qualitative research analysis of interview

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Chapter 10: Qualitative Data Collection & Analysis Methods

10.5 Analysis of Qualitative Interview Data

Analysis of qualitative interview data typically begins with a set of transcripts of the interviews conducted. Obtaining said transcripts requires either having taken exceptionally good notes during an interview or, preferably, recorded the interview and then transcribed it. To transcribe an interview means to create a complete, written copy of the recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. In general, it is best to aim for a verbatim transcription, i.e., one that reports word for word exactly what was said in the recorded interview. If possible, it is also best to include nonverbal responses in the written transcription of an interview (if the interview is completed face-to-face, or some other form of visual contact is maintained, such as with Skype). Gestures made by respondents should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by respondents.

If you have the time, it is best to transcribe your interviews yourself. If the researcher who conducted the interviews transcribes them herself, that person will also be able to record associated nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. Interviewees may roll their eyes, wipe tears from their face, and even make obscene gestures that speak volumes about their feelings; however, such non-verbal gestures cannot be recorded, and being able to remember and record in writing these details as it relates to the transcribing of interviews is invaluable.

Overall, the goal of analysis is to reach some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. Analysis of qualitative interview data often works inductively (Glaser & Strauss, 1967; Patton, 2001). To move from the specific observations an interviewer collects to identifying patterns across those observations, qualitative interviewers will often begin by reading through transcripts of their interviews and trying to identify codes. A code is a shorthand representation of some more complex set of issues or ideas. The process of identifying codes in one’s qualitative data is often referred to as coding . Coding involves identifying themes across interview data by reading and re-reading (and re-reading again) interview transcripts, until the researcher has a clear idea about what sorts of themes come up across the interviews. Coding helps to achieve the goal of data management and data reduction (Palys & Atchison, 2014, p. 304).

Coding can be inductive or deductive. Deductive coding is the approach used by research analysts who have a well-specified or pre-defined set of interests (Palys & Atchison, 2014, P. 304). The process of deductive coding begins with the analyst utilizing those specific or pre-defined interests to identify “relevant” passages, quotes, images, scenes, etc., to develop a set of preliminary codes (often referred to as descriptive coding ). From there, the analyst elaborates on these preliminary codes, making finer distinctions within each coding category (known as interpretative coding ). Pattern coding is another step an analyst might take as different associations become apparent. For example, if you are studying at-risk behaviours in youth, and you discover that the various behaviours have different characteristics and meanings depending upon the social context (e.g., school, family, work) in which the various behaviours occur, you have identified a pattern (Palys & Atchison, 2014, p. 304).

In contrast, inductive coding begins with the identification of general themes and ideas that emerge as the researcher reads through the data. This process is also referred to as open coding (Palys & Atchison, 2014, p. 305), because it will probably require multiple analyses. As you read through your transcripts, it is likely that you will begin to see some commonalities across the categories or themes that you’ve jotted down (Saylor Academy, 2012). The open coding process can go one of two ways: either the researcher elaborates on a category by making finer, and then even finer distinctions, or the researcher starts with a very specific descriptive category that is subsequently collapsed into another category (Palys & Atchison, 2014, p. 305). In other words, the development and elaboration of codes arise out of the material that is being examined.

The next step for the research analyst is to begin more specific coding, which is known as focused or axial coding . Focused coding involves collapsing or narrowing themes and categories identified in open coding by reading through the notes you made while conducting open coding, identifying themes or categories that seem to be related, and perhaps merging some. Then give each collapsed/merged theme or category a name (or code) and identify passages of data that fit each named category or theme. To identify passages of data that represent your emerging codes, you will need to read through your transcripts several times. You might also write up brief definitions or descriptions of each code. Defining codes is a way of giving meaning to your data, and developing a way to talk about your findings and what your data means (Saylor Academy, 2012).

As tedious and laborious as it might seem to read through hundreds of pages of transcripts multiple times, sometimes getting started with the coding process is actually the hardest part. If you find yourself struggling to identify themes at the open coding stage, ask yourself some questions about your data. The answers should give you a clue about what sorts of themes or categories you are reading (Saylor Academy, 2012). (Lofland and Lofland,1995, p. 2001) identify a set of questions that are useful when coding qualitative data. They suggest asking the following:

• Of what topic, unit, or aspect is this an instance?
• What question about a topic does this item of data suggest?
• What sort of answer to a question about a topic does this item of data suggest (i.e., what proposition is suggested)?

Asking yourself these questions about the passages of data that you are reading can help you begin to identify and name potential themes and categories.

Table 10.3 “ Interview coding” example is drawn from research undertaken by Saylor Academy (Saylor Academy, 2012) where she presents two codes that emerged from her inductive analysis of transcripts from her interviews with child-free adults. Table 10.3 also includes a brief description of each code and a few (of many) interview excerpts from which each code was developed.

Table 10.3 Interview coding

Just as quantitative researchers rely on the assistance of special computer programs designed to help sort through and analyze their data, so, do qualitative researchers. Where quantitative researchers have SPSS and MicroCase (and many others), qualitative researchers have programs such as NVivo ( http://www.qsrinternational.com ) and Atlasti ( http://www.atlasti.com ). These are programs specifically designed to assist qualitative researchers to organize, manage, sort, and analyze large amounts of qualitative data. The programs allow researchers to import interview transcripts contained in an electronic file and then label or code passages, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Published: 15 September 2022

Interviews in the social sciences

• Eleanor Knott   ORCID: orcid.org/0000-0002-9131-3939 1 ,
• Aliya Hamid Rao   ORCID: orcid.org/0000-0003-0674-4206 1 ,
• Kate Summers   ORCID: orcid.org/0000-0001-9964-0259 1 &
• Chana Teeger   ORCID: orcid.org/0000-0002-5046-8280 1  

Nature Reviews Methods Primers volume  2 , Article number:  73 ( 2022 ) Cite this article

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In-depth interviews are a versatile form of qualitative data collection used by researchers across the social sciences. They allow individuals to explain, in their own words, how they understand and interpret the world around them. Interviews represent a deceptively familiar social encounter in which people interact by asking and answering questions. They are, however, a very particular type of conversation, guided by the researcher and used for specific ends. This dynamic introduces a range of methodological, analytical and ethical challenges, for novice researchers in particular. In this Primer, we focus on the stages and challenges of designing and conducting an interview project and analysing data from it, as well as strategies to overcome such challenges.

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Introduction

In-depth interviews are a qualitative research method that follow a deceptively familiar logic of human interaction: they are conversations where people talk with each other, interact and pose and answer questions 1 . An interview is a specific type of interaction in which — usually and predominantly — a researcher asks questions about someone’s life experience, opinions, dreams, fears and hopes and the interview participant answers the questions 1 .

Interviews will often be used as a standalone method or combined with other qualitative methods, such as focus groups or ethnography, or quantitative methods, such as surveys or experiments. Although interviewing is a frequently used method, it should not be viewed as an easy default for qualitative researchers 2 . Interviews are also not suited to answering all qualitative research questions, but instead have specific strengths that should guide whether or not they are deployed in a research project. Whereas ethnography might be better suited to trying to observe what people do, interviews provide a space for extended conversations that allow the researcher insights into how people think and what they believe. Quantitative surveys also give these kinds of insights, but they use pre-determined questions and scales, privileging breadth over depth and often overlooking harder-to-reach participants.

In-depth interviews can take many different shapes and forms, often with more than one participant or researcher. For example, interviews might be highly structured (using an almost survey-like interview guide), entirely unstructured (taking a narrative and free-flowing approach) or semi-structured (using a topic guide ). Researchers might combine these approaches within a single project depending on the purpose of the interview and the characteristics of the participant. Whatever form the interview takes, researchers should be mindful of the dynamics between interviewer and participant and factor these in at all stages of the project.

In this Primer, we focus on the most common type of interview: one researcher taking a semi-structured approach to interviewing one participant using a topic guide. Focusing on how to plan research using interviews, we discuss the necessary stages of data collection. We also discuss the stages and thought-process behind analysing interview material to ensure that the richness and interpretability of interview material is maintained and communicated to readers. The Primer also tracks innovations in interview methods and discusses the developments we expect over the next 5–10 years.

We wrote this Primer as researchers from sociology, social policy and political science. We note our disciplinary background because we acknowledge that there are disciplinary differences in how interviews are approached and understood as a method.

Experimentation

Here we address research design considerations and data collection issues focusing on topic guide construction and other pragmatics of the interview. We also explore issues of ethics and reflexivity that are crucial throughout the research project.

Research design

Participant selection.

Participants can be selected and recruited in various ways for in-depth interview studies. The researcher must first decide what defines the people or social groups being studied. Often, this means moving from an abstract theoretical research question to a more precise empirical one. For example, the researcher might be interested in how people talk about race in contexts of diversity. Empirical settings in which this issue could be studied could include schools, workplaces or adoption agencies. The best research designs should clearly explain why the particular setting was chosen. Often there are both intrinsic and extrinsic reasons for choosing to study a particular group of people at a specific time and place 3 . Intrinsic motivations relate to the fact that the research is focused on an important specific social phenomenon that has been understudied. Extrinsic motivations speak to the broader theoretical research questions and explain why the case at hand is a good one through which to address them empirically.

Next, the researcher needs to decide which types of people they would like to interview. This decision amounts to delineating the inclusion and exclusion criteria for the study. The criteria might be based on demographic variables, like race or gender, but they may also be context-specific, for example, years of experience in an organization. These should be decided based on the research goals. Researchers should be clear about what characteristics would make an individual a candidate for inclusion in the study (and what would exclude them).

The next step is to identify and recruit the study’s sample . Usually, many more people fit the inclusion criteria than can be interviewed. In cases where lists of potential participants are available, the researcher might want to employ stratified sampling , dividing the list by characteristics of interest before sampling.

When there are no lists, researchers will often employ purposive sampling . Many researchers consider purposive sampling the most useful mode for interview-based research since the number of interviews to be conducted is too small to aim to be statistically representative 4 . Instead, the aim is not breadth, via representativeness, but depth via rich insights about a set of participants. In addition to purposive sampling, researchers often use snowball sampling . Both purposive and snowball sampling can be combined with quota sampling . All three types of sampling aim to ensure a variety of perspectives within the confines of a research project. A goal for in-depth interview studies can be to sample for range, being mindful of recruiting a diversity of participants fitting the inclusion criteria.

Study design

The total number of interviews depends on many factors, including the population studied, whether comparisons are to be made and the duration of interviews. Studies that rely on quota sampling where explicit comparisons are made between groups will require a larger number of interviews than studies focused on one group only. Studies where participants are interviewed over several hours, days or even repeatedly across years will tend to have fewer participants than those that entail a one-off engagement.

Researchers often stop interviewing when new interviews confirm findings from earlier interviews with no new or surprising insights (saturation) 4 , 5 , 6 . As a criterion for research design, saturation assumes that data collection and analysis are happening in tandem and that researchers will stop collecting new data once there is no new information emerging from the interviews. This is not always possible. Researchers rarely have time for systematic data analysis during data collection and they often need to specify their sample in funding proposals prior to data collection. As a result, researchers often draw on existing reports of saturation to estimate a sample size prior to data collection. These suggest between 12 and 20 interviews per category of participant (although researchers have reported saturation with samples that are both smaller and larger than this) 7 , 8 , 9 . The idea of saturation has been critiqued by many qualitative researchers because it assumes that meaning inheres in the data, waiting to be discovered — and confirmed — once saturation has been reached 7 . In-depth interview data are often multivalent and can give rise to different interpretations. The important consideration is, therefore, not merely how many participants are interviewed, but whether one’s research design allows for collecting rich and textured data that provide insight into participants’ understandings, accounts, perceptions and interpretations.

Sometimes, researchers will conduct interviews with more than one participant at a time. Researchers should consider the benefits and shortcomings of such an approach. Joint interviews may, for example, give researchers insight into how caregivers agree or debate childrearing decisions. At the same time, they may be less adaptive to exploring aspects of caregiving that participants may not wish to disclose to each other. In other cases, there may be more than one person interviewing each participant, such as when an interpreter is used, and so it is important to consider during the research design phase how this might shape the dynamics of the interview.

Data collection

Semi-structured interviews are typically organized around a topic guide comprised of an ordered set of broad topics (usually 3–5). Each topic includes a set of questions that form the basis of the discussion between the researcher and participant (Fig.  1 ). These topics are organized around key concepts that the researcher has identified (for example, through a close study of prior research, or perhaps through piloting a small, exploratory study) 5 .

a | Elaborated topics the researcher wants to cover in the interview and example questions. b | An example topic arc. Using such an arc, one can think flexibly about the order of topics. Considering the main question for each topic will help to determine the best order for the topics. After conducting some interviews, the researcher can move topics around if a different order seems to make sense.

Topic guide

One common way to structure a topic guide is to start with relatively easy, open-ended questions (Table  1 ). Opening questions should be related to the research topic but broad and easy to answer, so that they help to ease the participant into conversation.

After these broad, opening questions, the topic guide may move into topics that speak more directly to the overarching research question. The interview questions will be accompanied by probes designed to elicit concrete details and examples from the participant (see Table  1 ).

Abstract questions are often easier for participants to answer once they have been asked more concrete questions. In our experience, for example, questions about feelings can be difficult for some participants to answer, but when following probes concerning factual experiences these questions can become less challenging. After the main themes of the topic guide have been covered, the topic guide can move onto closing questions. At this stage, participants often repeat something they have said before, although they may sometimes introduce a new topic.

Interviews are especially well suited to gaining a deeper insight into people’s experiences. Getting these insights largely depends on the participants’ willingness to talk to the researcher. We recommend designing open-ended questions that are more likely to elicit an elaborated response and extended reflection from participants rather than questions that can be answered with yes or no.

Questions should avoid foreclosing the possibility that the participant might disagree with the premise of the question. Take for example the question: “Do you support the new family-friendly policies?” This question minimizes the possibility of the participant disagreeing with the premise of this question, which assumes that the policies are ‘family-friendly’ and asks for a yes or no answer. Instead, asking more broadly how a participant feels about the specific policy being described as ‘family-friendly’ (for example, a work-from-home policy) allows them to express agreement, disagreement or impartiality and, crucially, to explain their reasoning 10 .

For an uninterrupted interview that will last between 90 and 120 minutes, the topic guide should be one to two single-spaced pages with questions and probes. Ideally, the researcher will memorize the topic guide before embarking on the first interview. It is fine to carry a printed-out copy of the topic guide but memorizing the topic guide ahead of the interviews can often make the interviewer feel well prepared in guiding the participant through the interview process.

Although the topic guide helps the researcher stay on track with the broad areas they want to cover, there is no need for the researcher to feel tied down by the topic guide. For instance, if a participant brings up a theme that the researcher intended to discuss later or a point the researcher had not anticipated, the researcher may well decide to follow the lead of the participant. The researcher’s role extends beyond simply stating the questions; it entails listening and responding, making split-second decisions about what line of inquiry to pursue and allowing the interview to proceed in unexpected directions.

Optimizing the interview

The ideal place for an interview will depend on the study and what is feasible for participants. Generally, a place where the participant and researcher can both feel relaxed, where the interview can be uninterrupted and where noise or other distractions are limited is ideal. But this may not always be possible and so the researcher needs to be prepared to adapt their plans within what is feasible (and desirable for participants).

Another key tool for the interview is a recording device (assuming that permission for recording has been given). Recording can be important to capture what the participant says verbatim. Additionally, it can allow the researcher to focus on determining what probes and follow-up questions they want to pursue rather than focusing on taking notes. Sometimes, however, a participant may not allow the researcher to record, or the recording may fail. If the interview is not recorded we suggest that the researcher takes brief notes during the interview, if feasible, and then thoroughly make notes immediately after the interview and try to remember the participant’s facial expressions, gestures and tone of voice. Not having a recording of an interview need not limit the researcher from getting analytical value from it.

As soon as possible after each interview, we recommend that the researcher write a one-page interview memo comprising three key sections. The first section should identify two to three important moments from the interview. What constitutes important is up to the researcher’s discretion 9 . The researcher should note down what happened in these moments, including the participant’s facial expressions, gestures, tone of voice and maybe even the sensory details of their surroundings. This exercise is about capturing ethnographic detail from the interview. The second part of the interview memo is the analytical section with notes on how the interview fits in with previous interviews, for example, where the participant’s responses concur or diverge from other responses. The third part consists of a methodological section where the researcher notes their perception of their relationship with the participant. The interview memo allows the researcher to think critically about their positionality and practice reflexivity — key concepts for an ethical and transparent research practice in qualitative methodology 11 , 12 .

Ethics and reflexivity

All elements of an in-depth interview can raise ethical challenges and concerns. Good ethical practice in interview studies often means going beyond the ethical procedures mandated by institutions 13 . While discussions and requirements of ethics can differ across disciplines, here we focus on the most pertinent considerations for interviews across the research process for an interdisciplinary audience.

Ethical considerations prior to interview

Before conducting interviews, researchers should consider harm minimization, informed consent, anonymity and confidentiality, and reflexivity and positionality. It is important for the researcher to develop their own ethical sensitivities and sensibilities by gaining training in interview and qualitative methods, reading methodological and field-specific texts on interviews and ethics and discussing their research plans with colleagues.

Researchers should map the potential harm to consider how this can be minimized. Primarily, researchers should consider harm from the participants’ perspective (Box  1 ). But, it is also important to consider and plan for potential harm to the researcher, research assistants, gatekeepers, future researchers and members of the wider community 14 . Even the most banal of research topics can potentially pose some form of harm to the participant, researcher and others — and the level of harm is often highly context-dependent. For example, a research project on religion in society might have very different ethical considerations in a democratic versus authoritarian research context because of how openly or not such topics can be discussed and debated 15 .

The researcher should consider how they will obtain and record informed consent (for example, written or oral), based on what makes the most sense for their research project and context 16 . Some institutions might specify how informed consent should be gained. Regardless of how consent is obtained, the participant must be made aware of the form of consent, the intentions and procedures of the interview and potential forms of harm and benefit to the participant or community before the interview commences. Moreover, the participant must agree to be interviewed before the interview commences. If, in addition to interviews, the study contains an ethnographic component, it is worth reading around this topic (see, for example, Murphy and Dingwall 17 ). Informed consent must also be gained for how the interview will be recorded before the interview commences. These practices are important to ensure the participant is contributing on a voluntary basis. It is also important to remind participants that they can withdraw their consent at any time during the interview and for a specified period after the interview (to be decided with the participant). The researcher should indicate that participants can ask for anything shared to be off the record and/or not disseminated.

In terms of anonymity and confidentiality, it is standard practice when conducting interviews to agree not to use (or even collect) participants’ names and personal details that are not pertinent to the study. Anonymizing can often be the safer option for minimizing harm to participants as it is hard to foresee all the consequences of de-anonymizing, even if participants agree. Regardless of what a researcher decides, decisions around anonymity must be agreed with participants during the process of gaining informed consent and respected following the interview.

Although not all ethical challenges can be foreseen or planned for 18 , researchers should think carefully — before the interview — about power dynamics, participant vulnerability, emotional state and interactional dynamics between interviewer and participant, even when discussing low-risk topics. Researchers may then wish to plan for potential ethical issues, for example by preparing a list of relevant organizations to which participants can be signposted. A researcher interviewing a participant about debt, for instance, might prepare in advance a list of debt advice charities, organizations and helplines that could provide further support and advice. It is important to remember that the role of an interviewer is as a researcher rather than as a social worker or counsellor because researchers may not have relevant and requisite training in these other domains.

Box 1 Mapping potential forms of harm

Social: researchers should avoid causing any relational detriment to anyone in the course of interviews, for example, by sharing information with other participants or causing interview participants to be shunned or mistreated by their community as a result of participating.

Economic: researchers should avoid causing financial detriment to anyone, for example, by expecting them to pay for transport to be interviewed or to potentially lose their job as a result of participating.

Physical: researchers should minimize the risk of anyone being exposed to violence as a result of the research both from other individuals or from authorities, including police.

Psychological: researchers should minimize the risk of causing anyone trauma (or re-traumatization) or psychological anguish as a result of the research; this includes not only the participant but importantly the researcher themselves and anyone that might read or analyse the transcripts, should they contain triggering information.

Political: researchers should minimize the risk of anyone being exposed to political detriment as a result of the research, such as retribution.

Professional/reputational: researchers should minimize the potential for reputational damage to anyone connected to the research (this includes ensuring good research practices so that any researchers involved are not harmed reputationally by being involved with the research project).

The task here is not to map exhaustively the potential forms of harm that might pertain to a particular research project (that is the researcher’s job and they should have the expertise most suited to mapping such potential harms relative to the specific project) but to demonstrate the breadth of potential forms of harm.

Ethical considerations post-interview

Researchers should consider how interview data are stored, analysed and disseminated. If participants have been offered anonymity and confidentiality, data should be stored in a way that does not compromise this. For example, researchers should consider removing names and any other unnecessary personal details from interview transcripts, password-protecting and encrypting files and using pseudonyms to label and store all interview data. It is also important to address where interview data are taken (for example, across borders in particular where interview data might be of interest to local authorities) and how this might affect the storage of interview data.

Examining how the researcher will represent participants is a paramount ethical consideration both in the planning stages of the interview study and after it has been conducted. Dissemination strategies also need to consider questions of anonymity and representation. In small communities, even if participants are given pseudonyms, it might be obvious who is being described. Anonymizing not only the names of those participating but also the research context is therefore a standard practice 19 . With particularly sensitive data or insights about the participant, it is worth considering describing participants in a more abstract way rather than as specific individuals. These practices are important both for protecting participants’ anonymity but can also affect the ability of the researcher and others to return ethically to the research context and similar contexts 20 .

Reflexivity and positionality

Reflexivity and positionality mean considering the researcher’s role and assumptions in knowledge production 13 . A key part of reflexivity is considering the power relations between the researcher and participant within the interview setting, as well as how researchers might be perceived by participants. Further, researchers need to consider how their own identities shape the kind of knowledge and assumptions they bring to the interview, including how they approach and ask questions and their analysis of interviews (Box  2 ). Reflexivity is a necessary part of developing ethical sensibility as a researcher by adapting and reflecting on how one engages with participants. Participants should not feel judged, for example, when they share information that researchers might disagree with or find objectionable. How researchers deal with uncomfortable moments or information shared by participants is at their discretion, but they should consider how they will react both ahead of time and in the moment.

Researchers can develop their reflexivity by considering how they themselves would feel being asked these interview questions or represented in this way, and then adapting their practice accordingly. There might be situations where these questions are not appropriate in that they unduly centre the researchers’ experiences and worldview. Nevertheless, these prompts can provide a useful starting point for those beginning their reflexive journey and developing an ethical sensibility.

Reflexivity and ethical sensitivities require active reflection throughout the research process. For example, researchers should take care in interview memos and their notes to consider their assumptions, potential preconceptions, worldviews and own identities prior to and after interviews (Box  2 ). Checking in with assumptions can be a way of making sure that researchers are paying close attention to their own theoretical and analytical biases and revising them in accordance with what they learn through the interviews. Researchers should return to these notes (especially when analysing interview material), to try to unpack their own effects on the research process as well as how participants positioned and engaged with them.

Box 2 Aspects to reflect on reflexively

For reflexive engagement, and understanding the power relations being co-constructed and (re)produced in interviews, it is necessary to reflect, at a minimum, on the following.

Ethnicity, race and nationality, such as how does privilege stemming from race or nationality operate between the researcher, the participant and research context (for example, a researcher from a majority community may be interviewing a member of a minority community)

Gender and sexuality, see above on ethnicity, race and nationality

Social class, and in particular the issue of middle-class bias among researchers when formulating research and interview questions

Economic security/precarity, see above on social class and thinking about the researcher’s relative privilege and the source of biases that stem from this

Educational experiences and privileges, see above

Disciplinary biases, such as how the researcher’s discipline/subfield usually approaches these questions, possibly normalizing certain assumptions that might be contested by participants and in the research context

Political and social values

Lived experiences and other dimensions of ourselves that affect and construct our identity as researchers

In this section, we discuss the next stage of an interview study, namely, analysing the interview data. Data analysis may begin while more data are being collected. Doing so allows early findings to inform the focus of further data collection, as part of an iterative process across the research project. Here, the researcher is ultimately working towards achieving coherence between the data collected and the findings produced to answer successfully the research question(s) they have set.

The two most common methods used to analyse interview material across the social sciences are thematic analysis 21 and discourse analysis 22 . Thematic analysis is a particularly useful and accessible method for those starting out in analysis of qualitative data and interview material as a method of coding data to develop and interpret themes in the data 21 . Discourse analysis is more specialized and focuses on the role of discourse in society by paying close attention to the explicit, implicit and taken-for-granted dimensions of language and power 22 , 23 . Although thematic and discourse analysis are often discussed as separate techniques, in practice researchers might flexibly combine these approaches depending on the object of analysis. For example, those intending to use discourse analysis might first conduct thematic analysis as a way to organize and systematize the data. The object and intention of analysis might differ (for example, developing themes or interrogating language), but the questions facing the researcher (such as whether to take an inductive or deductive approach to analysis) are similar.

Preparing data

Data preparation is an important step in the data analysis process. The researcher should first determine what comprises the corpus of material and in what form it will it be analysed. The former refers to whether, for example, alongside the interviews themselves, analytic memos or observational notes that may have been taken during data collection will also be directly analysed. The latter refers to decisions about how the verbal/audio interview data will be transformed into a written form, making it suitable for processes of data analysis. Typically, interview audio recordings are transcribed to produce a written transcript. It is important to note that the process of transcription is one of transformation. The verbal interview data are transformed into a written transcript through a series of decisions that the researcher must make. The researcher should consider the effect of mishearing what has been said or how choosing to punctuate a sentence in a particular way will affect the final analysis.

Box  3 shows an example transcript excerpt from an interview with a teacher conducted by Teeger as part of her study of history education in post-apartheid South Africa 24 (Box  3 ). Seeing both the questions and the responses means that the reader can contextualize what the participant (Ms Mokoena) has said. Throughout the transcript the researcher has used square brackets, for example to indicate a pause in speech, when Ms Mokoena says “it’s [pause] it’s a difficult topic”. The transcription choice made here means that we see that Ms Mokoena has taken time to pause, perhaps to search for the right words, or perhaps because she has a slight apprehension. Square brackets are also included as an overt act of communication to the reader. When Ms Mokoena says “ja”, the English translation (“yes”) of the word in Afrikaans is placed in square brackets to ensure that the reader can follow the meaning of the speech.

Decisions about what to include when transcribing will be hugely important for the direction and possibilities of analysis. Researchers should decide what they want to capture in the transcript, based on their analytic focus. From a (post)positivist perspective 25 , the researcher may be interested in the manifest content of the interview (such as what is said, not how it is said). In that case, they may choose to transcribe intelligent verbatim . From a constructivist perspective 25 , researchers may choose to record more aspects of speech (including, for example, pauses, repetitions, false starts, talking over one another) so that these features can be analysed. Those working from this perspective argue that to recognize the interactional nature of the interview setting adequately and to avoid misinterpretations, features of interaction (pauses, overlaps between speakers and so on) should be preserved in transcription and therefore in the analysis 10 . Readers interested in learning more should consult Potter and Hepburn’s summary of how to present interaction through transcription of interview data 26 .

The process of analysing semi-structured interviews might be thought of as a generative rather than an extractive enterprise. Findings do not already exist within the interview data to be discovered. Rather, researchers create something new when analysing the data by applying their analytic lens or approach to the transcripts. At a high level, there are options as to what researchers might want to glean from their interview data. They might be interested in themes, whereby they identify patterns of meaning across the dataset 21 . Alternatively, they may focus on discourse(s), looking to identify how language is used to construct meanings and therefore how language reinforces or produces aspects of the social world 27 . Alternatively, they might look at the data to understand narrative or biographical elements 28 .

A further overarching decision to make is the extent to which researchers bring predetermined framings or understandings to bear on their data, or instead begin from the data themselves to generate an analysis. One way of articulating this is the extent to which researchers take a deductive approach or an inductive approach to analysis. One example of a truly inductive approach is grounded theory, whereby the aim of the analysis is to build new theory, beginning with one’s data 6 , 29 . In practice, researchers using thematic and discourse analysis often combine deductive and inductive logics and describe their process instead as iterative (referred to also as an abductive approach ) 30 , 31 . For example, researchers may decide that they will apply a given theoretical framing, or begin with an initial analytic framework, but then refine or develop these once they begin the process of analysis.

Box 3 Excerpt of interview transcript (from Teeger 24 )

Interviewer : Maybe you could just start by talking about what it’s like to teach apartheid history.

Ms Mokoena : It’s a bit challenging. You’ve got to accommodate all the kids in the class. You’ve got to be sensitive to all the racial differences. You want to emphasize the wrongs that were done in the past but you also want to, you know, not to make kids feel like it’s their fault. So you want to use the wrongs of the past to try and unite the kids …

Interviewer : So what kind of things do you do?

Ms Mokoena : Well I normally highlight the fact that people that were struggling were not just the blacks, it was all the races. And I give examples of the people … from all walks of life, all races, and highlight how they suffered as well as a result of apartheid, particularly the whites… . What I noticed, particularly my first year of teaching apartheid, I noticed that the black kids made the others feel responsible for what happened… . I had a lot of fights…. A lot of kids started hating each other because, you know, the others are white and the others were black. And they started saying, “My mother is a domestic worker because she was never allowed an opportunity to get good education.” …

Interviewer : I didn’t see any of that now when I was observing.

Ms Mokoena : … Like I was saying I think that because of the re-emphasis of the fact that, look, everybody did suffer one way or the other, they sort of got to see that it was everybody’s struggle … . They should now get to understand that that’s why we’re called a Rainbow Nation. Not everybody agreed with apartheid and not everybody suffered. Even all the blacks, not all blacks got to feel what the others felt . So ja [yes], it’s [pause] it’s a difficult topic, ja . But I think if you get the kids to understand why we’re teaching apartheid in the first place and you show the involvement of all races in all the different sides , then I think you have managed to teach it properly. So I think because of my inexperience then — that was my first year of teaching history — so I think I — maybe I over-emphasized the suffering of the blacks versus the whites [emphasis added].

Reprinted with permission from ref. 24 , Sage Publications.

From data to codes

Coding data is a key building block shared across many approaches to data analysis. Coding is a way of organizing and describing data, but is also ultimately a way of transforming data to produce analytic insights. The basic practice of coding involves highlighting a segment of text (this may be a sentence, a clause or a longer excerpt) and assigning a label to it. The aim of the label is to communicate some sort of summary of what is in the highlighted piece of text. Coding is an iterative process, whereby researchers read and reread their transcripts, applying and refining their codes, until they have a coding frame (a set of codes) that is applied coherently across the dataset and that captures and communicates the key features of what is contained in the data as it relates to the researchers’ analytic focus.

What one codes for is entirely contingent on the focus of the research project and the choices the researcher makes about the approach to analysis. At first, one might apply descriptive codes, summarizing what is contained in the interviews. It is rarely desirable to stop at this point, however, because coding is a tool to move from describing the data to interpreting the data. Suppose the researcher is pursuing some version of thematic analysis. In that case, it might be that the objects of coding are aspects of reported action, emotions, opinions, norms, relationships, routines, agreement/disagreement and change over time. A discourse analysis might instead code for different types of speech acts, tropes, linguistic or rhetorical devices. Multiple types of code might be generated within the same research project. What is important is that researchers are aware of the choices they are making in terms of what they are coding for. Moreover, through the process of refinement, the aim is to produce a set of discrete codes — in which codes are conceptually distinct, as opposed to overlapping. By using the same codes across the dataset, the researcher can capture commonalities across the interviews. This process of refinement involves relabelling codes and reorganizing how and where they are applied in the dataset.

From coding to analysis and writing

Data analysis is also an iterative process in which researchers move closer to and further away from the data. As they move away from the data, they synthesize their findings, thus honing and articulating their analytic insights. As they move closer to the data, they ground these insights in what is contained in the interviews. The link should not be broken between the data themselves and higher-order conceptual insights or claims being made. Researchers must be able to show evidence for their claims in the data. Figure  2 summarizes this iterative process and suggests the sorts of activities involved at each stage more concretely.

As well as going through steps 1 to 6 in order, the researcher will also go backwards and forwards between stages. Some stages will themselves be a forwards and backwards processing of coding and refining when working across different interview transcripts.

At the stage of synthesizing, there are some common quandaries. When dealing with a dataset consisting of multiple interviews, there will be salient and minority statements across different participants, or consensus or dissent on topics of interest to the researcher. A strength of qualitative interviews is that we can build in these nuances and variations across our data as opposed to aggregating them away. When exploring and reporting data, researchers should be asking how different findings are patterned and which interviews contain which codes, themes or tropes. Researchers should think about how these variations fit within the longer flow of individual interviews and what these variations tell them about the nature of their substantive research interests.

A further consideration is how to approach analysis within and across interview data. Researchers may look at one individual code, to examine the forms it takes across different participants and what they might be able to summarize about this code in the round. Alternatively, they might look at how a code or set of codes pattern across the account of one participant, to understand the code(s) in a more contextualized way. Further analysis might be done according to different sampling characteristics, where researchers group together interviews based on certain demographic characteristics and explore these together.

When it comes to writing up and presenting interview data, key considerations tend to rest on what is often termed transparency. When presenting the findings of an interview-based study, the reader should be able to understand and trace what the stated findings are based upon. This process typically involves describing the analytic process, how key decisions were made and presenting direct excerpts from the data. It is important to account for how the interview was set up and to consider the active part that the researcher has played in generating the data 32 . Quotes from interviews should not be thought of as merely embellishing or adding interest to a final research output. Rather, quotes serve the important function of connecting the reader directly to the underlying data. Quotes, therefore, should be chosen because they provide the reader with the most apt insight into what is being discussed. It is good practice to report not just on what participants said, but also on the questions that were asked to elicit the responses.

Researchers have increasingly used specialist qualitative data analysis software to organize and analyse their interview data, such as NVivo or ATLAS.ti. It is important to remember that such software is a tool for, rather than an approach or technique of, analysis. That said, software also creates a wide range of possibilities in terms of what can be done with the data. As researchers, we should reflect on how the range of possibilities of a given software package might be shaping our analytical choices and whether these are choices that we do indeed want to make.

Applications

This section reviews how and why in-depth interviews have been used by researchers studying gender, education and inequality, nationalism and ethnicity and the welfare state. Although interviews can be employed as a method of data collection in just about any social science topic, the applications below speak directly to the authors’ expertise and cutting-edge areas of research.

When it comes to the broad study of gender, in-depth interviews have been invaluable in shaping our understanding of how gender functions in everyday life. In a study of the US hedge fund industry (an industry dominated by white men), Tobias Neely was interested in understanding the factors that enable white men to prosper in the industry 33 . The study comprised interviews with 45 hedge fund workers and oversampled women of all races and men of colour to capture a range of experiences and beliefs. Tobias Neely found that practices of hiring, grooming and seeding are key to maintaining white men’s dominance in the industry. In terms of hiring, the interviews clarified that white men in charge typically preferred to hire people like themselves, usually from their extended networks. When women were hired, they were usually hired to less lucrative positions. In terms of grooming, Tobias Neely identifies how older and more senior men in the industry who have power and status will select one or several younger men as their protégés, to include in their own elite networks. Finally, in terms of her concept of seeding, Tobias Neely describes how older men who are hedge fund managers provide the seed money (often in the hundreds of millions of dollars) for a hedge fund to men, often their own sons (but not their daughters). These interviews provided an in-depth look into gendered and racialized mechanisms that allow white men to flourish in this industry.

Research by Rao draws on dozens of interviews with men and women who had lost their jobs, some of the participants’ spouses and follow-up interviews with about half the sample approximately 6 months after the initial interview 34 . Rao used interviews to understand the gendered experience and understanding of unemployment. Through these interviews, she found that the very process of losing their jobs meant different things for men and women. Women often saw job loss as being a personal indictment of their professional capabilities. The women interviewed often referenced how years of devaluation in the workplace coloured their interpretation of their job loss. Men, by contrast, were also saddened by their job loss, but they saw it as part and parcel of a weak economy rather than a personal failing. How these varied interpretations occurred was tied to men’s and women’s very different experiences in the workplace. Further, through her analysis of these interviews, Rao also showed how these gendered interpretations had implications for the kinds of jobs men and women sought to pursue after job loss. Whereas men remained tied to participating in full-time paid work, job loss appeared to be a catalyst pushing some of the women to re-evaluate their ties to the labour force.

In a study of workers in the tech industry, Hart used interviews to explain how individuals respond to unwanted and ambiguously sexual interactions 35 . Here, the researcher used interviews to allow participants to describe how these interactions made them feel and act and the logics of how they interpreted, classified and made sense of them 35 . Through her analysis of these interviews, Hart showed that participants engaged in a process she termed “trajectory guarding”, whereby they sought to monitor unwanted and ambiguously sexual interactions to avoid them from escalating. Yet, as Hart’s analysis proficiently demonstrates, these very strategies — which protect these workers sexually — also undermined their workplace advancement.

Drawing on interviews, these studies have helped us to understand better how gendered mechanisms, gendered interpretations and gendered interactions foster gender inequality when it comes to paid work. Methodologically, these studies illuminate the power of interviews to reveal important aspects of social life.

Nationalism and ethnicity

Traditionally, nationalism has been studied from a top-down perspective, through the lens of the state or using historical methods; in other words, in-depth interviews have not been a common way of collecting data to study nationalism. The methodological turn towards everyday nationalism has encouraged more scholars to go to the field and use interviews (and ethnography) to understand nationalism from the bottom up: how people talk about, give meaning, understand, navigate and contest their relation to nation, national identification and nationalism 36 , 37 , 38 , 39 . This turn has also addressed the gap left by those studying national and ethnic identification via quantitative methods, such as surveys.

Surveys can enumerate how individuals ascribe to categorical forms of identification 40 . However, interviews can question the usefulness of such categories and ask whether these categories are reflected, or resisted, by participants in terms of the meanings they give to identification 41 , 42 . Categories often pitch identification as a mutually exclusive choice; but identification might be more complex than such categories allow. For example, some might hybridize these categories or see themselves as moving between and across categories 43 . Hearing how people talk about themselves and their relation to nations, states and ethnicities, therefore, contributes substantially to the study of nationalism and national and ethnic forms of identification.

One particular approach to studying these topics, whether via everyday nationalism or alternatives, is that of using interviews to capture both articulations and narratives of identification, relations to nationalism and the boundaries people construct. For example, interviews can be used to gather self–other narratives by studying how individuals construct I–we–them boundaries 44 , including how participants talk about themselves, who participants include in their various ‘we’ groupings and which and how participants create ‘them’ groupings of others, inserting boundaries between ‘I/we’ and ‘them’. Overall, interviews hold great potential for listening to participants and understanding the nuances of identification and the construction of boundaries from their point of view.

Education and inequality

Scholars of social stratification have long noted that the school system often reproduces existing social inequalities. Carter explains that all schools have both material and sociocultural resources 45 . When children from different backgrounds attend schools with different material resources, their educational and occupational outcomes are likely to vary. Such material resources are relatively easy to measure. They are operationalized as teacher-to-student ratios, access to computers and textbooks and the physical infrastructure of classrooms and playgrounds.

Drawing on Bourdieusian theory 46 , Carter conceptualizes the sociocultural context as the norms, values and dispositions privileged within a social space 45 . Scholars have drawn on interviews with students and teachers (as well as ethnographic observations) to show how schools confer advantages on students from middle-class families, for example, by rewarding their help-seeking behaviours 47 . Focusing on race, researchers have revealed how schools can remain socioculturally white even as they enrol a racially diverse student population. In such contexts, for example, teachers often misrecognize the aesthetic choices made by students of colour, wrongly inferring that these students’ tastes in clothing and music reflect negative orientations to schooling 48 , 49 , 50 . These assessments can result in disparate forms of discipline and may ultimately shape educators’ assessments of students’ academic potential 51 .

Further, teachers and administrators tend to view the appropriate relationship between home and school in ways that resonate with white middle-class parents 52 . These parents are then able to advocate effectively for their children in ways that non-white parents are not 53 . In-depth interviews are particularly good at tapping into these understandings, revealing the mechanisms that confer privilege on certain groups of students and thereby reproduce inequality.

In addition, interviews can shed light on the unequal experiences that young people have within educational institutions, as the views of dominant groups are affirmed while those from disadvantaged backgrounds are delegitimized. For example, Teeger’s interviews with South African high schoolers showed how — because racially charged incidents are often framed as jokes in the broader school culture — Black students often feel compelled to ignore and keep silent about the racism they experience 54 . Interviews revealed that Black students who objected to these supposed jokes were coded by other students as serious or angry. In trying to avoid such labels, these students found themselves unable to challenge the racism they experienced. Interviews give us insight into these dynamics and help us see how young people understand and interpret the messages transmitted in schools — including those that speak to issues of inequality in their local school contexts as well as in society more broadly 24 , 55 .

The welfare state

In-depth interviews have also proved to be an important method for studying various aspects of the welfare state. By welfare state, we mean the social institutions relating to the economic and social wellbeing of a state’s citizens. Notably, using interviews has been useful to look at how policy design features are experienced and play out on the ground. Interviews have often been paired with large-scale surveys to produce mixed-methods study designs, therefore achieving both breadth and depth of insights.

In-depth interviews provide the opportunity to look behind policy assumptions or how policies are designed from the top down, to examine how these play out in the lives of those affected by the policies and whose experiences might otherwise be obscured or ignored. For example, the Welfare Conditionality project used interviews to critique the assumptions that conditionality (such as, the withdrawal of social security benefits if recipients did not perform or meet certain criteria) improved employment outcomes and instead showed that conditionality was harmful to mental health, living standards and had many other negative consequences 56 . Meanwhile, combining datasets from two small-scale interview studies with recipients allowed Summers and Young to critique assumptions around the simplicity that underpinned the design of Universal Credit in 2020, for example, showing that the apparently simple monthly payment design instead burdened recipients with additional money management decisions and responsibilities 57 .

Similarly, the Welfare at a (Social) Distance project used a mixed-methods approach in a large-scale study that combined national surveys with case studies and in-depth interviews to investigate the experience of claiming social security benefits during the COVID-19 pandemic. The interviews allowed researchers to understand in detail any issues experienced by recipients of benefits, such as delays in the process of claiming, managing on a very tight budget and navigating stigma and claiming 58 .

These applications demonstrate the multi-faceted topics and questions for which interviews can be a relevant method for data collection. These applications highlight not only the relevance of interviews, but also emphasize the key added value of interviews, which might be missed by other methods (surveys, in particular). Interviews can expose and question what is taken for granted and directly engage with communities and participants that might otherwise be ignored, obscured or marginalized.

Reproducibility and data deposition

There is a robust, ongoing debate about reproducibility in qualitative research, including interview studies. In some research paradigms, reproducibility can be a way of interrogating the rigour and robustness of research claims, by seeing whether these hold up when the research process is repeated. Some scholars have suggested that although reproducibility may be challenging, researchers can facilitate it by naming the place where the research was conducted, naming participants, sharing interview and fieldwork transcripts (anonymized and de-identified in cases where researchers are not naming people or places) and employing fact-checkers for accuracy 11 , 59 , 60 .

In addition to the ethical concerns of whether de-anonymization is ever feasible or desirable, it is also important to address whether the replicability of interview studies is meaningful. For example, the flexibility of interviews allows for the unexpected and the unforeseen to be incorporated into the scope of the research 61 . However, this flexibility means that we cannot expect reproducibility in the conventional sense, given that different researchers will elicit different types of data from participants. Sharing interview transcripts with other researchers, for instance, downplays the contextual nature of an interview.

Drawing on Bauer and Gaskell, we propose several measures to enhance rigour in qualitative research: transparency, grounding interpretations and aiming for theoretical transferability and significance 62 .

Researchers should be transparent when describing their methodological choices. Transparency means documenting who was interviewed, where and when (without requiring de-anonymization, for example, by documenting their characteristics), as well as the questions they were asked. It means carefully considering who was left out of the interviews and what that could mean for the researcher’s findings. It also means carefully considering who the researcher is and how their identity shaped the research process (integrating and articulating reflexivity into whatever is written up).

Second, researchers should ground their interpretations in the data. Grounding means presenting the evidence upon which the interpretation relies. Quotes and extracts should be extensive enough to allow the reader to evaluate whether the researcher’s interpretations are grounded in the data. At each step, researchers should carefully compare their own explanations and interpretations with alternative explanations. Doing so systematically and frequently allows researchers to become more confident in their claims. Here, researchers should justify the link between data and analysis by using quotes to justify and demonstrate the analytical point, while making sure the analytical point offers an interpretation of quotes (Box  4 ).

An important step in considering alternative explanations is to seek out disconfirming evidence 4 , 63 . This involves looking for instances where participants deviate from what the majority are saying and thus bring into question the theory (or explanation) that the researcher is developing. Careful analysis of such examples can often demonstrate the salience and meaning of what appears to be the norm (see Table  2 for examples) 54 . Considering alternative explanations and paying attention to disconfirming evidence allows the researcher to refine their own theories in respect of the data.

Finally, researchers should aim for theoretical transferability and significance in their discussions of findings. One way to think about this is to imagine someone who is not interested in the empirical study. Articulating theoretical transferability and significance usually takes the form of broadening out from the specific findings to consider explicitly how the research has refined or altered prior theoretical approaches. This process also means considering under what other conditions, aside from those of the study, the researcher thinks their theoretical revision would be supported by and why. Importantly, it also includes thinking about the limitations of one’s own approach and where the theoretical implications of the study might not hold.

Box 4 An example of grounding interpretations in data (from Rao 34 )

In an article explaining how unemployed men frame their job loss as a pervasive experience, Rao writes the following: “Unemployed men in this study understood unemployment to be an expected aspect of paid work in the contemporary United States. Robert, a white unemployed communications professional, compared the economic landscape after the Great Recession with the tragic events of September 11, 2001:

Part of your post-9/11 world was knowing people that died as a result of terrorism. The same thing is true with the [Great] Recession, right? … After the Recession you know somebody who was unemployed … People that really should be working.

The pervasiveness of unemployment rendered it normal, as Robert indicates.”

Here, the link between the quote presented and the analytical point Rao is making is clear: the analytical point is grounded in a quote and an interpretation of the quote is offered 34 .

Limitations and optimizations

When deciding which research method to use, the key question is whether the method provides a good fit for the research questions posed. In other words, researchers should consider whether interviews will allow them to successfully access the social phenomena necessary to answer their question(s) and whether the interviews will do so more effectively than other methods. Table  3 summarizes the major strengths and limitations of interviews. However, the accompanying text below is organized around some key issues, where relative strengths and weaknesses are presented alongside each other, the aim being that readers should think about how these can be balanced and optimized in relation to their own research.

Breadth versus depth of insight

Achieving an overall breadth of insight, in a statistically representative sense, is not something that is possible or indeed desirable when conducting in-depth interviews. Instead, the strength of conducting interviews lies in their ability to generate various sorts of depth of insight. The experiences or views of participants that can be accessed by conducting interviews help us to understand participants’ subjective realities. The challenge, therefore, is for researchers to be clear about why depth of insight is the focus and what we should aim to glean from these types of insight.

Naturalistic or artificial interviews

Interviews make use of a form of interaction with which people are familiar 64 . By replicating a naturalistic form of interaction as a tool to gather social science data, researchers can capitalize on people’s familiarity and expectations of what happens in a conversation. This familiarity can also be a challenge, as people come to the interview with preconceived ideas about what this conversation might be for or about. People may draw on experiences of other similar conversations when taking part in a research interview (for example, job interviews, therapy sessions, confessional conversations, chats with friends). Researchers should be aware of such potential overlaps and think through their implications both in how the aims and purposes of the research interview are communicated to participants and in how interview data are interpreted.

Further, some argue that a limitation of interviews is that they are an artificial form of data collection. By taking people out of their daily lives and asking them to stand back and pass comment, we are creating a distance that makes it difficult to use such data to say something meaningful about people’s actions, experiences and views. Other approaches, such as ethnography, might be more suitable for tapping into what people actually do, as opposed to what they say they do 65 .

Dynamism and replicability

Interviews following a semi-structured format offer flexibility both to the researcher and the participant. As the conversation develops, the interlocutors can explore the topics raised in much more detail, if desired, or pass over ones that are not relevant. This flexibility allows for the unexpected and the unforeseen to be incorporated into the scope of the research.

However, this flexibility has a related challenge of replicability. Interviews cannot be reproduced because they are contingent upon the interaction between the researcher and the participant in that given moment of interaction. In some research paradigms, replicability can be a way of interrogating the robustness of research claims, by seeing whether they hold when they are repeated. This is not a useful framework to bring to in-depth interviews and instead quality criteria (such as transparency) tend to be employed as criteria of rigour.

Accessing the private and personal

Interviews have been recognized for their strength in accessing private, personal issues, which participants may feel more comfortable talking about in a one-to-one conversation. Furthermore, interviews are likely to take a more personable form with their extended questions and answers, perhaps making a participant feel more at ease when discussing sensitive topics in such a context. There is a similar, but separate, argument made about accessing what are sometimes referred to as vulnerable groups, who may be difficult to make contact with using other research methods.

There is an associated challenge of anonymity. There can be types of in-depth interview that make it particularly challenging to protect the identities of participants, such as interviewing within a small community, or multiple members of the same household. The challenge to ensure anonymity in such contexts is even more important and difficult when the topic of research is of a sensitive nature or participants are vulnerable.

Increasingly, researchers are collaborating in large-scale interview-based studies and integrating interviews into broader mixed-methods designs. At the same time, interviews can be seen as an old-fashioned (and perhaps outdated) mode of data collection. We review these debates and discussions and point to innovations in interview-based studies. These include the shift from face-to-face interviews to the use of online platforms, as well as integrating and adapting interviews towards more inclusive methodologies.

Collaborating and mixing

Qualitative researchers have long worked alone 66 . Increasingly, however, researchers are collaborating with others for reasons such as efficiency, institutional incentives (for example, funding for collaborative research) and a desire to pool expertise (for example, studying similar phenomena in different contexts 67 or via different methods). Collaboration can occur across disciplines and methods, cases and contexts and between industry/business, practitioners and researchers. In many settings and contexts, collaboration has become an imperative 68 .

Cheek notes how collaboration provides both advantages and disadvantages 68 . For example, collaboration can be advantageous, saving time and building on the divergent knowledge, skills and resources of different researchers. Scholars with different theoretical or case-based knowledge (or contacts) can work together to build research that is comparative and/or more than the sum of its parts. But such endeavours also carry with them practical and political challenges in terms of how resources might actually be pooled, shared or accounted for. When undertaking such projects, as Morse notes, it is worth thinking about the nature of the collaboration and being explicit about such a choice, its advantages and its disadvantages 66 .

A further tension, but also a motivation for collaboration, stems from integrating interviews as a method in a mixed-methods project, whether with other qualitative researchers (to combine with, for example, focus groups, document analysis or ethnography) or with quantitative researchers (to combine with, for example, surveys, social media analysis or big data analysis). Cheek and Morse both note the pitfalls of collaboration with quantitative researchers: that quality of research may be sacrificed, qualitative interpretations watered down or not taken seriously, or tensions experienced over the pace and different assumptions that come with different methods and approaches of research 66 , 68 .

At the same time, there can be real benefits of such mixed-methods collaboration, such as reaching different and more diverse audiences or testing assumptions and theories between research components in the same project (for example, testing insights from prior quantitative research via interviews, or vice versa), as long as the skillsets of collaborators are seen as equally beneficial to the project. Cheek provides a set of questions that, as a starting point, can be useful for guiding collaboration, whether mixed methods or otherwise. First, Cheek advises asking all collaborators about their assumptions and understandings concerning collaboration. Second, Cheek recommends discussing what each perspective highlights and focuses on (and conversely ignores or sidelines) 68 .

A different way to engage with the idea of collaboration and mixed methods research is by fostering greater collaboration between researchers in the Global South and Global North, thus reversing trends of researchers from the Global North extracting knowledge from the Global South 69 . Such forms of collaboration also align with interview innovations, discussed below, that seek to transform traditional interview approaches into more participatory and inclusive (as part of participatory methodologies).

Digital innovations and challenges

The ongoing COVID-19 pandemic has centred the question of technology within interview-based fieldwork. Although conducting synchronous oral interviews online — for example, via Zoom, Skype or other such platforms — has been a method used by a small constituency of researchers for many years, it became (and remains) a necessity for many researchers wanting to continue or start interview-based projects while COVID-19 prevents face-to-face data collection.

In the past, online interviews were often framed as an inferior form of data collection for not providing the kinds of (often necessary) insights and forms of immersion face-to-face interviews allow 70 , 71 . Online interviews do tend to be more decontextualized than interviews conducted face-to-face 72 . For example, it is harder to recognize, engage with and respond to non-verbal cues 71 . At the same time, they broaden participation to those who might not have been able to access or travel to sites where interviews would have been conducted otherwise, for example people with disabilities. Online interviews also offer more flexibility in terms of scheduling and time requirements. For example, they provide more flexibility around precarious employment or caring responsibilities without having to travel and be away from home. In addition, online interviews might also reduce discomfort between researchers and participants, compared with face-to-face interviews, enabling more discussion of sensitive material 71 . They can also provide participants with more control, enabling them to turn on and off the microphone and video as they choose, for example, to provide more time to reflect and disconnect if they so wish 72 .

That said, online interviews can also introduce new biases based on access to technology 72 . For example, in the Global South, there are often urban/rural and gender gaps between who has access to mobile phones and who does not, meaning that some population groups might be overlooked unless researchers sample mindfully 71 . There are also important ethical considerations when deciding between online and face-to-face interviews. Online interviews might seem to imply lower ethical risks than face-to-face interviews (for example, they lower the chances of identification of participants or researchers), but they also offer more barriers to building trust between researchers and participants 72 . Interacting only online with participants might not provide the information needed to assess risk, for example, participants’ access to a private space to speak 71 . Just because online interviews might be more likely to be conducted in private spaces does not mean that private spaces are safe, for example, for victims of domestic violence. Finally, online interviews prompt further questions about decolonizing research and engaging with participants if research is conducted from afar 72 , such as how to include participants meaningfully and challenge dominant assumptions while doing so remotely.

A further digital innovation, modulating how researchers conduct interviews and the kinds of data collected and analysed, stems from the use and integration of (new) technology, such as WhatsApp text or voice notes to conduct synchronous or asynchronous oral or written interviews 73 . Such methods can provide more privacy, comfort and control to participants and make recruitment easier, allowing participants to share what they want when they want to, using technology that already forms a part of their daily lives, especially for young people 74 , 75 . Such technology is also emerging in other qualitative methods, such as focus groups, with similar arguments around greater inclusivity versus traditional offline modes. Here, the digital challenge might be higher for researchers than for participants if they are less used to such technology 75 . And while there might be concerns about the richness, depth and quality of written messages as a form of interview data, Gibson reports that the reams of transcripts that resulted from a study using written messaging were dense with meaning to be analysed 75 .

Like with online and face-to-face interviews, it is important also to consider the ethical questions and challenges of using such technology, from gaining consent to ensuring participant safety and attending to their distress, without cues, like crying, that might be more obvious in a face-to-face setting 75 , 76 . Attention to the platform used for such interviews is also important and researchers should be attuned to the local and national context. For example, in China, many platforms are neither legal nor available 76 . There, more popular platforms — like WeChat — can be highly monitored by the government, posing potential risks to participants depending on the topic of the interview. Ultimately, researchers should consider trade-offs between online and offline interview modalities, being attentive to the social context and power dynamics involved.

The next 5–10 years

Continuing to integrate (ethically) this technology will be among the major persisting developments in interview-based research, whether to offer more flexibility to researchers or participants, or to diversify who can participate and on what terms.

Pushing the idea of inclusion even further is the potential for integrating interview-based studies within participatory methods, which are also innovating via integrating technology. There is no hard and fast line between researchers using in-depth interviews and participatory methods; many who employ participatory methods will use interviews at the beginning, middle or end phases of a research project to capture insights, perspectives and reflections from participants 77 , 78 . Participatory methods emphasize the need to resist existing power and knowledge structures. They broaden who has the right and ability to contribute to academic knowledge by including and incorporating participants not only as subjects of data collection, but as crucial voices in research design and data analysis 77 . Participatory methods also seek to facilitate local change and to produce research materials, whether for academic or non-academic audiences, including films and documentaries, in collaboration with participants.

In responding to the challenges of COVID-19, capturing the fraught situation wrought by the pandemic and the momentum to integrate technology, participatory researchers have sought to continue data collection from afar. For example, Marzi has adapted an existing project to co-produce participatory videos, via participants’ smartphones in Medellin, Colombia, alongside regular check-in conversations/meetings/interviews with participants 79 . Integrating participatory methods into interview studies offers a route by which researchers can respond to the challenge of diversifying knowledge, challenging assumptions and power hierarchies and creating more inclusive and collaborative partnerships between participants and researchers in the Global North and South.

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Acknowledgements

The authors are grateful to the MY421 team and students for prompting how best to frame and communicate issues pertinent to in-depth interview studies.

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A pre-written interview outline for a semi-structured interview that provides both a topic structure and the ability to adapt flexibly to the content and context of the interview and the interaction between the interviewer and participant. Others may refer to the topic guide as an interview protocol.

Here we refer to the participants that take part in the study as the sample. Other researchers may refer to the participants as a participant group or dataset.

This involves dividing a population into smaller groups based on particular characteristics, for example, age or gender, and then sampling randomly within each group.

A sampling method where the guiding logic when deciding who to recruit is to achieve the most relevant participants for the research topic, in terms of being rich in information or insights.

Researchers ask participants to introduce the researcher to others who meet the study’s inclusion criteria.

Similar to stratified sampling, but participants are not necessarily randomly selected. Instead, the researcher determines how many people from each category of participants should be recruited. Recruitment can happen via snowball or purposive sampling.

A method for developing, analysing and interpreting patterns across data by coding in order to develop themes.

An approach that interrogates the explicit, implicit and taken-for-granted dimensions of language as well as the contexts in which it is articulated to unpack its purposes and effects.

A form of transcription that simplifies what has been said by removing certain verbal and non-verbal details that add no further meaning, such as ‘ums and ahs’ and false starts.

The analytic framework, theoretical approach and often hypotheses, are developed prior to examining the data and then applied to the dataset.

The analytic framework and theoretical approach is developed from analysing the data.

An approach that combines deductive and inductive components to work recursively by going back and forth between data and existing theoretical frameworks (also described as an iterative approach). This approach is increasingly recognized not only as a more realistic but also more desirable third alternative to the more traditional inductive versus deductive binary choice.

A theoretical apparatus that emphasizes the role of cultural processes and capital in (intergenerational) social reproduction.

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Interviews in Qualitative Research

• Nigel King - University of Huddersfield, UK
• Christine Horrocks - Manchester Metropolitan University, UK
• Joanna Brooks - University of Manchester, UK
• Description

This dynamic user-focused book will help you to get the data you want from your interviews. It provides practical guidance regarding technique, gives top-tips from real world case studies and shares achievable checklists and interview plans.

Whether you are doing interviews in your own research or just using other researchers’ data, this book will tell you everything you need to know about designing, planning, conducting and analyzing quality interviews. It explains how to:

-          Construct ethical research designs

-          Record and manage your data

-          Transcribe your notes

-          Analyse your findings

-          Disseminate your conclusions

Written using clear, jargon-free terminology and with coverage of practical, theoretical and philosophical issues all grounded in examples from real interviews, this is the ideal guide for new and experienced researchers alike.

Nigel King  is Professor of Applied Psychology at the University of Huddersfield.

Christine Horrocks  is Professor of Applied Social Psychology and Head of the Department of Psychology at Manchester Metropolitan University. 

Joanna Brooks  is Lecturer in the Manchester Centre for Health Psychology at the University of Manchester. 

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

As a researcher and educator I find this book to be an invaluable resource, a detailed yet accessible guide for all those engaged in qualitative research . 

Interviews in Qualitative Research (Second Edition)  should be your first line of defense as you voyage through the minefield of using the method of interviewing. As a supervisor and qualitative researcher, this is a must-have, accessible read to support researchers at any stage. A helping, comforting hand to hold for qualitative researchers!

This is an impressive and comprehensive exploration of the use of interviews in qualitative research  –  theoretically rich, practically sensible, robust in application and academically relevant. It is essential reading for researchers at all levels of their research journey – an excellent resource and contribution to the literature.

Interviews in Qualitative Research is a must read for ambitious students and developing researchers. The authors holistically detail the interview method whilst remaining clear, concise and coherent. I am confident this book will help produce high quality qualitative research!

A comprehensive and accessible introduction to qualitative interviews. It is particularly strong on the practical aspects of designing and conducting interview-based studies using a wide range of settings, methods and approaches. It will become a trusted companion to many an undergraduate or postgraduate student, especially those who value an applied perspective. The new edition therefore deserves to defend its place on many a qualitative methods reading list – including my own.

This is an accessible and authoritative text that provides a detailed outline of the interview process from start to finish [...] The book usefully adopts an applied perspective and draws on ‘real world’ case studies to exemplify the possibilities and challenges that might arise throughout the interview process. It provides much practical guidance about how interviews might be planned, conducted and analysed – with boxes, tables and figures proving a particularly useful feature

This is a very good resource for students and teaching

This is a very good reference in qualitative research. The chapters are nicely laid and it is very easy to follow both from students and teachers' perspectives. I will highly recommend this book for students, teachers and researchers.

A useful book on using qualitative interviews in research for my students who spend this year undertaking a research project.

this books outlines the benefits of conducting interviews within the area of field data. The suggestion allow students to identify areas that they are interested in and also highlights potential pitfalls that they may encounter on the way to completing a dissertation.

Preview this book

For instructors, select a purchasing option, related products.

• Harvard Library
• Research Guides
• Faculty of Arts & Sciences Libraries

Library Support for Qualitative Research

• Interview Research
• Resources for Methodology
• Remote Research & Virtual Fieldwork

Resources for Research Interviewing

Nih-funded qualitative research.

• Oral History
• Data Management & Repositories
• Campus Access

Types of Interviews

• Engaging Participants

Interview Questions

• Conducting Interviews
• Transcription
• Coding and Analysis
• Managing & Finding Interview Data
• UX & Market Research Interviews

Textbooks, Guidebooks, and Handbooks  

• The Ethnographic Interview by James P. Spradley  “Spradley wrote this book for the professional and student who have never done ethnographic fieldwork (p. 231) and for the professional ethnographer who is interested in adapting the author’s procedures (p. iv). Part 1 outlines in 3 chapters Spradley’s version of ethnographic research, and it provides the background for Part 2 which consists of 12 guided steps (chapters) ranging from locating and interviewing an informant to writing an ethnography. Most of the examples come from the author’s own fieldwork among U.S. subcultures . . . Steps 6 and 8 explain lucidly how to construct a domain and a taxonomic analysis” (excerpted from book review by James D. Sexton, 1980).  
• Fundamentals of Qualitative Research by Johnny Saldana (Series edited by Patricia Leavy)  Provides a soup-to-nuts overview of the qualitative data collection process, including interviewing, participant observation, and other methods.  
• InterViews by Steinar Kvale  Interviewing is an essential tool in qualitative research and this introduction to interviewing outlines both the theoretical underpinnings and the practical aspects of the process. After examining the role of the interview in the research process, Steinar Kvale considers some of the key philosophical issues relating to interviewing: the interview as conversation, hermeneutics, phenomenology, concerns about ethics as well as validity, and postmodernism. Having established this framework, the author then analyzes the seven stages of the interview process - from designing a study to writing it up.  
• Practical Evaluation by Michael Quinn Patton  Surveys different interviewing strategies, from, a) informal/conversational, to b) interview guide approach, to c) standardized and open-ended, to d) closed/quantitative. Also discusses strategies for wording questions that are open-ended, clear, sensitive, and neutral, while supporting the speaker. Provides suggestions for probing and maintaining control of the interview process, as well as suggestions for recording and transcription.  
• The SAGE Handbook of Interview Research by Amir B. Marvasti (Editor); James A. Holstein (Editor); Jaber F. Gubrium (Editor); Karyn D. McKinney (Editor)  The new edition of this landmark volume emphasizes the dynamic, interactional, and reflexive dimensions of the research interview. Contributors highlight the myriad dimensions of complexity that are emerging as researchers increasingly frame the interview as a communicative opportunity as much as a data-gathering format. The book begins with the history and conceptual transformations of the interview, which is followed by chapters that discuss the main components of interview practice. Taken together, the contributions to The SAGE Handbook of Interview Research: The Complexity of the Craft encourage readers simultaneously to learn the frameworks and technologies of interviewing and to reflect on the epistemological foundations of the interview craft.  
• The SAGE Handbook of Online Research Methods by Nigel G. Fielding, Raymond M. Lee and Grant Blank (Editors) Bringing together the leading names in both qualitative and quantitative online research, this new edition is organised into nine sections: 1. Online Research Methods 2. Designing Online Research 3. Online Data Capture and Data Collection 4. The Online Survey 5. Digital Quantitative Analysis 6. Digital Text Analysis 7. Virtual Ethnography 8. Online Secondary Analysis: Resources and Methods 9. The Future of Online Social Research

ONLINE RESOURCES, COMMUNITIES, AND DATABASES  

• Interviews as a Method for Qualitative Research (video) This short video summarizes why interviews can serve as useful data in qualitative research.  
• Companion website to Bloomberg and Volpe's  Completing Your Qualitative Dissertation: A Road Map from Beginning to End,  4th ed Provides helpful templates and appendices featured in the book, as well as links to other useful dissertation resources.
• International Congress of Qualitative Inquiry Annual conference hosted by the International Center for Qualitative Inquiry at the University of Illinois at Urbana-Champaign, which aims to facilitate the development of qualitative research methods across a wide variety of academic disciplines, among other initiatives.  
• METHODSPACE ​​​​​​​​An online home of the research methods community, where practicing researchers share how to make research easier.  
• SAGE researchmethods ​​​​​​​Researchers can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. A "methods map" facilitates finding content on methods.

The decision to conduct interviews, and the type of interviewing to use, should flow from, or align with, the methodological paradigm chosen for your study, whether that paradigm is interpretivist, critical, positivist, or participative in nature (or a combination of these).

Structured:

• Structured Interview. Entry in The SAGE Encyclopedia of Social Science Research Methodsby Floyd J. Fowler Jr., Editors: Michael S. Lewis-Beck; Alan E. Bryman; Tim Futing Liao (Editor)  A concise article noting standards, procedures, and recommendations for developing and testing structured interviews. For an example of structured interview questions, you may view the Current Population Survey, May 2008: Public Participation in the Arts Supplement (ICPSR 29641), Apr 15, 2011 at https://doi.org/10.3886/ICPSR29641.v1 (To see the survey questions, preview the user guide, which can be found under the "Data and Documentation" tab. Then, look for page 177 (attachment 8).

Semi-Structured:

• Semi-Structured Interview. Entry in The SAGE Encyclopedia of Qualitative Research Methodsby Lioness Ayres; Editor: Lisa M. Given  The semi-structured interview is a qualitative data collection strategy in which the researcher asks informants a series of predetermined but open-ended questions. The researcher has more control over the topics of the interview than in unstructured interviews, but in contrast to structured interviews or questionnaires that use closed questions, there is no fixed range of responses to each question.

Unstructured:

• Unstructured Interview. Entry in The SAGE Encyclopedia of Qualitative Research Methodsby Michael W. Firmin; Editor: Lisa M. Given  Unstructured interviews in qualitative research involve asking relatively open-ended questions of research participants in order to discover their percepts on the topic of interest. Interviews, in general, are a foundational means of collecting data when using qualitative research methods. They are designed to draw from the interviewee constructs embedded in his or her thinking and rationale for decision making. The researcher uses an inductive method in data gathering, regardless of whether the interview method is open, structured, or semi-structured. That is, the researcher does not wish to superimpose his or her own viewpoints onto the person being interviewed. Rather, inductively, the researcher wishes to understand the participant's perceptions, helping him or her to articulate percepts such that they will be understood clearly by the journal reader.

Genres and Uses

Focus groups:.

• "Focus Groups." Annual Review of Sociology 22 (1996): 129-1524.by David L. Morgan  Discusses the use of focus groups and group interviews as methods for gathering qualitative data used by sociologists and other academic and applied researchers. Focus groups are recommended for giving voice to marginalized groups and revealing the group effect on opinion formation.  
• Qualitative Research Methods: A Data Collector's Field Guide (See Module 4: "Focus Groups")by Mack, N., et al.  This field guide is based on an approach to doing team-based, collaborative qualitative research that has repeatedly proven successful in research projects sponsored by Family Health International (FHI) throughout the developing world. With its straightforward delivery of information on the main qualitative methods being used in public health research today, the guide speaks to the need for simple yet effective instruction on how to do systematic and ethically sound qualitative research. The aim of the guide is thus practical. In bypassing extensive discussion on the theoretical underpinnings of qualitative research, it distinguishes itself as a how-to guide to be used in the field.

In-Depth (typically One-on-One):

• A Practical Introduction to in-Depth Interviewingby Alan Morris  Are you new to qualitative research or a bit rusty and in need of some inspiration? Are you doing a research project involving in-depth interviews? Are you nervous about carrying out your interviews? This book will help you complete your qualitative research project by providing a nuts and bolts introduction to interviewing. With coverage of ethics, preparation strategies and advice for handling the unexpected in the field, this handy guide will help you get to grips with the basics of interviewing before embarking on your research. While recognising that your research question and the context of your research will drive your approach to interviewing, this book provides practical advice often skipped in traditional methods textbooks.  
• Qualitative Research Methods: A Data Collector's Field Guide (See Module 3: "In-Depth Interviews")by Mack, N., et al.  This field guide is based on an approach to doing team-based, collaborative qualitative research that has repeatedly proven successful in research projects sponsored by Family Health International (FHI) throughout the developing world. With its straightforward delivery of information on the main qualitative methods being used in public health research today, the guide speaks to the need for simple yet effective instruction on how to do systematic and ethically sound qualitative research. The aim of the guide is thus practical. In bypassing extensive discussion on the theoretical underpinnings of qualitative research, it distinguishes itself as a how-to guide to be used in the field.

Folklore Research and Oral Histories:

In addition to the following resource, see the  Oral History   page of this guide for helpful resources on Oral History interviewing.

American Folklife Center at the Library of Congress. Folklife and Fieldwork: A Layman’s Introduction to Field Techniques Interviews gathered for purposes of folklore research are similar to standard social science interviews in some ways, but also have a good deal in common with oral history approaches to interviewing. The focus in a folklore research interview is on documenting and trying to understand the interviewee's way of life relative to a culture or subculture you are studying. This guide includes helpful advice and tips for conducting fieldwork in folklore, such as tips for planning, conducting, recording, and archiving interviews.

An interdisciplinary scientific program within the Institute for Quantitative Social Science which encourages and facilitates research and instruction in the theory and practice of survey research. The primary mission of PSR is to provide survey research resources to enhance the quality of teaching and research at Harvard.

• Internet, Phone, Mail, and Mixed-Mode Surveysby Don A. Dillman; Jolene D. Smyth; Leah Melani Christian  The classic survey design reference, updated for the digital age. The new edition is thoroughly updated and revised, and covers all aspects of survey research. It features expanded coverage of mobile phones, tablets, and the use of do-it-yourself surveys, and Dillman's unique Tailored Design Method is also thoroughly explained. This new edition is complemented by copious examples within the text and accompanying website. It includes: Strategies and tactics for determining the needs of a given survey, how to design it, and how to effectively administer it. How and when to use mail, telephone, and Internet surveys to maximum advantage. Proven techniques to increase response rates. Guidance on how to obtain high-quality feedback from mail, electronic, and other self-administered surveys. Direction on how to construct effective questionnaires, including considerations of layout. The effects of sponsorship on the response rates of surveys. Use of capabilities provided by newly mass-used media: interactivity, presentation of aural and visual stimuli. The Fourth Edition reintroduces the telephone--including coordinating land and mobile.

User Experience (UX) and Marketing:

• See the  "UX & Market Research Interviews"  tab on this guide, above. May include  Focus Groups,  above.

Screening for Research Site Selection:

• Research interviews are used not only to furnish research data for theoretical analysis in the social sciences, but also to plan other kinds of studies. For example, interviews may allow researchers to screen appropriate research sites to conduct empirical studies (such as randomized controlled trials) in a variety of fields, from medicine to law. In contrast to interviews conducted in the course of social research, such interviews do not typically serve as the data for final analysis and publication.

ENGAGING PARTICIPANTS

Research ethics  .

• Human Subjects (IRB) The Committee on the Use of Human Subjects (CUHS) serves as the Institutional Review Board for the University area which includes the Cambridge and Allston campuses at Harvard. Find your IRB  contact person , or learn about  required ethics training.  You may also find the  IRB Lifecycle Guide  helpful. This is the preferred IRB portal for Harvard graduate students and other researchers. IRB forms can be downloaded via the  ESTR Library  (click on the "Templates and Forms" tab, then navigate to pages 2 and 3 to find the documents labelled with “HUA” for the Harvard University Area IRB. Nota bene: You may use these forms only if you submit your study to the Harvard University IRB). The IRB office can be reached through email at [email protected] or by telephone at (617) 496-2847.  
• Undergraduate Research Training Program (URTP) Portal The URTP at Harvard University is a comprehensive platform to create better prepared undergraduate researchers. The URTP is comprised of research ethics training sessions, a student-focused curriculum, and an online decision form that will assist students in determining whether their project requires IRB review. Students should examine the  URTP's guide for student researchers: Introduction to Human Subjects Research Protection.  
• Ethics reports From the Association of Internet Researchers (AoIR)  
• Respect, Beneficence, and Justice: QDR General Guidance for Human Participants If you are hoping to share your qualitative interview data in a repository after it has been collected, you will need to plan accordingly via informed consent, careful de-identification procedures, and data access controls. Consider  consulting with the Qualitative Research Support Group at Harvard Library  and consulting with  Harvard's Dataverse contacts  to help you think through all of the contingencies and processes.  
• "Conducting a Qualitative Child Interview: Methodological Considerations." Journal of Advanced Nursing 42/5 (2003): 434-441 by Kortesluoma, R., et al.  The purpose of this article is to illustrate the theoretical premises of child interviewing, as well as to describe some practical methodological solutions used during interviews. Factors that influence data gathered from children and strategies for taking these factors into consideration during the interview are also described.  
• "Crossing Cultural Barriers in Research Interviewing." Qualitative Social Work 63/3 (2007): 353-372 by Sands, R., et al.  This article critically examines a qualitative research interview in which cultural barriers between a white non-Muslim female interviewer and an African American Muslim interviewee, both from the USA, became evident and were overcome within the same interview.  
• Decolonizing Methodologies: Research and Indigenous Peoples by Linda Tuhiwai Smith  This essential volume explores intersections of imperialism and research - specifically, the ways in which imperialism is embedded in disciplines of knowledge and tradition as 'regimes of truth.' Concepts such as 'discovery' and 'claiming' are discussed and an argument presented that the decolonization of research methods will help to reclaim control over indigenous ways of knowing and being. The text includes case-studies and examples, and sections on new indigenous literature and the role of research in indigenous struggles for social justice.  

This resource, sponsored by University of Oregon Libraries, exemplifies the use of interviewing methodologies in research that foregrounds traditional knowledge. The methodology page summarizes the approach.

• Ethics: The Need to Tread Carefully. Chapter in A Practical Introduction to in-Depth Interviewing by Alan Morris  Pay special attention to the sections in chapter 2 on "How to prevent and respond to ethical issues arising in the course of the interview," "Ethics in the writing up of your interviews," and "The Ethics of Care."  
• Handbook on Ethical Issues in Anthropology by Joan Cassell (Editor); Sue-Ellen Jacobs (Editor)  This publication of the American Anthropological Association presents and discusses issues and sources on ethics in anthropology, as well as realistic case studies of ethical dilemmas. It is meant to help social science faculty introduce discussions of ethics in their courses. Some of the topics are relevant to interviews, or at least to studies of which interviews are a part. See chapters 3 and 4 for cases, with solutions and commentary, respectively.  
• Research Ethics from the Chanie Wenjack School for Indigenous Studies, Trent University  (Open Access) An overview of Indigenous research ethics and protocols from the across the globe.  
• Resources for Equity in Research Consult these resources for guidance on creating and incorporating equitable materials into public health research studies that entail community engagement.

The SAGE Handbook of Qualitative Research Ethics by Ron Iphofen (Editor); Martin Tolich (Editor)  This handbook is a much-needed and in-depth review of the distinctive set of ethical considerations which accompanies qualitative research. This is particularly crucial given the emergent, dynamic and interactional nature of most qualitative research, which too often allows little time for reflection on the important ethical responsibilities and obligations. Contributions from leading international researchers have been carefully organized into six key thematic sections: Part One: Thick Descriptions Of Qualitative Research Ethics; Part Two: Qualitative Research Ethics By Technique; Part Three: Ethics As Politics; Part Four: Qualitative Research Ethics With Vulnerable Groups; Part Five: Relational Research Ethics; Part Six: Researching Digitally. This Handbook is a one-stop resource on qualitative research ethics across the social sciences that draws on the lessons learned and the successful methods for surmounting problems - the tried and true, and the new.

RESEARCH COMPLIANCE AND PRIVACY LAWS

Research Compliance Program for FAS/SEAS at Harvard : The Faculty of Arts and Sciences (FAS), including the School of Engineering and Applied Sciences (SEAS), and the Office of the Vice Provost for Research (OVPR) have established a shared Research Compliance Program (RCP). An area of common concern for interview studies is international projects and collaboration . RCP is a resource to provide guidance on which international activities may be impacted by US sanctions on countries, individuals, or entities and whether licenses or other disclosure are required to ship or otherwise share items, technology, or data with foreign collaborators.

• Harvard Global Support Services (GSS) is for students, faculty, staff, and researchers who are studying, researching, or working abroad. Their services span safety and security, health, culture, outbound immigration, employment, financial and legal matters, and research center operations. These include travel briefings and registration, emergency response, guidance on international projects, and managing in-country operations.

Generative AI: Harvard-affiliated researchers should not enter data classified as confidential ( Level 2 and above ), including non-public research data, into publicly-available generative AI tools, in accordance with the University’s Information Security Policy. Information shared with generative AI tools using default settings is not private and could expose proprietary or sensitive information to unauthorized parties.

Privacy Laws: Be mindful of any potential privacy laws that may apply wherever you conduct your interviews. The General Data Protection Regulation is a high-profile example (see below):

• General Data Protection Regulation (GDPR) This Regulation lays down rules relating to the protection of natural persons with regard to the processing of personal data and rules relating to the free movement of personal data. It protects fundamental rights and freedoms of natural persons and in particular their right to the protection of personal data. The free movement of personal data within the Union shall be neither restricted nor prohibited for reasons connected with the protection of natural persons with regard to the processing of personal data. For a nice summary of what the GDPR requires, check out the GDPR "crash course" here .

SEEKING CONSENT  

If you would like to see examples of consent forms, ask your local IRB, or take a look at these resources:

• Model consent forms for oral history, suggested by the Centre for Oral History and Digital Storytelling at Concordia University  
• For NIH-funded research, see this  resource for developing informed consent language in research studies where data and/or biospecimens will be stored and shared for future use.

POPULATION SAMPLING

If you wish to assemble resources to aid in sampling, such as the USPS Delivery Sequence File, telephone books, or directories of organizations and listservs, please contact our  data librarian  or write to  [email protected] .

• Research Randomizer   A free web-based service that permits instant random sampling and random assignment. It also contains an interactive tutorial perfect for students taking courses in research methods.  
• Practical Tools for Designing and Weighting Survey Samples by Richard Valliant; Jill A. Dever; Frauke Kreuter  Survey sampling is fundamentally an applied field. The goal in this book is to put an array of tools at the fingertips of practitioners by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This book serves at least three audiences: (1) Students seeking a more in-depth understanding of applied sampling either through a second semester-long course or by way of a supplementary reference; (2) Survey statisticians searching for practical guidance on how to apply concepts learned in theoretical or applied sampling courses; and (3) Social scientists and other survey practitioners who desire insight into the statistical thinking and steps taken to design, select, and weight random survey samples. Several survey data sets are used to illustrate how to design samples, to make estimates from complex surveys for use in optimizing the sample allocation, and to calculate weights. Realistic survey projects are used to demonstrate the challenges and provide a context for the solutions. The book covers several topics that either are not included or are dealt with in a limited way in other texts. These areas include: sample size computations for multistage designs; power calculations related to surveys; mathematical programming for sample allocation in a multi-criteria optimization setting; nuts and bolts of area probability sampling; multiphase designs; quality control of survey operations; and statistical software for survey sampling and estimation. An associated R package, PracTools, contains a number of specialized functions for sample size and other calculations. The data sets used in the book are also available in PracTools, so that the reader may replicate the examples or perform further analyses.  
• Sampling: Design and Analysis by Sharon L. Lohr  Provides a modern introduction to the field of sampling. With a multitude of applications from a variety of disciplines, the book concentrates on the statistical aspects of taking and analyzing a sample. Overall, the book gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys.  
• Sampling Techniques by William G. Cochran  Clearly demonstrates a wide range of sampling methods now in use by governments, in business, market and operations research, social science, medicine, public health, agriculture, and accounting. Gives proofs of all the theoretical results used in modern sampling practice. New topics in this edition include the approximate methods developed for the problem of attaching standard errors or confidence limits to nonlinear estimates made from the results of surveys with complex plans.  
• "Understanding the Process of Qualitative Data Collection" in Chapter 13 (pp. 103–1162) of 30 Essential Skills for the Qualitative Researcher by John W. Creswell  Provides practical "how-to" information for beginning researchers in the social, behavioral, and health sciences with many applied examples from research design, qualitative inquiry, and mixed methods.The skills presented in this book are crucial for a new qualitative researcher starting a qualitative project.  
• Survey Methodology by Robert M. Groves; Floyd J. Fowler; Mick P. Couper; James M. Lepkowski; Eleanor Singer; Roger Tourangeau; Floyd J. Fowler  coverage includes sampling frame evaluation, sample design, development of questionnaires, evaluation of questions, alternative modes of data collection, interviewing, nonresponse, post-collection processing of survey data, and practices for maintaining scientific integrity.

The way a qualitative researcher constructs and approaches interview questions should flow from, or align with, the methodological paradigm chosen for the study, whether that paradigm is interpretivist, critical, positivist, or participative in nature (or a combination of these).

Constructing Your Questions

Helpful texts:.

• "Developing Questions" in Chapter 4 (pp. 98–108) of Becoming Qualitative Researchers by Corrine Glesne  Ideal for introducing the novice researcher to the theory and practice of qualitative research, this text opens students to the diverse possibilities within this inquiry approach, while helping them understand how to design and implement specific research methods.  
• "Learning to Interview in the Social Sciences" Qualitative Inquiry, 9(4) 2003, 643–668 by Roulston, K., deMarrais, K., & Lewis, J. B. See especially the section on "Phrasing and Negotiating Questions" on pages 653-655 and common problems with framing questions noted on pages 659 - 660.  
• Qualitative Research Interviewing: Biographic Narrative and Semi-Structured Methods (See sections on “Lightly and Heavily Structured Depth Interviewing: Theory-Questions and Interviewer-Questions” and “Preparing for any Interviewing Sequence") by Tom Wengraf  Unique in its conceptual coherence and the level of practical detail, this book provides a comprehensive resource for those concerned with the practice of semi-structured interviewing, the most commonly used interview approach in social research, and in particular for in-depth, biographic narrative interviewing. It covers the full range of practices from the identification of topics through to strategies for writing up research findings in diverse ways.  
• "Scripting a Qualitative Purpose Statement and Research Questions" in Chapter 12 (pp. 93–102) of 30 Essential Skills for the Qualitative Researcher by John W. Creswell  Provides practical "how-to" information for beginning researchers in the social, behavioral, and health sciences with many applied examples from research design, qualitative inquiry, and mixed methods.The skills presented in this book are crucial for a new qualitative researcher starting a qualitative project.  
• Some Strategies for Developing Interview Guides for Qualitative Interviews by Sociology Department, Harvard University Includes general advice for conducting qualitative interviews, pros and cons of recording and transcription, guidelines for success, and tips for developing and phrasing effective interview questions.  
• Tip Sheet on Question Wording by Harvard University Program on Survey Research

Let Theory Guide You:

The quality of your questions depends on how you situate them within a wider body of knowledge. Consider the following advice:

A good literature review has many obvious virtues. It enables the investigator to define problems and assess data. It provides the concepts on which percepts depend. But the literature review has a special importance for the qualitative researcher. This consists of its ability to sharpen his or her capacity for surprise (Lazarsfeld, 1972b). The investigator who is well versed in the literature now has a set of expectations the data can defy. Counterexpectational data are conspicuous, readable, and highly provocative data. They signal the existence of unfulfilled theoretical assumptions, and these are, as Kuhn (1962) has noted, the very origins of intellectual innovation. A thorough review of the literature is, to this extent, a way to manufacture distance. It is a way to let the data of one's research project take issue with the theory of one's field.

McCracken, G. (1988), The Long Interview, Sage: Newbury Park, CA, p. 31

When drafting your interview questions, remember that everything follows from your central research question. Also, on the way to writing your "operationalized" interview questions, it's  helpful to draft broader, intermediate questions, couched in theory. Nota bene:  While it is important to know the literature well before conducting your interview(s), be careful not to present yourself to your research participant(s) as "the expert," which would be presumptuous and could be intimidating. Rather, the purpose of your knowledge is to make you a better, keener listener.

If you'd like to supplement what you learned about relevant theories through your coursework and literature review, try these sources:

• Annual Reviews   Review articles sum up the latest research in many fields, including social sciences, biomedicine, life sciences, and physical sciences. These are timely collections of critical reviews written by leading scientists.  
• HOLLIS - search for resources on theories in your field   Modify this example search by entering the name of your field in place of "your discipline," then hit search.  
• Oxford Bibliographies   Written and reviewed by academic experts, every article in this database is an authoritative guide to the current scholarship in a variety of fields, containing original commentary and annotations.  
• ProQuest Dissertations & Theses (PQDT)   Indexes dissertations and masters' theses from most North American graduate schools as well as some European universities. Provides full text for most indexed dissertations from 1990-present.  
• Very Short Introductions   Launched by Oxford University Press in 1995, Very Short Introductions offer concise introductions to a diverse range of subjects from Climate to Consciousness, Game Theory to Ancient Warfare, Privacy to Islamic History, Economics to Literary Theory.

CONDUCTING INTERVIEWS

Equipment and software:  .

• Lamont Library  loans microphones and podcast starter kits, which will allow you to capture audio (and you may record with software, such as Garage Band). 
• Cabot Library  loans digital recording devices, as well as USB microphones.

If you prefer to use your own device, you may purchase a small handheld audio recorder, or use your cell phone.

• Audio Capture Basics (PDF)  - Helpful instructions, courtesy of the Lamont Library Multimedia Lab.
• Getting Started with Podcasting/Audio:  Guidelines from Harvard Library's Virtual Media Lab for preparing your interviewee for a web-based recording (e.g., podcast, interview)
• ​ Camtasia Screen Recorder and Video Editor
• Zoom: Video Conferencing, Web Conferencing
• Visit the Multimedia Production Resources guide! Consult it to find and learn how to use audiovisual production tools, including: cameras, microphones, studio spaces, and other equipment at Cabot Science Library and Lamont Library.
• Try the virtual office hours offered by the Lamont Multimedia Lab!

TIPS FOR CONDUCTING INTERVIEWS

Quick handout:  .

• Research Interviewing Tips (Courtesy of Dr. Suzanne Spreadbury)

Remote Interviews:  

• For Online or Distant Interviews, See "Remote Research & Virtual Fieldwork" on this guide .  
• Deborah Lupton's Bibliography: Doing Fieldwork in a Pandemic

Seeking Consent:

Books and articles:  .

• "App-Based Textual Interviews: Interacting With Younger Generations in a Digitalized Social Reallity."International Journal of Social Research Methodology (12 June 2022). Discusses the use of texting platforms as a means to reach young people. Recommends useful question formulations for this medium.  
• "Learning to Interview in the Social Sciences." Qualitative Inquiry, 9(4) 2003, 643–668 by Roulston, K., deMarrais, K., & Lewis, J. B. See especially the section on "Phrasing and Negotiating Questions" on pages 653-655 and common problems with framing questions noted on pages 659-660.  
• "Slowing Down and Digging Deep: Teaching Students to Examine Interview Interaction in Depth." LEARNing Landscapes, Spring 2021 14(1) 153-169 by Herron, Brigette A. and Kathryn Roulston. Suggests analysis of videorecorded interviews as a precursor to formulating one's own questions. Includes helpful types of probes.  
• Using Interviews in a Research Project by Nigel Joseph Mathers; Nicholas J Fox; Amanda Hunn; Trent Focus Group.  A work pack to guide researchers in developing interviews in the healthcare field. Describes interview structures, compares face-to-face and telephone interviews. Outlines the ways in which different types of interview data can be analysed.  
• “Working through Challenges in Doing Interview Research.” International Journal of Qualitative Methods, (December 2011), 348–66 by Roulston, Kathryn.  The article explores (1) how problematic interactions identified in the analysis of focus group data can lead to modifications in research design, (2) an approach to dealing with reported data in representations of findings, and (3) how data analysis can inform question formulation in successive rounds of data generation. Findings from these types of examinations of interview data generation and analysis are valuable for informing both interview practice as well as research design.

Videos:  

The way a qualitative researcher transcribes interviews should flow from, or align with, the methodological paradigm chosen for the study, whether that paradigm is interpretivist, critical, positivist, or participative in nature (or a combination of these).

TRANSCRIPTION

Before embarking on a transcription project, it's worthwhile to invest in the time and effort necessary to capture good audio, which will make the transcription process much easier. If you haven't already done so, check out the  audio capture guidelines from Harvard Library's Virtual Media Lab , or  contact a media staff member  for customized recommendations. First and foremost, be mindful of common pitfalls by watching this short video that identifies  the most common errors to avoid!

SOFTWARE:  

• Adobe Premiere Pro Speech-To-Text  automatically generates transcripts and adds captions to your videos. Harvard affiliates can download Adobe Premiere in the Creative Cloud Suite.  
• GoTranscript  provides cost-effective human-generated transcriptions.  
• pyTranscriber  is an app for generating automatic transcription and/or subtitles for audio and video files. It uses the Google Cloud Speech-to-Text service, has a friendly graphical user interface, and is purported to work nicely with Chinese.   
• Otter  provides a new way to capture, store, search and share voice conversations, lectures, presentations, meetings, and interviews. The startup is based in Silicon Valley with a team of experienced Ph.Ds and engineers from Google, Facebook, Yahoo and Nuance (à la Dragon). Free accounts available. This is the software that  Zoom  uses to generate automated transcripts, so if you have access to a Zoom subscription, you have access to Otter transcriptions with it (applicable in several  languages ). As with any automated approach, be prepared to correct any errors after the fact, by hand.  
• Panopto  is available to Harvard affiliates and generates  ASR (automated speech recognition) captions . You may upload compatible audio files into it. As with any automatically generated transcription, you will need to make manual revisions. ASR captioning is available in several  languages . Panopto maintains robust security practices, including strong authentication measures and end-to-end encryption, ensuring your content remains private and protected.  
• REV.Com  allows you to record and transcribe any calls on the iPhone, both outgoing and incoming. It may be useful for recording phone interviews. Rev lets you choose whether you want an AI- or human-generated transcription, with a fast turnaround. Rev has Service Organization Controls Type II (SOC2) certification (a SOC2 cert looks at and verifies an organization’s processing integrity, privacy practices, and security safeguards).   
• Scribie Audio/Video Transcription  provides automated or manual transcriptions for a small fee. As with any transcription service, some revisions will be necessary after the fact, particularly for its automated transcripts.  
• Sonix  automatically transcribes, translates, and helps to organize audio and video files in over 40 languages. It's fast and affordable, with good accuracy. The free trial includes 30 minutes of free transcription.  
• TranscriptionWing  uses a human touch process to clean up machine-generated transcripts so that the content will far more accurately reflect your audio recording.   
• Whisper is a tool from OpenAI that facilitates transcription of sensitive audiovisual recordings (e.g., of research interviews) on your own device. Installation and use depends on your operating system and which version you install. Important Note: The Whisper API, where audio is sent to OpenAI to be processed by them and then sent back (usually through a programming language like Python) is NOT appropriate for sensitive data. The model should be downloaded with tools such as those described in this FAQ , so that audio is kept to your local machine. For assistance, contact James Capobianco .

EQUIPMENT:  

• Transcription pedals  are in circulation and available to borrow from the Circulation desk at Lamont, or use at Lamont Library's Media Lab on level B. For hand-transcribing your interviews, they work in conjunction with software such as  Express Scribe , which is loaded on Media Lab computers, or you may download for free on your own machine (Mac or PC versions; scroll down the downloads page for the latter). The pedals are plug-and-play USB, allow a wide range of playback speeds, and have 3 programmable buttons, which are typically set to rewind/play/fast-forward. Instructions are included in the bag that covers installation and set-up of the software, and basic use of the pedals.

NEED HELP?  

• Try the virtual office hours offered by the Lamont Multimedia Lab!    
• If you're creating podcasts, login to  Canvas  and check out the  Podcasting/Audio guide . 

Helpful Texts:  

• "Transcription as a Crucial Step of Data Analysis" in Chapter 5 of The SAGE Handbook of Qualitative Data Analysisby Uwe Flick (Editor)  Covers basic terminology for transcription, shares caveats for transcribers, and identifies components of vocal behavior. Provides notation systems for transcription, suggestions for transcribing turn-taking, and discusses new technologies and perspectives. Includes a bibliography for further reading.  
• "Transcribing the Oral Interview: Part Art, Part Science " on p. 10 of the Centre for Community Knowledge (CCK) newsletter: TIMESTAMPby Mishika Chauhan and Saransh Srivastav

QUALITATIVE DATA ANALYSIS

Software  .

• Free download available for Harvard Faculty of Arts and Sciences (FAS) affiliates
• Desktop access at Lamont Library Media Lab, 3rd floor
• Desktop access at Harvard Kennedy School Library (with HKS ID)
• Remote desktop access for Harvard affiliates from  IQSS Computer Labs . Email them at  [email protected] and ask for a new lab account and remote desktop access to NVivo.
• Virtual Desktop Infrastructure (VDI) access available to Harvard T.H. Chan School of Public Health affiliates

CODING AND THEMEING YOUR DATA

Data analysis methods should flow from, or align with, the methodological paradigm chosen for your study, whether that paradigm is interpretivist, critical, positivist, or participative in nature (or a combination of these). Some established methods include Content Analysis, Critical Analysis, Discourse Analysis, Gestalt Analysis, Grounded Theory Analysis, Interpretive Analysis, Narrative Analysis, Normative Analysis, Phenomenological Analysis, Rhetorical Analysis, and Semiotic Analysis, among others. The following resources should help you navigate your methodological options and put into practice methods for coding, themeing, interpreting, and presenting your data.

• Users can browse content by topic, discipline, or format type (reference works, book chapters, definitions, etc.). SRM offers several research tools as well: a methods map, user-created reading lists, a project planner, and advice on choosing statistical tests.  
• Abductive Coding: Theory Building and Qualitative (Re)Analysis by Vila-Henninger, et al.  The authors recommend an abductive approach to guide qualitative researchers who are oriented towards theory-building. They outline a set of tactics for abductive analysis, including the generation of an abductive codebook, abductive data reduction through code equations, and in-depth abductive qualitative analysis.  
• Analyzing and Interpreting Qualitative Research: After the Interview by Charles F. Vanover, Paul A. Mihas, and Johnny Saldana (Editors)   Providing insight into the wide range of approaches available to the qualitative researcher and covering all steps in the research process, the authors utilize a consistent chapter structure that provides novice and seasoned researchers with pragmatic, "how-to" strategies. Each chapter author introduces the method, uses one of their own research projects as a case study of the method described, shows how the specific analytic method can be used in other types of studies, and concludes with three questions/activities to prompt class discussion or personal study.   
• "Analyzing Qualitative Data." Theory Into Practice 39, no. 3 (2000): 146-54 by Margaret D. LeCompte   This article walks readers though rules for unbiased data analysis and provides guidance for getting organized, finding items, creating stable sets of items, creating patterns, assembling structures, and conducting data validity checks.  
• "Coding is Not a Dirty Word" in Chapter 1 (pp. 1–30) of Enhancing Qualitative and Mixed Methods Research with Technology by Shalin Hai-Jew (Editor)   Current discourses in qualitative research, especially those situated in postmodernism, represent coding and the technology that assists with coding as reductive, lacking complexity, and detached from theory. In this chapter, the author presents a counter-narrative to this dominant discourse in qualitative research. The author argues that coding is not necessarily devoid of theory, nor does the use of software for data management and analysis automatically render scholarship theoretically lightweight or barren. A lack of deep analytical insight is a consequence not of software but of epistemology. Using examples informed by interpretive and critical approaches, the author demonstrates how NVivo can provide an effective tool for data management and analysis. The author also highlights ideas for critical and deconstructive approaches in qualitative inquiry while using NVivo. By troubling the positivist discourse of coding, the author seeks to create dialogic spaces that integrate theory with technology-driven data management and analysis, while maintaining the depth and rigor of qualitative research.   
• The Coding Manual for Qualitative Researchers by Johnny Saldana   An in-depth guide to the multiple approaches available for coding qualitative data. Clear, practical and authoritative, the book profiles 32 coding methods that can be applied to a range of research genres from grounded theory to phenomenology to narrative inquiry. For each approach, Saldaña discusses the methods, origins, a description of the method, practical applications, and a clearly illustrated example with analytic follow-up. Essential reading across the social sciences.  
• Flexible Coding of In-depth Interviews: A Twenty-first-century Approach by Nicole M. Deterding and Mary C. Waters The authors suggest steps in data organization and analysis to better utilize qualitative data analysis technologies and support rigorous, transparent, and flexible analysis of in-depth interview data.  
• From the Editors: What Grounded Theory is Not by Roy Suddaby Walks readers through common misconceptions that hinder grounded theory studies, reinforcing the two key concepts of the grounded theory approach: (1) constant comparison of data gathered throughout the data collection process and (2) the determination of which kinds of data to sample in succession based on emergent themes (i.e., "theoretical sampling").  
• “Good enough” methods for life-story analysis, by Wendy Luttrell. In Quinn N. (Ed.), Finding culture in talk (pp. 243–268). Demonstrates for researchers of culture and consciousness who use narrative how to concretely document reflexive processes in terms of where, how and why particular decisions are made at particular stages of the research process.   
• The Ethnographic Interview by James P. Spradley  “Spradley wrote this book for the professional and student who have never done ethnographic fieldwork (p. 231) and for the professional ethnographer who is interested in adapting the author’s procedures (p. iv) ... Steps 6 and 8 explain lucidly how to construct a domain and a taxonomic analysis” (excerpted from book review by James D. Sexton, 1980). See also:  Presentation slides on coding and themeing your data, derived from Saldana, Spradley, and LeCompte Click to request access.  
• Qualitative Data Analysis by Matthew B. Miles; A. Michael Huberman   A practical sourcebook for researchers who make use of qualitative data, presenting the current state of the craft in the design, testing, and use of qualitative analysis methods. Strong emphasis is placed on data displays matrices and networks that go beyond ordinary narrative text. Each method of data display and analysis is described and illustrated.  
• "A Survey of Qualitative Data Analytic Methods" in Chapter 4 (pp. 89–138) of Fundamentals of Qualitative Research by Johnny Saldana   Provides an in-depth introduction to coding as a heuristic, particularly focusing on process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Includes advice on writing analytic memos, developing categories, and themeing data.   
• "Thematic Networks: An Analytic Tool for Qualitative Research." Qualitative Research : QR, 1(3), 385–405 by Jennifer Attride-Stirling Details a technique for conducting thematic analysis of qualitative material, presenting a step-by-step guide of the analytic process, with the aid of an empirical example. The analytic method presented employs established, well-known techniques; the article proposes that thematic analyses can be usefully aided by and presented as thematic networks.  
• Using Thematic Analysis in Psychology by Virginia Braun and Victoria Clark Walks readers through the process of reflexive thematic analysis, step by step. The method may be adapted in fields outside of psychology as relevant. Pair this with One Size Fits All? What Counts as Quality Practice in Reflexive Thematic Analysis? by Virginia Braun and Victoria Clark

TESTING OR GENERATING THEORIES

The quality of your data analysis depends on how you situate what you learn within a wider body of knowledge. Consider the following advice:

Once you have coalesced around a theory, realize that a theory should  reveal  rather than  color  your discoveries. Allow your data to guide you to what's most suitable. Grounded theory  researchers may develop their own theory where current theories fail to provide insight.  This guide on Theoretical Models  from Alfaisal University Library provides a helpful overview on using theory.

MANAGING & FINDING INTERVIEW DATA

Managing your elicited interview data, general guidance:  .

• Research Data Management @ Harvard A reference guide with information and resources to help you manage your research data. See also: Harvard Research Data Security Policy , on the Harvard University Research Data Management website.  
• Data Management For Researchers: Organize, Maintain and Share Your Data for Research Success by Kristin Briney. A comprehensive guide for scientific researchers providing everything they need to know about data management and how to organize, document, use and reuse their data.  
• Open Science Framework (OSF) An open-source project management tool that makes it easy to collaborate within and beyond Harvard throughout a project's lifecycle. With OSF you can manage, store, and share documents, datasets, and other information with your research team. You can also publish your work to share it with a wider audience. Although data can be stored privately, because this platform is hosted on the Internet and designed with open access in mind, it is not a good choice for highly sensitive data.  
• Free cloud storage solutions for Harvard affiliates to consider include:  Google Drive ,  DropBox , or  OneDrive ( up to DSL3 )  

Data Confidentiality and Secure Handling:  

• Data Security Levels at Harvard - Research Data Examples This resource provided by Harvard Data Security helps you determine what level of access is appropriate for your data. Determine whether it should be made available for public use, limited to the Harvard community, or be protected as either "confidential and sensitive," "high risk," or "extremely sensitive." See also:  Harvard Data Classification Table  
• Harvard's Best Practices for Protecting Privacy and  Harvard Information Security Collaboration Tools Matrix Follow the nuts-and-bolts advice for privacy best practices at Harvard. The latter resource reveals the level of security that can be relied upon for a large number of technological tools and platforms used at Harvard to conduct business, such as email, Slack, Accellion Kiteworks, OneDrive/SharePoint, etc.  
• “Protecting Participant Privacy While Maintaining Content and Context: Challenges in Qualitative Data De‐identification and Sharing.” Proceedings of the ASIST Annual Meeting 57 (1) (2020): e415-420 by Myers, Long, and Polasek Presents an informed and tested protocol, based on the De-Identification guidelines published by the Qualitative Data Repository (QDR) at Syracuse University. Qualitative researchers may consult it to guide their data de-identification efforts.  
• QDS Qualitative Data Sharing Toolkit The Qualitative Data Sharing (QDS) project and its toolkit was funded by the NIH National Human Genome Research Institute (R01HG009351). It provides tools and resources to help researchers, especially those in the health sciences, share qualitative research data while protecting privacy and confidentiality. It offers guidance on preparing data for sharing through de-identification and access control. These health sciences research datasets in ICPSR's Qualitative Data Sharing (QDS) Project Series were de-identified using the QuaDS Software and the project’s QDS guidelines.  
• Table of De-Identification Techniques  
• Generative AI Harvard-affiliated researchers should not enter data classified as confidential ( Level 2 and above ), including non-public research data, into publicly-available generative AI tools, in accordance with the University’s Information Security Policy. Information shared with generative AI tools using default settings is not private and could expose proprietary or sensitive information to unauthorized parties.  
• Harvard Information Security Quick Reference Guide Storage guidelines, based on the data's security classification level (according to its IRB classification) is displayed on page 2, under "handling."  
• Email Encryption Harvard Microsoft 365 users can now send encrypted messages and files directly from the Outlook web or desktop apps. Encrypting an email adds an extra layer of security to the message and its attachments (up to 150MB), and means only the intended recipient (and their inbox delegates with full access) can view it. Message encryption in Outlook is approved for sending high risk ( level 4 ) data and below.  

Sharing Qualitative Data:  

• Repositories for Qualitative Data If you have cleared this intention with your IRB, secured consent from participants, and properly de-identified your data, consider sharing your interviews in one of the data repositories included in the link above. Depending on the nature of your research and the level of risk it may present to participants, sharing your interview data may not be appropriate. If there is any chance that sharing such data will be desirable, you will be much better off if you build this expectation into your plans from the beginning.  
• Guide for Sharing Qualitative Data at ICPSR The Inter-university Consortium for Political and Social Research (ICPSR) has created this resource for investigators planning to share qualitative data at ICPSR. This guide provides an overview of elements and considerations for archiving qualitative data, identifies steps for investigators to follow during the research life cycle to ensure that others can share and reuse qualitative data, and provides information about exemplars of qualitative data  

International Projects:

• Research Compliance Program for FAS/SEAS at Harvard The Faculty of Arts and Sciences (FAS), including the School of Engineering and Applied Sciences (SEAS), and the Office of the Vice Provost for Research (OVPR) have established a shared Research Compliance Program (RCP). An area of common concern for interview studies is international projects and collaboration . RCP is a resource to provide guidance on which international activities may be impacted by US sanctions on countries, individuals, or entities and whether licenses or other disclosure are required to ship or otherwise share items, technology, or data with foreign collaborators.

Finding Extant Interview Data

Finding journalistic interviews:  .

• Academic Search Premier This all-purpose database is great for finding articles from magazines and newspapers. In the Advanced Search, it allows you to specify "Document Type":  Interview.  
• Guide to Newspapers and Newspaper Indexes Use this guide created to Harvard Librarians to identify newspapers collections you'd like to search. To locate interviews, try adding the term  "interview"  to your search, or explore a database's search interface for options to  limit your search to interviews.  Nexis Uni  and  Factiva  are the two main databases for current news.   
• Listen Notes Search for podcast episodes at this podcast aggregator, and look for podcasts that include interviews. Make sure to vet the podcaster for accuracy and quality! (Listen Notes does not do much vetting.)  
• NPR  and  ProPublica  are two sites that offer high-quality long-form reporting, including journalistic interviews, for free.

Finding Oral History and Social Research Interviews:  

• To find oral histories, see the Oral History   page of this guide for helpful resources on Oral History interviewing.  
• Repositories for Qualitative Data It has not been a customary practice among qualitative researchers in the social sciences to share raw interview data, but some have made this data available in repositories, such as the ones listed on the page linked above. You may find published data from structured interview surveys (e.g., questionnaire-based computer-assisted telephone interview data), as well as some semi-structured and unstructured interviews.  
• If you are merely interested in studies interpreting data collected using interviews, rather than finding raw interview data, try databases like  PsycInfo ,  Sociological Abstracts , or  Anthropology Plus , among others. 

Finding Interviews in Archival Collections at Harvard Library:

In addition to the databases and search strategies mentioned under the  "Finding Oral History and Social Research Interviews" category above,  you may search for interviews and oral histories (whether in textual or audiovisual formats) held in archival collections at Harvard Library.

• HOLLIS searches all documented collections at Harvard, whereas HOLLIS for Archival Discovery searches only those with finding aids. Although HOLLIS for Archival Discovery covers less material, you may find it easier to parse your search results, especially when you wish to view results at the item level (within collections). Try these approaches:

Search in  HOLLIS :  

• To retrieve items available online, do an Advanced Search for  interview* OR "oral histor*" (in Subject), with Resource Type "Archives/Manuscripts," then refine your search by selecting "Online" under "Show Only" on the right of your initial result list.  Revise the search above by adding your topic in the Keywords or Subject field (for example:  African Americans ) and resubmitting the search.  
•  To enlarge your results set, you may also leave out the "Online" refinement; if you'd like to limit your search to a specific repository, try the technique of searching for  Code: Library + Collection on the "Advanced Search" page .   

Search in  HOLLIS for Archival Discovery :  

• To retrieve items available online, search for   interview* OR "oral histor*" limited to digital materials . Revise the search above by adding your topic (for example:  artist* ) in the second search box (if you don't see the box, click +).  
• To preview results by collection, search for  interview* OR "oral histor*" limited to collections . Revise the search above by adding your topic (for example:  artist* ) in the second search box (if you don't see the box, click +). Although this method does not allow you to isolate digitized content, you may find the refinement options on the right side of the screen (refine by repository, subject or names) helpful.  Once your select a given collection, you may search within it  (e.g., for your topic or the term interview).

UX & MARKET RESEARCH INTERVIEWS

Ux at harvard library  .

• User Experience and Market Research interviews can inform the design of tangible products and services through responsive, outcome-driven insights. The  User Research Center  at Harvard Library specializes in this kind of user-centered design, digital accessibility, and testing. They also offer guidance and  resources  to members of the Harvard Community who are interested in learning more about UX methods. Contact [email protected] or consult the URC website for more information.

Websites  

• User Interviews: The Beginner’s Guide (Chris Mears)  
• Interviewing Users (Jakob Nielsen)

Books  

• Interviewing Users: How to Uncover Compelling Insights by Steve Portigal; Grant McCracken (Foreword by)  Interviewing is a foundational user research tool that people assume they already possess. Everyone can ask questions, right? Unfortunately, that's not the case. Interviewing Users provides invaluable interviewing techniques and tools that enable you to conduct informative interviews with anyone. You'll move from simply gathering data to uncovering powerful insights about people.  
• Rapid Contextual Design by Jessamyn Wendell; Karen Holtzblatt; Shelley Wood  This handbook introduces Rapid CD, a fast-paced, adaptive form of Contextual Design. Rapid CD is a hands-on guide for anyone who needs practical guidance on how to use the Contextual Design process and adapt it to tactical projects with tight timelines and resources. Rapid Contextual Design provides detailed suggestions on structuring the project and customer interviews, conducting interviews, and running interpretation sessions. The handbook walks you step-by-step through organizing the data so you can see your key issues, along with visioning new solutions, storyboarding to work out the details, and paper prototype interviewing to iterate the design all with as little as a two-person team with only a few weeks to spare *Includes real project examples with actual customer data that illustrate how a CD project actually works.

Videos  

Instructional Presentations on Interview Skills  

• Interview/Oral History Research for RSRA 298B: Master's Thesis Reading and Research (Spring 2023) Slideshow covers: Why Interviews?, Getting Context, Engaging Participants, Conducting the Interview, The Interview Guide, Note Taking, Transcription, File management, and Data Analysis.  
• Interview Skills From an online class on February 13, 2023:  Get set up for interview research. You will leave prepared to choose among the three types of interviewing methods, equipped to develop an interview schedule, aware of data management options and their ethical implications, and knowledgeable of technologies you can use to record and transcribe your interviews. This workshop complements Intro to NVivo, a qualitative data analysis tool useful for coding interview data.

NIH Data Management & Sharing Policy (DMSP) This policy, effective January 25, 2023, applies to all research, funded or conducted in whole or in part by NIH, that results in the generation of  scientific data , including NIH-funded qualitative research. Click here to see some examples of how the DMSP policy has been applied in qualitative research studies featured in the 2021 Qualitative Data Management Plan (DMP) Competition . As a resource for the community, NIH has developed a resource for developing informed consent language in research studies where data and/or biospecimens will be stored and shared for future use. It is important to note that the DMS Policy does NOT require that informed consent obtained from research participants must allow for broad sharing and the future use of data (either with or without identifiable private information). See the FAQ for more information.

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Except where otherwise noted, this work is subject to a Creative Commons Attribution 4.0 International License , which allows anyone to share and adapt our material as long as proper attribution is given. For details and exceptions, see the Harvard Library Copyright Policy ©2021 Presidents and Fellows of Harvard College.

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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How to Analyze Interview Transcripts in Qualitative Research

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Studies take time, accuracy, and a drive to provide excellent information, and qualitative research is a critical part of any successful study. You may be wondering how qualitative data adds to a paper or report, given that it’s not the hard “science” we often see highlighted the most often.

How Do You Analyze Qualitative Interviews?

There are two main approaches to qualitative analysis: inductive and deductive . What’s more, there are two types of inductive qualitative analysis to choose from. These are called thematic content analysis and narrative analysis, both of which call for an unstructured approach to research.

Inductive Methods of Analyzing Interview Transcripts

A thematic content analysis begins with weeding out biases and establishing your overarching impressions of the data. Rather than approaching your data with a predetermined framework, identify common themes as you search the materials organically. Your goal is to find common patterns across the data set.

A narrative analysis involves making sense of your interview respondents’ individual stories. Use this type of qualitative data analysis to highlight important aspects of their stories that will best resonate with your readers. And, highlight critical points you have found in other areas of your research.

Deductive Approach to Qualitative Analysis

Deductive analysis , on the other hand, requires a structured or predetermined approach. In this case, the researcher will build categories in advance of their analysis. Then, they’ll map connections in the data to those specific categories.

Each of these qualitative analysis methods lends its benefits to the research effort. Inductive analyses will produce more nuanced findings. Meanwhile, deductive analyses allow the researcher to point to key themes essential to their research.

Successful qualitative research hinges on the accuracy of your data. This can be harder to achieve than with quantitative research. It’s easy to lose important facts and meaning as you transition qualitative data from the source to your published content. This makes transcription a vital tool in maintaining integrity and relaying information in an unbiased way that’s useful for readers and adds appropriate context to the journal or study.

How to Transcribe a Qualitative Interview

Accurate transcription begins early in the interview process, even before you start interviewing. Here are the steps to transcribing a qualitative interview.

1. Collect Feedback for Qualitative Research

There are dozens of ways to gather qualitative data. Recording and accurately transcribing interviews is among the best methods to avoid inaccuracies and data loss, and researchers should consider this approach over simply taking notes firsthand.

Make sure you have a reliable way to record, whether the interview takes place in person, over the phone, or as part of a video call. Depending on the interview method, you may record a video or an audio-only format. Here are some tips depending on where the interview takes place:

• These apps can also be used for over-the-phone interviews.
• For video interviews , we recommend taking advantage of one of our transcription integrations , such as Zoom. Rev also has an API available for those who want to streamline their workflow even further by integrating Rev directly into their processes and platforms.

2. Organize Your Research Recordings

You should ensure that your audio or video files are easy to save, compile, and share. To do this, be sure to adopt easy-to-remember naming conventions as well to ensure they stay organized. An example of a naming convention that is simple to remember and recreate includes “Date.LastNameofSource.Topic”.

3. Transcribe All the Interviews and Focus Group Recordings

The next critical step is transcription. Done manually, this is a long and tedious process that can add hours, days, or even months to your report-writing process. There are dozens of pitfalls when performing transcriptions manually as well, as it can be hard to pick up words spoken in a heavy dialect or quiet tone. You also want to avoid having to transcribe all the “umms” and “ems” that occur when a source is speaking naturally.

Rev provides a variety of transcription services that take the tedium and guesswork out of the research process. You can choose to edit out all of the “umms,” while ensuring that heavy accents or muffled voices are picked up by the recording service.

You can order transcripts from Rev with both audio and video recordings. Once you’ve received your professional transcripts from Rev, you can begin your qualitative analysis.

The 6 Steps of Qualitative Interview Data Analysis

Among qualitative interview data analysis methods, thematic content analysis is perhaps the most common and effective method. It can also be one of the most trustworthy , increasing the traceability and verification of an analysis when done correctly. The following are the six main steps of a successful thematic analysis of your transcripts.

1. Read the Transcripts

By now, you will have accessed your transcript files as digital files in the cloud or have downloaded them to your computer for offline viewing. Start by browsing through your transcripts and making notes of your first impressions. You will be able to identify common themes. This will help you with your final summation of the data.

Next, read through each transcript carefully. Evidence of themes will become stronger, helping you to hone in on important insights.

You must identify bias during this step as well. Biases can appear in the data, among the interviewees, and even within your objectives and methodologies. According to SAGE Publishing , researchers should “acknowledge preconceived notions and actively work to neutralize them” at this early step.

2. Annotate the Transcripts

Annotation is the process of labeling relevant words, phrases, sentences, or sections with codes. These codes help identify important qualitative data types and patterns. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant.  Annotations will help you organize your data for dissemination .

Be generous with your annotations—don’t hold back. You will have an opportunity to eliminate or consolidate them later. It’s best to do more here, so you don’t have to come back to find more opportunities later.

3. Conceptualize the Data

Conceptualizing qualitative data is the process of aligning data with critical themes you will use in your published content. You will have identified many of these themes during your initial review of the transcripts.

To conceptualize,  create categories and subcategories  by grouping the codes you created during annotation. You may eliminate or combine certain codes rather than using all the codes you created. Keep only the codes you deem relevant to your analysis.

4. Segment the Data

Segmentation is the process of positioning and  connecting your categories . This allows you to establish the bulk of your data cohesively. Start by labeling your categories and then describe the connections between them.

You can use these descriptions to improve your final published content.

• Create a spreadsheet  to easily compile your data.
• Then, use the columns to structure important variables of your data analysis using codes as tools for reference.
• Create a separate tab for the front of the document that contains a coding table. This glossary contains important codes used in the segmentation process. This will help you and others quickly identify what the codes are referring to.

5. Analyze the Segments

You’re now ready to take a  deep dive into your data segments . Start by determining if there is a hierarchy among your categories. Determine if one is more important than the other, or draw a figure to summarize the results. At this stage, you may also want to align qualitative data with any quantitative data you collected.

6. Write the Results

Your analysis of the content is complete—you’re ready to transition your findings into the real body of your content. Use your insights to build and verify theories, answer key questions in your field, and back aims and objectives. Describe your categories and how they are connected using a neutral, objective voice.

Although you will pull heavily from your own research, be sure to publish content in the context of your field. Interpret your results in light of relevant studies, theories, and concepts related to your study.

Why Use Interviews for Qualitative Data

Unlike quantitative data, which is certainly important, a qualitative analysis adds color to academic and business reports. It offers perspective and can make a report more readable, add context, and inspire thoughtful discussion beyond the report.

As we’ve observed, transcribing qualitative interviews is crucial to getting less measurable data from direct sources. They allow researchers to provide relatable stories and perspectives and even quote important contributors directly. Lots of qualitative data from interviews enables authors to avoid embellishment and maintain the integrity of their content as well.

So, how do you conduct interview data analysis on qualitative data to pull key insights and strengthen your reports? Transcribing interviews is one of the most useful tools available for this task.

As a researcher, you need to make the most of recorded interviews . Interview transcripts allow you to use the best qualitative analysis methods. Plus, you can focus only on tasks that add value to your research effort.

Transcription is Essential to Qualitative Research Analysis

Qualitative data is often elusive to researchers. Transcripts allow you to capture original, nuanced responses from your respondents. You get their response naturally using their own words—not a summarized version in your notes.

You can also go back to the original transcript at any time to see what was said as you gain new context. The editable digital transcript files are incredibly easy to work with, saving you time and giving you speaker tags, time marks, and other tools to ensure you can find what you need within a transcript quickly.

When creating a report, accuracy matters, but efficiency matters, as well. Rev offers a seamless way of doing the transcription for you, saving you time and allowing you to focus on high-quality work instead. Consider Rev as your transcription service provider for qualitative research analysis — try Rev’s AI or Human Transcription services today.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Analyze Interview Data In Qualitative Research

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Deductive analysis , on the other hand, requires a structured or predetermined approach. In this case, the researcher will build categories in advance of his or her analysis. Then, theyll map connections in the data to those specific categories.

Each of these qualitative analysis methods lends its own benefits to the research effort. Inductive analyses will produce more nuanced findings. Meanwhile, deductive analyses allow the researcher to point to key themes essential to his or her research.

Analysing As You Collect

As we saw in the section on introductory considerations, qualitative research differs from quantitative in being non linear, with the activities of data collection and analysis intertwined. Most researchers advocate starting some coding before all the data comes in, for two reasons:

• You avoid ‘drowning in data’ qualitative research can generate voluminous data, and the researcher can be faced with literally 100s, even 1,000s of pages.
• You get to develop your analysis concepts and themes start to emerge, and if you have decided to use a particular method, such as content or discouse analysis, you have a chance to see how that will work, and whether it might be better to adopt another approach.

Thus when you get a certain way through your collection, say after the first few interviews or first major site visit, you could make your initial analysis. The next section, Carrying out the analysis, goes into more detail on methodology.

Difficulties In Presenting Interview Data

The following might be the type of difficulties that you will face in presenting the interview data

• Interview data is almost always detailed and the manuscript has a limitation on the number of words that you can use. You have to present only the key elements of the interview in your manuscripts so it does not take a lot of it. It means that the the researcher needs to have an expertise in analyzing data in a manner that yields only useful details.
• Some students face difficulty in understanding the difference between what part of the interview is directly related to the research question and what is not.
• As interview data is non-numerical presenting in tabulated form is not easier. There are complex concepts that cannot be broken down into numerical method. Tabulated data is far easier to analyze and interpret.

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How Not To Assess Qualitative Research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

How To Create High

1. Cover as many survey responses as possible.

The code should be generic enough to apply to multiple comments, but specific enough to be useful in your analysis. For example, Product is a broad code that will cover a variety of responses but its also pretty vague. What about the product? On the other hand, Product stops working after using it for 3 hours is very specific and probably wont apply to many responses. Poor product quality or short product lifespan might be a happy medium.

2. Avoid commonalities.

Having similar codes is okay as long as they serve different purposes. Customer service and Product are different enough from one another, while Customer service and Customer support may have subtle differences but should likely be combined into one code.

3. Capture the positive and the negative.

Try to create codes that contrast with each other to track both the positive and negative elements of a topic separately. For example, Useful product features and Unnecessary product features would be two different codes to capture two different themes.

4. Reduce data to a point.

Lets look at the two extremes: There are as many codes as there are responses, or each code applies to every single response. In both cases, the coding exercise is pointless you dont learn anything new about your data or your customers. To make your analysis as useful as possible, try to find a balance between having too many and too few codes.

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Inductive Methods Of Analyzing Interview Transcripts

Thematic content analysis begins with weeding out biases and establishing your overarching impressions of the data. Rather than approaching your data with a predetermined framework, identify common themes as you search the materials organically. Your goal is to find common patterns across the data set.

A narrative analysis involves making sense of your interview respondents individual stories. Use this type of qualitative data analysis to highlight important aspects of their stories that will best resonate with your readers. And, highlight critical points you have found in other areas of your research.

Using Nvivo In Qualitative Data Analysis

NVivo is one of the computer-assisted qualitative data analysis softwares developed by QSR International , the worlds largest qualitative research software developer. This software allows for qualitative inquiry beyond coding, sorting and retrieval of data. It was also designed to integrate coding with qualitative linking, shaping and modelling. The following sections discuss the fundamentals of the NVivo software and illustrates the primary tools in NVivo which assist qualitative researchers in managing their data.

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What Are The Steps In Analyzing Quantitative Data

• Data validation is the first step in the process. The purpose of data validation is to find out if the data collection was done in line with the pre-set standards.
• The second step is data editing. There are errors in large data sets.
• Data coding is part of the third step.

The brand will analyze the data to find out what young women want, for example, if they would like to see more variety of jeans. Depending on the type of research there are many different data analysis methods. Researchers need to pick a random sample of surveys to get the data they need.

The researcher can get in touch with them through email or phone, and check their responses to questions. Its important to fill all the empty fields while editing the data.

It is important to think about which one is best suited for your research question and what you want to show before applying descriptive statistics. When the research is limited to the sample and doesnt need to be generalized to a larger populationDescriptive statistics are most helpful when the research is limited to the sample and doesnt need to be generalized to a larger population If you are comparing the percentage of children in two different villages, descriptive statistics is enough.

It can be used to analyze documented information in the form of texts, media or even physical items. Narrative analysis is a method used to analyze content from various sources.

Identify Trends & Analyze

There are literally thousands of different ways to analyze qualitative data, and in most situations, the best technique depends on the information you want to get out of the research.

Nevertheless, there are a few go-to techniques. The most important of this is occurrences . In this short video, we finish the example from above by counting the number of times our codes appear. In this way, its very similar to word frequency .

A few other options include:

• Ranking each code on a set of relevant criteria and clustering
• Pure cluster analysis
• Causal analysis

We cover different types of analysis like this on the website, so be sure to check out other articles on the home page .

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Checklist For Qualitative Papers

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

So Qualitative Analysis Is Easier Than Quantitative Right

Well. not quite . In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase , youll likely have many pages of text-based data or hours upon hours of audio to work through. You might have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes.

Making sense of all of this is no small task and you shouldnt underestimate it. Long story short qualitative analysis can be a lot of work!

In this post, we will explore qualitative data analysis by looking at the general methodological approaches used for dealing with qualitative data. Were not going to cover every possible qualitative approach and were not going to go into heavy detail were just going to give you the big picture. These approaches can be used on primary data or secondary data .

Without further delay, lets get into it.

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Importance Of Qualitative Data

Qualitative data is important in determining the particular frequency of traits or characteristics. It allows the statistician or the researchers to form parameters through which larger data sets can be observed. Qualitative data provides the means by which observers can quantify the world around them.

For a market researcher, collecting qualitative data helps in answering questions like, who their customers are, what issues or problems they are facing, and where do they need to focus their attention, so problems or issues are resolved.

Qualitative data is about the emotions or perceptions of people , what they feel. In quantitative data, these perceptions and emotions are documented. It helps the market researchers understand the language their consumers speak and deal with the problem effectively and efficiently.

Qualitative Data Collection Methods Types Of Qualitative Data

Qualitative data collection is exploratory it involves in-depth analysis and research. Qualitative data collection methods are mainly focused on gaining insights, reasoning, and motivations hence they go deeper in terms of research . Since the qualitative data cannot be measured, researchers prefer methods or data collection tools that are structured to a limited extent.

Here are the qualitative data collection methods :

1. One-to-One Interviews: It is one of the most commonly used data collection instruments for qualitative research, mainly because of its personal approach. The interviewer or the researcher collects data directly from the interviewee on a one-to-one basis. The interview may be informal and unstructured conversational. Mostly the open-ended questions are asked spontaneously, with the interviewer letting the flow of the interview dictate the questions to be asked.

2. Focus groups: This is done in a group discussion setting. The group is limited to 6-10 people, and a moderator is assigned to moderate the ongoing discussion.

Depending on the data which is sorted, the members of a group may have something in common. For example, a researcher conducting a study on track runners will choose athletes who are track runners or were track runners and have sufficient knowledge of the subject matter.

Besides taking notes, other documentation methods, such as video and audio recording, photography, and similar methods, can be used.

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Qualitative Research In Medicine

Qualitative research has seen an increased popularity in the last two decades and is becoming widely accepted across a wide range of medical and health disciplines, including health services research, health technology assessment, nursing, and allied health. There has also been a corresponding rise in the reporting of qualitative research studies in medical and health related journals.

The increasing popularity of qualitative methods is a result of failure of quantitative methods to provide insight into in-depth information about the attitudes, beliefs, motives, or behaviours of people, for example in understanding the emotions, perceptions and actions of people who suffer from a medical condition. Qualitative methods explore the perspective and meaning of experiences, seek insight and identify the social structures or processes that explain peoples behavioural meaning., Most importantly, qualitative research relies on extensive interaction with the people being studied, and often allows researchers to uncover unexpected or unanticipated information, which is not possible in the quantitative methods. In medical research, it is particularly useful, for example, in a health behaviour study whereby health or education policies can be effectively developed if reasons for behaviours are clearly understood when observed or investigated using qualitative methods.

The Qualitative Data Analysis Methods Big 6

There are many different types of qualitative data analysis , all of which serve different purposes and have unique strengths and weaknesses . Well start by outlining the analysis methods and then well dive into the details for each one.

The 6 most popular QDA methods or at least the ones we see at Grad Coach are:

Lets take a look at them

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The First Step In Qualitative Research: Determine Your Goal

Determine what you want to study:

• A current or potential product, service or brand positioning
• Strengths and weaknesses in products
• Purchasing decisions
• Reactions to advertising or marketing campaigns
• Usability of a website or other interactive services
• Perceptions about the company, brand or product
• Reactions to packaging and design

Straightforward Methods For Analyzing Qualitative Interview Data

To some qualitative data analysis may seem like a daunting task. Some quantitative researchers openly admit they would not know where to begin if given the job, and that the unfamiliar process scares them a bit. Unlike most quantitative methodologies, qualitative analysis does not follow a formula-like procedure that can be systematically and analytically applied. When we embark on a qualitative journey, we need to be prepared to work in a slightly more intuitive and not always tangible way. But that does not imply qualitative methodology lacks rigor. On the contrary it just achieves results in a different way to a quantitative study.

Do not let this posts title fool you, qualitative analysis is not an easy task. Often time-consuming and at times slightly chaotic, the researcher generally never knows where the study will take them. But, hey, thats also the beauty of the qualitative method and its hidden potential.

Reading interviews multiple times to get familiar with your data is where most qualitative researchers start. In qualitative research, we immerse ourselves into the study we do not first start to seek objectivity , but rather closeness . Remember, as a qualitative researcher you are the research tool.

Which method of analyzing to choose?

If you have conducted qualitative interviews, here are three methods that can be used to analyze your data:

• Thematic content analysis
• Narrative analysis
• A deductive approach

Using computer software for data analysis

Recommended Reading: What Are Some Good Interview Questions To Ask

How To Analyze A Qualitative Interview

Analyze your qualitative research data early. That way, you can identify emerging themes to shape future interviews. Consider adding these to each interview report:

• The goal of the interview
• Details about the interview participant
• Questions asked, summarized responses and key findings
• Recommendations

Relate the analysis to the goal of the qualitative research interview.

Complementing Audio/visual Data With Written Data

Most qualitative data collection includes some form of note-taking in addition to audio or video recording. In one-on-one interviews, the note-taker is usually the interviewer. For focus groups, a dedicated note-taker is usually present . This note-taker can also serve a dual role of assisting with focus group logistics on the day of the session, such as directing lost participants by telephone, assisting with consent processes, and greeting late arrivals. For observational studies, a data collector may take notes in real time , or after leaving the study site.

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How To Do A Thematic Analysis Of User Interviews

You have been in the field talking to users and you now find yourself with a massive amount of audio, notes, video, pictures, and interesting impressions. All that information can be overwhelming, and its difficult to know where to start to make sense of all the data. Here, we will teach you how to go from information chaos to patterns and themes that represent the most interesting aspects of your data and which you can use as the foundation for personas , user scenarios and design decisions.

No matter which type of study you are doing and for what purpose, the most important thing in your analysis is that you respect the data and try to represent your interview as honestly as possible. When you share your results with others, you should be transparent about everything in your research process, from how you recruited participants to how you performed the analysis. This will make it easier for people to trust in the validity of your results. People who dont agree with your conclusion might be critical of your research results, but if you know that you have done everything possible to represent your participants and your research process honestly, you should have no problem defending your results.

How To Assess Qualitative Research

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness . However, none of these has been elevated to the gold standard in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

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Create New Codes That Encapsulate Potential Themes

Look across all the codes and explore any causal relationships, similarities, differences, or contradictions to see if you can uncover underlying themes. While doing so, some of the codes will be set aside and new interpretive codes will be created. If youre using a physical-mapping approach like that discussed in step 3, then some of these initial groupings may collapse or expand as you look for themes.

Ask yourself the following questions:

• Whats going on in each group?
• How are these codes related?
• How do these relate to my research questions?

Returning to our cooking topic, when analyzing the text within each grouping and looking for relationships between the data, I noticed that two participants said that they liked ingredients that can be prepared in different ways and go well with other different ingredients. A third participant talked about wishing she could have a set of ingredients that can be used for many different meals throughout the week, rather than having to buy separate ingredients for each meal plan. Thus, a new theme about the flexibility of ingredients emerged. For this theme, I came up with the code one ingredient fits all, for which I then wrote a detailed description.

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Effectiveness of Psychosocial Skills Training and Community Mental Health Services: A Qualitative Research

• Original Paper
• Published: 22 April 2024

Cite this article

• Halil İbrahim Bilkay   ORCID: orcid.org/0000-0002-8231-960X 1 ,
• Burak Şirin   ORCID: orcid.org/0000-0002-8485-5756 2 &
• Nermin Gürhan   ORCID: orcid.org/0000-0002-3472-7115 2  

This study employs a phenomenological approach to investigate the experiences of individuals who access services at a community mental health center (CHMC) in Türkiye The aim of this study is to comprehend the experiences of individuals who participate in psychosocial skills training at the CHMC. Thematic analysis of data from sixteen in-depth interviews revealed three main themes and eight sub-themes. Functionality theme emphasizes the positive impact of CHMC services and training on daily life and social functioning. Effective Factors theme encompasses the elements that improve the effectiveness of CHMC services. Participants have provided suggestions for the content of the training under the theme of Recommendations. Study results show that CHMC services and psychosocial skills training benefit individuals' daily lives and functioning, but that opportunities for improvement exist. It is crucial to incorporate participant feedback, and further research should be conducted to investigate the effectiveness of these services in this area.

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Acknowledgements

The authors express gratitude to the staff of the mental health center for their support and assistance during the study, as well as to the participants who generously provided responses to the questions posed.

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Material preparation, data collection and analysis were performed by Halil İbrahim BİLKAY and Burak ŞİRİN. The first draft of the manuscript was written by Halil İbrahim BİLKAY and all authors commented on previous versions of the manuscript. Nermin GÜRHAN read and approved the final manuscript.

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Bilkay, H.İ., Şirin, B. & Gürhan, N. Effectiveness of Psychosocial Skills Training and Community Mental Health Services: A Qualitative Research. Community Ment Health J (2024). https://doi.org/10.1007/s10597-024-01278-3

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Sustaining the nursing workforce - exploring enabling and motivating factors for the retention of returning nurses: a qualitative descriptive design

• Kumiko Yamamoto 1 ,
• Katsumi Nasu 1 ,
• Yoko Nakayoshi 1 &
• Miyuki Takase 1  

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The nursing shortage represents a persistent and urgent challenge within the healthcare industry. One of the most cost-effective and time-efficient solutions to address this issue is the recruitment of inactive nurses to rejoin the nursing workforce, while simultaneously ensuring the long-term sustainability of their careers following their return to work. The aim of this study is to explore the factors that facilitate the retention of nurses who have returned to work, from their perspective.

To achieve this aim, a qualitative descriptive design was employed. A total of 15 registered nurses who had not practiced nursing for a minimum of three years prior to their return to work, and had been working as nurses for at least three months following their return, were selected from seven healthcare institutions using convenience sampling. Face-to-face or online semi-structured interviews were conducted, and qualitative inductive analysis was employed to analyze the collected data.

The analysis revealed five key themes, two of which were related to the enabling factors making it possible for the nurses to continue their work, while the remaining three pertained to the motivating factors driving the pursuit of professional careers. The two themes associated with enabling factors were identified as “Conditions and support that sustain work-life balance” and “A workplace that acknowledges my career, and encourages my growth as an experienced nurse”. The three themes related to motivating factors were entitled “Pride in reconnecting with and contributing to society,” “Cultivating confidence through incremental professional development and future envisioning,” and “Enrichment of my own and my family’s life”.

Conclusions

Returning nurses constitute a valuable asset for healthcare institutions. To effectively retain these nurses, it is crucial to implement multi-dimensional approaches that enable and motivate them to sustain and enrich their professional and personal lives while continuing their work in the nursing field.

Peer Review reports

Nurses constitute a vital cornerstone of the healthcare system, assuming a foundational role in providing patient care, and notably representing over half of the entire healthcare workforce [ 1 ]. The global nurse population was estimated at 27.9 million in 2018 [ 1 ], and there was a notable growth of 4.7 million nurses between 2013 and 2018 [ 1 ]. Simultaneously, the WHO [ 1 ] reported a deficit of 5.9 million nurses in 2018, with the shortfall in the number of nurses expected to reach 10.6 million by 2030 [ 2 ]. This trend is primarily driven by the mounting demand for nursing services stemming from population aging dynamics. Moreover, the aging composition of the nursing workforce exacerbates the existing shortage of nurses. Currently, 17% of the global nursing population is aged 55 years or over [ 2 ], and projections indicate that within the upcoming decade, approximately 4.7 million nurses are expected to retire [ 3 ]. This means that an estimated annual influx of 47,000 new nurses is required just to sustain the current nursing workforce. Failure to meet this demand will probably intensify the nursing shortage at an accelerating pace. There is an immediate need for cost-effective measures aimed at mitigating the shortage of nurses.

Numerous policies have been implemented on a global scale to address the persistent shortage of nursing professionals. These policy measures encompass creating new registered nurses through education; facilitating re-entry into the nursing workforce for currently inactive registered nurses, and recruiting nurses from other countries [ 4 ]. Among the aforementioned strategies, one particularly promising approach to overcoming the nursing shortage involves the recruitment of inactive nurses, which has been implemented in many countries [ 4 , 5 ]. The reintegration of inactive nurses into the labor force is advantageous in terms of cost and time, as it obviates the need to invest social capital and years of resources in educating and nurturing new nursing students. Countries have implemented Return to Practice Programs designed for inactive nurses, each varying in educational content and duration [e.g., 6 , 7 ], and these initiatives have demonstrated notable success in augmenting the nursing labor force [ 8 , 9 , 10 , 11 ].

The reintegration of these nurses into the labor force holds significant importance in addressing the nursing shortage in Japan in particular. Japan is currently facing the challenge of a super-aging population, with 29.0% of its total population being 65 years and older [ 12 ]. This demographic shift has imposed increasing demands on nursing professionals, as older people often experience multiple chronic illnesses that result in physical and cognitive decline [ 13 ], necessitating substantial medical support and assistance in daily activities. In response to this demand, the Japanese government has actively pursued strategies to increase enrollment in nursing schools, reduce attrition rates, promote the retention of currently practicing nurses, and encourage inactive nurses to return to nursing practice [ 14 ]. However, the declining birth rate in Japan has led to a decrease in the number of students enrolling in nursing schools since 2018 [ 15 ]. Although the improvement in the workplace environment has contributed to a reduction in the turnover rate of full-time nursing personnel from 11.0% in 2013 to 10.6% in 2021, which is slightly lower than the average turnover rate across all occupations (i.e., 11.3%) [ 16 ], this alone cannot address the issue of the nursing shortage. Consequently, an inevitable imbalance between demand and supply persists. The Ministry of Health, Labor, and Welfare in Japan [ 14 ] projected a demand for 1.88–2.02 million nurses by 2025, when the baby boomer generation reaches 75 years old or older, while the projected supply would be 1.75–1.82 million nurses, resulting in a shortage of 60,000 to 250,000 nurses. Therefore, the recruitment of inactive nurses has emerged as a pivotal measure to rectify this imbalance promptly.

Available statistics show that there is an estimated population of approximately 700,000–860,000 inactive nurses in Japan [ 17 ], the United States [ 18 ] and Germany [ 19 ]. Several studies have demonstrated that a significant proportion of surveyed inactive nurses, ranging from 43 to 85%, expressed a desire to return to nursing practice [ 20 , 21 ]. The motivations behind their return or desire to return to nursing practice encompass factors such as no longer having childcare responsibilities [ 22 ], a yearning for nursing practice [ 22 ], seeking a renewed purpose in life after completing child-rearing [ 23 ], financial incentives [ 10 , 22 , 23 ], and a desire to update skills and knowledge in acute care nursing [ 24 ]. Similarly, a more recent study conducted in Taiwan reported that incentives for returning to practice included the improvement of the nurse staffing level, and the provision of a safer working environment and re-entry preparation programs [ 20 ].

However, it should be noted that despite the expressed intentions, many inactive nurses have faced challenges in returning to practice as well as in sustaining their employment [ 25 ]. These challenges related to returning to work include difficulties in balancing work with childcare and household responsibilities, anxiety arising from a perceived lack of competency, concern about heavy work responsibilities, and fears of committing medical errors [ 15 ]. Consequently, previous research findings have indicated that only 57–69% of nurses who completed the Return to Practice Program were able to successfully re-enter the nursing workforce [ 26 ]. These challenges persist even after returning to work, as reported in subsequent studies [ 27 , 28 , 29 ], exacerbated by the absence of family-friendly working conditions, inadequate on-the-job training opportunities, and insufficient ongoing education and mental support to overcome anxiety and regain confidence [ 30 ]. As a consequence, nurses who have returned to work experience a sense of guilt toward both their colleagues and patients for perceived inadequacies in care provision, as well as feelings of guilt toward their families due to the sacrifices necessitated by their work obligations [ 31 ], all of which contribute to higher attrition rates among returners. In fact, the findings from a small-scale survey conducted in Japan revealed that 25% of nurses who participated in refresher programs and returned to work were unable to sustain their employment [ 32 ]. This retention rate is significantly higher compared to the turnover rates observed among newly graduated nurses (7.8%) and nurses with prior experience (17.7%) [ 16 ].

While it is crucial to address the barriers encountered by nurses who wish to return to practice and have successfully done so, it is equally imperative to ensure the long-term sustainability of their careers following their return to work. However, the factors that contribute to the retention of these returners have not been thoroughly investigated. For instance, Barriball et al. [ 33 ] and Elwin [ 27 ] investigated the experiences of nurses returning to practice, although their focus was primarily on the experiences within the Return to Practice Program, rather than the process of returning to the workplace itself. Conversely, Durand and Randhawa [ 34 ], Hammer and Craig [ 23 ] and Costantini, et al. [ 35 ] explored the experiences of nurses returning to work; however, they did not focus on the specific factors that facilitate retention. In fact, only a limited number of studies have endeavored to identify factors that facilitate the retention of inactive nurses. The key findings facilitating their retention were preceptors fulfilling their learning needs [ 28 , 31 ], support on nursing units [ 31 ], flexible working atmosphere [ 28 ], and re-building a new family life [ 28 ] or re-negotiation with both work and home life [ 36 ]. Nevertheless, these studies are based on a relatively small sample of five to eight nurses who have returned to practice, thus leaving the possibility that some factors remain undiscovered. A comprehensive understanding of the factors that not only prompt nurses to leave their positions but also motivate them to remain is crucial for the development of strategies that ensure a sufficient nursing workforce and the provision of high-quality nursing care in countries grappling with nursing shortages.

Therefore, the aim of this study is to explore the factors that facilitate the retention of nurses who have returned to work, from their perspectives.

Methodology

This study employed a qualitative descriptive design [ 37 ]. The qualitative descriptive approach produces “findings closer to the data as given, or data-near” [ 38 , p. 78], without commitment to any theoretical views and without being bounded by preconceptions [ 38 ]. As such, this approach provides straightforward and comprehensive descriptive summaries of participants’ experiences and perceptions [ 39 , 40 ], thus it is suitable for areas where little is known about the topic under investigation [ 39 ]. We applied this approach to investigate the factors that contributed to the retention of these returners.

Participants

The participants were selected from seven healthcare institutions located in the southwestern region of Japan, and using convenience sampling and snowball sampling. The participants comprised re-entry nurses employed in five community hospitals and two long-term care facilities situated across metropolitan, urban, and rural areas of Japan with populations ranging from 0.4 million to 2.7 million. Inclusion criteria for the nurses were that they (1) had not practiced nursing for a minimum of three years prior to returning to work (based on the Japanese childcare policy allowing a maximum three-year leave), (2) had been working as nurses for a minimum of three months after returning to work, and (3) were able to participate in interviews conducted in Japanese. Exclusion criteria included: (1) working as nursing managers after returning to work, and (2) being without prior experience of working in Japanese healthcare institutions (i.e., those who only had overseas experience). Participants were recruited until saturation was reached, i.e., no further new information emerged during the interviews. A total of 15 participants were recruited as a result.

Data collection

The research team approached the Directors of Nursing and obtained permission to recruit potential participants. Written statements were distributed to the potential participants to explain the purpose and methods of the study.

Semi-structured interviews (see Table  1 for the interview guide) were conducted face-to-face or online, between November 2021 and July 2022. The interview guide was developed based on the research purpose and the review of existing literature. The first author conducted all interviews because her 16-year career hiatus from nursing for child-rearing would help her establish a mutually respectful relationship with the participants and foster an environment free from intimidation. These conditions are crucial for eliciting participants’ genuine sentiments. Throughout the interviews, the author demonstrated respect and empathy toward the participants by openly sharing her own feelings. Additionally, she skilfully guided the discussions to extract the participants’ experiences, concurrently undergoing a process of reintegration in tandem with them. Conversely, the dynamic between the interviewer and participants could be impacted by the assumptions and biases inherent in the interviewer’s background. To mitigate this potential influence, data analysis was performed independently by two researchers (refer to the Data Analysis section).

The interviews were conducted in private rooms, and all sessions were audio-recorded. Nonverbal data, such as the participants’ posture during the interviews, were recorded in an observation notebook. Each participant underwent a single interview session and received a book voucher valued at ¥2500 as a token of appreciation. The interviews lasted between 18 and 49 min (Mean = 39.2 min). Audio-recorded data were transcribed verbatim.

Data analysis

Qualitative inductive analysis [ 41 ] was conducted. Verbatim transcripts were thoroughly reviewed to develop an overall understanding of the participants’ statements. Meaningful words and paragraphs related to the factors that had facilitated the retention of these re-entry nurses were extracted, and codes were assigned to represent the symbolic meanings of the data segments (first-cycle coding). Subsequently, the codes were compared and contrasted to group them into categories based on their similarities in meaning. These categories were further integrated into themes that captured the essence of the factors facilitating the retention of nurses who returned to the nursing workforce (second cycle coding). The first-cycle coding was conducted by the first author (KY) by utilizing her understandings of the participants’ context and their experiences. In the second cycle of coding, the first (KY)and second (KN) authors independently categorized the codes, and the congruencies or discrepancies between them were discussed among all the research team members (KY, KN, YN, and MT), who possessed nursing backgrounds and qualitative research experience. Discussion continued until consensus was reached among all the research members. NVivo12 (QSR International, Melbourne, Australia) software was used for data management.

The trustworthiness of the study

Ensuring credibility, confirmability, transferability and dependability contributes to the trustworthiness of the study [ 42 ]. To enhance the credibility, we applied method triangulation. The interviewer (i.e., the first author) took notes on the participants’ facial expressions and eye movements during the interview, which were included in the analysis along with the verbatim transcripts of the interview data. During the analysis process, the first author repeatedly read the transcripts and observational notes to code the data. For confirmability, two researchers independently categorized the codes, and discussions among the research team took place repeatedly to ensure the elimination of any preconceptions or biases. Any disagreements that arose during this process were resolved through discussions among the research team. To enhance the transferability of the findings, participants were recruited from diverse practice areas and various regions. Furthermore, detailed information was provided regarding the participants’ characteristics and their practicing contexts. In addition, the dependability of the findings was assured by providing detailed descriptions of the data collection and analysis process.

Ethical considerations

This study was approved by the Review Board of Yasuda Women’s University (approval number: 210007), and ethical approval was waived by the participating institutions. This study was conducted in accordance with the Declaration of Helsinki. The participants were fully informed about the study’s purpose, methods, potential risks, and benefits of participation as well as their right to decline participation or withdraw from the study. Written informed consent was obtained from each participant before the data collection. The interview schedule and location were prioritized according to the preferences of the participants, as many were balancing work and childcare responsibilities. Participants were assured that they could refrain from answering any questions that made them feel uncomfortable. Additionally, they were informed that they could end the interview session at any time if they experienced emotional distress. The collected data were securely stored in a locked cabinet, and pseudonyms were used to maintain the participants’ anonymity and protect their privacy.

All 15 eligible participants were female. The reasons cited for leaving employment were childbirth/child-rearing in 11 cases, caring for older family members in three cases, and pursuing a postgraduate degree in one case. The range of length of clinical experience before leaving employment was 3–20 (Mean = 8.2, SD = 4.2) years, that of career breaks was 3–19 (Mean = 6.6, SD = 4.0) years, and that of work after returning was 7 months to 8 years (Mean = 2.6 years, SD = 1.7 years). During the period of data collection, only two participants worked full-time, and 13 worked part-time. The areas of practice encompassed outpatient departments in hospitals ( n  = 8), hospital wards ( n  = 4), and long-term care facilities ( n  = 3) (see Table  2 ).

The data analysis revealed five themes that facilitated the continuation of work for these participants. These themes include “Conditions and support that sustain work-life balance,” “A workplace that acknowledges my career, and encourages my growth as an experienced nurse,” “Pride in reconnecting with and contributing to society,” “Cultivating confidence through incremental professional development and future envisioning,” and “Enrichment of my own and family’s life.” The first two themes represent conditions that enabled the participants to continue their work. Thus, these conditions are referred to as “enablers”. The latter three themes describe factors that motivated the participants to pursue their professional careers. Thus, these factors are referred to as “motivators”.

Theme 1: conditions and support that sustain work-life balance

The participants identified support systems at home, in the workplace, and within society as prerequisites for maintaining a work-life balance, essential for sustaining their employment. This theme encompasses crucial elements that allow nurses to balance their work and family responsibilities, such as work conditions that consider their family circumstances, and support from family and friends. The theme consists of three categories: “Work (i.e., hours and location) and childcare conditions that meet my preferences,” “A family-friendly work environment,” and “Instrumental and emotional support from family and friends.”

Most participants juggled work, household, and childcare responsibilities. Therefore, effectively managing childcare duties while fulfilling work roles became a priority in their lives. Access to childcare facilities was deemed a basic requirement for them to work, as well as conditions such as workplaces located close to their homes and offering flexible working hours to address child-related matters promptly.

“When I was contemplating returning to work, one requirement was that I should be able to look after my two children, so it was important for me that all the conditions related to my children were in place, such as time restrictions and being able to go home immediately if something happens to them.” (ID 10) .

The participants also emphasized the need for a family-friendly work environment, where colleagues and supervisors understood their family circumstances and provided support in balancing work and family duties.

“When I returned to work, I wondered if I would be allowed to take a sudden leave if my child was ill. And they told me, ‘We take turns (taking a leave) so you can do it now, it’s fine,’ as well as ‘We can’t do it for you (take care of your child) but we can do the work in your place.’ Here at my current workplace, we can say such things to each other.” (ID 06) .

Given that most participants were engaged in multiple tasks both at home and work, they experienced physical and mental fatigue and strain. However, they managed to overcome these challenges by receiving instrumental and emotional support from their families and friends. Examples of such assistance included husbands and children sharing household chores and friends providing emotional support during conflicts arising from the intersection of family and work responsibilities.

“Regarding my husband, yes. When I started working, I was no longer a full-time housewife. But I’ve been working alongside him, and he’s been supporting me a lot, such as by taking the kids to school and picking them up after, things like that.” (ID 13) .

Ensuring the effective management of household responsibilities, particularly childcare, was a fundamental prerequisite for the participants to continue their employment. Consequently, the provision of “Conditions and support that sustain work-life balance” acted as an enabler, facilitating their continued engagement in work by sustaining their personal lives.

Theme 2: a workplace that acknowledges my career, and encourages my growth as an experienced nurse

The participants asserted that receiving support to cultivate their professional competencies within their work environment facilitated their transition through a process of reorientation. The participants were returners who had prior nursing experience and possessed a certain level of nursing competence required for professional practice. Initially apprehensive about their competence level, they desired recognition and appreciation for their previous experience and expertise from their supervisors and colleagues. They also expressed a preference for on-the-job refresher training that helped them regain necessary knowledge and skills. This training differed from that provided to newly graduated nurses. This theme represents the importance of receiving educational support to function as a nurse and opportunities for further growth, both of which facilitated the continuation of their work. The theme comprises three categories: “Supervisors and colleagues who appreciate and accept me,” “Support for myself as both a beginner and someone with experience,” and “Comprehensive manual and training.”

The participants emphasized the significance of being recognized and accepted by their colleagues and supervisors. The acknowledgment of their efforts by supervisors and the understanding of their hard work by colleagues served as encouragement to sustain their work. Furthermore, perceiving themselves as individuals who were relied upon by others and striving to meet those expectations facilitated their professional growth and their desire to contribute to the workplace.

“One thing is that um, I also discussed this with the Head Nurse, regarding training, that maybe we should improve the training even more, and the Head Nurse feels the same way, and so, she said I can go ahead and think about a program or something. When I’m entrusted with making these kinds of decisions, the work becomes fulfilling.” (ID 09) .

The participants also expressed the importance of receiving support from their colleagues as newcomers while appreciating their prior experience. The participants were often perceived as fully capable individuals and were assigned a workload equivalent to that of experienced nurses. However, the participants stressed the need for support from their colleagues during the initial phase of readjustment to their duties. Simultaneously, they sought appropriate levels of support while valuing their previous work experience and expertise. They felt reassured when their supervisors or colleagues offered support, recognizing them as both a beginner but also as someone with experience.

“From the day after I started working, I had my own room, and on that day, someone from the day shift always made it a point to talk to me and support me, and it felt like fate. I thought if I were being supported this much, I should do the same, and well, everyone in the ward helped me understand the patients within the week, so much that I thought I already remember them. I felt that I should make an effort to do so, since they supported me so much.” (ID 06) .

Additionally, they desired to receive training and manuals tailored to their skill set, enabling them to effectively perform their roles as staff members.

“Although it was only 3 years, I did have a work gap, so I was thinking that my skills and knowledge might be obsolete and that I might have forgotten some things, but this hospital has a very detailed manual.” (ID 06) .

Acceptance and support from both managers and colleagues, coupled with access to on-the-job training and manuals, emerged as crucial resources enabling participants to realign with their work responsibilities, especially in cases where they lacked up-to-date knowledge and skills. Additionally, feeling valued and trusted by colleagues played a pivotal role in bolstering their confidence, an essential attribute for navigating through challenging periods. Consequently, the provision of “A workplace that acknowledges my career, and encourages my growth as an experienced nurse” served as the pivotal enabler that sustained their professional life though continued commitment to their careers.

Theme 3: pride in reconnecting with and contributing to society

The participants described working as nurses as giving them a sense of pride and of being valuable to society, which motivated them to continue their work. Prior to returning to work, the participants experienced social isolation due to their engagement in various household responsibilities. However, returning to the nursing profession allows the participants to reclaim their roles as active members of society and regain confidence in their contribution to society. The theme comprises three categories: “Desire to contribute as a nurse,” “Expansion of relationships resulting from stepping out of the home,” and “My children feeling proud of me for being an active nurse.”

The participants maintained a strong sense of pride in their profession and were motivated by the desire to contribute to society as nurses, utilizing their nursing qualifications. As the demand for nurses increased during the COVID-19 pandemic, their determination to support patients as nurses grew even stronger. They also expressed a desire to share their expertise with younger nurses and provide guidance to other inactive nurses who were considering returning to work.

“Nurses are needed in situations like COVID-19, and I had gone through the trouble of getting my license, and all that.” (ID 03) . “Well, I’d like to be in a position where people feel they can ask me and maybe find a bit of a solution. I work with the mindset that someone a bit older, like me, should take a role of listening to and giving advice to younger colleagues.” (ID 8) .

Moreover, returning to work reaffirmed their sense of belonging to society not only as mothers but also as nurses. When they were solely focused on child-rearing, their social interactions were limited to those associated with their children. However, by returning to work and establishing their own place in the workplace, their social connections expanded beyond the confines of their homes. The opportunity to reconnect with broader society and experience personal freedom outside of their domestic responsibilities served as a motivation for the participants to continue their work.

“It definitely connects me to society. Until now, my connections with society were through my child. I think I couldn’t have had that without my child, and now it feels like I have a separate community of my own. I feel like that.” (ID 08) .

Furthermore, their pride in being nurses was reinforced by the admiration of their children, who proudly spoke of their mothers’ profession, especially during the challenging times of the pandemic. This alleviated any guilt associated with not having enough time to devote to their children and not fulfilling their maternal roles to the same extent as before. On the contrary, their professional engagement enhanced their self-esteem as proud mothers to their children.

“When I think of these moments, it makes me really happy. Like those moments when I feel that my children have become interested in me (omitted). For example, when they say things like, ‘Nurses are really cool,’ or ‘My mom works in a hospital.’ They’ve even written about me in their diaries.” (ID 01) .

Reclaiming a sense of pride and expanding their professional network through contributions to society represented profoundly fulfilling experiences for the participants. These experiences not only brought them joy in their work but also transcended the mere facilitation of work continuation. Consequently, “Pride in reconnecting with and contributing to society” operated as a potent motivator, driving their commitment to pursue their professional careers and advance, thus enriching their professional life.

Theme 4: cultivating confidence through incremental professional development and future envisioning

The participants were motivated to continue their work by their passion for professional growth and self-actualization. The participants engaged in introspection regarding their journey from the moment they returned to work up until the present. Despite encountering challenging circumstances, they swiftly reacquired previously possessed skills and knowledge, thus restoring their self-assurance in the practice of nursing. This newfound confidence propelled them to envision their future career paths. The following three categories encompass this overarching theme: “Confidence arising from successfully surmounting challenges upon restarting,” “Realization that I have finally made my comeback as a nurse,” and “Personal aspirations for the future.”

According to the participants, they encountered arduous situations upon re-entering the workforce, as they were frequently required to perform tasks that exceeded their current skill sets. Irrespective of their absence from work, their colleagues often regarded them as seasoned nurses. Struggling to fulfill assigned responsibilities, they engaged in negotiations with colleagues and supervisors, asserting their capabilities and limitations. These challenging experiences facilitated the recovery and enhancement of the necessary skills and knowledge, bolstering their confidence, and motivating them to persevere in their work.

“After returning to work, for about half a year, I struggled for a while before getting used to it again. It took me more than six months to understand why I was struggling. But when I got used to the working life, I was able to gain self-confidence.” (ID 04) .

Through introspection and self-comparison between the time of restarting and the present, the participants recognized their continuous development as nursing professionals, observing their ability to provide a sufficient level of patient care.

“In the sense that my intuition has returned, um, it was definitely the fact that before I started working, all I had was anxiety, but when I was actually able to perform my work by myself again, I think that was when I became confident.” (ID 10) .

This developmental process stimulated their anticipation of future career prospects. Some participants expressed aspirations to acquire advanced qualifications and pursue managerial positions, thus making career advancement their future objective.

“There was definitely something different about me, internally, before and after returning to work. It seems like I was lively, like I was going to set my goals, and that I was doing my best. There was a sense of certainty (omitted) and I was able to find what I wanted to do, too.” (ID 11) .

The successful completion of the readjustment journey played a pivotal role in bolstering the participants’ confidence, and encouraged them to envisage future professional goals. The process of “Cultivating confidence through incremental professional development and future envisioning” emerged as a critical motivating factor (i.e., motivator), propelling the participants towards continued professional growth, and thereafter enriching their professional life.

Theme 5. Enrichment of my own and my family’s life

The participants perceived added value when their own lives and their families were enriched by their work, which encouraged them to continue their jobs. The participants acknowledged the positive transformations in their physical and emotional well-being, as well as in the lives of their families, following their return to work. They perceived an overall improvement in their daily lives. This theme encompasses three categories: “A healthy mind and body attained by adding variety to life,” “Positive influence on family dynamics,” and “Income that enriches my life.”

The participants said that resuming employment contributed to a well-rounded lifestyle and positively impacted their physical and mental health. Specifically, those who were responsible for raising children noted that having time away from their children reduced feelings of irritability and enabled them to engage with their children in a more compassionate and nurturing manner upon returning home from work.

“I feel like my day has become balanced. I do feel a little sad that I’m spending a lot more time away from my children (omitted). I make up for it when I see them, and I think I’ve become a little less irritable.” (ID 10) .

Furthermore, having a job established a consistent rhythm to their lives and facilitated physical fitness, thus promoting a balanced existence. They also perceived the involvement of others in caring for their children as an opportunity for their children to interact with a broader network of individuals, fostering their growth and healthy development. Moreover, the up-to-date medical knowledge gained through their work served to safeguard the health of their families.

“Because I want to know about cutting-edge technology. You know, if I quit this job, it will affect my life directly, because it’s a job that involves the body after all. I think it’s always gonna be useful (in my life).” (ID 13) .

By earning their own income, they were able to provide economic security to their families, which was closely linked to their mental well-being.

“Before I was reinstated, we were living on my husband’s salary alone. I felt bad about it, but now we have some financial leeway, so that definitely was a benefit for me.” (ID 11) .

Resuming employment engendered an ‘Enrichment of my own and my family’s life,’ demonstrated by enhancements in physical and mental well-being, the wholesome development of children, and economic incentives. Consequently, this theme illustrates the enrichment of the participants’ personal lives as a result of having fulfilling professional lives, and emerged as an additional motivator.

This study explored factors contributing to the retention of nurses re-entering the workforce after a career break, resulting in the identification of five themes. The first two, “Conditions and support that sustain work-life balance” and “A workplace that acknowledges my career, and encourages my growth as an experienced nurse,” were identified as enablers, sustaining the participants’ continued engagement in work. The next three themes, “Pride in reconnecting with and contributing to society,” “Cultivating confidence through incremental professional development and future envisioning,” and “Enrichment of my own and family’s life,” served as motivators, propelling them toward a professional career.

The concept of enablers and motivators parallels Herzberg’s Two-Factor Theory of Motivation [ 43 ], where hygiene factors, including salary and work conditions, are essential but their absence leads to dissatisfaction, while motivation factors, like achievement and recognition, promote job satisfaction and enhanced performance [ 43 ]. Similarly, enablers such as family-friendly work conditions, peer support, and on-the-job training played pivotal roles in the participants’ job continuity, and their absence could result in dissatisfaction or job exit. Likewise, motivators such as pride and confidence yielded personal fulfillment, motivating participants to pursue their professional goals. However, distinctions arise. While the Two-Factor Theory focuses on work components, our study contends that healthcare institutions must address both professional and personal factors for nurse retention. This is critical, particularly for returning nurses, often with caregiving responsibilities, necessitating a balance between sustaining and enriching their professional and personal lives. Another distinction lies in the relationship between the enablers and motivators. According to the Two-Factor Theory, hygiene and motivation factors exist independently, while motivators do not exist without the presence of enablers. For example, without adequate support for nurses to achieve work-life balance, they are unable to enhance their own or their family’s quality of life. Similarly, lacking encouragement in professional development, nurses are unable to cultivate pride or confidence, or envision their future. These relationships are depicted in Fig.  1 . The subsequent sections provide a detailed explanation of each of these factors.

Framework for the sustainability of career for returners

The first theme, “Conditions and support that sustain work-life balance,” functions as an enabler that sustains nurses’ personal life. Nurses are prominent double-duty caregivers, tending to family and patients [ 44 ]. The majority of our participants had children, reflecting the fact that in Japan, 55–66% of nurses are parents [ 16 , 45 ]. Therefore, balancing family and work is crucial, regardless of career breaks. Specifically, nurses who temporarily left their employment due to childcare responsibilities had various reasons such as the absence of available childcare support. Especially in Japan, women often prioritize their childcare responsibilities over work commitments, or may feel societal pressure to remain at home and care for their children [ 46 ]. These cultural practices and norms could potentially elucidate their career hiatus. Therefore, family-friendly working conditions (e.g., flexible hours, location, childcare support) are vital for returning and sustaining work. This finding is consistent with previous studies indicating that workplace flexibility, which helps alleviate childcare concerns, is crucial for enabling nurses to sustain their work [ 28 , 30 , 35 , 36 ]. Furthermore, nurses who juggle dual caregiving roles often experience fatigue and stress [ 44 ]. Therefore, receiving instrumental and emotional support from their spouses is essential for maintaining a healthy work-life balance. In fact, recent studies have highlighted that support from their families enables nurses to effectively manage the demands of both their family and work spheres, facilitating their re-entry into professional practice [ 28 , 35 ]. The successful sharing of household responsibilities and childcare is indispensable for returners who aspire to continue their professional work, particularly those with young children.

The second theme, “A workplace that acknowledges my career, and encourages my growth as an experienced nurse,” serves as an enabler that sustains the professional practice of returners. This finding is also in line with previous studies that have highlighted the significance of a supportive work environment in aiding individuals to manage their jobs and regain confidence [ 28 , 35 ]. Although returners are often perceived as experienced nurses capable of functioning independently, the literature indicates that they encounter significant challenges in reacquiring their previous knowledge and skills, while also adapting to the rapidly advancing field of medical technology [ 21 , 33 , 35 ]. Reintegrating into the nursing workforce is arduous, and returners often experience anxiety and confidence issues [ 27 , 31 ]. This was also evident among our participants. Consequently, receiving appropriate initial training and access to manuals are critical factors enabling returners to fulfill their duties and sustain their professional work [ 30 ]. On the other hand, the majority of the participants had achieved an expert nurse level, possessing more than five years of previous clinical experience [ 47 ], thus they desired recognition and acceptance of this. The need for acceptance and respect was also identified in previous studies on returning nurses [ 27 , 30 ]. Appreciating their skills, efforts, and contributions while identifying areas for professional development represents the ideal “just-right preceptorship” for returners. Organizational support of this nature promotes work engagement [ 48 ], thus sustaining their professional practice.

While the existing literature commonly highlights the enablers necessary for nurses to return to work and continue their professional roles, previous studies have overlooked the motivating factors that drive them to work. Merely creating a sustainable environment for their return is insufficient. Internal drivers are essential to maintain their motivation to work, especially during challenging times. The following three themes describe the motivators that encourage nurses to pursue their professional careers, thus enriching their professional life.

“Pride in reconnecting with and contributing to society” stimulates nurses’ work motivation and enriches their professional lives. Previous studies have demonstrated that returning to work helps them regain self-esteem through their contribution to society, increasing pride as valuable society members [ 35 , 36 ]. This study contributed new knowledge by highlighting how this sense of pride motivates returning nurses to pursue their professional careers. Nurses who had previously been inactive cited the desire to utilize their qualifications and contribute to the welfare of society as the main reason for returning to work [ 16 ]. They took pride in being nurses and were eager to apply their professional knowledge and skills, supported by their abundant clinical experience. This aligns with previous studies emphasizing their high levels of clinical and leadership skills [ 20 , 28 ] and the enthusiasm exhibited by returners [ 30 ]. While initially struggling to adjust, their experience enables them to quickly adapt [ 33 ]. Once they regain competence, they contribute to healthcare and society by providing competent nursing care, educating colleagues, and serving as successful examples for potential returners. These experiences may instill a career calling characterized by self-actualization, personal fulfillment, and passion for their work [ 49 ], which promote job satisfaction [ 50 ] and engagement [ 51 ]. Returning to work also allows them to establish their societal position and expand their network, which is limited when solely fulfilling household responsibilities. According to the Self-Determination Theory [ 52 ], relating to others by engaging in employment outside the home not only alleviates isolation but also enhances their motivation. Additionally, contributing to society as valued members of the healthcare profession enhances their self-esteem [ 36 ] and allows them to cultivate a professional identity. If their children or significant others take pride in the nursing profession, their identification with nursing becomes stronger. During the COVID-19 pandemic, nurses were portrayed as heroes combating the crisis, which enhanced their professional identity and the pride their families had in them. Professional identity is known to enhance individual motivation to remain in the profession [ 53 , 54 ]. Therefore, reconnecting with and making contributions to society enrich nurses’ professional lives.

“Cultivating confidence through incremental professional development and future envisioning” represents another motivator that enriches the professional lives of returners. Previous studies have shown the struggles and challenges that returning nurses faced in their journey towards reintegration, and in reaffirming their identity as nursing professionals [ 28 , 31 , 35 ]. When restarting their careers, returning nurses often experience anxiety due to changes within healthcare institutions, such as the introduction of new medical equipment and technology, shifts in insurance policies, increased demands for high-level physical assessment skills, and the expanded scope of responsibilities they now carry [ 55 ]. Nevertheless, the participants in this study successfully overcame numerous challenges and navigated the journey of reintegration. This experience of triumph and the acquisition of new knowledge and skills enabled them to regain the confidence they had in their previous career. Reflecting on their hard work and learning trajectory also instilled a sense of professional growth. Possessing confidence and a sense of self-worth has enhanced their self-efficacy, which, in turn, has promoted affective organizational commitment [ 56 ] and work engagement [ 57 ]. Furthermore, a successful reintegration fulfills their need for competence, thereby bolstering their motivation [ 52 ]. In addition. their learning achievements foster expectations for their future career goals. Having a clear goal enhances their professional development and further enriches their professional life. This study contributes new insights by demonstrating that perceiving their own professional development and embracing future goals motivates them to continue their work.

The final theme, “Enrichment of my own and family’s life,” highlights the reciprocity between personal and professional aspects for returners. Returning to work enables a balanced lifestyle, which improves mental and physical health and reduces strain and fatigue for double-duty caregivers. Employment also provides financial stability and enriches personal life, aligning with the previous findings [ 35 ]. Financial incentives are often cited as reasons for nurses to consider returning [ 23 , 33 ]. While extrinsic, these incentives improve individuals’ quality of life, enriching their minds and energizing their work. Furthermore, work positively influences family dynamics, countering feelings of guilt at leaving children, often portrayed as a negative consequence of returning to work [ 31 ]. The participants in this study recognized the benefits, such as positive effects on their children’s healthy development, and how it led to an improved relationship with their children. Another study also observes a positive reciprocal relationship between work and family [ 35 ]. The theory of work-family enrichment asserts that " experiences in one role improve the quality of life in the other role” [ 58 ]. Work enriches personal life, while fulfillment in personal life motivates job continuation. Positive family experiences also enhance work performance [ 59 ]. Enrichment of personal life forms the foundation for individual professional life, and vice versa. This study reveals a new insight: returning to work can yield positive outcomes for nurses’ own lives and those of their families, particularly concerning child development.

Implications for nursing management

The findings of this study suggest that in order to retain returners in the current nursing force, it is imperative to maximize both the enablers and motivators that contribute to the sustainability and enrichment of their personal and professional lives. In order to maximize the enablers, the establishment of a family-friendly environment is crucial. Nurse managers should strive to comprehend the personal and professional lifestyles that returners desire and should provide support accordingly. Furthermore, the formation of a mutual support group among returners can facilitate the exchange of experiences and encouragement, as well as make it possible to accommodate shift changes when family-related issues arise. The provision of adequate training is also of paramount importance. Unlike new graduate nurses, returning nurses possess diverse nursing skills and experience, necessitating a comprehensive evaluation by managers and colleagues to determine their competencies, while simultaneously providing them with the necessary knowledge and skills required for current clinical practice.

To enhance motivators, nursing managers should actively encourage returners to revive their professional pride and sense of fulfillment as nurses. One effective approach involves providing positive and constructive feedback on their contributions to the well-being of patients, thereby bolstering their pride. Additionally, managers need to assist returners in regaining their confidence and should support their progress toward achieving personal goals. Encouraging self-reflection on their clinical experiences can serve as a powerful means to help them realize the extent of their growth and subsequently enhance their confidence [ 31 ]. Assisting them in setting future professional goals represents another important strategy. Finally, managers should help returners recognize the positive changes that have occurred in their family dynamics as a result of their return to work. Engaging in discussions about personal life with managers or other returners may prove beneficial in this regard.

Limitations

Efforts were made to enhance the transferability of the findings, by recruiting a heterogeneous sample of returning nurses, considering factors such as the duration of their career breaks, the length of clinical experience after returning, their employment status, and their area of practice. However, it cannot be assured that our sample is truly representative of Japanese returning nurses due to the relatively limited number of participants in this study. To enhance the transferability of the results, future studies should aim to replicate this research by encompassing diverse characteristics of returning nurses from various geographical locations. This approach would facilitate the aggregation of findings and the formulation of more robust programs designed to promote the retention of re-entering nurses.

The nursing shortage is a persistent issue that is anticipated to worsen in the foreseeable future. The available solutions to alleviate this problem are limited, and a cost-effective approach involves incentivizing inactive nurses to rejoin the nursing workforce [ 60 ]. Returning nurses constitute a valuable asset for hospitals, as they possess a renewed professional commitment and can quickly regain nursing competence. Furthermore, their diverse experience in various clinical areas and organizations has the potential to introduce innovative clinical and managerial solutions within the current healthcare setting, thereby enhancing clinical outcomes and improving patient satisfaction. Therefore, it is imperative to implement multi-dimensional approaches aimed at retaining and harnessing the potential of these valuable human resources.

Data availability

The data are not publicly available because they contain information that could compromise the privacy of the research participants.

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Acknowledgements

The authors would like to thank the participants for participating in the study and for sharing their experiences.

This work was supported by JSPS KAKENHI Grant Number 22K10697.　The funder had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

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KY designed this study under the supervision of MT. KY performed the data collection and the initial data analysis. KY, KN, YN and MT contributed to the data analysis. KY, KN and MT wrote the manuscript. All co-authors reviewed the manuscript and approved the final manuscript for publication.

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This study was approved by the Review Board of Yasuda Women’s University (approval number: 210007). This study was conducted in accordance with the Declaration of Helsinki. The participants were fully informed about the study’s purpose, methods, potential risks, and benefits of participation as well as their right to decline participation or withdraw from the study. Written informed consent was obtained from each participant before the data collection. The collected data were securely stored in a locked cabinet, and pseudonyms were used throughout the paper to maintain the participants’ anonymity and protect their privacy.

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Yamamoto, K., Nasu, K., Nakayoshi, Y. et al. Sustaining the nursing workforce - exploring enabling and motivating factors for the retention of returning nurses: a qualitative descriptive design. BMC Nurs 23 , 248 (2024). https://doi.org/10.1186/s12912-024-01900-5

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Qualitative analysis of mothers’ perception related to the delivery of information regarding preterm births

• Doriane Randriamboarison 1   na1 ,
• Elisa Fustec 2   na1 ,
• Isabelle Enderlé 2 , 3 ,
• Mathilde Yverneau 1 , 3 ,
• Karine Le Breton 1 , 2 ,
• Linda Lassel 2 ,
• Nadia Mazille-Orfanos 1 &
• Patrick Pladys 1 , 3  

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Preterm birth is a major health issue due to its potential outcomes and socioeconomic impact. Prenatal counseling is of major importance for parents because it is believed that the risk of preterm birth is associated with a higher parental mental burden. Nowadays in France, the content and delivery of antenatal counseling is based on personal experience since there is a lack of official guidelines. The goal of the study was to evaluate maternal perception of antenatal information delivered in the setting of preterm births.

A qualitative study was performed using semi-structured individual interviews of 15 mothers with a child born > 26–34 GW. Data analysis was based on a constant comparative method.

Concerning prenatal counseling content, parents wanted to be informed of their role in the care of their preterm child more so than statistics that were not always considered relevant. Parents’ reactions to the announcement of the risk of a preterm birth was dominated by stupefaction, uncertainty and anxiety. When it comes to the setting of prenatal counseling, patients’ room was deemed an appropriate setting by parents and ideally the presence of a coparent was appreciated as it increased patients’ understanding. The physicians’ attitude during the counseling was considered appropriate and described as empathic and optimistic. The importance of support throughout the hospitalization in the form of other parents’ experiences, healthcare professionals and the possibility to preemptively visit the NICU was emphasized by participants. Delivery experience was dominated by a sense of uncertainty, and urgency. Some leads for improvement included additional support of information such as virtual NICU visit; participants also insisted on continuity of care and the multidisciplinary aspect of counseling (obstetrician, neonatologist, midwife, nurse, lactation consultant and psychologist).

Highlighting parents’ expectations about prenatal counseling could lead to the establishment of overall general guidelines. However, some topics like the use of statistics and mentioning the risk of death underline the importance of a personalized information.

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Introduction

According to the latest French National Prenatal Survey (NPS), the rate of preterm birth was 7% in 2021, which represents about 46,000 newborns [ 1 ].This rate has remained stable since 2016. Most of preterm deliveries happen between 32 and 36 GW (5.3%), and1.7% occurbetween 22 and 31 GW [ 1 ].

Preterm birth has long term effects and even though survival without neuromotor or sensory disabilities has improved in the last decades from 45.5% in 1997 to 62% in 2011 [ 2 ], ex-preterm infants present more neurodevelopmental complications and motor disorders such as cerebral palsy, cognitive disabilities, school learning disabilities [ 3 , 4 ]. Considering all these outcomes and their socioeconomic impact,preterm birth represents a major health issue. Therefore, preventingpreterm labor and neonatal complications associated with a preterm birth is of utmost importance [ 2 ]. An integral part of high-risk pregnancy management is announcing to the parents that their pregnancy is no longer as they had envisioned it. It is the healthcare professional’s responsibility to make sure the patient understands all the information necessary to apprehend the medical course and to make informed decisions [ 5 ]. In this setting, communication in addition to bringing emotional support and conveying empathy also plays a legal role. This information is delivered during an antenatal consultation.

During the antenatal consultation, neonatologists focus on neonatal complications and how to manage them. This has been shown to be helpful for parents [ 6 ]. It contributes to psycho-social support, lowers risk of postpartum depression and mother-infant bonding disorder [ 7 ]. However, this new knowledge may also contribute to parents’ anxiety [ 8 ]. Parents’ needs and expectations regarding antenatal counseling are not always correctly understood by clinicians [ 9 , 10 ]. Furthermore, this information is often delivered in a stressful environment, where there is a concomitant concern for the mother’s health. Information concerning obstetrical outcomes must also be provided including causes of preterm birth, treatments, and prognosis. In this setting, delivery becomes an abrupt and unanticipated event which can generate an important amount of stress. High-risk pregnancies and emergency deliveries are more at risk to generate posttraumatic stress disorder than regular pregnancies (18.5% versus 4%) [ 11 ].Neonatal outcomes are deeply connected to the obstetrical path and perinatal collaboration between neonatal and maternal caregivers improves families’ experience in all aspects of preterm birth [ 12 ]. Therefore, the way in which information is delivered in the antenatal period has a major impact on the parents’ experience throughout their subsequent path.

Nowadays in France, the content and delivery of antenatal counseling is based on personal experience since there is a lack of official guidelines. Most studies evaluating antenatal counseling are focusing on extreme preterm birth [ 13 , 14 , 15 , 16 ]. However, even though preterm infants born after 26 GW are at lower risk of adverse outcomes, they represent an important population in terms of prognosis. Moreover, these studies essentially focus on parents’ role in deciding between active support and palliative care [ 17 ], overlooking all other aspects of prenatal information. Most research has also been conducted from a neonatological point of view without the obstetricians’ input.

The aim of this qualitative study is to evaluate maternal perception of antenatal information delivery in the setting of preterm birth between 26 and 34 GW. Our goal is to improve our practice by bringing some insights on how to best counsel patients at risk of preterm birth and help them understand complex information [ 18 ].

Study context

The neonatal and obstetric departments of the university hospital of Rennes offer prenatal counseling and maternal care to all patients at risk of preterm delivery. Information delivered is based on healthcare professionals’ experience. Concerning neonatal antenatal counseling, interviews are conducted by a senior neonatologist as soon as possible after patient’s hospital admission. If the situation evolves or if patient asks for an update, follow-up consultations may be conducted by the neonatal physician. The obstetric team (senior obstetricians and neonatologists, residents and midwives) informs patients on obstetrical care and prognostication. All information given is adjusted on clinical context and patient’s history.

A qualitative study was performed. We followed Consolidated Criteria for Reporting Qualitative Studies (COREQ) guidelines [ 19 ]. Then we analyzed quantitative population’s characteristics.

Participants

Mothers with a child born between 26 and 34 gestation weeks admitted to the NICU at the tertiary care university hospital of Rennes from January 2019 to April 2020 and discharged from the hospital at the time of inclusion were selected. The time lag between birth and interview ranged from 6 months post-discharge to a maximum of 18 months, in order to minimize memory bias We included in our study mothers who had been hospitalized in the level 3 high risk pregnancy unit of the University hospital of Rennes and received prenatal counseling from a neonatal attending physician. Some patients had their first medical care at another hospital and then were transferred to the hospital of Rennes before birth. Transferred patients were also included. Our exclusion criteria were children born before 26 GW, deceased children, deceased mother, patients under 18 years old, patients who did not speak fluently French, patients with cognitive disabilities, patients without contact information, patients who gave birth in another hospital and patients whose child (or one of the children in case of multiple pregnancy) was still hospitalized at the time of the study.

Setting and sample

A physician was responsible for explaining the research project to potential participants and for sending an email newsletter describing the purpose and outline of the research. Mothers were invited to participate in a semi-structured interview. Participants responded to this invitation via email. Investigators who conducted the interviews informed participants, in the letter and then orally, about the aim of the study and their right to withdraw their participation at any time without giving any reason. Reminders were then sent via emails to the participants who did not respond to the letter. Patients who did not have an email address were recruited by phone calls made by one of the investigators. All mothers gave their informed consent before participating. We planned on stopping inclusion of patients when saturation was achieved (i.e. no new themes or ideas were generated by subsequent interviews).

Considering the difficulty for patients to come to the hospital for the interview, we initially let participants choose between a face-to-face interview or over the phone according to their convenience. In the face of the Sars-CoV-2 pandemic and its associated restrictions, all interviews were then conducted over the phone.

Data collection

Semi-structured interviews were conducted in French by one or both interviewers who were a neonatal resident (DR) and an obstetrician-gynecologist (OB/GYN) resident (EF). Data collection spanned from June 2020 to March 2021. Interviews were semi-structured, with a predefined list of open-ended questions focusing first on the information received concerning the hospitalization, treatments, and prenatal counseling, and then on desired improvements, and open suggestions. The interview guide was developed by authors (DR, EF, NM, IE and KL) after a review of the literature before starting the study. If applicable, face-to-face interviews were conducted at a private office space located in the NICU.

To ensure consistency, we used the same interview guide in every interview (Table  1 ). The interviewers received preliminary training on reformulation to carry out the in-depth interviews with qualitative method referents. They reported their involvement after each interview. Sessions were recorded with the consent of each participant and then transcribed verbatim and de-identified. The aims and rational for the research were disclosed to the participants in the newsletter. We confirmed patient’s understanding during the interview.

Throughout the session, the moderator summarized and reformulated the results and presented them back to the participants to ensure information was accurate and that their comments had been correctly understood. At the end of the session, participants completed a short quantitative questionnaire to obtain their socio-demographic characteristics. We obtained remaining socio-demographic data from the patient electronic medical record.

Data analysis

The analysis procedure was conducted byfour researchers (EF, DR, NM and IE) using an inductive approach to identify themes that emerged from the data. Each transcript was independently read several times to facilitate immersion in the data.The thematic analysis of the data promoted a logic of emergence. The interviews were first analyzed using a manual method of coding the themes and sub-themes. The researchers used open coding process to summarize participants’ views by assigning words to quotes or paragraphs. The coding of the researchers were then compared and in the event of any discrepancies or a disagreement, other physicians (MY, KL, LL and PP) adjudicated. This method enhances the validity of the assigned themes. We kept including participants in the study until saturation was achieved (i.e. no new themes or ideas were generated by subsequent interviews).

The list of themes and sub-themes was then generated and extracted in tabular form. Constant comparative analysis was used to assess overall saturation [ 20 ]. Authors selected verbatim quotes to illustrate the thematic findings. We coded data from transcripts using the Saldaña method [ 21 ] To ensure the reliability of the coding and analysis of the data, findings were discussed among the authors. At the same time we used the NVivo® 12 Plus software interface (QSR International) to support the coding tree. The software was also used to check the frequency of occurrence of themes and to ensure that our main themes were consistent. NVivo’s contribution was also to facilitate the link between the highlighted themes and the verbatim references.

Ethical considerations

The study was approved by the local Ethics Committee (reference number 20.61). Participation was on a voluntary basis. The university hospital of Rennes recorded the material in accordance with all French ethical regulations (ref: MR-003).

We conducted a total of 15 interviews, which took place between June 2020 and March 2021.We obtained data saturation after 12 interviews. Amongst the three first participants who were given the choice of the interview setting, two of them decided on a face-to-face interview, and the last one over the phone. For all remaining participants, we only conducted phone interviews.Average length of interviews is 44 min ± 11 min (minimum 25 min, maximum 66 min). Face to face interviews lasted 32 and 42 min each.

Participant’s characteristics are presented in Table  2 . On average, participating mothers were 31.4 years old (± 4.9 years). Newborns were on average 30.2 ± 2.5 weeks of gestation at birth.

Characteristics of prenatal counseling

Circumstances of prenatal counseling are reported in Table  3 . Interviews mostly took place in the patient’s hospital room, and within the first days after admission.

Thematic analysis

Seven themes were extracted from our data analysis. We subdivided each theme into sub-themes and illustrated some of them with participants’ quotes from the interview (presented in Table  4 ).

Prenatal counseling content.

Neonatal complications and care .

Information delivered during prenatal counseling was the most mentioned during the interviews. Participants recalled being told about neonatal complications. They talked about respiratory outcomes first, short and long term. Then neurological complications were evoked including specific follow-up and neurosensorial risks. Mothers also reported receiving information concerning the NICU: the rooms, the equipment, the incubator. They remembered being told about the usual medical course and the steps during hospitalization.

Parents’ role .

How participants should act with their preterm newborn is commonly addressed during antenatal consultation. Mentioning the baby’s future life makes parenthood more real. For instance, practical aspects such as transferring parents’ smell through comfort blankets and cuddly toys were greatly appreciated. Being able to spend unlimited time with their child was also reassuring. Breastfeeding is another important topic, especially knowing that it is feasible even in case of preterm delivery. This notion was carried by the obstetric team. Midwives adapted their support to patients’ need, no matter what they first wished. A participant explained that she changed her mind based on the information she received about the role of breast milk for preterm babies: ‘Midwives who listened, who taught me how to pump my milk even though I was totally reluctant to breastfeed’ (patient no 2).

Finally, participants mentioned skin to skin as a beneficial act to their child’s well-being. Mothers report highly on it, as shown by patient no 8’s quote: “they told me that I could stay close to her, that I was going to be able to touch her […] to hold her against me. When I was told that, I felt a lot better because I didn’t know I would have the opportunity to hold her.”

Use of statistics .

To participants, statistics and numbers were either not mentioned or considered irrelevant. Indeed, seven patients reported not receiving any and seven had no recall of any statistics. Only one patient was looking for statistical data in the prenatal counseling and insisted on receiving some. When asked if they wished they were given some, four participants were against, four would have appreciated it and seven had no opinion. The ones in favor explained they wanted to hear positive numbers such as survival rates. Some participants described themselves as wanting to know everything and be as informed as possible. Participants who did not wish to receive any statistics argued that it would have scared them, and made them worry about worst case scenario.

Risk of death .

Mortality of preterm children was not mentioned to every participant as four participants reported death not being talked about during antenatal counseling. Avoiding this subject was appreciated by some participants. One mentioned they felt like practitioners could sense which information was relevant to them. To other participants, not talking about death could lead parents to imagine the worst-case scenario.

Mothers’ feelings and reactions.

Announcement of a risk of preterm birth .

Participants often reported feeling paradoxically in good health while being diagnosed with a risk of preterm birth. Therefore, such a diagnosis was reported as being a shock. Another feeling commonly mentioned is fear for the child’s health. On the contrary, some participants felt optimistic.

Prenatal hospitalization experience .

When asked how their hospital stay went, participants reported as many positive aspects as negative ones. They generally appreciated the close medical attention and support which were reassuring. However, some of them also mentioned the difficulty to accept the fact that they needed to stay in the hospital. Feelings mentioned by order of frequency were stupefaction, uncertainty, hope and anxiety. The sudden change during their pregnancy brought disorientation to some participants. Another feeling described was not knowing exactly what would happen to them and when delivery would occur. Participants also mentioned developing some hope during their hospital stay, especially for participants who were hospitalized for the longest period of time. As time went by and nothing serious was happening, they found themselves hoping they would slowly escape preterm birth’s adverse outcomes. The whole experience of a risk of preterm birth generated anxiety for several participants. They continuously feared for their child’s life. Moreover, being hospitalized, away from their homes and relatives, could enhance this anxiety.

Circumstances of prenatal counseling.

Co-parent present .

Both parents being present during antenatal counseling was the most frequent situation. Having the other parent present allowed to reflect further on what had just been said. It kept the information alive and encouraged questions.

Organization of prenatal counseling .

All participants could describe how prenatal counseling went. Consultations happened in their hospital room, which participants found appropriate.

Counselor’s attitude .

Participants commented on the physician’s skills. Fourteen of them defined the neonatologist as optimistic, and showing empathy. They reported the physician using understandable language to them. According to participants, the counselor also personalized information according to the patient and the situation, as Patient no 1 mentioned: ‘I think they really understood [me] and told me what I needed to know without telling me too much.’

Support during prenatal hospitalization.

Close relatives seemed to be the most important emotional support throughout hospitalization. The other parent was the most mentioned, followed by first-degree family members, especially mothers and sisters, and for some participants, friends. Healthcare professionals were also referred as supportive. Midwives and assistant nurses were in the first line of patient’s care and mothers relied on them. The psychologist was also cited, bringing moral, psychological, and emotional support. Shared experience with other parents who went through a similar path were appreciated by participants. They mentioned feedback from relatives who had a preterm delivery, letters, and pictures from former parents of NICU’s babies, who are now doing well. One patient said she had the need to search the internet, even though it did not necessarily bring her comfort. The tour of the NICU was also appreciated by mothers and considered as a real source of support.

Delivery experience.

Participants described information on delivery as clear but mentioned the difficulty dealing with delivery’s unpredictability. They had questions on how far in their pregnancy they could possibly go, whether they were going to deliver vaginally or by cesarean, if they were going to be induced. Mothers also talked a lot about the urgency of delivery and reported a feeling of being rushed. The need for support in this difficult situation was important. The presence of the co-parent was requested by participants, although it may not always have been possible if delivery was impending. They counted on the midwives and the obstetric team to support them as well.

Additional sources of information.

The most mentioned source of information was the tour of the NICU, when the patient’s health allowed it, and delivery was not impending. Written documents were also presented to patients and appreciated. Most participants mentioned receiving paper documents, including one on breastfeeding and one explaining planned cesarean section. Some participants reported searching information on the internet.

Suggestions for improvement.

Additional support of information .

Participants suggested pictures and videos. A virtual tour of the NICU to show the rooms with their equipment was also mentioned. The expectation of what the photographs should describe was controversial. Pamphlets with pictures of staff members to help identify each professional’s face and tasks were suggested. Written documents about local neonatal units, from highly intensive care to current care, and how they connect to each other, would be appreciated as well. Explanations on milk collection centers (lactarium) were also requested as several participants did not have a complete understanding of their functioning.

Antenatal information .

Participants wanted the same practitioners to perform the consultation, as they sought continuity of care and commitment from healthcare professionals. Several participants also mentioned that the presence of a neonatal nurse during the neonatologist’s counseling would be beneficial. One participant suggested having the psychologist present to adjust psychological follow-up after the meeting. Sharing other parents’ experiences was also brought up. Participants wished they could have joined talk groups in the high pregnancy risks unit. Participant no 9 suggested to tell future parents confronted with a risk of preterm birth about the care of a preterm child: “And to tell them it’s a fight for the baby and it’s a fight for the parents.”

Postpartum care .

Several participants addressed postpartum mothers’ care. They expressed the need to be hospitalized in a unit without any newborn instead of the usual post-delivery maternity units, as it made the absence of their child harder to endure. Some of them even wished to be in the same room as their infant, included in the intensive care unit, such as Kangaroo Mother Care (KMC) units. Another commonly mentioned topic was breastfeeding: they wished for more help and support during the first steps of setting breastfeeding.

This study on the information related to preterm birth and its consequences, delivered during prenatal care, gives a thorough insight into the perception of mothers faced with the care of a preterm infant. The announcement of a risk ofpreterm birth came as a shock for patients, as there often was no forerunner. However, the information delivered byneonatologists was overall described as clear, adapted, and carried out with optimism and empathy. Concerning hospitalization in the high-risk pregnancy unit, participants emphasized the importance of having different sources of support to help them cope with anxiety and unpredictability. The feedback provided by participants to improve the delivery of information included the development of visual sources of information.

Providing information on a situation that cannot be predicted is a difficult task. Parents need to be aware that the ability to give an accurate prognosis before delivery remains limited [ 22 ]. Our study shows that some parents wish to have as much information as possible to be fully prepared, whereas others would like to only hear what is very necessary. Many studies on prenatal counseling have shown the importance of personalized information. Most of them focus on the field of extreme prematurity. However, Gaucher et al. demonstrated, in a preliminary qualitative study of 5 interviews [ 23 ], results comparable to our own on the content of patients’ expectations during this antenatal interview. This initial study was followed by a quantitative study [ 24 ] designed to verify their results on a larger scale using a quantitative method. This is one of the few studies which has focused on the maternal experience beyond extreme prematurity, but with a quantitative approach. Healthcare professionals must try to identify parents’ expectations and adapt their speech accordingly [ 18 ]. Culture and social background should also be taken into consideration, as well as level of understanding [ 25 ]. Personalization is probably the most important aspect and should be applied to all parts of antenatal care [ 26 , 27 ](. We also found these results in our study, but our qualitative approach, which is relevant for assessing mothers’ experiences, provided additional data on the way in which parents wish to receive this information. Learning how to identify parents’ wishes should be a part of residents’ training as it is not an easy task. Moreover, delivering unwanted information can create the wrong environment and hinder the parents and healthcare providers relationship [ 13 , 14 ]. Misunderstanding can generate miscommunication and dissatisfaction which can lead to suboptimal care [ 18 ]. What practicians think parents understood may not reflect what parents actually report being told [ 22 ].

In our findings, the wish for statistics and figures varies from one participant to another. Physicians may be confronted with the question of whether or not to share them. A study showed that some mothers, especially those with a high education level appreciated exact statistics more than general facts [ 6 ]. It brings us back to the idea of personalizing our counsel. Geurtzen et al. showed that parents’ choice on statistics was divided, and if given, these should be well explained [ 26 ]. However, a systematic review on parent communication needs during antenatal consultations found that parents wished for more than only quantitative data concerning mortality and morbidity. For instance, they expect information on their role [ 14 ]. So before giving statistical data, physicians should seek if parents want them and provide them in a way that is understandable and relevant to this individual situation.

In our study, physician’s skills and attitude are well remembered by mothers, suggesting the idea that if parents feel in a safe and trusting environment, they will be more willing to listen, understand and ask questions. Other studies found that in order to improve pedagogy, the speaker should be compassionate, empathic, honest, and caring [ 10 ]. Nevertheless, parents also expect truth and real outcomes and importantly, in words they can understand [ 14 ]. Our study shows that participants had a positive experience with well conducted antenatal counseling, even though the risk of neonatal death was brought up by the physician. As pointed in previous research work, truthful information, even when difficult, can be expected from physicians regarding prenatal information. Some hope should also be provided, however some physicians may fear giving false hope [ 16 ]. The timing of the information delivery is another aspect of prenatal counseling that also needs to be personalized. Too soon can be stressful if the patient is still accommodating to their new situation [ 18 ]. On the contrary, too late may increase mother’s stress. Uncertainty of the prognosis and the possible threat of sudden emergency delivery add difficulty to the timing of antenatal counseling.

Several participants from our study brought up the positive impact of a nurse being present during counseling, which illustrates the importance of multidisciplinarity. Indeed, it has been shown that nurses can rephrase and check parents’ understanding [ 14 ]. Moreover, in the Netherlands, guidelines mention antenatal counseling should be performed with both an obstetrician and aneonatologist [ 15 ]. An American study supports the idea that optimal communication between the obstetric and neonatal teams improves outcomes and safety during the peripartum period [ 28 ]. When combined with an obstetric expertise, neonatal information can be more accurate and adapted to the degree of emergency. .

The use of multiple means of information delivery was supported by our participants including written, oral and visual. A study on the use of a decision aid in antenatal counseling showed that written information was often too complicated and understanding relied on parents’ educational level. Consequently, written information should be completed by oral explanations from a professional [ 18 ]. This has proven its efficacy in the obstetric field [ 25 ]. Such documents should be preferably personalized and adjusted to parents’ needs [ 26 ]. A visual support can decrease mothers’ anxiety [ 7 ]. Indeed, the time between antenatal counseling and the actual day of delivery can be long and mothers’ memory of the information delivered may fade. Visual aid can help parents remember information, even more so in a stressful environment [ 29 ]. A. D. Muthusamy et al. [ 30 ] found that submission of the medium before or while the information is being delivered improved recall of the information and decreased anxiety. However, providing this support after the information has been delivered is not very effective. Written information may not improve factual recall after verbal counseling of mothers in preterm labor [ 31 ]. Concerning the support of written information, Nicole M Rau and al provided that a paper handout and multimedia tablet were equally effective in the labor unit to supplement verbal preterm birth counseling and decrease parental anxiety [ 32 ]. This approach could be used in the setting of antenatal counseling. Alongside official documents provided by the hospital, the use of the Internet as a means of information has become increasingly important for pregnant women over the years [ 28 ]. In our study, the internet was depicted as negative because mothers mostly reported on their “worst case scenario” findings. However, other research show that even though internet findings may generate anxiety, they can also reassure mothers-to-be and be a rich and accessible source of support [ 33 ].

Strengths and limitations

Our study is novel as it explores the obstetric side, and the research team was multidisciplinary, including neonatologists, obstetricians, and a psychologist. Moreover, the fact that we did not focus on periviable terms enabled us to study several aspects of prenatal counseling other than decision-making. Even though our interviews were conducted over the phone for the most part due to the sanitary conditions, the interview durations were satisfactory which shows participants’ trust towards researchers. Furthermore, we included patients who gave birth at least 6 months before the interview, and whose child was discharged which gave participants time to process what happened, allowing them to tell their experience. Another asset of this study is that it reflects real world experience and not a simulation like many previous studies [ 34 ].

One of the limitations of our study is that our results are impacted by some mothers’ characteristics: our participants mostly had preeclampsia. In consequence, we cannot generalize our results to all high-risk pregnancy hospitalizations, in particular spontaneous preterm labor. However, preeclampsia causes longer hospital stays and thus allows deeper insights on the hospital experience. Patients who present with spontaneous preterm labor sometimes don’t have time to receive antenatal counseling before delivery. Other biases to consider are gestational age at admission and delivery, and pathology of the newborns, as they may have influenced participants’ experiences. We also did not include mothers who had lost their child. They probably have a very different insight that is important to consider. This stems from the fact that we decided to not include very extreme preterm children, therefore mortality was less important in our population. In order to explore mothers’ point of view after the loss of their child, the research team would have to be well prepared to deal with grief and bring emotional support during the study. Another population that was not included was mothers who had gotten prenatal counseling but ended up delivering at full term. We did not explore the impact of such information on preterm birth and the stress generated on those patients. Fish et al. showed that prenatal counseling improved parental knowledge and satisfaction without increasing anxiety [ 35 ]. Finally, in this study we focused on mothers’ experiences. It would be interesting to compare them with the coparents’ point of views, as there could be differences in psychosocial perceptions between them.

To improve the delivery of information related to preterm births, several leads could be explored. Using simulation to personalize the information in prenatal counseling remains interesting and has been widely described in the literature, but an evaluation of the clinical implementation after this simulation training is essential. Furthermore, multidisciplinarity could be developed by training different specialists to perform prenatal counseling. Written documents and videos may be elaborated to improve patients’ understanding.

The risk of preterm birth is a complex situation and all involved healthcare professionals should reflect on the best way to inform and support patients. Providing some general guidelines on how to respond to mothers’ expectations could be relevant, however personalization is the most fundamental aspect to keep in mind when delivering information on preterm birth. Hence the skills associated with information delivery in preterm births could benefit from the development and improvement of tools like: healthcare professionals’ training, interview guide for physicians that integrates parents’ expectations, and multidisciplinary counseling including all actors involved in the care of the mother and the child.

Data availability

All authors had full access to the data and materials. Data is available from Nadia Mazille-Orfanos ([email protected]) upon reasonable request.

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Doriane Randriamboarison, Elisa Fustec Contributed equally.

Authors and Affiliations

Department of Neonatology, University Hospital of Rennes, Rennes, 35000, France

Doriane Randriamboarison, Mathilde Yverneau, Karine Le Breton, Nadia Mazille-Orfanos & Patrick Pladys

Department of Obstetrics and Gynecology, University Hospital of Rennes, Rennes, 35000, France

Elisa Fustec, Isabelle Enderlé, Karine Le Breton & Linda Lassel

Faculty of Medicine Rennes 1 University, Rennes, France

Isabelle Enderlé, Mathilde Yverneau & Patrick Pladys

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Contributions

DR, EF, IE, KL, NM participated in the study design, collection and analysis of the data and the writing of the report. NM, IE and MY participated in the study design, trained the interviewers, guaranteed the expertise of the qualitative method and participated in the analysis of the data through N Vivo software. LL and PP participated in study design, data collection, writing and the interpretation of the data. DR and EF participated in organisation of the interviews and collection of the data. All authors revised this article critically, approved the final manuscript and agreed to its being submitted for publication. DR, EF, IE, KL, MY, LL, NM, and PP had complete access to the study data that support the publication.

Corresponding author

Correspondence to Nadia Mazille-Orfanos .

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The study was reviewed and approved by the Rennes University Hospital Ethics Committee (reference number 20.61). The University Hospital of Rennes recorded the material in accordance with all French ethical regulations (ref: MR-003). All methods were carried out in accordance with relevant guidelines and regulations. Participation was on a voluntary basis. All participants provided written informed consent to participate in this study.

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Randriamboarison, D., Fustec, E., Enderlé, I. et al. Qualitative analysis of mothers’ perception related to the delivery of information regarding preterm births. BMC Pregnancy Childbirth 24 , 272 (2024). https://doi.org/10.1186/s12884-024-06404-3

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Falling and rising in the vortex of cancer: children’s adaptation with cancer: a qualitative study

• Fatemeh Sepahvand 1 ,
• Fatemeh Valizadeh 2 , 4 ,
• Kimia Karami 4 ,
• Babak Abdolkarimi 3 &
• Fatemeh Ghasemi 4  

BMC Psychology volume  12 , Article number:  221 ( 2024 ) Cite this article

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Cancer is a considerable health problem worldwide and the second leading cause of death in children. It has many physical, psychological, and social consequences for children and their families. The ability to adapt to cancer plays a vital role in the recovery and quality of life of affected children. This study aimed to explain the adaptation of children with cancer to their disease.

This qualitative study adopted the directed content analysis approach based on the Roy nursing model. The participants were nine children with cancer aged 6–18 years old, five family members, four nurses, one doctor, one teacher, and two charity association members, recruited by purposive sampling method. The information was collected via individual semi-structured interviews, a focus group discussion, and field notes. The data were analyzed simultaneously with data collection using the Elo and Kyngäs method. The study rigor was ensured based on the Guba and Lincoln criteria.

Of the four categories of physical challenges, fragile self-concept, the difficulty of role transition, and disruption of the path to independence, the theme of Falling and rising in the cancer vortex was abstracted.

Based on the Roy model, the children in the present study were at the compensatory level of adaptation. This research demonstrates that the adaptation of children being treated for cancer is fragile and not constant. With each hospitalization and exacerbation of the disease, they made efforts to adapt to their disease using regulatory and cognitive subsystems. Paying attention to different stimulants and the effects of support systems on physical challenges, fragile self-concept, difficult role transition, and disruption of the path to independence for each child, as well as providing individualized care for these children, can help their adaptation to and healthy transition from the vortex of cancer. The Roy adaptation model was helpful and efficient for elucidating the adaptation of children with cancer. Providing care for children by healthcare specialists, especially nurses, should be theory-based and individualized.

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Cancer is a major health problem and its increasing growth in recent decades, along with its negative impacts on physical, psychological, social, and economic aspects of human life, have greatly concerned experts [ 1 ]. Nowadays, with the development of medicine, cancer is gradually transforming from an acute and fatal disease into a chronic one in children and adolescents. Despite the increased survival rate, cancer causes life-threatening conditions [ 2 ]. Children and their families often report psychosocial stressors of cancer, such as loss of control over emotions, fear, distancing from the family, disrupted family practices, family conflicts, financial problems, and loss of social relationships with peers [ 3 ]. When a child is diagnosed with cancer, it can lead to psychological distress, including adaptation problems and its destructive consequences such as lack of adherence to health recommendations that is highly prevalent among the patients with chronic diseases [ 4 , 5 ]. Lack of treatment adherence can continually reduce the patients’ quality of life and may even endanger their life [ 6 ]. Therefore, checking and helping to manage adaptation in children with cancer is one of the important nursing measures.

Nursing theories and conceptual models regulate nurses’ activities and direct them in research, practice, education, and management [ 7 , 8 ]. The Roy adaptation model (2009) (Fig.  1 ) views individuals and groups as adaptive comprehensive systems that are continually in relationship and interaction with the environment and its stimulants [ 9 , 10 ]. Adaptive behaviors are positive responses to the focal, contextual, and residual stimulants [ 11 ]. Roy introduces two regulatory and cognitive subsystems to help the process of adaptation [ 12 ]. She has described three levels of adaptation: 1- integration level, which structures, performances, and processes are activated to meet one’s needs in life; 2- compensatory level, which the subsystems are activated to integrate the processes of one’s life; and 3- corrective level, which compensatory systems are inefficient and a negative response is formed [ 7 ]. The general goal of nursing is to focus on promoting individual or group health by increasing adaptation in four physiological, self-concept, role function, and interdependence modes [ 11 ]. Kohi et al. conducted a qualitative study to determine the concerns and needs related to cancer among young adults and children with cancer in Tanzania. They stated that more studies are required to understand and elucidate this group’s daily needs and concerns, especially in third world and low-income countries [ 13 ]. Children’s adaptation to cancer has so far been largely based on the experiences of survivors and parents. The adaptation of children with cancer during the treatment phase has been less studied based on their own experiences. Therefore, this study aimed to elucidate the adaptation of children with cancer based on the Roy adaptation model.

The Roy Adaptation Model (2009)

This was a qualitative study using directed content analysis. It was conducted in the pediatric oncology ward in Shahid Madani hospital, Khorramabad, Iran. Children with cancer in Iran are treated in 32 hospitals under the supervision of universities of medical sciences. Treatments such as surgery, chemotherapy, radiotherapy, hormone therapy, and transplantation are performed for cancer patients in big cities in Iran. Childhood cancer treatment costs are covered by special and incurable disease insurance and non-governmental organizations (NGOs) such as the Mahak charity. Mahak provides support, psychosocial and welfare services to cancer children and their families [ 14 , 15 ]. Shahid Madani Hospital has a children’s oncology ward. In this ward chemotherapy and medical treatments are done for children with cancer. Patients go to Tehran and big cities for other treatments such as surgery, radiotherapy, and…. In this hospital, Hamin charity affiliated to Mahak was established in 2015. Benevolent people and volunteers cooperate with this charity. Their purpose is to entertain and sometimes help with children’s educational issues, and to a limited extent financial aid to patients who are introduced to them by the head nurse or ward oncologist. Providing the services usually is limited to underprivileged ones and the lack of a formal system for notifying the patient and family has caused some families to be unaware of these services and bear the treatment costs themselves.

Participants

There were 22 participants, 15 females and 7 males, the main participants were nine children aged 6–18 with Acute lymphoblastic leukemia, Hodgkin’s lymphoma, or Osteosarcoma or etc.; however, based on the created concepts, other participants including five family members, four nurses, one oncologist, one teacher and two members of Hamin charity were also recruited later (appendix 1). Purposive sampling was performed from June 2019 to September 2020. Efforts were made to have maximum diversity in sampling in terms of sex, age, diagnosis, etc., so that the data would better represent the population. The sampling continued until the data reached saturation. The inclusion criteria for children were willingness to participate, have the age of 6–18 years old; 6 months have passed since their definitive diagnosis of cancer, lack of communication problems and no known history of psychological diseases. The exclusion criteria were the incidence of any events during the interview that would prevent the interview from moving on and unwillingness to continue participation.

Data collection

The main data collection method was in-depth, semi-structured interviews. A preliminary interview guide (Box 1 ) based on the four modes of the Roy adaptation model was used. During the interviews, based on the analysis, the questions were gradually added to this preliminary guide. The questions were posed to the participants in the form of reminding and recounting a memory. For children, the questions were made suitable to their developmental stage. The location and time of conducting the interviews were set with the participants and the interviews were audio-recorded with their permission. Moreover, notes were taken on the gestures, pauses, and other non-verbal communications of the participants. Each interview lasted for 30–45 min based on the participants’ cooperation. In addition, for brainstorming and further encouraging the children to talk, the focus group discussion with three children was also held but because of the COVID-19 pandemic, the focus group discussion was held online. When present in the field, the researcher recorded everything observed, heard, or experienced in relation to the research topic in the form of six field notes, which served as another means of data collection.

Data Analysis

The interviews and field notes were transcribed on paper verbatim as soon as possible, then typed in Microsoft Word software and then entered into MaxQDA software version 2010. The data were analyzed simultaneously with data collection using the Elo and Kyngäs method. According to Elo and Kyngäs, content analysis comprises three main phase: preparation, organization, and reporting [ 16 , 17 ]. In total, 1010 codes were obtained upon data analysis. Upon continuous comparison and placement of similar codes in a previously prepared matrix based on the Roy model, four themes of physical challenges, fragile self-concept, difficulty of role transition, and disruption of the path to independence were determined (Table  1 ).

The Guba and Lincoln trustworthiness criteria, credibility, dependability, confirmability, and transferability were used to ensure the rigor of the study [ 18 ]. To enhance the credibility of the findings, prolonged engagement with the participants and member checks (the findings of the study were seen by three participants for confirmation and they stated that they were accurate in their perception and transfer of experiences) were used. The dependability of the data was confirmed by transcribing the interviews as soon as possible, seeking peers’ opinions, and reviewing the entire data. To guarantee confirmability, a voice recorder was used to record all the interviews. In addition to the comments and participation of the research team, the comments of two external appraisers were used. In all the stages of the study, parts of the codes, subcategories, and categories were given to them, and they were requested to examine the process. Then, their comments for confirmation or modification were applied. To guarantee data transferability, sampling was performed with maximum diversity.

Experiences of children with cancer in terms of adaptation were described as falling and rising in the vortex of cancer. Based on this concept, children are constantly trying to adapt to their disease based on their disease recovery/exacerbation status and support systems.

Physical challenges

The physical challenges experienced by these children included disruption in breathing pattern, disruption in nutritional pattern, disruption in activity and rest pattern, collapse of the protective system, unpleasant accompaniment of pain, and agitated nerves. The mother of a participant mentioned: “Every time his disease relapses, he breathes with difficulty and must receive oxygen all the time.” (Participant 4).

The field note about disruption in nutritional pattern: “The child felt nauseated and loudly gagged several times.” The mother stated: “She hasn’t even had water and her stomach is completely empty.” (Field note 3).

A 17-year-old adolescent said: “I experience weakness and malaise because of the medication. I feel dizzy”. (Participant 2)

A doctor said: “They experience hair loss due to the side-effects of the medications. Their skin is dried or discolored on the face, eyebrows, and eyelids. White lines may appear on the nails.” (Participant 20).

A 13-year-old boy said: “I was in a lot of pain. I could not sleep at night because of it. I had to take a bath and pour warm water on it to relieve pain. My mom kept massaging my feet, but the pain wouldn’t go away.” (Participant 21).

Fragile self-concept

Based on the findings, extensive and debilitating cancer can lead to a “distorted body image” in the physical self and a “fragile personal self” in the personal self-component. “Feeling of ugliness following hair loss” and “efforts to regain fitness” were the major concepts of distorted body image.

A 10-year-old boy said, “I’m not happy about my hair loss; I feel embarrassed. I have become ugly. I was very pretty when I had hair.” (Participant 7).

The aunt of a patient mentioned: “He is really sad because he has been obese due to the medications. He hardly do exercise to lose weight with the help of his father.” (Participant 12).

“Apprehension”, “emotional behaviors”, “fragile spirituality”, “threatened future”, and “efforts for positive thinking” lead to an “unstable personal self” in children with cancer.

“Apprehension” was abstracted from “rumination of hospitalization” and “distress and torment of diagnostic-therapeutic methods”. The thought or experience of painful diagnostic and therapeutic methods made these children feel distressed and tormented constantly. A 13-year-old girl stated, “I couldn’t sleep until 5 in the morning last night because I knew I’d be hospitalized the next day. I kept thinking how many venous catheters they’d be placing.” (Participant 22).

These children demonstrated “emotional behaviors” such as “crying”, “agitation”, “impatience”, “discomfort”, and “grumpiness”. A 17-year-old boy stated, “I’ve become angry, stubborn, and cross. I do not want to use my disease as an excuse. However, it’s something others have to accept. Anyone who sits on these beds will start nagging and being stubborn.” (Participant 3).

The patients and their families took refuge in religious and cultural beliefs to control and manage the stress caused by cancer. These children and their families believe in invoking God and saints for recovery. Thus, when their patients are not recovered and the treatment is prolonged, they start complaining to God and the saints. One of the participants said: “I used to tell God, ‘Cure all the patients who are at the hospital. Then cure me too. Don’t let me suffer too much. Sometimes, I complained to Him about being ill.” (Participant 22).

Upon experiencing “lost health and beauty”, “endangered academic-occupational future”, “disrupted life routine”, and “endangered life expectancy” as well as with the prolongation of the treatment process and lack of recovery, some children lost their hope and spirits, and became depressed over time. A nurse declared the following about a 17-year-old boy: “His mother says he has thought about suicide several times.” (Participant 18).

Some children believed that maintaining their spirits affected their recovery. One of the participants said: “When this happened to me, I decided to make myself happy, instead of being sad. I keep telling myself it’s like a cold and I’ll be fine one day. I imagine the future when I’m fine.” (Participant 21).

Difficulty of role transition

A child’s primary role in the family is that of a child and a student, and playing these roles is difficult under the effect of cancer. However, in most cases, children tried to demonstrate “responsibility despite cancer” by “adjusting responsibility at home” and “continuing education despite difficulties”. A mother mentioned: “During treatment, she kept cleaning her room; even when she was in a poor physical condition.” (Participant 22).

A 16-year-old girl stated: “At the beginning of the disease, I asked my doctor, ‘What will happen to my studies?’ He replied, ‘A teacher will attend the ward.’ When we went back home, we hired a tutor, also my elder sister and brothers helped me. I went to school only for the exams.” (Participant 1).

Loss of income (secondary role): Some adolescents used to work and earn money for themselves and their families, and the disease led to the loss of their jobs and income. A 17-year-old boy stated, “I used to be a street vendor, but, physically, I can’t do it anymore.” (Participant 5).

Trying to tolerate cancer (tertiary role) expresses the participants’ experiences in making efforts to accept the patient role. Primary categories were: “in search of diagnosis”, “cancer bottlenecks”, “treatment adherence”, and “reduction the social activities”. Many children and families ignored the primary physical symptoms and turned to “self-treatment”; this led to a “delayed visit to the doctor”. A participant mentioned: “My foot hurt so much. I took a pack of Ibuprofen a week, with no effect. Then, I told my dad, ‘Let’s go to a doctor or hospital and see what it is.’ (Participant 21).

The participants declared that, after the diagnosis of cancer, they faced “cancer bottlenecks” including efforts to understand cancer, hiding the disease from others, not accepting the limitations, and the need for the passage of time. These children were making “efforts to understand cancer” by understanding it through experience, asking others, searching the Internet, and reading books. A 17-year-old boy said, “No one has told me its cancer. I searched the Internet and found what it was. When I ask my mother, she says it’s not cancer.” (Participant 5).

Many children did not wish for others to know their diagnosis and the reason was the fear of being abandoned by them. This is why they did not wear a hat or mask because it would attract attention, and it would bother them if their family would tell others about their disease. A 13-year-old boy said, “People kept asking me why my hair was falling. I was fed up with all these questions. I was afraid they’d leave me if they’d know what my disease was.” (Participant 21).

“Lack of adherence to doctors’ orders”, “lack of adherence to isolation and “lack of adherence to dietary restrictions” were evidence for “not accepting the limitations”. A mother said, “The doctor wouldn’t allow me to send him to school, but I do because he really likes to go.” (Participant 4).

The adaptive behaviors experienced by these children included “adherence to doctors’ orders”, “performing diagnostic and therapeutic tests”, “adherence to isolation”, and “searching for a healthy lifestyle”. When the researcher was present in the field and during patient visits, a 17-year-old patient posed the following questions to his doctor: “What can I eat? Would take-out be harmful? Can I go to my friend’s birthday party? Would riding a motorbike hurt me? Can I go to the gym?” (Field note 3).

Due to the nature of the disease or the medication side effects, these children experienced a “reduction in social activities”. A participant mentioned: “Before this disease, I had a black belt in Karate. But after the disease, I had to quit it.” (Participant 5).

Disruption of the path to independence

In this study, significant others included the family, friends, relatives, teachers, ward psychologists, nurses, and doctors.

“Others’ educational role”, “others’ dutifulness”, “boosting morale”, “normalization”, and “continuing relationships with others” were among the children’s empowering behaviors by significant others, while “a sense of debt to the family” and “entertaining oneself alone” were the children adaptive behaviors.

Children felt a debt to the family because the family took the trouble of maintaining hygiene, took care of them, felt sad because of their illness, and had financial problems because of the disease. Some children made “efforts to compensate for the family’s troubles” by thanking their mothers for always accompanying them in the hospital, improving their relationship with the family because of their support, and studying during treatment. The aunt of a participant said: “My niece used to say, ‘I should compensate for this disease and the trouble it has caused for my family by studying and education’” (Participant 12).

“Social isolation” and “emotional conflict with others” were the significant others’ limiting behaviors towards the children, whereas “envying others’ health” and “having expectations of others” were among the children’s maladaptive behaviors. A 10-year-old girl noted, “My friends laugh at me; they call me bald. I don’t like to be called bald. It offends me. I don’t play with them anymore.” (Participant 6).

A 10-year-old boy declared: “When I watch healthy children playing from the window, I feel sad because I can’t play like them” (Participant 23).

In the focus group discussion, one of the participants mentioned the following about the nurse’s crying during cannulation: “Ms.…[the nurse] has cannulated half my veins; she cried when she saw my state.” (Participant 23).

According to the participants, support systems included the family, the healthcare system, the educational system, the members of charity associations, and insurance companies. Almost all the significant others for the children were part of their support system as well. The preliminary categories of this subcategory were “the financial problems of the family”, “a lack of an integrated and purposive healthcare system”, “lack of specialized insurance”, “sufficiency of the educational system”, and “benefits of the charity associations”.

A nurse said: “We had a patient, the family of whom did not have a good economic status; he was really sad and had not coped with his disease. He was hurt more because there was also a financial burden to bear. There was once a boy, almost the same age as him, with the same disease, but better economic status. Our other patient had coped with his disease better because of the better economic status of his family” (Participant 17).

The “lack of an integrated and purposive treatment system” included a delay in providing services to patients, inappropriate time of some nursing and therapeutic care, and lack of facilities in the ward and medication challenges. The most important concerns of the children admitted to the ward were “inappropriate time of some therapeutic and diagnostic measures or nursing cares”. A participant mentioned: “They used to wake me up at 3 in the morning for cannulation” (Participant 23).

Uninsured families or those with low-coverage insurance were in trouble paying for the treatments. A mother said, “His treatment is too expensive and there’s no one to support us. We visited State Welfare Organization and The Mahak Foundation; they said, ‘It’s a difficult-to-treat disease and not covered.’…. only pays a part of the costs of medications. We pay for the rest of the treatment costs, even though my husband is a worker” (Participant 9).

The stimulants belonging to each mode are presented in Table  2 .

Based on this study, children with cancer adapting to their disease did not have a steady and continuous condition, rather they had ups and downs in this area with every hospitalization or based on their disease recovery/exacerbation status and how much their social network was supportive. Based on Roy’s adaptation model, children with cancer experience numerous physical challenges in the physiological mode, especially in terms of activity, sleep and rest, nutrition, senses (pain), protection system, etc. Sibulwa et al. also declared that children with cancer experience physical challenges [ 2 ]. In the study by Kohi et al., one of the needs identified for children with cancer was a concern for physical problems [ 13 ]. These experiences impact all aspects of cancer patients’ life such as daily activities, level of independence, cognitive and physical activities, work, intimate relationships [ 5 ], quality of life, and emotional status. The ability to perform routine and daily activities is a determinant of cancer patients’ quality of life [ 2 ]. Thus, healthcare specialists should develop care measures and support strategies that assist patients’ health and functioning in daily life, both during and after the course of treatment.

The participants had a “fragile self-concept” because children’s image of their bodies was distorted. The effects of hair loss [ 19 ] and a change in children with cancer’s body image [ 20 ] lead to psychological challenges and severe alterations in self-concept [ 2 , 21 ]. In the study of Negussie et al the prevalence of psychological distress among patients with cancer was high [ 22 ]. Based on the participants’ experiences, the negative impact of the loss of beauty increased as they approached puberty. Still, the consequences of hair loss were fewer for girls because they wore scarves, wigs, and headbands, and used makeup such as kohl and eyebrow pencils. Thus, it is recommended that the proper use of these covers be trained for children with hair loss, especially when they are in public.

Personal self of the participants was “Unstable”. The stress of hospitalization and diagnostic-therapeutic measures persisted like a mental obsession even when they were at home. It is known that diagnostic and therapeutic measures such as lumbar puncture and intrathecal create high levels of pain, fear, anxiety, and emotional distress in children [ 23 ]. In the present study, some children demonstrated adaptive behavior of “efforts for positive thinking”. Therefore, in addition to drug measures, nurses should extensively use storytelling, playing, jokes, and other non-drug strategies for pain management and coping with children’s emotional distress in these cases. They should also teach children relaxation methods and positive thinking to control their negative thoughts. Moreover, they should respect the patients’ religious and cultural beliefs, and seek the help of religious missionaries in the ward near the children and their families for offering spiritual support.

“Difficulty of role transition” shows the children’s efforts to adjust themselves to problems of changing roles from being a student and an offspring to a person with those roles despite cancer. They tried to adapt their responsibilities based on their physical condition, and the support of family, hospital, and school while adhering to the principles of infection control in the right condition. Continuing education was important for the children due to the public attitude and the sense of value attached to it. Chao, Chen, et al. stated that, in the Taiwanese culture, academic performance is the most important criterion for evaluating children and adolescents [ 24 ]. Thus, healthcare specialists should actively support the child’s education during treatment.

As for secondary roles, some boys aged 15–17 years old played the role of the “source of income for themselves, and the family” by working before the disease and “quitting their job after the disease” had led to the “loss of income”. Knox et al. also noted that the experience of advanced cancer disrupted developmental responsibilities [ 25 ]. Thus, it is suggested that support systems financially support these children and their families, so that financial problems would not make them delay or quit treatment.

The most important tertiary role for children was being a patient. Participants either ignored the initial symptoms of the disease themselves or spent a lot of time to confirm the diagnosis. According to Kohi et al., one of the greatest challenges identified in relation to cancer had to do with inaccurate diagnosis or treatments during the patients’ first visits, which delayed the diagnosis and onset of treatment [ 13 ]. Therefore, it is recommended that the general and specialized education for the healthcare team focus on taking the initial symptoms of pediatric cancer seriously.

Children tried to understand cancer by asking others about its nature, searching the media and the Internet, and reading books; sometimes, only the experience of living with cancer formed their understanding. Some parents tried to hide the diagnosis of cancer from their children; therefore, some participants had a vague understanding of their disease. The children themselves sometimes hid their disease to prevent pity, curiosity, and abandonment by others. After understanding the disease and a change in their body image, the children tried to hide the disease to maintain a sense of integrity and reduce harm from others. Children with cancer hope that they can be viewed as normal people and refuse to be labeled as patients [ 19 ]. Over time, they gradually get used to the diagnosis and painful and invasive treatments. Then, they talk to others about the disease or the difficult treatment becomes tolerable for them. To them, immediate hospitalization with no prior preparation and starting with painful tests on their first visit to the hospital were very unpleasant; they suggested that the child be prepared to some extent before the first hospitalization and onset of treatment. Sometimes they did not accept the limitations of the disease and showed the maladaptive behaviors in the role of the patient. Lack of adherence to treatment is a major concern in oncology because it can expose the patient to a greater risk of relapse, side effects, and poorer treatment outcomes. Adherence to treatment can affect survival, especially in types of cancer, for which chemotherapy plays a key role [ 26 ]. Results of the study by Naghavi et al. revealed that information about the nature of the disease, the side-effects of the disease, treatments, prevention of disease exacerbation, and awareness of the outcomes of lack of adherence to therapeutic recommendations, promote adherence to treatment and optimal treatment outcomes [ 27 ]. As a result, the healthcare team should prioritize measures to promote adherence to treatment. It is therefore, suggested that booklets explaining these points be developed and provided to children. Moreover, to boost the children’s spirits and, thus, promote their adherence to treatment, movies about children surviving cancer, new scientific achievements to enhance cancer treatment, and the effect of hope on recovery should be prepared and shown to the children.

Children’s social activities and interactions in exercise and religious groups were limited due to fatigue and immobility of cancer. The majority of adolescents in the study by Sibulwa et al. declared that cancer treatment and surgery negatively influence their daily activities and social interactions [ 2 ]. Thus, it seems these children should be guided about continuing their social activities without exertion while adhering to the principles of infection control.

The participants’ experiences showed a disruption of independence. Yi et al. noted that children with cancer cling to their parents because of the anxiety they experience [ 28 ]. Decker et al. also mentioned that it is essential to pay attention to the threatened independence of adolescents with cancer [ 29 ]. In today’s societies, social support is regarded as the most important facilitator of health-related behaviors; it is the most powerful method for successfully coping with stressful situations and makes it easier for people to bear difficulties [ 30 ]. Woodgate et al. stated that the need for further acceptance, attention, and care by family and friends at the time of illness are among the children’s fundamental needs after facing the disease [ 31 ]. It is, therefore, suggested that healthcare specialists prepare programs for continuing the children’s relationship with their peers and social support network virtually and in person, whether at the hospital or at home, and prepare children for continuing social interactions after discharge.

Financial support systems were insufficient. According to Kohi et al., cancer imposes an additional financial burden on the family and this concerns the child, leading to maladaptive behaviors such as quitting treatment [ 13 ]. Before assuming the role of caregiver for a child with cancer, family caregivers need to be educated and sensitized about the potential pressures they may face. Also, the formal participation of non-governmental organizations and religious institutions also ensures that family caregivers benefit from adequate community support to cope with the pressures of caregiving [ 32 ]. Thus, it is suggested that authorities and charity associations pay more attention to resolving these families’ financial problems. Support networks can positively affect the quality of life of these children and their families through financial aids and further ensuring their economic-social security. Support systems should also support children and their families in different modes.

Stimulants on each child differed from another child. Evidently, children of different age groups reacted to these stimulants differently based on their developmental age and the accessibility of regulatory and cognitive subsystems. Based on the findings, the children in the present study were at the compensatory level of adaptation; with each hospitalization and exacerbation of the disease, they made efforts to adapt to their disease by using regulatory and cognitive subsystems. Due to the difference in stimulants on each child, the existence of different subsystems, and their different support levels, these children should receive individualized care. The results of this research can be used in future studies to design intervention studies to improve the adaptation of children with cancer.

Roy’s adaptation model was efficient in elucidating the experiences of children with cancer in adaptation to their disease. Children with cancer experienced almost the same physical challenges, but their experiences in the other three modes of adaptation differed depending on the stimulants as well as access to and use of subsystems. Children with influential others who reinforced their empowering behaviors and with better and more support resources were more positive and showed more adherence to treatment in adapting to their disease. On the other hand, children who were influenced by the limiting behaviors of their influential others and insufficiency of support systems considered their future as threatened. Maladaptive behaviors such as suicidal thoughts were also observed in some children. These children did not accept their role as a patient and had lower adherence to treatment. Due to the difference in stimulants on each child, the healthcare specialists, and especially nurses, should develop and implement their care and support measures in proportion to each child’s conditions to help the child adapt and safely pass the vortex of cancer.

Data availability

If someone wants to request, the data from this study should contact Dr. Fatemeh Valizadeh and Miss Fatemeh Sepahvand, who the data set used during the study is kept by them.

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Acknowledgements

This article is the result of a master’s degree thesis in the field of pediatric nursing approved by the Vice Chancellor for Research and Technology of Lorestan University of Medical Sciences with the code of ethics: (IR.LUMS.REC.1398.071). We would like to thank all the participants and the staff of the Pediatric oncology ward in Shahid Madani hospital, for their cooperation to perform the interviews in a suitable environment.

This research was funded by Lorestan University of Medical Sciences.

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Miss Fatemeh Sepahvand conducted the research and interviews and write the manuscript. Dr. Fatemeh Valizadeh led the design and supervised the conduct of the research, performed data analysis and interpretation, and write and revise the manuscript. Dr. Fatemeh Ghasemi helped with data analysis and interpretation. Dr. Kimia Karami helped with data analysis and interpretation. Dr Babak Abolkarimi consulted us on the project. All of the authors read and approved the study and the manuscript.

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All methods were carried out in accordance with the Helsinki Declaration. The study received ethics approval (IR.LUMS.REC.1398.071) from Ethics Committee of Lorestan University of Medical Sciences. After explaining the objective of the study, the researchers obtained father’s or mother’s informed consent to participation of his/her child in research, children’s verbal assent and the written informed consent of the other participants and getting permission to record the interview. The information letter stated that participation was confidential and voluntary and that the choice to participate or not, would not influence the forthcoming care at the ward. All methods were performed in accordance with the relevant guidelines and regulations.

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Sepahvand, F., Valizadeh, F., Karami, K. et al. Falling and rising in the vortex of cancer: children’s adaptation with cancer: a qualitative study. BMC Psychol 12 , 221 (2024). https://doi.org/10.1186/s40359-024-01722-9

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How does the external context affect an implementation processes? A qualitative study investigating the impact of macro-level variables on the implementation of goal-oriented primary care

• Ine Huybrechts   ORCID: orcid.org/0000-0003-0288-1756 1 , 2 ,
• Anja Declercq 3 , 4 ,
• Emily Verté 1 , 2 ,
• Peter Raeymaeckers 5   na1 &
• Sibyl Anthierens 1   na1

on behalf of the Primary Care Academy

Implementation Science volume  19 , Article number:  32 ( 2024 ) Cite this article

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Although the importance of context in implementation science is not disputed, knowledge about the actual impact of external context variables on implementation processes remains rather fragmented. Current frameworks, models, and studies merely describe macro-level barriers and facilitators, without acknowledging their dynamic character and how they impact and steer implementation. Including organizational theories in implementation frameworks could be a way of tackling this problem. In this study, we therefore investigate how organizational theories can contribute to our understanding of the ways in which external context variables shape implementation processes. We use the implementation process of goal-oriented primary care in Belgium as a case.

A qualitative study using in-depth semi-structured interviews was conducted with actors from a variety of primary care organizations. Data was collected and analyzed with an iterative approach. We assessed the potential of four organizational theories to enrich our understanding of the impact of external context variables on implementation processes. The organizational theories assessed are as follows: institutional theory, resource dependency theory, network theory, and contingency theory. Data analysis was based on a combination of inductive and deductive thematic analysis techniques using NVivo 12.

Institutional theory helps to understand mechanisms that steer and facilitate the implementation of goal-oriented care through regulatory and policy measures. For example, the Flemish government issued policy for facilitating more integrated, person-centered care by means of newly created institutions, incentives, expectations, and other regulatory factors. The three other organizational theories describe both counteracting or reinforcing mechanisms. The financial system hampers interprofessional collaboration, which is key for GOC. Networks between primary care providers and health and/or social care organizations on the one hand facilitate GOC, while on the other hand, technology to support interprofessional collaboration is lacking. Contingent variables such as the aging population and increasing workload and complexity within primary care create circumstances in which GOC is presented as a possible answer.

Conclusions

Insights and propositions that derive from organizational theories can be utilized to expand our knowledge on how external context variables affect implementation processes. These insights can be combined with or integrated into existing implementation frameworks and models to increase their explanatory power.

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Contributions to literature

Knowledge on how external context variables affect implementation processes tends to be rather fragmented. Insights on external context in implementation research often remain limited to merely describing macro-context barriers and facilitators.

Organizational theories contribute to our understanding on the impact of external context to an implementation process by explaining the complex interactions between organizations and their environments.

Findings can be utilized to help explain the mechanism of change in an implementation process and can be combined with or integrated into existing implementation frameworks and models to gain a broader picture on how external context affects implementation processes.

In this study, we integrate organizational theories to provide a profound analysis on how external context influences the implementation of complex interventions. There is a growing recognition that the context in which an intervention takes place highly influences implementation outcomes [ 1 , 2 ]. Despite its importance, researchers are challenged by the lack of a clear definition of context. Most implementation frameworks and models do not define context as such, but describe categories or elements of context, without capturing it as a whole [ 2 , 3 ]. Studies often distinguish between internal and external context: micro- and meso-level internal context variables are specific to a person, team, or organization. Macro-level external context variables consist of variables on a broader, socio-economic and policy level that are beyond one’s control [ 4 ].

Overall, literature provides a rather fragmented and limited perspective on how external context influences the implementation process of a complex intervention. Attempts are made to define, categorize, and conceptualize external context [ 5 , 6 ]. Certain implementation frameworks and models specifically mention external context, such as the conceptual model of evidence-based practice implementation in public service sectors [ 7 ], the Consolidated Framework for Implementation Research [ 8 ], or the i-PARiHS framework [ 9 ]. However, they remain limited to identifying and describing external context variables. Few studies are conducted that specifically point towards the actual impact of macro-level barriers and facilitators [ 10 , 11 , 12 ] but only provide limited insights in how these shape an implementation process. Nonetheless, external contextual variables can be highly disruptive for an organization’s implementation efforts, for example, when fluctuations in funding occur or when new legislation or technology is introduced [ 13 ]. In order to build a more comprehensive view on external context influences, we need an elaborative theoretical perspective.

Organizational theories as a frame of reference

To better understand how the external context affects the implementation process of a primary care intervention, we build upon research of Birken et al. [ 13 ] who demonstrate the explanatory power of organizational theories. Organizational theories can help explain the complex interactions between organizations and their environments [ 13 ], providing understanding on the impact of external context on the mechanism of change in an implementation process. We focus on three of the theories Birken et al. [ 8 ] put forward: institutional theory, resource dependency theory, and contingency theory. We also include network theory in recognition of the importance of interorganizational context and social ties between various actors, especially in primary care settings which are characterized by a multitude of diverse actors (meaning: participants of a process).

These four organizational theories demonstrate the ways in which organizations interact with their external environment in order to sustain and fulfill their core activities. All four of them do this with a different lens. Institutional theory states that an organization will aim to fulfil the expectations, values, or norms that are posed upon them in order to achieve a fit with their environment [ 14 ]. This theory helps to understand the relationships between organizations and actors and the institutional context in which they operate. Institutions can broadly be defined as a set of expectations for social or organizational behavior that can take the form formal structures such as regulatory entities, legislation, or procedures [ 15 ]. Resource dependency theory explains actions and decisions of organizations in terms of their dependence on critical and important resources. It postulates that organizations will respond to their external environment to secure the resources they need to operate [ 16 , 17 ]. This theory helps to gain insight in how fiscal variables can shape the adoption of an innovation. Contingency theory presupposes that an organizations’ effectiveness depends on the congruence between situational factors and organizational characteristics [ 18 ]. External context variables such as social and economic change and pressure can impact the way in which an innovation will be integrated. Lastly, network theory in its broader sense underlines the strength of networks: collaborating in networks can establish an effectiveness in which outcomes are achieved that could not be realized by individual organizations acting independently. Networks are about connecting or sharing information, resources, activities, and competences of three or more organizations aiming to achieve a shared goal or outcome [ 19 , 20 ]. Investigating networks helps to gain understanding of the importance of the interorganizational context and how social ties between organizations affect the implementation process of a complex intervention.

Goal-oriented care in Flanders as a case

In this study, we focus on the implementation of the approach goal-oriented care (GOC) in primary care in Flanders, the Dutch-speaking region in Belgium. Primary care is a highly institutionalized and regulated setting with a high level of professionalism. Healthcare organizations can be viewed as complex adaptive systems that are increasingly interdependent [ 21 ]. The primary care landscape in Flanders is characterized by many primary care providers (PCPs) being either self-employed or working in group practices or community health centers. They are organized and financed at different levels (federal, regional, local). In 2015–2019, a primary care reform was initiated in Flanders in which the region was geographically divided into 60 primary care zones that are governed by care councils. The Flemish Institute of Primary Care was created as a supporting institution aiming to strengthen the collaboration between primary care health and welfare actors. The complex and multisectoral nature of primary care in Flanders forms an interesting setting to gain understanding in how macro-level context variables affect implementation processes.

The concept of GOC implies a paradigm shift [ 22 ] that shifts away from a disease or problem-oriented focus towards a person-centered focus that departs from “what matters to the patient.” Boeykens et al. [ 23 ] state in their concept analysis that GOC could be described as a healthcare approach encompassing a multifaceted, dynamic, and iterative process underpinned by the patient’s context and values. The process is characterized by three stages: goal elicitations, goal setting, and goal evaluation in which patients’ needs and preferences form the common thread. It is an approach in which PCPs and patients collaborate to identify personal life goals and to align care with those goals [ 23 ]. An illustration of how this manifests at individual level can be found in Table 1 . The concept of GOC was incorporated in Flemish policies and included in the primary care reform in 2015–2019. It has gained interest in research and policy as a potential catalyst for integrated care [ 24 ]. As such, the implementation of GOC in Flanders provides an opportunity to investigate the external context of a complex primary care intervention. Our main research question is as follows: what can organizational theories tell us about the influence of external context variables on the implementation process of GOC?

We assess the potential of four organizational theories to enrich our understanding of the impact of external context variables on implementation processes. The organizational theories assessed are as follows: institutional theory, resource dependency theory, network theory, and contingency theory. Qualitative research methods are most suitable to investigate such complex matters, as they can help answer “how” and “why” questions on implementation [ 25 ]. We conducted online, semi-structured in-depth interviews with various primary care actors. These actors all had some level of experience at either meso- or micro-level with GOC implementation efforts.

Sample selection

For our purposive sample, we used the following inclusion criteria: 1) working in a Flemish health/social care context in which initiatives are taken to implement GOC and 2) having at least 6 months of experience. For recruitment, we made an overview of all possible stakeholders that are active in GOC by calling upon the network of the Primary Care Academy (PCA) Footnote 1 . Additionally, a snowballing approach was used in which respondents could refer to other relevant stakeholders at the end of each interview. This leads to respondents with different backgrounds (not only medical) and varying roles, such as being a staff member, project coordinator, or policy maker. We aimed at a maximum variation in the type of organizations which were represented by respondents, such as different governmental institutions and a variety of healthcare/social care organizations. In some cases, paired interviews were conducted [ 26 ] if the respondents were considered complementary in terms of expertise, background, and experience with the topic. An information letter and a request to participate was send to each stakeholder by e-mail. One reminder was sent in case of nonresponse.

Data collection

Interviews were conducted between January and June 2022 by a sociologist trained in qualitative research methods. Interviewing took place online using the software Microsoft Teams and were audio-recorded and transcribed verbatim. A semi-structured interview guide was used, which included (1) an exploration of the concept of GOC and how the respondent relates to this topic, (2) questions on how GOC became a topic of interest and initiatives within the respondent’s setting, and (3) the perceived barriers and facilitators for implementation. An iterative approach was used between data collection and data analysis, meaning that the interview guide underwent minor adjustments based on proceeding insights from earlier interviews in order to get richer data.

Data analysis

All data were thematically analyzed, both inductively and deductively, supported by the software NVivo 12©. For the inductive part, implicit and explicit ideas within the qualitative data were identified and described [ 27 ]. The broader research team, with backgrounds in sociology, medical sciences, and social work, discussed these initial analyses and results. The main researcher then further elaborated this into a broad understanding. This was followed by a deductive part, in which characteristics and perspectives from organizational theories were used as sensitizing concepts, inspired by research from Birken et al. [ 13 ]. This provided a frame of reference and direction, adding interpretive value to our analysis [ 28 ]. These analyses were subject of peer debriefing with our cooperating research team to validate whether these results aligned with their knowledge of GOC processes. This enhances the trustworthiness and credibility of our results [ 29 , 30 ]. Data analysis was done in Dutch, but illustrative quotes were translated into English.

In-depth interviews were performed with n = 23 respondents (see Table 2 ): five interviews were duo interviews, and one interview took place with n = 3 respondents representing one organization. We had n = 6 refusals: n = 3 because of time restraints, n = 1 did not feel sufficiently knowledgeable about the topic, n = 1 changed professional function, and there was n = 1 nonresponse. Respondents had various ways in which they related towards the macro-context: we included actors that formed part of external context (e.g., the Flemish Agency of Care and Health), actors that facilitate and strengthen organizations in the implementation of GOC (e.g., the umbrella organization for community health centers), and actors that actively convey GOC inside and outside their setting (e.g., an autonomous and integral home care service). Interviews lasted between 47 and 72 min. Table 3 gives an overview on the main findings of our deductive analysis with their respective links to the propositions of each of the organizational theories that we applied as a lens.

Institutional theory: laying foundations for a shift towards GOC

For the implementation of GOC in primary care, looking at the data with an institutional theory lens helps us understand the way in which primary care organizations will respond to social structures surrounding them. Institutional theory describes the influence of institutions, which give shape to organizational fields: “organizations that, in the aggregate, constitute a recognized area of institutional life [ 31 ], p. 148. Prevailing institutions within primary care in Flanders can affect how organizations within such organizational fields fulfil their activities. Throughout our interviews, we recognized several dynamics that are being described in institutional theory.

First of all, the changing landscape of primary care in Flanders (see 1.2) was often brought up as a dynamic in which GOC is intertwined with other changes. Respondents mention an overall tendency to reform primary care to becoming more integrated and the ideas of person-centered care becoming more upfront. These expectations in how primary care should be approached seem to affect the organizational field of primary care: “You could tell that in people’s minds they are ready to look into what it actually means to put the patient, the person central. — INT01” Various policy actors are committed to further steer towards these approaches: “the government has called it the direction that we all have to move towards. — INT23” It was part of the foundations for the most recent primary care reform, leading to the creation of demographic primary care zones governed by care councils and the Flemish Institute of Primary Care as supporting institution.

These newly established actors were viewed by our respondents as catalysts of GOC. They pushed towards the aims to depart from local settings and to establish connections between local actors. Overall, respondents emphasized their added value as they are close to the field and they truly connect primary care actors. “They [care councils] have picked up these concepts and have started working on it. At the moment they are truly the incubators and ecosystems, as they would call it in management slang. — INT04” For an innovation such as GOC to be diffused, they are viewed as the ideal actors who can function as a facilitator or conduit. They are uniquely positioned as they are closely in contact with the practice field and can be a top-down conduit for governmental actors but also are able to address the needs from bottom-up. “In this respect, people look at the primary care zones as the ideal partners. […] We can start bringing people together and have that helicopter view: what is it that truly connects you? — INT23” However, some respondents also mentioned their difficult governance structure due to representation of many disciplines and organizations.

Other regulatory factors were mentioned by respondents were other innovations or changes in primary care that were intentionally linked to GOC: e.g., the BelRAI Footnote 2 or Flemish Social Protection Footnote 3 . “The government also provides incentives. For example, family care services will gradually be obliged to work with the BelRAI screener. This way, you actually force them to start taking up GOC. — INT23” For GOC to be embedded in primary care, links with other regulatory requirements can steer PCPs towards GOC. Furthermore, it was sometimes mentioned that an important step would be for the policy level to acknowledge GOC as quality of care and to include the concept in quality standards. This would further formalize and enforce the institutional expectation to go towards person-centered care.

Currently, a challenge on institutional level as viewed by most respondents is that GOC is not or only to a limited extent incorporated in the basic education of most primary care disciplines. This leads to most of PCPs only having a limited understanding of GOC and different disciplines not having a shared language in this matter. “You have these primary health and welfare actors who each have their own approach, history and culture. To bring them together and to align them is challenging. — INT10” The absence of GOC as a topic in basic education is mentioned by various respondents as a current shortcoming in effectively implementing GOC in the wider primary care landscape.

Overall, GOC is viewed as our respondents as a topic that has recently gained a lot interest, both by individual PCPS, organizations, and governmental actors. The Flemish government has laid some foundations to facilitate this change with newly created institutions and incentives. However, other external context variables can interfere in how the concept of GOC is currently being picked up and what challenges arise.

Resource dependency theory: in search for a financial system that accommodates interprofessional collaboration

Another external context variable that affects how GOC can be introduced is the financial system that is at place. To analyze themes that were raised during the interviews with regard to finances, we utilized a resource dependency perspective. This theory presumes that organizations are dependent on financial resources and are seeking ways to ensure their continued functioning [ 16 , 17 ]. To a certain extent, this collides with the assumptions of institutional theory that foregrounds organization’s conformity to institutional pressures [ 32 ]. Resource dependency theory in contrast highlights differentiation of organizations that seek out competitive advantages [ 32 ].

In this context, respondents mention that their interest and willingness to move towards a GOC approach are held back by the current dominant system of pay for performance in the healthcare system. This financial system is experienced as restrictive, as it does not provide any incentive to PCPs for interprofessional collaboration, which is key for GOC. A switch to a flat fee system (in which a fixed fee is charged for each patient) or bundled payment was often mentioned as desirable. PCPs and health/social care organizations working in a context where they are financially rewarded for a trajectory or treatment of a patient in its entirety ensure that there is no tension with their necessity to obtain financial resources, as described in the resource dependency theory. Many of our respondents voice that community health centers are a good example. They cover different healthcare disciplines and operate with a fixed price per enrolled patient, regardless of the number of services for that patient. This promotes setting up preventive and health-promoting actions, which confirms our finding on the relevance of dedicated funding.

At the governmental level, the best way to finance and give incentives is said to be a point of discussion: “For years, we have been arguing about how to finance. Are we going to fund counsel coordination? Or counsel organization? Or care coordination? — INT04” Macro-level respondents do however mention financial incentives that are already in place to stimulate interprofessional collaboration: fees for multidisciplinary consultation being the most prominent. Other examples were given in which certain requirements were set for funding (e.g., Impulseo Footnote 4 , VIPA Footnote 5 ) that stimulate actors or settings in taking steps towards more interprofessional collaboration.

Nowadays, financial incentives to support organizations to engage in GOC tend to be project grants. However, a structural way to finance GOC approaches is currently lacking, according to our respondents. As a consequence, a long-term perspective for organizations is lacking; there is no stable financing and organizations are obliged to focus on projects instead of normalizing GOC in routine practice. According to a resource dependency perspective, the absence of financial incentives for practicing GOC hinders organizations in engaging with the approach, as they are focused on seeking out resources in order to fulfil their core activities.

A network-theory perspective: the importance of connectedness for the diffusion of an innovation

Throughout the interviews, interorganizational contextual elements were often addressed. A network theory lens states that collaborating in networks can lead to outcomes that could not be realized by individual organizations acting independently [ 19 , 20 ]. Networks consist of a set of actors such as PCPs or health/social care organizations along with a set of ties that link them [ 33 ]. These ties can be state-type ties (e.g., role based, cognitive) or event-type ties (e.g., through interactions, transactions). Both type of ties can enable a flow in which information or innovations can pass, as actors interact [ 33 ]. To analyze the implementation process of GOC and how this is diffused through various actors, a network theory perspective can help understand the importance of the connection between actors.

A first observation throughout the interviews in which we notice the importance of networks was in the mentioning of local initiatives that already existed before the creation of the primary care zones/care councils. In the area around Ghent, local multidisciplinary networks already organized community meetings, bringing together different PCPs on overarching topics relating to long-term care for patients with chronic conditions. These regions have a tradition of collaboration and connectedness of PCPs, which respondents mention to be highly valuable: “This ensures that we are more decisive, speaking from one voice with regards to what we want to stand for. — INT23” Respondents voice that the existence of such local networks has had a positive effect on the diffusion of ideas such as GOC, as trust between different actors was already established.

Further mentioning of the importance of networks could be found in respondents acknowledging one of the presumptions of network theory: working collaboratively towards a specific objective leads to outcomes that cannot be realized independently. This is especially true for GOC, an approach that in essence requires different disciplines to work together: “When only one GP, nurse or social worker starts working on it, it makes no sense. Everyone who is involved with that person needs to be on board. Actually, you need to finetune teams surrounding a person — INT11.” This is why several policy-level respondents mentioned that emphasis was placed on organizing GOC initiatives in a neighborhood-oriented way, in which accessible, inclusive care is aimed at by strengthening social cohesion. This way, different types of PCPs got to know each other through these sessions an GOC and would start to get aligned on what it means to provide GOC. However, in particular, self-employed PCPs are hard to reach. According to our respondents, occupational groups and care councils are suitable actors to engage these self-employed PCPs, but they are not always much involved in such a network .

To better connect PCPs and health/social care organizations, the absence of connectedness through the technological landscape is also mentioned. Current technological systems and platforms for documenting patient information do not allow for aligning and sharing between disciplines. In Flanders, there is a history of each discipline developing its own software, which lacks centralization or unification: “For years, they have decided to just leave it to the market, in such a way that you ended up with a proliferation of software, each discipline having its own package. — INT06” Most of the respondents mentioning this were aware that Flanders government is currently working on a unified digital care and support platform and were optimistic about its development.

Contingency theory: how environmental pressure can be a trigger for change

Our interviews were conducted during a rather dynamic and unique period of time in which the impact of social change and pressure was clearly visible: the Flemish primary care reform was ongoing which leads to the creation of care councils and VIVEL (see 3.1.1), and the COVID crisis impacted the functioning of these and other primary care actors. These observed effects of societal changes are reminiscent of the assumptions that are made in contingency theory. In essence, contingency theory presupposes that “organizational effectiveness results from fitting characteristics of the organization, such as its structure, to contingencies that reflect the situation of the organization [ 34 ], p. 1.” When it comes to the effects of the primary care reform and the COVID crisis, there were several mentions on how primary care actors reorganized their activities to adapt to these circumstances. Representatives of care councils/primary care zones whom we interviewed underlined that they were just at the point where they could again engage with their original action plans, not having to take up so many COVID-related tasks anymore. On the one hand, the COVID crisis had however forced them to immediately become functional and has also contributed that various primary care actors quickly got to know them. On the other hand, the COVID crisis has also kept them from their core activities for a while. On top of that, the crisis has also triggered a change the overall view towards data sharing. Some respondents mention a rather protectionist approach towards data sharing, while data sharing has become more normalized during the COVID crisis. This discussion was also relevant for the creation of a unified shared patient record in terms of documenting and sharing patient goals.

Other societal factors that were mentioned having an impact on the uptake of GOC are the demographic composition of a certain area. It was suggested that areas that are characterized by a patient population with more chronic care needs will be more likely to steer towards GOC as a way of coping with these complex cases. “You always have these GPs who blow it away immediately and question whether this is truly necessary. They will only become receptive to this when they experience needs for which GOC can be a solution — INT11.” On a macro-level, several respondents have mentioned how a driver for change is to have the necessity for change becoming very tangible. As PCPs are confronted with increasing numbers of patients with complex, chronic needs and their work becomes more demanding, the need for change becomes more acute. This finding is in line with what contingency theory underlines: changes in contingency (e.g., the population that is increasingly characterized by aging and multimorbidity) are an impetus for change for health/social care organizations to resolve this by adopting a structure that better fits the current environmental characteristics [ 34 ].

Our research demonstrates the applicability of organizational theories to help explain the impact that macro-level context variables have on an implementation process. These insights can be integrated into existing implementation frameworks and models to add the explanatory power of macro-level context variables, which is to date often neglected. The organizational theories demonstrate the ways in which organizations interact with their external environment in order to sustain and fulfill their core activities. As demonstrated in Fig. 1 , institutional theory largely explains how social expectations in the form of institutions lead towards the adoption or implementation of innovation, such as GOC. However, other organizational theories demonstrate how other macro-context elements on different areas can either strengthen or hamper the implementation process.

How organizational theories can help explain the way in which macro-level context variables affect implementation of an intervention

Departing from the mechanisms that are postulated by institutional theory, we observed that the shift towards GOC is part of a larger Flemish primary care reform in which and new institutions have been established and polices have been drawn up to go towards more integrated, person-centered care. To achieve this, governmental actors have placed emphasis on socialization of care, the local context, and establishing ties between organizations in order to become more complementary in providing primary health care [ 35 ]. With various initiatives surrounding this aim, the Flemish government is steering towards GOC. This is reminiscent of the mechanisms that are posed within institutional theory: organizations adapt to prevailing norms and expectations and mimic behaviors that are surrounding them [ 15 , 36 ].

Throughout our data, we came across concrete examples of how institutionalization takes place. DiMaggio and Powell [ 31 ] describe the subsequent process of isomorphism: organizations start to resemble each other as they are conforming to their institutional environment. A first mechanism through which this change occurs is coercive isomorphism and is clearly noticeable in our data. This type of isomorphism results from both formal and informal pressure coming from organizations from which a dependency relationship exists and from cultural expectations in the society [ 31 ]. Person-centered, GOC care is both formally propagated by governmental institutions and procedures and informally expected by current social tendencies. Care councils within primary care zones explicitly propagate and disseminate ideas and approaches that are desirable on policy level. Another form of isomorphism is professional isomorphism and relates to our finding that incorporation of GOC in basic education is currently lacking. The presumptions of professional isomorphism back up the importance of this: values, norms, and ideas that are developed during education are bound to find entrance within organizations as professionals start operating along these views.

Although many observations in our data back up the assumptions of institutional theory, it should be noticed that new initiatives such as the promotion of person-centered care and GOC can collide with earlier policy trends. Martens et al. [ 12 ] have examined the Belgian policy process relating three integrated care projects and concluded that although there is a strong support for a change towards a more patient-centered system, the current provider-driven system and institutional design complicate this objective. Furthermore, institutional theory tends to simplify actors as passive adopters of institutional norms and expectations and overlook the human agency and sensemaking that come with it [ 37 ]. For GOC, it is particularly true that PCPs will actively have to seek out their own style and fit the approach in their own way of working. Moreover, GOC was not just addressed as a governmental expectation but for many PCPs something they inherently stood behind.

Resources dependency theory poses that organizations are dependent on critical resources and adapt their way of working in response to those resources [ 17 ]. From our findings, it seems that the current financial system does not promote GOC, meaning that the mechanisms that are put forward in resources dependency theory are not set in motion. A macro-level analysis of barriers and facilitators in the implementation of integrated care in Belgium by Danhieux et al. [ 10 ] also points towards the financial system and data sharing as two of the main contextual determinants that affect implementation.

Throughout our data, the importance of a network approach was frequently mentioned. Interprofessional collaboration came forward as a prerequisite to make GOC happen, as well as active commitment on different levels. Burns, Nembhard, and Shortell [ 38 ] argue that research efforts on implementing person-centered, integrated care should have more focus on the use of social networks to study relational coordination. In terms of interprofessional collaboration, to date, Belgium has a limited tradition of working team-based with different disciplines [ 35 ]. However, when it comes to strengthening a cohesive primary care network, the recently established care councils have become an important facilitator. As a network governance structure, they resemble mostly a Network Administrative Organization (NAO): a separate, centralized administrative entity that is externally governed and not another member providing its own services [ 19 ]. According to Provan and Kenis [ 19 ], this type of governance form is most effective in a rather dense network with many participants, when the goal consensus is moderately high, characteristics that are indeed representative for the Flemish primary care landscape. This strengthens our observation that care councils have favorable characteristics and are well-positioned to facilitate the interorganizational context to implement GOC.

Lastly, the presumptions within contingency theory became apparent as respondents talked about how the need for change needs to become tangible for PCPs and organizations to take action, as they are increasingly faced with a shortage of time and means and more complex patient profiles. Furthermore, De Maeseneer [ 39 ] affirms our findings that the COVID-19 crisis could be employed as an opportunity to strengthen primary health care, as health becomes prioritized and its functioning becomes re-evaluated. Overall, contingency theory can help gain insight in how and why certain policy trends or decisions are made. A study of Bruns et al. [ 40 ] found that modifiable external context variables such as interagency collaboration were predictive for policy support for intervention adoption, while unmodifiable external context variable such as socio-economic composition of a region was more predictive for fiscal investments that are made.

Strengths and limitations

This study contributes to our overall understanding of implementation processes by looking into real-life implementation efforts for GOC in Flanders. It goes beyond a mere description of external context variables that affect implementation processes but aims to grasp which and how external context variables influence implementation processes. A variety of respondents from different organizations, with different backgrounds and perspectives, were interviewed, and results were analyzed by researchers with backgrounds in sociology, social work, and medical sciences. Results can not only be applied to further develop sustainable implementation plans for GOC but also enhance our understanding of how the external context influences and shapes implementation processes. As most research on contextual variables in implementation processes has until now mainly focused on internal context variables, knowledge on external context variables contributes to gaining a bigger picture of the mechanism of change.

However, this study is limited to the Flemish landscape, and external context variables and their dynamics might differ from other regions or countries. Furthermore, our study has examined and described how macro-level context variables affect the overall implementation processes of GOC. Further research is needed on the link between outer and inner contexts during implementation and sustainment, as explored by Lengninck-Hall et al. [ 41 ]. Another important consideration is that our sample only includes the “believers” in GOC and those who are already taking steps towards its implementation. It is possible that PCPs themselves or other relevant actors who are more skeptical about GOC have a different view on the policy and organizational processes that we explored. Furthermore, data triangulations in which this data is complemented with document analysis could have expanded our understanding and verified subjective perceptions of respondents.

Insights and propositions that derive from organizational theories can be utilized to expand our knowledge on how external context variables affect implementation processes. Our research demonstrates that the implementation of GOC in Flanders is steered and facilitated by regulatory and policy variables, which sets in motion mechanisms that are described in institutional theory. However, other external context variables interact with the implementation process and can further facilitate or hinder the overall implementation process. Assumptions and mechanisms explained within resource dependency theory, network theory, and contingency theory contribute to our understanding on how fiscal, technological, socio-economic, and interorganizational context variables affect an implementation process.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality guaranteed to participants but are available from the corresponding author on reasonable request.

The Primary Care Academy (PCA) is a research and teaching network of four Flemish universities, six university colleges, the White and Yellow Cross (an organization for home nursing), and patient representatives that have included GOC as one of their main research domains.

BelRAI, the Belgian implementation of the interRAI assessment tools; these are scientific, internationally validated instruments enabling an assessment of social, psychological, and physical needs and possibilities of individuals in different care settings. The data follows the person and is shared between care professionals and care organizations.

The Flemish Social Protection is a mandatory insurance established by the Flemish government to provide a range of concessions to individuals with long-term care and support needs due to illness or disability.

Impulseo, financial support for general practitioners who start an individual practice or join a group practice

VIPA, grants for the realization of sustainable, accessible, and affordable healthcare infrastructure

Abbreviations

• Goal-oriented care

Primary care provider

Primary Care Academy

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Acknowledgements

We are grateful for the partnership with the Primary Care Academy (academie-eerstelijn.be) and want to thank the King Baudouin Foundation and Fund Daniël De Coninck for the opportunity they offer us for conducting research and have impact on the primary care of Flanders, Belgium. The consortium of the Primary Care Academy consists of the following: lead author: Roy Remmen—[email protected]—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Emily Verté—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium, and Department of Family Medicine and Chronic Care, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussel, Belgium; Muhammed Mustafa Sirimsi—Centre for Research and Innovation in Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Peter Van Bogaert—Workforce Management and Outcomes Research in Care, Faculty of Medicine and Health Sciences, University of Antwerp, Belgium; Hans De Loof—Laboratory of Physio-Pharmacology, Faculty of Pharmaceutical Biomedical and Veterinary Sciences, University of Antwerp, Belgium; Kris Van den Broeck—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Sibyl Anthierens—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Ine Huybrechts—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Peter Raeymaeckers—Department of Sociology, Faculty of Social Sciences, University of Antwerp, Belgium; Veerle Bufel—Department of Sociology, Centre for Population, Family and Health, Faculty of Social Sciences, University of Antwerp, Belgium; Dirk Devroey—Department of Family Medicine and Chronic Care, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussel; Bert Aertgeerts—Academic Centre for General Practice, Faculty of Medicine, KU Leuven, Leuven, and Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven; Birgitte Schoenmakers—Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium; Lotte Timmermans—Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium; Veerle Foulon—Department of Pharmaceutical and Pharmacological Sciences, Faculty Pharmaceutical Sciences, KU Leuven, Leuven, Belgium; Anja Declercq—LUCAS-Centre for Care Research and Consultancy, Faculty of Social Sciences, KU Leuven, Leuven, Belgium; Dominique Van de Velde, Department of Rehabilitation Sciences, Occupational Therapy, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, and Department of Occupational Therapy, Artevelde University of Applied Sciences, Ghent, Belgium; Pauline Boeckxstaens—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; An De Sutter—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Patricia De Vriendt—Department of Rehabilitation Sciences, Occupational Therapy, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, and Frailty in Ageing (FRIA) Research Group, Department of Gerontology and Mental Health and Wellbeing (MENT) Research Group, Faculty of Medicine and Pharmacy, Vrije Universiteit, Brussels, Belgium, and Department of Occupational Therapy, Artevelde University of Applied Sciences, Ghent, Belgium; Lies Lahousse—Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium; Peter Pype—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, End-of-Life Care Research Group, Faculty of Medicine and Health Sciences, Vrije Universiteit Brussel and Ghent University, Ghent, Belgium; Dagje Boeykens—Department of Rehabilitation Sciences, Occupational Therapy, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, and Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Ann Van Hecke—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, University Centre of Nursing and Midwifery, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Peter Decat—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Rudi Roose—Department of Social Work and Social Pedagogy, Faculty of Psychology and Educational Sciences, University Ghent, Belgium; Sandra Martin—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Erica Rutten—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Sam Pless—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Anouk Tuinstra—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Vanessa Gauwe—Department of Occupational Therapy, Artevelde University of Applied Sciences, Ghent, Belgium; Didier ReynaertE-QUAL, University College of Applied Sciences Ghent, Ghent, Belgium; Leen Van Landschoot—Department of Nursing, University of Applied Sciences Ghent, Ghent, Belgium; Maja Lopez Hartmann—Department of Welfare and Health, Karel de Grote University of Applied Sciences and Arts, Antwerp, Belgium; Tony Claeys—LiveLab, VIVES University of Applied Sciences, Kortrijk, Belgium; Hilde Vandenhoudt—LiCalab, Thomas University of Applied Sciences, Turnhout, Belgium; Kristel De Vliegher—Department of Nursing–Homecare, White-Yellow Cross, Brussels, Belgium; and Susanne Op de Beeck—Flemish Patient Platform, Heverlee, Belgium.

This research was funded by fund Daniël De Coninck, King Baudouin Foundation, Belgium. The funder had no involvement in this study. Grant number: 2019-J5170820-211,588.

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Peter Raeymaeckers and Sibyl Anthierens have contributed equally to this work and share senior last authorship.

Authors and Affiliations

Department of Family Medicine and Population Health, University of Antwerp, Doornstraat 331, 2610, Antwerp, Belgium

Ine Huybrechts, Emily Verté & Sibyl Anthierens

Department of Family Medicine and Chronic Care, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Jette/Brussels, Belgium

Ine Huybrechts & Emily Verté

LUCAS — Centre for Care Research and Consultancy, KU Leuven, Minderbroedersstraat 8/5310, 3000, Leuven, Belgium

Anja Declercq

Center for Sociological Research, Faculty of Social Sciences, KU Leuven, Parkstraat 45/3601, 3000, Leuven, Belgium

Department of Social Work, University of Antwerp, St-Jacobstraat 2, 2000, Antwerp, Belgium

Peter Raeymaeckers

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• , Emily Verté
• , Muhammed Mustafa Sirimsi
• , Peter Van Bogaert
• , Hans De Loof
• , Kris Van den Broeck
• , Sibyl Anthierens
• , Ine Huybrechts
• , Peter Raeymaeckers
• , Veerle Bufel
• , Dirk Devroey
• , Bert Aertgeerts
• , Birgitte Schoenmakers
• , Lotte Timmermans
• , Veerle Foulon
• , Anja Declerq
• , Dominique Van de Velde
• , Pauline Boeckxstaens
• , An De Sutter
• , Patricia De Vriendt
• , Lies Lahousse
• , Peter Pype
• , Dagje Boeykens
• , Ann Van Hecke
• , Peter Decat
• , Rudi Roose
• , Sandra Martin
• , Erica Rutten
• , Sam Pless
• , Anouk Tuinstra
• , Vanessa Gauwe
• , Leen Van Landschoot
• , Maja Lopez Hartmann
• , Tony Claeys
• , Hilde Vandenhoudt
• , Kristel De Vliegher
•  & Susanne Op de Beeck

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IH wrote the main manuscript text. AD, EV, PR, and SA contributed to the different steps of the making of this manuscript. All authors reviewed the manuscript.

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Correspondence to Ine Huybrechts .

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Huybrechts, I., Declercq, A., Verté, E. et al. How does the external context affect an implementation processes? A qualitative study investigating the impact of macro-level variables on the implementation of goal-oriented primary care. Implementation Sci 19 , 32 (2024). https://doi.org/10.1186/s13012-024-01360-0

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Perceptions of medical students on narrow learning objectives and structured debriefing in medical escape rooms: a qualitative study

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Escape rooms are increasingly used in medical education as a complementary learning technique or even alternative to traditional educational approaches. Few studies focus on debriefing following medical escape rooms and how escape rooms can be used to achieve pre-defined learning objectives. Evaluating the use of narrow learning objectives may increase the depth of reflections and transform an engaging team event into an effective learning opportunity. This study aimed to explore participants’ experiences and perceived learning outcomes of narrow learning objectives in a medical escape room with debriefing.

In this explorative, qualitative study, participants saw a video lecture, participated in an escape room experience, and in a following debriefing. Throughout this learning session, the learning objectives concerned “exchange of information” and are therefore relatively narrow. Participants then participated in a semi-structured focus group interview and completed a demographic questionnaire. Participants were volunteer final-year medical students. Focus group interview recordings were transcribed and analysed using systematic text condensation.

Thirty-two students in eight groups completed the study. Five themes were described in the analysis of the focus group interviews: Experience with the narrow learning objectives, topics discussed in the debriefing, learning mechanisms, learning outcomes concerning exchange of information and influences of the learning approach.

Conclusions

Narrow learning objectives and structured debriefing seem to increase perceived learning depth of medical escape room sessions. Using semi-structured debriefing still allows for discussions of other elements relevant to the students.

Clinical trials

Clinical.trials ID NCT04783259.

Peer Review reports

There is increasing evidence that medical students prefer interactive education styles with elements of gamification [ 1 , 2 , 3 ]. Gamification is applying game mechanics to a non-gaming environment [ 4 ], which improves achievement of learning goals compared to traditional teaching methods [ 5 , 6 , 7 ].

One interactive gamification technique is escape rooms, a themed exercise that involves solving puzzles and riddles to get out of a room within a specific time limit [ 8 ]. An example of an educational escape room is the Medical Escape Room Game Experience (MERGE) [ 9 ]. It is designed to raise awareness about non-technical skills (NTS) [ 10 , 11 ] among healthcare students by presenting medically themed logic- and skill-based puzzles to be solved as a team. NTS are defined as the social, cognitive and personal management skills necessary for safe and effective performance. These skills are important across various high risk industries including nuclear power, aviation and healthcare [ 12 ].

Like simulations, escape rooms are experiential learning settings. Compared to a simulation, however, participants engage less in role-play and more in a game. Where a simulation at least sometimes asks participants to assume a professional role other than their own, participants in an escape room typically enter as “themselves.”

In such a learning situation, participants share the experience but perceive it from different angles. Debriefing can enlighten differences and strengthen the learning outcome from experiential learning situations by allowing reflection on the educational experience [ 13 , 14 , 15 ]. Therefore, debriefing will supplement an escape room’s inherent entertainment value to increase learning [ 16 ]. Further, the debriefer can be a peer to the learners as peer-to-peer feedback is suggested to affect the learning outcome positively [ 17 ].

The considerable number of debriefing structures published indicate that there is value in organising the debriefing in one way or another. The research group also experienced that structure in the debriefing is appreciated by participants and facilitators. On a theoretical basis, structured debriefings might positively affect the collaboration between facilitators and participants, as both know what to expect, once the structure is established [ 18 , 19 ].

In a debriefing, learning objectives can be predetermined [ 14 , 20 ] with narrow or broad wording. The research group differentiates between narrow and broad learning objectives. Narrow learning objectives concern focused and well-defined questions as opposed to broad learning objectives that are more open and likely to spur many different discussions depending on the learner. The “breadth” metaphor is always relative: “Knowing how errors occur” or “Discussing communication” are examples of broader learning goals with many possible subtopics whereas “Understanding the role of eye contact in non-verbal communication” in contrast is relatively narrow. When using narrow rather than broad learning objectives, the discussion can reach deeper reflection levels, as fewer topics are covered [ 21 , 22 ]. The discussion might not cover topics of interest to participants if they are outside the narrow learning objectives, resulting in discussions being terminated during a debriefing.

Only four studies evaluated escape room debriefing [ 23 , 24 , 25 , 26 ] and concluded that participants would have preferred more structured debriefing relating to specific outcomes for the escape room sessions.

Published studies applied broad learning objectives or had no pre-set learning objective. Thus, no knowledge exists about how narrow learning objectives in a medical escape room are perceived by participants and how they affect the learning experience. The research group believes this knowledge might optimise the overall learning outcome of medical escape rooms by helping educators choose suitable learning objectives. Focusing on learning objectives during debriefing can optimise learning and emphasise the educational character of these entertaining activities. When having learning objectives tailored to the needs of the participants, it is, in the research group’s experience, easier for the educator to provide a high-quality learning session. This can be done by emphasising certain aspects of the learning objectives (e.g., spending more time on discussing them) to satisfy the learning needs and wishes of the participants.

For other experiential learning settings, like simulation, debriefing was declared the “heart and soul” of learning [ 27 ]. Therefore, the research group assumes that debriefing is valuable for escape rooms as well. Given the richness and openness of the learning situation in an escape room it is unclear whether the debriefing should focus on “everything” or specific potentials in the situation. Both approaches likely have advantages and disadvantages.

This study aimed to explore participants’ experiences and perceived learning outcomes of narrow learning objectives in a medical escape room with debriefing.

This was a qualitative study using semi-structured focus group interviews and text condensing. The research group was interested in exploring participants’ perceptions and needed a method that allowed participants to express those experiences. Given the character of the learning objectives, the cognitive aspects of participants’ learning were of interest. Therefore, verbal descriptions in an interview would be a valuable method to collect data and answer the research question [ 28 ]. The research group operated within the constructivist paradigm as it tried to understand a phenomenon from the perspective of those experiencing it.

This section describes the approach, but the supplementary material should be read to understand the experimental work clearly.

The escape room followed the MERGE manual [ 9 ] and was conducted at Copenhagen Academy of Medical Education and Simulation (CAMES) at Herlev Hospital, Denmark. The theme was a zombie apocalypse. It consisted of seven medically themed, logic- and skill-based puzzles that had to be solved sequentially, and the award at the end was the cure for the fictive zombie virus. The MERGE ‘Triage’ puzzle was exchanged with a puzzle box with laparoscopic forceps, focussing on teamwork (see Appendix 2 ). Behind a see-through mirror, a facilitator monitored the escape room events. Participants had 45 min to solve the puzzles. If they struggled in progress, the facilitator provided planned scenario lifesavers to help keep the time frame [ 29 ]. All the faculty had experience facilitating experiential learning settings, including simulation and escape room experiences.

Data collection

Following the escape room, participants were interviewed semi-structured in focus groups and the individuals involved answered a questionnaire about their experience, perceived learning outcome, and demographic information (see Appendix 1 ).

The puzzles in the escape room were in English, while participants communicated in Danish. The video lecture, debriefing, focus group interview, and questionnaire were in Danish. Illustrative citations from the condensation process were translated from Danish into English.

Participants

Participants were medical students who had completed four out of six years of their studies at the University of Copenhagen (UCPH), Denmark. Participants had completed at least four months of internship, experienced clinical practice close to that experienced by young physicians, and had some experience with simulation. Participants were recruited via social media, signed up in groups of four to five, and chosen based on the order of application. Participants did not receive any compensation.

Intervention

The intervention was a structured learning session comprising four parts: a video lecture, focused instructions before the escape room, the escape room scenario, and a post-session debriefing. It was conducted in March 2021.

The learning session focused on two narrow learning objectives: “ Recognising the different ways of exchanging information ” and “ Discussing the impact of exchanging information on problem solving ”. These were chosen based on previous focus points and learning wishes by former participants [ 9 ]. The first learning objective concerned knowledge and comprehension of Bloom’s taxonomy, and the second concerned application and analysis [ 30 ].

The video lecture concerned theory of exchange of information in general terms, thus preparing participants to work with the concrete learning objectives and was developed within the research team (see Appendix 3 ). The focused instructions included practical information on the escape room’s course and emphasised the need to focus on exchange of information, as it was the learning objective. Debriefing was a semi-structured conversation steered by TJ, who has practical experience in the peer-to-peer debriefing of medical students and facilitated the discussion following a manual (Appendix 4 ) based on an established debriefing model [ 13 ].

Semi-structured focus group interviews

Immediately after debriefing, participants were focus group interviewed with a semi-structured interview guide by KE or PD (see interview guide, Appendix 5 ). Focus group interviews concerned participants’ experiences and perceived learning outcomes of narrow learning objectives in a medical escape room with debriefing. Some of the main questions explored how they felt about the format, if and why participants would have preferred a less structured format and whether or not they felt limited by the narrow focus of the debriefing. Furthermore, participants were asked when they experienced learning outcomes and what these were.

Focus group interviews were estimated to last 30 to 45 min and were video and audio recorded. Interviewers emphasised that all points of view were relevant and essential, including perceived challenges.

Focus group interviews were transcribed ad verbatim by TJ and OR and analysed using systematic text condensation [ 31 ]. Condensation focussed on participants’ statements. Unclear and explicitly irrelevant citations (e.g. chit-chat) were excluded. The coding was done in Microsoft Excel. The citations were loaded into one column, where each row represented a different speaker. After initially reading the focus group interview transcripts several times, the coding proceeded with paraphrasing each cell in the next column on a similar level of abstraction by TJ. Themes were assigned to each paraphrase, condensing content of the focus group interviews. Themes were used by TJ and OR to identify all citations relevant to the study aim. These steps were repeated until researchers concluded that saturation had been reached by watching the remaining focus group interviews, and no more codes or themes were identified. TJ condensed the statements, selecting and translating representative citations from Danish to English before grouping them into main themes. Three research group members not involved in the coding and condensation (KE, TWJ, PD) cross-checked the coding and condensation process.

Because of the qualitative character of this study, the purpose was to describe participants’ perceptions as detailed as possible but not to describe how widespread each perception was. Further quantifications were avoided, as the semi-structured nature of the focus group interviews possibly would strongly influence how often a point was made (e.g. when a follow-up question was posed). Points made by a single participant were therefore reported and treated equally important as those made by “some” or “all”.

The questionnaire provided some quantifiable information used in the discussion and conclusion to describe the general tendencies.

Several themes of interest not directly associated with the narrow learning objectives were included in a separate analysis, as they provided valuable insights into escape rooms and debriefings in general; the protocol did not cover this. The study protocol was uploaded to clinicaltrials.gov on 05/03/2021 (ClinicalTrials ID: NCT04783259).

Focus group interviews and participants

Eight groups, with a total of 32 participants completed the study. Participants were in their late twenties and evenly distributed amongst gender (see Table  1 ). In the post-interview questionnaire, they reported prior experience, educational preferences and familiarity (see Table  2 ). Focus group interview duration had a median of 36 min and ranged from 23 to 43 min. After coding and analysing six focus group interviews, saturation was reached, as no new themes could be identified from the last two focus group interviews. This was confirmed by TJ and OR watching the remaining focus group interviews on video. The remaining two focus group interviews were neither transcribed nor analysed.

Themes related to narrow learning objectives

Five main themes were identified from the focus group interviews (Table  2 ).

Within the first theme, experience with narrow learning objectives , participants expressed that they did not feel restricted by the narrow learning objectives but experienced the possibility of discussing other topics important to them.

Topics discussed in the debriefing were mainly about exchange of information . Participants understood the term communication as broader than exchange of information . Participants also discussed leadership and situational awareness .

Learning mechanisms : The single and narrow focus was seen to increase the depth and perceived outcome of the debriefing. Participants explained that the debriefer helped maintain focus on the learning objectives and increased the perceived learning outcome by guiding participants in their reflection.

Perceived learning outcomes in relation to exchange of information that were identified included: Knowledge of different ways of communicating and the importance of optimising communication when working together; skills in ignoring redundant information; and change of attitude by becoming aware that others perceive a situation differently. A few participants reported no learning outcome due to being familiar with the learning objectives prior to the intervention.

Influence of the learning approach covered the parts of the whole learning experience besides the debriefing, focussing on how each of the different phases affected perceived learning of participants. Participants expressed that the video lecture contained little educational value but supported the rest of the approach by setting the scene. Regarding the scenario briefing, some participants wished for more emphasis on the learning objectives just before entering the room. Some participants explained that they got caught up in the game and did not focus on the learning objectives during the escape room. Finally, participants indicated that they liked the coherence of the experience in that each part supported the next and enabled deeper reflection.

Other findings

The focus group interviews provided points beyond discussing the narrow learning objectives (Appendix 6 ). Two main themes were identified. Meta-learning regarding the debriefing itself, where participants realised the usefulness of debriefings in an educational context. And the general experience of the escape room , where participants stated that the experience was relevant to clinical practice. Some participants also described how learning within an escape room differed from conventional communication training because the lack of formal pressure promoted more genuine communication that reflected real-life behaviour. Furthermore, the format was engaging and fun, and the low requirements on medical expertise were appreciated as they did not steal focus.

Post-interview questionnaire

The questionnaire results concerning the learning objectives and their perceived learning outcome are presented in Table  3 . It shows that all participants experienced learning about exchange of information and many about other topics as well. The vast majority liked the narrow focus of the learning objectives and would not have preferred a broader learning objective.

This qualitative study identified narrow learning objectives and structured debriefing to increase perceived learning depth and general outcome of medical escape room sessions. Using semi-structured debriefing allowed for discussions of other elements relevant to the students.

Narrow learning objectives were not restricting

Unstructured game-like learning exercises allows for many different learning objectives catering to participants’ interests but can result in superficial and erratic discussions with frequent changes in topics. To increase the learning outcome, there is a need for some structure. According to the questionnaire, most participants preferred a narrow learning objective though they did not have a comparable experience with a broad learning objective. During the focus group interviews, participants did not feel restricted by the narrow learning objectives and felt free to discuss other topics of their interest. This is a benefit of the semi-structured rather than fully-structured debriefing format and illustrates an educational duality: participants feel a need for autonomy but also for being paced by the educator to focus on the learning objectives and return to the topic when getting off-topic. The results suggest that many educators’ fear– that guiding the debriefing is seen as negative by participants [ 32 ]– might not have an empirical basis. However, the current setting took several steps to focus on the narrow learning objectives (video lecture, scenario briefing, and debriefing). Therefore, this focus was more stringent than is typical in simulation practice.

Structuring debriefings affect perceived learning outcomes

By making participants verbalise perceptions and experiences during the escape room and their perception of aspects of the experience related to the narrow learning objective, the educator increased the perceived learning outcome by increasing the depth of the debriefing. Though the research group defines this as facilitation techniques, the participants refer to it as structure . This complies with others’ findings that participants prefer structured debriefing sessions [ 23 , 24 , 25 ]. This study emphasises that such structure indeed can improve– at least the perceived– learning outcome.

Medical expertise in the escape room

Participants expressed it as an advantage that the level of ambition for medical expertise in the escape room puzzles was low. If there had been difficult medical challenges, these could have reduced learning related to exchange of information . This could be related to matching the amount of new information to avoid an overload, as described in cognitive load theory [ 33 ]. It can also be challenging, especially for novice facilitators, not to overwhelm learners, as they might do so to avoid risking the participants perceiving the learning session as boring [ 34 ]. This study can make it easier for educators to accept that less can be more: participants see the value of discussing fewer topics in more depth.

Exchange of information as a learning objective

The learning objectives were “Recognising the different ways of exchanging information” and “Discussing the impact of exchanging information on problem solving”. Participants were thoroughly introduced to the definition of exchanging information and reminded of the learning objectives throughout the learning experience, yet participants widely used the term communication during the focus group interview. When asked, participants explained that they perceived exchange of information as a more narrow and instrumental term than communication . Participants considered the reflections in the debriefing to concern both the instrumental factors, such as structuring a message and taking notes, and elements, such as non-verbal communication and the distribution of roles within the group.

This exemplifies a challenge in concept learning [ 35 ]: Educators need to balance conceptual sharpness and keep learners motivated about a new concept. The literature on learning (second) languages shows that it may lead to steeper learning and acceptance curves if skills are presented practically with a focus on implementation instead of insisting on conceptual sharpness in using terms [ 36 , 37 , 38 ]. However, this may increase the risk of misunderstanding concepts and terms. Focusing on definitions can be frustrating for many and may slow down learning.

Limitations

In participant recruitment, the research group may have created a selection bias by having voluntary admissions for the study, thus risking a sample of the general population with a specific interest in innovative and interactive education. This potential bias is of little concern since the aim concerned the learning objectives, not the innovative and interactive education style.

The study design increases the risk of a social-desirability bias. The researchers attempted to pre-empt this by explicitly informing the participants of the importance of enlightening both positive and negative aspects.

As a medical student at UPCH, TJ had met some of the participants before, but none of the interviewers had met the participants. Although it cannot be ruled out that familiarity between participants and TJ affected the debriefing, the data collected during the focus group interview session is without this bias.

Narrow learning objectives and structured debriefing can increase perceived learning depth of medical escape room sessions. Using semi-structured debriefing still allows for discussions of other elements relevant to the students.

The findings of this study encourage the use of narrow learning objectives and semi-structured debriefings in future conductions of medical escape room sessions. This will hopefully aid educators in choosing suitable learning objectives to optimise the overall learning outcome of medical escape rooms.

Data availability

Not applicable.

Abbreviations

Copenhagen Academy of Medical Education and Simulation

Medical Escape Room Gaming Experience

Non-technical skills

University of Copenhagen

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Acknowledgements

We thank CAMES for funding the expenses required to set up and run the escape room and the participants for their time and insights.

No external funding was received for the conduct of the study.

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Copenhagen Academy for Medical Education and Simulation (CAMES), Capital Region of Denmark, Borgmester Ib Juuls Vej 1, 2730, Copenhagen, Denmark

Tami Jørgensen, Oscar Rosenkrantz, Kristine Elisabeth Eberhard, Theo Walther Jensen & Peter Dieckmann

Department of Cardiology, Bispebjerg Hospital, Copenhagen, Denmark

Tami Jørgensen

Department of Anaesthesia, Center of Head and Orthopaedics, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark

Oscar Rosenkrantz

Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark

Kristine Elisabeth Eberhard

Prehospital Center, Region Zealand, Denmark

Theo Walther Jensen

Department of Public Health, University of Copenhagen, Copenhagen , Denmark

Peter Dieckmann

Department of Quality and Health Technology, University of Stavanger, Stavanger, Norway

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T.J. wrote the main manuscript and prepared Tables 1, 2, 3 and 4. All authors participated in conducting the intervention, analysing the data and reviewing the manuscript.

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Correspondence to Tami Jørgensen .

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A formal review was waived by the National Committee on Health Research Ethics (nr.: 21014792). Participants were informed about study procedures and publication plans and were informed that they could withdraw consent at any moment without consequences. They also gave written consent before participation. Thus, informed consent was obtained from all the participants in the study. All methods were performed in accordance with the relevant guidelines and regulations.

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Competing interests.

Dieckmann holds a professorship with the University of Stavanger, Norway, which was established by an unconditional grant to the university by the Laerdal Foundation and is today financed by the university itself. Dieckmann is part of the leadership of the EuSim group, a network of simulation educators and centres providing faculty development courses. We believe that these activities do not substantially impact the study presented here. The other authors report no conflicts of interest.

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Jørgensen, T., Rosenkrantz, O., Eberhard, K.E. et al. Perceptions of medical students on narrow learning objectives and structured debriefing in medical escape rooms: a qualitative study. BMC Med Educ 24 , 403 (2024). https://doi.org/10.1186/s12909-024-05295-4

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DOI : https://doi.org/10.1186/s12909-024-05295-4

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

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Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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