main methods of data collection in qualitative research

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Qualitative Data Collection: What it is + Methods to do it

Qualitative data collection is vital in qualitative research. It helps researchers understand individuals’ attitudes, beliefs, and behaviors in a specific context.

Several methods are used to collect qualitative data, including interviews, surveys, focus groups, and observations. Understanding the various methods used for gathering qualitative data is essential for successful qualitative research.

In this post, we will discuss qualitative data and its collection methods of it.

Content Index

What is Qualitative Data?

What is qualitative data collection, what is the need for qualitative data collection, effective qualitative data collection methods, qualitative data analysis, advantages of qualitative data collection.

Qualitative data is defined as data that approximates and characterizes. It can be observed and recorded.

This data type is non-numerical in nature. This type of data is collected through methods of observations, one-to-one interviews, conducting focus groups, and similar methods.

Qualitative data in statistics is also known as categorical data – data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.

It’s pretty easy to understand the difference between qualitative and quantitative data. Qualitative data does not include numbers in its definition of traits, whereas quantitative research data is all about numbers.

The cake is orange, blue, and black in color (qualitative).
Females have brown, black, blonde, and red hair (qualitative).

Qualitative data collection is gathering non-numerical information, such as words, images, and observations, to understand individuals’ attitudes, behaviors, beliefs, and motivations in a specific context. It is an approach used in qualitative research. It seeks to understand social phenomena through in-depth exploration and analysis of people’s perspectives, experiences, and narratives. In statistical analysis , distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

The data collected through qualitative methods are often subjective, open-ended, and unstructured and can provide a rich and nuanced understanding of complex social phenomena.

Qualitative research is a type of study carried out with a qualitative approach to understand the exploratory reasons and to assay how and why a specific program or phenomenon operates in the way it is working. A researcher can access numerous qualitative data collection methods that he/she feels are relevant.

LEARN ABOUT: Best Data Collection Tools

Qualitative data collection methods serve the primary purpose of collecting textual data for research and analysis , like the thematic analysis. The collected research data is used to examine:

Knowledge around a specific issue or a program, experience of people.
Meaning and relationships.
Social norms and contextual or cultural practices demean people or impact a cause.

The qualitative data is textual or non-numerical. It covers mostly the images, videos, texts, and written or spoken words by the people. You can opt for any digital data collection methods , like structured or semi-structured surveys, or settle for the traditional approach comprising individual interviews, group discussions, etc.

Data at hand leads to a smooth process ensuring all the decisions made are for the business’s betterment. You will be able to make informed decisions only if you have relevant data.

Well! With quality data, you will improve the quality of decision-making. But you will also enhance the quality of the results expected from any endeavor.

Qualitative data collection methods are exploratory. Those are usually more focused on gaining insights and understanding the underlying reasons by digging deeper.

Although quantitative data cannot be quantified, measuring it or analyzing qualitative data might become an issue. Due to the lack of measurability, collection methods of qualitative data are primarily unstructured or structured in rare cases – that too to some extent.

Let’s explore the most common methods used for the collection of qualitative data:

main methods of data collection in qualitative research

Individual interview

It is one of the most trusted, widely used, and familiar qualitative data collection methods primarily because of its approach. An individual or face-to-face interview is a direct conversation between two people with a specific structure and purpose.

The interview questionnaire is designed in the manner to elicit the interviewee’s knowledge or perspective related to a topic, program, or issue.

At times, depending on the interviewer’s approach, the conversation can be unstructured or informal but focused on understanding the individual’s beliefs, values, understandings, feelings, experiences, and perspectives on an issue.

More often, the interviewer chooses to ask open-ended questions in individual interviews. If the interviewee selects answers from a set of given options, it becomes a structured, fixed response or a biased discussion.

The individual interview is an ideal qualitative data collection method. Particularly when the researchers want highly personalized information from the participants. The individual interview is a notable method if the interviewer decides to probe further and ask follow-up questions to gain more insights.

Qualitative surveys

To develop an informed hypothesis, many researchers use qualitative research surveys for data collection or to collect a piece of detailed information about a product or an issue. If you want to create questionnaires for collecting textual or qualitative data, then ask more open-ended questions .

LEARN ABOUT: Research Process Steps

To answer such qualitative research questions , the respondent has to write his/her opinion or perspective concerning a specific topic or issue. Unlike other collection methods, online surveys have a wider reach. People can provide you with quality data that is highly credible and valuable.

Paper surveys

Online surveys, focus group discussions.

Focus group discussions can also be considered a type of interview, but it is conducted in a group discussion setting. Usually, the focus group consists of 8 – 10 people (the size may vary depending on the researcher’s requirement). The researchers ensure appropriate space is given to the participants to discuss a topic or issue in a context. The participants are allowed to either agree or disagree with each other’s comments.

With a focused group discussion, researchers know how a particular group of participants perceives the topic. Researchers analyze what participants think of an issue, the range of opinions expressed, and the ideas discussed. The data is collected by noting down the variations or inconsistencies (if any exist) in the participants, especially in terms of belief, experiences, and practice.

The participants of focused group discussions are selected based on the topic or issues for which the researcher wants actionable insights. For example, if the research is about the recovery of college students from drug addiction. The participants have to be college students studying and recovering from drug addiction.

Other parameters such as age, qualification, financial background, social presence, and demographics are also considered, but not primarily, as the group needs diverse participants. Frequently, the qualitative data collected through focused group discussion is more descriptive and highly detailed.

Record keeping

This method uses reliable documents and other sources of information that already exist as the data source. This information can help with the new study. It’s a lot like going to the library. There, you can look through books and other sources to find information that can be used in your research.

Case studies

In this method, data is collected by looking at case studies in detail. This method’s flexibility is shown by the fact that it can be used to analyze both simple and complicated topics. This method’s strength is how well it draws conclusions from a mix of one or more qualitative data collection methods.

Observations

Observation is one of the traditional methods of qualitative data collection. It is used by researchers to gather descriptive analysis data by observing people and their behavior at events or in their natural settings. In this method, the researcher is completely immersed in watching people by taking a participatory stance to take down notes.

There are two main types of observation:

Covert: In this method, the observer is concealed without letting anyone know that they are being observed. For example, a researcher studying the rituals of a wedding in nomadic tribes must join them as a guest and quietly see everything.
Overt: In this method, everyone is aware that they are being watched. For example, A researcher or an observer wants to study the wedding rituals of a nomadic tribe. To proceed with the research, the observer or researcher can reveal why he is attending the marriage and even use a video camera to shoot everything around him.

Observation is a useful method of qualitative data collection, especially when you want to study the ongoing process, situation, or reactions on a specific issue related to the people being observed.

When you want to understand people’s behavior or their way of interaction in a particular community or demographic, you can rely on the observation data. Remember, if you fail to get quality data through surveys, qualitative interviews , or group discussions, rely on observation.

It is the best and most trusted collection method of qualitative data to generate qualitative data as it requires equal to no effort from the participants.

LEARN ABOUT: Behavioral Research

You invested time and money acquiring your data, so analyze it. It’s necessary to avoid being in the dark after all your hard work. Qualitative data analysis starts with knowing its two basic techniques, but there are no rules.

Deductive Approach: The deductive data analysis uses a researcher-defined structure to analyze qualitative data. This method is quick and easy when a researcher knows what the sample population will say.
Inductive Approach: The inductive technique has no structure or framework. When a researcher knows little about the event, an inductive approach is applied.

Whether you want to analyze qualitative data from a one-on-one interview or a survey, these simple steps will ensure a comprehensive qualitative data analysis.

Step 1: Arrange your Data

After collecting all the data, it is mostly unstructured and sometimes unclear. Arranging your data is the first stage in qualitative data analysis. So, researchers must transcribe data before analyzing it.

Step 2: Organize all your Data

After transforming and arranging your data, the next step is to organize it. One of the best ways to organize the data is to think back to your research goals and then organize the data based on the research questions you asked.

Step 3: Set a Code to the Data Collected

Setting up appropriate codes for the collected data gets you one step closer. Coding is one of the most effective methods for compressing a massive amount of data. It allows you to derive theories from relevant research findings.

Step 4: Validate your Data

Qualitative data analysis success requires data validation. Data validation should be done throughout the research process, not just once. There are two sides to validating data:

The accuracy of your research design or methods.
Reliability—how well the approaches deliver accurate data.

Step 5: Concluding the Analysis Process

Finally, conclude your data in a presentable report. The report should describe your research methods, their pros and cons, and research limitations. Your report should include findings, inferences, and future research.

QuestionPro is a comprehensive online survey software that offers a variety of qualitative data analysis tools to help businesses and researchers in making sense of their data. Users can use many different qualitative analysis methods to learn more about their data.

Users of QuestionPro can see their data in different charts and graphs, which makes it easier to spot patterns and trends. It can help researchers and businesses learn more about their target audience, which can lead to better decisions and better results.

LEARN ABOUT: Steps in Qualitative Research

Qualitative data collection has several advantages, including:

In-depth understanding: It provides in-depth information about attitudes and behaviors, leading to a deeper understanding of the research.
Flexibility: The methods allow researchers to modify questions or change direction if new information emerges.
Contextualization: Qualitative research data is in context, which helps to provide a deep understanding of the experiences and perspectives of individuals.
Rich data: It often produces rich, detailed, and nuanced information that cannot capture through numerical data.
Engagement: The methods, such as interviews and focus groups, involve active meetings with participants, leading to a deeper understanding.
Multiple perspectives: This can provide various views and a rich array of voices, adding depth and complexity.
Realistic setting: It often occurs in realistic settings, providing more authentic experiences and behaviors.

LEARN ABOUT: 12 Best Tools for Researchers

Qualitative research is one of the best methods for identifying the behavior and patterns governing social conditions, issues, or topics. It spans a step ahead of quantitative data as it fails to explain the reasons and rationale behind a phenomenon, but qualitative data quickly does.

Qualitative research is one of the best tools to identify behaviors and patterns governing social conditions. It goes a step beyond quantitative data by providing the reasons and rationale behind a phenomenon that cannot be explored quantitatively.

With QuestionPro, you can use it for qualitative data collection through various methods. Using Our robust suite correctly, you can enhance the quality and integrity of the collected data.

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Chapter 10. Introduction to Data Collection Techniques

Introduction.

Now that we have discussed various aspects of qualitative research, we can begin to collect data. This chapter serves as a bridge between the first half and second half of this textbook (and perhaps your course) by introducing techniques of data collection. You’ve already been introduced to some of this because qualitative research is often characterized by the form of data collection; for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos. Thus, some of this chapter will operate as a review of material already covered, but we will be approaching it from the data-collection side rather than the tradition-of-inquiry side we explored in chapters 2 and 4.

Revisiting Approaches

There are four primary techniques of data collection used in qualitative research: interviews, focus groups, observations, and document review. [1] There are other available techniques, such as visual analysis (e.g., photo elicitation) and biography (e.g., autoethnography) that are sometimes used independently or supplementarily to one of the main forms. Not to confuse you unduly, but these various data collection techniques are employed differently by different qualitative research traditions so that sometimes the technique and the tradition become inextricably entwined. This is largely the case with observations and ethnography. The ethnographic tradition is fundamentally based on observational techniques. At the same time, traditions other than ethnography also employ observational techniques, so it is worthwhile thinking of “tradition” and “technique” separately (see figure 10.1).

Figure 10.1. Data Collection Techniques

Each of these data collection techniques will be the subject of its own chapter in the second half of this textbook. This chapter serves as an orienting overview and as the bridge between the conceptual/design portion of qualitative research and the actual practice of conducting qualitative research.

Overview of the Four Primary Approaches

Interviews are at the heart of qualitative research. Returning to epistemological foundations, it is during the interview that the researcher truly opens herself to hearing what others have to say, encouraging her interview subjects to reflect deeply on the meanings and values they hold. Interviews are used in almost every qualitative tradition but are particularly salient in phenomenological studies, studies seeking to understand the meaning of people’s lived experiences.

Focus groups can be seen as a type of interview, one in which a group of persons (ideally between five and twelve) is asked a series of questions focused on a particular topic or subject. They are sometimes used as the primary form of data collection, especially outside academic research. For example, businesses often employ focus groups to determine if a particular product is likely to sell. Among qualitative researchers, it is often used in conjunction with any other primary data collection technique as a form of “triangulation,” or a way of increasing the reliability of the study by getting at the object of study from multiple directions. [2] Some traditions, such as feminist approaches, also see the focus group as an important “consciousness-raising” tool.

If interviews are at the heart of qualitative research, observations are its lifeblood. Researchers who are more interested in the practices and behaviors of people than what they think or who are trying to understand the parameters of an organizational culture rely on observations as their primary form of data collection. The notes they make “in the field” (either during observations or afterward) form the “data” that will be analyzed. Ethnographers, those seeking to describe a particular ethnos, or culture, believe that observations are more reliable guides to that culture than what people have to say about it. Observations are thus the primary form of data collection for ethnographers, albeit often supplemented with in-depth interviews.

Some would say that these three—interviews, focus groups, and observations—are really the foundational techniques of data collection. They are far and away the three techniques most frequently used separately, in conjunction with one another, and even sometimes in mixed methods qualitative/quantitative studies. Document review, either as a form of content analysis or separately, however, is an important addition to the qualitative researcher’s toolkit and should not be overlooked (figure 10.1). Although it is rare for a qualitative researcher to make document review their primary or sole form of data collection, including documents in the research design can help expand the reach and the reliability of a study. Document review can take many forms, from historical and archival research, in which the researcher pieces together a narrative of the past by finding and analyzing a variety of “documents” and records (including photographs and physical artifacts), to analyses of contemporary media content, as in the case of compiling and coding blog posts or other online commentaries, and content analysis that identifies and describes communicative aspects of media or documents.

In addition to these four major techniques, there are a host of emerging and incidental data collection techniques, from photo elicitation or photo voice, in which respondents are asked to comment upon a photograph or image (particularly useful as a supplement to interviews when the respondents are hesitant or unable to answer direct questions), to autoethnographies, in which the researcher uses his own position and life to increase our understanding about a phenomenon and its historical and social context.

Taken together, these techniques provide a wide range of practices and tools with which to discover the world. They are particularly suited to addressing the questions that qualitative researchers ask—questions about how things happen and why people act the way they do, given particular social contexts and shared meanings about the world (chapter 4).

Triangulation and Mixed Methods

Because the researcher plays such a large and nonneutral role in qualitative research, one that requires constant reflectivity and awareness (chapter 6), there is a constant need to reassure her audience that the results she finds are reliable. Quantitative researchers can point to any number of measures of statistical significance to reassure their audiences, but qualitative researchers do not have math to hide behind. And she will also want to reassure herself that what she is hearing in her interviews or observing in the field is a true reflection of what is going on (or as “true” as possible, given the problem that the world is as large and varied as the elephant; see chapter 3). For those reasons, it is common for researchers to employ more than one data collection technique or to include multiple and comparative populations, settings, and samples in the research design (chapter 2). A single set of interviews or initial comparison of focus groups might be conceived as a “pilot study” from which to launch the actual study. Undergraduate students working on a research project might be advised to think about their projects in this way as well. You are simply not going to have enough time or resources as an undergraduate to construct and complete a successful qualitative research project, but you may be able to tackle a pilot study. Graduate students also need to think about the amount of time and resources they have for completing a full study. Masters-level students, or students who have one year or less in which to complete a program, should probably consider their study as an initial exploratory pilot. PhD candidates might have the time and resources to devote to the type of triangulated, multifaceted research design called for by the research question.

We call the use of multiple qualitative methods of data collection and the inclusion of multiple and comparative populations and settings “triangulation.” Using different data collection methods allows us to check the consistency of our findings. For example, a study of the vaccine hesitant might include a set of interviews with vaccine-hesitant people and a focus group of the same and a content analysis of online comments about a vaccine mandate. By employing all three methods, we can be more confident of our interpretations from the interviews alone (especially if we are hearing the same thing throughout; if we are not, then this is a good sign that we need to push a little further to find out what is really going on). [3] Methodological triangulation is an important tool for increasing the reliability of our findings and the overall success of our research.

Methodological triangulation should not be confused with mixed methods techniques, which refer instead to the combining of qualitative and quantitative research methods. Mixed methods studies can increase reliability, but that is not their primary purpose. Mixed methods address multiple research questions, both the “how many” and “why” kind, or the causal and explanatory kind. Mixed methods will be discussed in more detail in chapter 15.

Let us return to the three examples of qualitative research described in chapter 1: Cory Abramson’s study of aging ( The End Game) , Jennifer Pierce’s study of lawyers and discrimination ( Racing for Innocence ), and my own study of liberal arts college students ( Amplified Advantage ). Each of these studies uses triangulation.

Abramson’s book is primarily based on three years of observations in four distinct neighborhoods. He chose the neighborhoods in such a way to maximize his ability to make comparisons: two were primarily middle class and two were primarily poor; further, within each set, one was predominantly White, while the other was either racially diverse or primarily African American. In each neighborhood, he was present in senior centers, doctors’ offices, public transportation, and other public spots where the elderly congregated. [4] The observations are the core of the book, and they are richly written and described in very moving passages. But it wasn’t enough for him to watch the seniors. He also engaged with them in casual conversation. That, too, is part of fieldwork. He sometimes even helped them make it to the doctor’s office or get around town. Going beyond these interactions, he also interviewed sixty seniors, an equal amount from each of the four neighborhoods. It was in the interviews that he could ask more detailed questions about their lives, what they thought about aging, what it meant to them to be considered old, and what their hopes and frustrations were. He could see that those living in the poor neighborhoods had a more difficult time accessing care and resources than those living in the more affluent neighborhoods, but he couldn’t know how the seniors understood these difficulties without interviewing them. Both forms of data collection supported each other and helped make the study richer and more insightful. Interviews alone would have failed to demonstrate the very real differences he observed (and that some seniors would not even have known about). This is the value of methodological triangulation.

Pierce’s book relies on two separate forms of data collection—interviews with lawyers at a firm that has experienced a history of racial discrimination and content analyses of news stories and popular films that screened during the same years of the alleged racial discrimination. I’ve used this book when teaching methods and have often found students struggle with understanding why these two forms of data collection were used. I think this is because we don’t teach students to appreciate or recognize “popular films” as a legitimate form of data. But what Pierce does is interesting and insightful in the best tradition of qualitative research. Here is a description of the content analyses from a review of her book:

In the chapter on the news media, Professor Pierce uses content analysis to argue that the media not only helped shape the meaning of affirmative action, but also helped create white males as a class of victims. The overall narrative that emerged from these media accounts was one of white male innocence and victimization. She also maintains that this narrative was used to support “neoconservative and neoliberal political agendas” (p. 21). The focus of these articles tended to be that affirmative action hurt white working-class and middle-class men particularly during the recession in the 1980s (despite statistical evidence that people of color were hurt far more than white males by the recession). In these stories fairness and innocence were seen in purely individual terms. Although there were stories that supported affirmative action and developed a broader understanding of fairness, the total number of stories slanted against affirmative action from 1990 to 1999. During that time period negative stories always outnumbered those supporting the policy, usually by a ratio of 3:1 or 3:2. Headlines, the presentation of polling data, and an emphasis in stories on racial division, Pierce argues, reinforced the story of white male victimization. Interestingly, the news media did very few stories on gender and affirmative action. The chapter on the film industry from 1989 to 1999 reinforces Pierce’s argument and adds another layer to her interpretation of affirmative action during this time period. She sampled almost 60 Hollywood films with receipts ranging from four million to 184 million dollars. In this chapter she argues that the dominant theme of these films was racial progress and the redemption of white Americans from past racism. These movies usually portrayed white, elite, and male experiences. People of color were background figures who supported the protagonist and “anointed” him as a savior (p. 45). Over the course of the film the protagonists move from “innocence to consciousness” concerning racism. The antagonists in these films most often were racist working-class white men. A Time to Kill , Mississippi Burning , Amistad , Ghosts of Mississippi , The Long Walk Home , To Kill a Mockingbird , and Dances with Wolves receive particular analysis in this chapter, and her examination of them leads Pierce to conclude that they infused a myth of racial progress into America’s cultural memory. White experiences of race are the focus and contemporary forms of racism are underplayed or omitted. Further, these films stereotype both working-class and elite white males, and underscore the neoliberal emphasis on individualism. ( Hrezo 2012 )

With that context in place, Pierce then turned to interviews with attorneys. She finds that White male attorneys often misremembered facts about the period in which the law firm was accused of racial discrimination and that they often portrayed their firms as having made substantial racial progress. This was in contrast to many of the lawyers of color and female lawyers who remembered the history differently and who saw continuing examples of racial (and gender) discrimination at the law firm. In most of the interviews, people talked about individuals, not structure (and these are attorneys, who really should know better!). By including both content analyses and interviews in her study, Pierce is better able to situate the attorney narratives and explain the larger context for the shared meanings of individual innocence and racial progress. Had this been a study only of films during this period, we would not know how actual people who lived during this period understood the decisions they made; had we had only the interviews, we would have missed the historical context and seen a lot of these interviewees as, well, not very nice people at all. Together, we have a study that is original, inventive, and insightful.

My own study of how class background affects the experiences and outcomes of students at small liberal arts colleges relies on mixed methods and triangulation. At the core of the book is an original survey of college students across the US. From analyses of this survey, I can present findings on “how many” questions and descriptive statistics comparing students of different social class backgrounds. For example, I know and can demonstrate that working-class college students are less likely to go to graduate school after college than upper-class college students are. I can even give you some estimates of the class gap. But what I can’t tell you from the survey is exactly why this is so or how it came to be so . For that, I employ interviews, focus groups, document reviews, and observations. Basically, I threw the kitchen sink at the “problem” of class reproduction and higher education (i.e., Does college reduce class inequalities or make them worse?). A review of historical documents provides a picture of the place of the small liberal arts college in the broader social and historical context. Who had access to these colleges and for what purpose have always been in contest, with some groups attempting to exclude others from opportunities for advancement. What it means to choose a small liberal arts college in the early twenty-first century is thus different for those whose parents are college professors, for those whose parents have a great deal of money, and for those who are the first in their family to attend college. I was able to get at these different understandings through interviews and focus groups and to further delineate the culture of these colleges by careful observation (and my own participation in them, as both former student and current professor). Putting together individual meanings, student dispositions, organizational culture, and historical context allowed me to present a story of how exactly colleges can both help advance first-generation, low-income, working-class college students and simultaneously amplify the preexisting advantages of their peers. Mixed methods addressed multiple research questions, while triangulation allowed for this deeper, more complex story to emerge.

In the next few chapters, we will explore each of the primary data collection techniques in much more detail. As we do so, think about how these techniques may be productively joined for more reliable and deeper studies of the social world.

Advanced Reading: Triangulation

Denzin ( 1978 ) identified four basic types of triangulation: data, investigator, theory, and methodological. Properly speaking, if we use the Denzin typology, the use of multiple methods of data collection and analysis to strengthen one’s study is really a form of methodological triangulation. It may be helpful to understand how this differs from the other types.

Data triangulation occurs when the researcher uses a variety of sources in a single study. Perhaps they are interviewing multiple samples of college students. Obviously, this overlaps with sample selection (see chapter 5). It is helpful for the researcher to understand that these multiple data sources add strength and reliability to the study. After all, it is not just “these students here” but also “those students over there” that are experiencing this phenomenon in a particular way.

Investigator triangulation occurs when different researchers or evaluators are part of the research team. Intercoding reliability is a form of investigator triangulation (or at least a way of leveraging the power of multiple researchers to raise the reliability of the study).

Theory triangulation is the use of multiple perspectives to interpret a single set of data, as in the case of competing theoretical paradigms (e.g., a human capital approach vs. a Bourdieusian multiple capital approach).

Methodological triangulation , as explained in this chapter, is the use of multiple methods to study a single phenomenon, issue, or problem.

8 Essential Qualitative Data Collection Methods

Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement.

British mathematician and marketing mastermind Clive Humby once famously stated that “Data is the new oil.” He has a point. Without data, nonprofit organizations are left second-guessing what their clients and supporters think, how their brand compares to others in the market, whether their messaging is on-point, how their campaigns are performing, where improvements can be made, and how overall results can be optimized.

There are two primary data collection methodologies: qualitative data collection and quantitative data collection. At UpMetrics, we believe that relying on quantitative, static data is no longer an option to drive effective impact. In the nonprofit sector, where financial gain is not the sole purpose of your organization’s existence. In this guide, we’ll focus on qualitative data collection methods and how they can help you gather, analyze, and collate information that can help drive your organization forward.

What is Qualitative Data?

Data collection in qualitative research focuses on gathering contextual information. Unlike quantitative data, which focuses primarily on numbers to establish ‘how many’ or ‘how much,’ qualitative data collection tools allow you to assess the ‘why’s’ and ‘how’s’ behind those statistics. This is vital for nonprofits as it enables organizations to determine:

Existing knowledge surrounding a particular issue.
How social norms and cultural practices impact a cause.
What kind of experiences and interactions people have with your brand.
Trends in the way people change their opinions.
Whether meaningful relationships are being established between all parties.

In short, qualitative data collection methods collect perceptual and descriptive information that helps you understand the reasoning and motivation behind particular reactions and behaviors. For that reason, qualitative data methods are usually non-numerical and center around spoken and written words rather than data extrapolated from a spreadsheet or report.

Qualitative vs. Quantitative Data

Quantitative and qualitative data represent both sides of the same coin. There will always be some degree of debate over the importance of quantitative vs. qualitative research, data, and collection. However, successful organizations should strive to achieve a balance between the two.

Organizations can track their performance by collecting quantitative data based on metrics including dollars raised, membership growth, number of people served, overhead costs, etc. This is all essential information to have. However, the data lacks value without the additional details provided by qualitative research because it doesn’t tell you anything about how your target audience thinks, feels, and acts.

Qualitative data collection is particularly relevant in the nonprofit sector as the relationships people have with the causes they support are fundamentally personal and cannot be expressed numerically. Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement.

8 Types of Qualitative Data Collection Methods

As we have firmly established the need for qualitative data, it’s time to answer the next big question: how to collect qualitative data.

Here is a list of the most common qualitative data collection methods. You don’t need to use them all in your quest for gathering information. However, a foundational understanding of each will help you refine your research strategy and select the methods that are likely to provide the highest quality business intelligence for your organization.

1. Interviews

One-on-one interviews are one of the most commonly used data collection methods in qualitative research because they allow you to collect highly personalized information directly from the source. Interviews explore participants' opinions, motivations, beliefs, and experiences and are particularly beneficial in gathering data on sensitive topics because respondents are more likely to open up in a one-on-one setting than in a group environment.

Interviews can be conducted in person or by online video call. Typically, they are separated into three main categories:

Structured Interviews - Structured interviews consist of predetermined (and usually closed) questions with little or no variation between interviewees. There is generally no scope for elaboration or follow-up questions, making them better suited to researching specific topics.
Unstructured Interviews – Conversely, unstructured interviews have little to no organization or preconceived topics and include predominantly open questions. As a result, the discussion will flow in completely different directions for each participant and can be very time-consuming. For this reason, unstructured interviews are generally only used when little is known about the subject area or when in-depth responses are required on a particular subject.
Semi-Structured Interviews – A combination of the two interviews mentioned above, semi-structured interviews comprise several scripted questions but allow both interviewers and interviewees the opportunity to diverge and elaborate so more in-depth reasoning can be explored.

While each approach has its merits, semi-structured interviews are typically favored as a way to uncover detailed information in a timely manner while highlighting areas that may not have been considered relevant in previous research efforts. Whichever type of interview you utilize, participants must be fully briefed on the format, purpose, and what you hope to achieve. With that in mind, here are a few tips to follow:

Give them an idea of how long the interview will last
If you plan to record the conversation, ask permission beforehand
Provide the opportunity to ask questions before you begin and again at the end.

2. Focus Groups

Focus groups share much in common with less structured interviews, the key difference being that the goal is to collect data from several participants simultaneously. Focus groups are effective in gathering information based on collective views and are one of the most popular data collection instruments in qualitative research when a series of one-on-one interviews proves too time-consuming or difficult to schedule.

Focus groups are most helpful in gathering data from a specific group of people, such as donors or clients from a particular demographic. The discussion should be focused on a specific topic and carefully guided and moderated by the researcher to determine participant views and the reasoning behind them.

Feedback in a group setting often provides richer data than one-on-one interviews, as participants are generally more open to sharing when others are sharing too. Plus, input from one participant may spark insight from another that would not have come to light otherwise. However, here are a couple of potential downsides:

If participants are uneasy with each other, they may not be at ease openly discussing their feelings or opinions.
If the topic is not of interest or does not focus on something participants are willing to discuss, data will lack value.

The size of the group should be carefully considered. Research suggests over-recruiting to avoid risking cancellation, even if that means moderators have to manage more participants than anticipated. The optimum group size is generally between six and eight for all participants to be granted ample opportunity to speak. However, focus groups can still be successful with as few as three or as many as fourteen participants.

3. Observation

Observation is one of the ultimate data collection tools in qualitative research for gathering information through subjective methods. A technique used frequently by modern-day marketers, qualitative observation is also favored by psychologists, sociologists, behavior specialists, and product developers.

The primary purpose is to gather information that cannot be measured or easily quantified. It involves virtually no cognitive input from the participants themselves. Researchers simply observe subjects and their reactions during the course of their regular routines and take detailed field notes from which to draw information.

Observational techniques vary in terms of contact with participants. Some qualitative observations involve the complete immersion of the researcher over a period of time. For example, attending the same church, clinic, society meetings, or volunteer organizations as the participants. Under these circumstances, researchers will likely witness the most natural responses rather than relying on behaviors elicited in a simulated environment. Depending on the study and intended purpose, they may or may not choose to identify themselves as a researcher during the process.

Regardless of whether you take a covert or overt approach, remember that because each researcher is as unique as every participant, they will have their own inherent biases. Therefore, observational studies are prone to a high degree of subjectivity. For example, one researcher’s notes on the behavior of donors at a society event may vary wildly from the next. So, each qualitative observational study is unique in its own right.

4. Open-Ended Surveys and Questionnaires

Open-ended surveys and questionnaires allow organizations to collect views and opinions from respondents without meeting in person. They can be sent electronically and are considered one of the most cost-effective qualitative data collection tools. Unlike closed question surveys and questionnaires that limit responses, open-ended questions allow participants to provide lengthy and in-depth answers from which you can extrapolate large amounts of data.

The findings of open-ended surveys and questionnaires can be challenging to analyze because there are no uniform answers. A popular approach is to record sentiments as positive, negative, and neutral and further dissect the data from there. To gather the best business intelligence, carefully consider the presentation and length of your survey or questionnaire. Here is a list of essential considerations:

Number of questions : Too many can feel intimidating, and you’ll experience low response rates. Too few can feel like it’s not worth the effort. Plus, the data you collect will have limited actionability. The consensus on how many questions to include varies depending on which sources you consult. However, 5-10 is a good benchmark for shorter surveys that take around 10 minutes and 15-20 for longer surveys that take approximately 20 minutes to complete.
Personalization: Your response rate will be higher if you greet patients by name and demonstrate a historical knowledge of their interactions with your brand.
Visual elements : Recipients can be easily turned off by poorly designed questionnaires. Besides, it’s a good idea to customize your survey template to include brand assets like colors, logos, and fonts to increase brand loyalty and recognition.
Reminders : Sending survey reminders is the best way to improve your response rate. You don’t want to hassle respondents too soon, nor do you want to wait too long. Sending a follow-up at around the 3-7 mark is usually the most effective.
Building a feedback loop : Adding a tick-box requesting permission for further follow-ups is a proven way to elicit more in-depth feedback. Plus, it gives respondents a voice and makes their opinion feel valued.

5. Case Studies

Case studies are often a preferred method of qualitative research data collection for organizations looking to generate incredibly detailed and in-depth information on a specific topic. Case studies are usually a deep dive into one specific case or a small number of related cases. As a result, they work well for organizations that operate in niche markets.

Case studies typically involve several qualitative data collection methods, including interviews, focus groups, surveys, and observation. The idea is to cast a wide net to obtain a rich picture comprising multiple views and responses. When conducted correctly, case studies can generate vast bodies of data that can be used to improve processes at every client and donor touchpoint.

The best way to demonstrate the purpose and value of a case study is with an example: A Longitudinal Qualitative Case Study of Change in Nonprofits – Suggesting A New Approach to the Management of Change .

The researchers established that while change management had already been widely researched in commercial and for-profit settings, little reference had been made to the unique challenges in the nonprofit sector. The case study examined change and change management at a single nonprofit hospital from the viewpoint of all those who witnessed and experienced it. To gain a holistic view of the entire process, research included interviews with employees at every level, from nursing staff to CEOs, to identify the direct and indirect impacts of change. Results were collated based on detailed responses to questions about preparing for change, experiencing change, and reflecting on change.

6. Text Analysis

Text analysis has long been used in political and social science spheres to gain a deeper understanding of behaviors and motivations by gathering insights from human-written texts. By analyzing the flow of text and word choices, relationships between other texts written by the same participant can be identified so that researchers can draw conclusions about the mindset of their target audience. Though technically a qualitative data collection method, the process can involve some quantitative elements, as often, computer systems are used to scan, extract, and categorize information to identify patterns, sentiments, and other actionable information.

You might be wondering how to collect written information from your research subjects. There are many different options, and approaches can be overt or covert.

Examples include:

Investigating how often certain cause-related words and phrases are used in client and donor social media posts.
Asking participants to keep a journal or diary.
Analyzing existing interview transcripts and survey responses.

By conducting a detailed analysis, you can connect elements of written text to specific issues, causes, and cultural perspectives, allowing you to draw empirical conclusions about personal views, behaviors, and social relations. With small studies focusing on participants' subjective experience on a specific theme or topic, diaries and journals can be particularly effective in building an understanding of underlying thought processes and beliefs.

7. Audio and Video Recordings

Similarly to how data is collected from a person’s writing, you can draw valuable conclusions by observing someone’s speech patterns, intonation, and body language when you watch or listen to them interact in a particular environment or within specific surroundings.

Video and audio recordings are helpful in circumstances where researchers predict better results by having participants be in the moment rather than having them think about what to write down or how to formulate an answer to an email survey.

You can collect audio and video materials for analysis from multiple sources, including:

Previously filmed records of events
Interview recordings
Video diaries

Utilizing audio and video footage allows researchers to revisit key themes, and it's possible to use the same analytical sources in multiple studies – providing that the scope of the original recording is comprehensive enough to cover the intended theme in adequate depth.

It can be challenging to present the results of audio and video analysis in a quantifiable form that helps you gauge campaign and market performance. However, results can be used to effectively design concept maps that extrapolate central themes that arise consistently. Concept Mapping offers organizations a visual representation of thought patterns and how ideas link together between different demographics. This data can prove invaluable in identifying areas for improvement and change across entire projects and organizational processes.

8. Hybrid Methodologies

It is often possible to utilize data collection methods in qualitative research that provide quantitative facts and figures. So if you’re struggling to settle on an approach, a hybrid methodology may be a good starting point. For instance, a survey format that asks closed and open questions can collect and collate quantitative and qualitative data.

A Net Promoter Score (NPS) survey is a great example. The primary goal of an NPS survey is to collect quantitative ratings of various factors on a score of 1-10. However, they also utilize open-ended follow-up questions to collect qualitative data that helps identify insights into the trends, thought processes, reasoning, and behaviors behind the initial scoring.

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Collecting and Analyzing Qualitative Data

Brent Wolff, Frank Mahoney, Anna Leena Lohiniva, and Melissa Corkum

Choosing When to Apply Qualitative Methods
Commonly Used Qualitative Methods in Field Investigations
Sampling and Recruitment for Qualitative Research
Managing, Condensing, Displaying, and Interpreting Qualitative Data
Coding and Analysis Requirements

Qualitative research methods are a key component of field epidemiologic investigations because they can provide insight into the perceptions, values, opinions, and community norms where investigations are being conducted ( 1,2 ). Open-ended inquiry methods, the mainstay of qualitative interview techniques, are essential in formative research for exploring contextual factors and rationales for risk behaviors that do not fit neatly into predefined categories. For example, during the 2014–2015 Ebola virus disease outbreaks in parts of West Africa, understanding the cultural implications of burial practices within different communities was crucial to designing and monitoring interventions for safe burials ( Box 10.1 ). In program evaluations, qualitative methods can assist the investigator in diagnosing what went right or wrong as part of a process evaluation or in troubleshooting why a program might not be working as well as expected. When designing an intervention, qualitative methods can be useful in exploring dimensions of acceptability to increase the chances of intervention acceptance and success. When performed in conjunction with quantitative studies, qualitative methods can help the investigator confirm, challenge, or deepen the validity of conclusions than either component might have yielded alone ( 1,2 ).

Qualitative research was used extensively in response to the Ebola virus disease outbreaks in parts of West Africa to understand burial practices and to design culturally appropriate strategies to ensure safe burials. Qualitative studies were also used to monitor key aspects of the response.

In October 2014, Liberia experienced an abrupt and steady decrease in case counts and deaths in contrast with predicted disease models of an increased case count. At the time, communities were resistant to entering Ebola treatment centers, raising the possibility that patients were not being referred for care and communities might be conducting occult burials.

To assess what was happening at the community level, the Liberian Emergency Operations Center recruited epidemiologists from the US Department of Health and Human Services/Centers for Disease Control and Prevention and the African Union to investigate the problem.

Teams conducted in-depth interviews and focus group discussions with community leaders, local funeral directors, and coffin makers and learned that communities were not conducting occult burials and that the overall number of burials was less than what they had experienced in previous years. Other key findings included the willingness of funeral directors to cooperate with disease response efforts, the need for training of funeral home workers, and considerable community resistance to cremation practices. These findings prompted the Emergency Operations Center to open a burial ground for Ebola decedents, support enhanced testing of burials in the private sector, and train private-sector funeral workers regarding safe burial practices.

Source: Melissa Corkum, personal communication.

Similar to quantitative approaches, qualitative research seeks answers to specific questions by using rigorous approaches to collecting and compiling information and producing findings that can be applicable beyond the study population. The fundamental difference in approaches lies in how they translate real-life complexities of initial observations into units of analysis. Data collected in qualitative studies typically are in the form of text or visual images, which provide rich sources of insight but also tend to be bulky and time-consuming to code and analyze. Practically speaking, qualitative study designs tend to favor small, purposively selected samples ideal for case studies or in-depth analysis ( 1 ). The combination of purposive sampling and open-ended question formats deprive qualitative study designs of the power to quantify and generalize conclusions, one of the key limitations of this approach.

Qualitative scientists might argue, however, that the generalizability and precision possible through probabilistic sampling and categorical outcomes are achieved at the cost of enhanced validity, nuance, and naturalism that less structured approaches offer ( 3 ). Open-ended techniques are particularly useful for understanding subjective meanings and motivations underlying behavior. They enable investigators to be equally adept at exploring factors observed and unobserved, intentions as well as actions, internal meanings as well as external consequences, options considered but not taken, and unmeasurable as well as measurable outcomes. These methods are important when the source of or solution to a public health problem is rooted in local perceptions rather than objectively measurable characteristics selected by outside observers ( 3 ). Ultimately, such approaches have the ability to go beyond quantifying questions of how much or how many to take on questions of how or why from the perspective and in the words of the study subjects themselves ( 1,2 ).

Another key advantage of qualitative methods for field investigations is their flexibility ( 4 ). Qualitative designs not only enable but also encourage flexibility in the content and flow of questions to challenge and probe for deeper meanings or follow new leads if they lead to deeper understanding of an issue (5). It is not uncommon for topic guides to be adjusted in the course of fieldwork to investigate emerging themes relevant to answering the original study question. As discussed herein, qualitative study designs allow flexibility in sample size to accommodate the need for more or fewer interviews among particular groups to determine the root cause of an issue (see the section on Sampling and Recruitment in Qualitative Research). In the context of field investigations, such methods can be extremely useful for investigating complex or fast-moving situations where the dimensions of analysis cannot be fully anticipated.

Ultimately, the decision whether to include qualitative research in a particular field investigation depends mainly on the nature of the research question itself. Certain types of research topics lend themselves more naturally to qualitative rather than other approaches ( Table 10.1 ). These include exploratory investigations when not enough is known about a problem to formulate a hypothesis or develop a fixed set of questions and answer codes. They include research questions where intentions matter as much as actions and “why?” or “why not?” questions matter as much as precise estimation of measured outcomes. Qualitative approaches also work well when contextual influences, subjective meanings, stigma, or strong social desirability biases lower faith in the validity of responses coming from a relatively impersonal survey questionnaire interview.

The availability of personnel with training and experience in qualitative interviewing or observation is critical for obtaining the best quality data but is not absolutely required for rapid assessment in field settings. Qualitative interviewing requires a broader set of skills than survey interviewing. It is not enough to follow a topic guide like a questionnaire, in order, from top to bottom. A qualitative interviewer must exercise judgment to decide when to probe and when to move on, when to encourage, challenge, or follow relevant leads even if they are not written in the topic guide. Ability to engage with informants, connect ideas during the interview, and think on one’s feet are common characteristics of good qualitative interviewers. By far the most important qualification in conducting qualitative fieldwork is a firm grasp of the research objectives; with this qualification, a member of the research team armed with curiosity and a topic guide can learn on the job with successful results.

Semi-Structured Interviews

Semi-structured interviews can be conducted with single participants (in-depth or individual key informants) or with groups (focus group discussions [FGDs] or key informant groups). These interviews follow a suggested topic guide rather than a fixed questionnaire format. Topic guides typically consist of a limited number ( 10– 15 ) of broad, open-ended questions followed by bulleted points to facilitate optional probing. The conversational back-and-forth nature of a semi-structured format puts the researcher and researched (the interview participants) on more equal footing than allowed by more structured formats. Respondents, the term used in the case of quantitative questionnaire interviews, become informants in the case of individual semi-structured in-depth interviews (IDIs) or participants in the case of FGDs. Freedom to probe beyond initial responses enables interviewers to actively engage with the interviewee to seek clarity, openness, and depth by challenging informants to reach below layers of self-presentation and social desirability. In this respect, interviewing is sometimes compared with peeling an onion, with the first version of events accessible to the public, including survey interviewers, and deeper inner layers accessible to those who invest the time and effort to build rapport and gain trust. (The theory of the active interview suggests that all interviews involve staged social encounters where the interviewee is constantly assessing interviewer intentions and adjusting his or her responses accordingly [ 1 ]. Consequently good rapport is important for any type of interview. Survey formats give interviewers less freedom to divert from the preset script of questions and formal probes.)

Individual In-Depth Interviews and Key-Informant Interviews

The most common forms of individual semi-structured interviews are IDIs and key informant interviews (KIIs). IDIs are conducted among informants typically selected for first-hand experience (e.g., service users, participants, survivors) relevant to the research topic. These are typically conducted as one-on-one face-to-face interviews (two-on-one if translators are needed) to maximize rapport-building and confidentiality. KIIs are similar to IDIs but focus on individual persons with special knowledge or influence (e.g., community leaders or health authorities) that give them broader perspective or deeper insight into the topic area ( Box 10.2 ). Whereas IDIs tend to focus on personal experiences, context, meaning, and implications for informants, KIIs tend to steer away from personal questions in favor of expert insights or community perspectives. IDIs enable flexible sampling strategies and represent the interviewing reference standard for confidentiality, rapport, richness, and contextual detail. However, IDIs are time-and labor-intensive to collect and analyze. Because confidentiality is not a concern in KIIs, these interviews might be conducted as individual or group interviews, as required for the topic area.

Focus Group Discussions and Group Key Informant Interviews

FGDs are semi-structured group interviews in which six to eight participants, homogeneous with respect to a shared experience, behavior, or demographic characteristic, are guided through a topic guide by a trained moderator ( 6 ). (Advice on ideal group interview size varies. The principle is to convene a group large enough to foster an open, lively discussion of the topic, and small enough to ensure all participants stay fully engaged in the process.) Over the course of discussion, the moderator is expected to pose questions, foster group participation, and probe for clarity and depth. Long a staple of market research, focus groups have become a widely used social science technique with broad applications in public health, and they are especially popular as a rapid method for assessing community norms and shared perceptions.

Focus groups have certain useful advantages during field investigations. They are highly adaptable, inexpensive to arrange and conduct, and often enjoyable for participants. Group dynamics effectively tap into collective knowledge and experience to serve as a proxy informant for the community as a whole. They are also capable of recreating a microcosm of social norms where social, moral, and emotional dimensions of topics are allowed to emerge. Skilled moderators can also exploit the tendency of small groups to seek consensus to bring out disagreements that the participants will work to resolve in a way that can lead to deeper understanding. There are also limitations on focus group methods. Lack of confidentiality during group interviews means they should not be used to explore personal experiences of a sensitive nature on ethical grounds. Participants may take it on themselves to volunteer such information, but moderators are generally encouraged to steer the conversation back to general observations to avoid putting pressure on other participants to disclose in a similar way. Similarly, FGDs are subject by design to strong social desirability biases. Qualitative study designs using focus groups sometimes add individual interviews precisely to enable participants to describe personal experiences or personal views that would be difficult or inappropriate to share in a group setting. Focus groups run the risk of producing broad but shallow analyses of issues if groups reach comfortable but superficial consensus around complex topics. This weakness can be countered by training moderators to probe effectively and challenge any consensus that sounds too simplistic or contradictory with prior knowledge. However, FGDs are surprisingly robust against the influence of strongly opinionated participants, highly adaptable, and well suited to application in study designs where systematic comparisons across different groups are called for.

Like FGDs, group KIIs rely on positive chemistry and the stimulating effects of group discussion but aim to gather expert knowledge or oversight on a particular topic rather than lived experience of embedded social actors. Group KIIs have no minimum size requirements and can involve as few as two or three participants.

Egypt’s National Infection Prevention and Control (IPC) program undertook qualitative research to gain an understanding of the contextual behaviors and motivations of healthcare workers in complying with IPC guidelines. The study was undertaken to guide the development of effective behavior change interventions in healthcare settings to improve IPC compliance.

Key informant interviews and focus group discussions were conducted in two governorates among cleaning staff, nursing staff, and physicians in different types of healthcare facilities. The findings highlighted social and cultural barriers to IPC compliance, enabling the IPC program to design responses. For example,

Informants expressed difficulty in complying with IPC measures that forced them to act outside their normal roles in an ingrained hospital culture. Response: Role models and champions were introduced to help catalyze change.
Informants described fatalistic attitudes that undermined energy and interest in modifying behavior. Response: Accordingly, interventions affirming institutional commitment to change while challenging fatalistic assumptions were developed.
Informants did not perceive IPC as effective. Response: Trainings were amended to include scientific evidence justifying IPC practices.
Informants perceived hygiene as something they took pride in and were judged on. Response: Public recognition of optimal IPC practice was introduced to tap into positive social desirability and professional pride in maintaining hygiene in the work environment.

Qualitative research identified sources of resistance to quality clinical practice in Egypt’s healthcare settings and culturally appropriate responses to overcome that resistance.

____________________ Source: Anna Leena Lohiniva, personal communication.

Visualization Methods

Visualization methods have been developed as a way to enhance participation and empower interviewees relative to researchers during group data collection ( 7 ). Visualization methods involve asking participants to engage in collective problem- solving of challenges expressed through group production of maps, diagrams, or other images. For example, participants from the community might be asked to sketch a map of their community and to highlight features of relevance to the research topic (e.g., access to health facilities or sites of risk concentrations). Body diagramming is another visualization tool in which community members are asked to depict how and where a health threat affects the human body as a way of understanding folk conceptions of health, disease, treatment, and prevention. Ensuing debate and dialogue regarding construction of images can be recorded and analyzed in conjunction with the visual image itself. Visualization exercises were initially designed to accommodate groups the size of entire communities, but they can work equally well with smaller groups corresponding to the size of FGDs or group KIIs.

Selecting a Sample of Study Participants

Fundamental differences between qualitative and quantitative approaches to research emerge most clearly in the practice of sampling and recruitment of study participants. Qualitative samples are typically small and purposive. In-depth interview informants are usually selected on the basis of unique characteristics or personal experiences that make them exemplary for the study, if not typical in other respects. Key informants are selected for their unique knowledge or influence in the study domain. Focus group mobilization often seeks participants who are typical with respect to others in the community having similar exposure or shared characteristics. Often, however, participants in qualitative studies are selected because they are exceptional rather than simply representative. Their value lies not in their generalizability but in their ability to generate insight into the key questions driving the study.

Determining Sample Size

Sample size determination for qualitative studies also follows a different logic than that used for probability sample surveys. For example, whereas some qualitative methods specify ideal ranges of participants that constitute a valid observation (e.g., focus groups), there are no rules on how many observations it takes to attain valid results. In theory, sample size in qualitative designs should be determined by the saturation principle , where interviews are conducted until additional interviews yield no additional insights into the topic of research ( 8 ). Practically speaking, designing a study with a range in number of interviews is advisable for providing a level of flexibility if additional interviews are needed to reach clear conclusions.

Recruiting Study Participants

Recruitment strategies for qualitative studies typically involve some degree of participant self-selection (e.g., advertising in public spaces for interested participants) and purposive selection (e.g., identification of key informants). Purposive selection in community settings often requires authorization from local authorities and assistance from local mobilizers before the informed consent process can begin. Clearly specifying eligibility criteria is crucial for minimizing the tendency of study mobilizers to apply their own filters regarding who reflects the community in the best light. In addition to formal eligibility criteria, character traits (e.g., articulate and interested in participating) and convenience (e.g., not too far away) are legitimate considerations for whom to include in the sample. Accommodations to personality and convenience help to ensure the small number of interviews in a typical qualitative design yields maximum value for minimum investment. This is one reason why random sampling of qualitative informants is not only unnecessary but also potentially counterproductive.

Analysis of qualitative data can be divided into four stages: data management, data condensation, data display, and drawing and verifying conclusions ( 9 ).

Managing Qualitative Data

From the outset, developing a clear organization system for qualitative data is important. Ideally, naming conventions for original data files and subsequent analysis should be recorded in a data dictionary file that includes dates, locations, defining individual or group characteristics, interviewer characteristics, and other defining features. Digital recordings of interviews or visualization products should be reviewed to ensure fidelity of analyzed data to original observations. If ethics agreements require that no names or identifying characteristics be recorded, all individual names must be removed from final transcriptions before analysis begins. If data are analyzed by using textual data analysis software, maintaining careful version control over the data files is crucial, especially when multiple coders are involved.

Condensing Qualitative Data

Condensing refers to the process of selecting, focusing, simplifying, and abstracting the data available at the time of the original observation, then transforming the condensed data into a data set that can be analyzed. In qualitative research, most of the time investment required to complete a study comes after the fieldwork is complete. A single hour of taped individual interview can take a full day to transcribe and additional time to translate if necessary. Group interviews can take even longer because of the difficulty of transcribing active group input. Each stage of data condensation involves multiple decisions that require clear rules and close supervision. A typical challenge is finding the right balance between fidelity to the rhythm and texture of original language and clarity of the translated version in the language of analysis. For example, discussions among groups with little or no education should not emerge after the transcription (and translation) process sounding like university graduates. Judgment must be exercised about which terms should be translated and which terms should be kept in vernacular because there is no appropriate term in English to capture the richness of its meaning.

Displaying Qualitative Data

After the initial condensation, qualitative analysis depends on how the data are displayed. Decisions regarding how data are summarized and laid out to facilitate comparison influence the depth and detail of the investigation’s conclusions. Displays might range from full verbatim transcripts of interviews to bulleted summaries or distilled summaries of interview notes. In a field setting, a useful and commonly used display format is an overview chart in which key themes or research questions are listed in rows in a word processer table or in a spreadsheet and individual informant or group entry characteristics are listed across columns. Overview charts are useful because they allow easy, systematic comparison of results.

Drawing and Verifying Conclusions

Analyzing qualitative data is an iterative and ideally interactive process that leads to rigorous and systematic interpretation of textual or visual data. At least four common steps are involved:

Reading and rereading. The core of qualitative analysis is careful, systematic, and repeated reading of text to identify consistent themes and interconnections emerging from the data. The act of repeated reading inevitably yields new themes, connections, and deeper meanings from the first reading. Reading the full text of interviews multiple times before subdividing according to coded themes is key to appreciating the full context and flow of each interview before subdividing and extracting coded sections of text for separate analysis.
Coding. A common technique in qualitative analysis involves developing codes for labeling sections of text for selective retrieval in later stages of analysis and verification. Different approaches can be used for textual coding. One approach, structural coding , follows the structure of the interview guide. Another approach, thematic coding , labels common themes that appear across interviews, whether by design of the topic guide or emerging themes assigned based on further analysis. To avoid the problem of shift and drift in codes across time or multiple coders, qualitative investigators should develop a standard codebook with written definitions and rules about when codes should start and stop. Coding is also an iterative process in which new codes that emerge from repeated reading are layered on top of existing codes. Development and refinement of the codebook is inseparably part of the analysis.
Analyzing and writing memos. As codes are being developed and refined, answers to the original research question should begin to emerge. Coding can facilitate that process through selective text retrieval during which similarities within and between coding categories can be extracted and compared systematically. Because no p values can be derived in qualitative analyses to mark the transition from tentative to firm conclusions, standard practice is to write memos to record evolving insights and emerging patterns in the data and how they relate to the original research questions. Writing memos is intended to catalyze further thinking about the data, thus initiating new connections that can lead to further coding and deeper understanding.
Verifying conclusions. Analysis rigor depends as much on the thoroughness of the cross-examination and attempt to find alternative conclusions as on the quality of original conclusions. Cross-examining conclusions can occur in different ways. One way is encouraging regular interaction between analysts to challenge conclusions and pose alternative explanations for the same data. Another way is quizzing the data (i.e., retrieving coded segments by using Boolean logic to systematically compare code contents where they overlap with other codes or informant characteristics). If alternative explanations for initial conclusions are more difficult to justify, confidence in those conclusions is strengthened.

Above all, qualitative data analysis requires sufficient time and immersion in the data. Computer textual software programs can facilitate selective text retrieval and quizzing the data, but discerning patterns and arriving at conclusions can be done only by the analysts. This requirement involves intensive reading and rereading, developing codebooks and coding, discussing and debating, revising codebooks, and recoding as needed until clear patterns emerge from the data. Although quality and depth of analysis is usually proportional to the time invested, a number of techniques, including some mentioned earlier, can be used to expedite analysis under field conditions.

Detailed notes instead of full transcriptions. Assigning one or two note-takers to an interview can be considered where the time needed for full transcription and translation is not feasible. Even if plans are in place for full transcriptions after fieldwork, asking note-takers to submit organized summary notes is a useful technique for getting real-time feedback on interview content and making adjustments to topic guides or interviewer training as needed.
Summary overview charts for thematic coding. (See discussion under “Displaying Data.”) If there is limited time for full transcription and/or systematic coding of text interviews using textual analysis software in the field, an overview chart is a useful technique for rapid manual coding.
Thematic extract files. This is a slightly expanded version of manual thematic coding that is useful when full transcriptions of interviews are available. With use of a word processing program, files can be sectioned according to themes, or separate files can be created for each theme. Relevant extracts from transcripts or analyst notes can be copied and pasted into files or sections of files corresponding to each theme. This is particularly useful for storing appropriate quotes that can be used to illustrate thematic conclusions in final reports or manuscripts.
Teamwork. Qualitative analysis can be performed by a single analyst, but it is usually beneficial to involve more than one. Qualitative conclusions involve subjective judgment calls. Having more than one coder or analyst working on a project enables more interactive discussion and debate before reaching consensus on conclusions.
Systematic coding.
Selective retrieval of coded segments.
Verifying conclusions (“quizzing the data”).
Working on larger data sets with multiple separate files.
Working in teams with multiple coders to allow intercoder reliability to be measured and monitored.

The most widely used software packages (e.g., NVivo [QSR International Pty. Ltd., Melbourne, VIC, Australia] and ATLAS.ti [Scientific Software Development GmbH, Berlin, Germany]) evolved to include sophisticated analytic features covering a wide array of applications but are relatively expensive in terms of license cost and initial investment in time and training. A promising development is the advent of free or low-cost Web-based services (e.g., Dedoose [Sociocultural Research Consultants LLC, Manhattan Beach, CA]) that have many of the same analytic features on a more affordable subscription basis and that enable local research counterparts to remain engaged through the analysis phase (see Teamwork criteria). The start-up costs of computer-assisted analysis need to be weighed against their analytic benefits, which tend to decline with the volume and complexity of data to be analyzed. For rapid situational analyses or small scale qualitative studies (e.g. fewer than 30 observations as an informal rule of thumb), manual coding and analysis using word processing or spreadsheet programs is faster and sufficient to enable rigorous analysis and verification of conclusions.

Qualitative methods belong to a branch of social science inquiry that emphasizes the importance of context, subjective meanings, and motivations in understanding human behavior patterns. Qualitative approaches definitionally rely on open-ended, semistructured, non-numeric strategies for asking questions and recording responses. Conclusions are drawn from systematic visual or textual analysis involving repeated reading, coding, and organizing information into structured and emerging themes. Because textual analysis is relatively time-and skill-intensive, qualitative samples tend to be small and purposively selected to yield the maximum amount of information from the minimum amount of data collection. Although qualitative approaches cannot provide representative or generalizable findings in a statistical sense, they can offer an unparalleled level of detail, nuance, and naturalistic insight into the chosen subject of study. Qualitative methods enable investigators to “hear the voice” of the researched in a way that questionnaire methods, even with the occasional open-ended response option, cannot.

Whether or when to use qualitative methods in field epidemiology studies ultimately depends on the nature of the public health question to be answered. Qualitative approaches make sense when a study question about behavior patterns or program performance leads with why, why not , or how . Similarly, they are appropriate when the answer to the study question depends on understanding the problem from the perspective of social actors in real-life settings or when the object of study cannot be adequately captured, quantified, or categorized through a battery of closed-ended survey questions (e.g., stigma or the foundation of health beliefs). Another justification for qualitative methods occurs when the topic is especially sensitive or subject to strong social desirability biases that require developing trust with the informant and persistent probing to reach the truth. Finally, qualitative methods make sense when the study question is exploratory in nature, where this approach enables the investigator the freedom and flexibility to adjust topic guides and probe beyond the original topic guides.

Given that the conditions just described probably apply more often than not in everyday field epidemiology, it might be surprising that such approaches are not incorporated more routinely into standard epidemiologic training. Part of the answer might have to do with the subjective element in qualitative sampling and analysis that seems at odds with core scientific values of objectivity. Part of it might have to do with the skill requirements for good qualitative interviewing, which are generally more difficult to find than those required for routine survey interviewing.

For the field epidemiologist unfamiliar with qualitative study design, it is important to emphasize that obtaining important insights from applying basic approaches is possible, even without a seasoned team of qualitative researchers on hand to do the work. The flexibility of qualitative methods also tends to make them forgiving with practice and persistence. Beyond the required study approvals and ethical clearances, the basic essential requirements for collecting qualitative data in field settings start with an interviewer having a strong command of the research question, basic interactive and language skills, and a healthy sense of curiosity, armed with a simple open-ended topic guide and a tape recorder or note-taker to capture the key points of the discussion. Readily available manuals on qualitative study design, methods, and analysis can provide additional guidance to improve the quality of data collection and analysis.

Patton MQ. Qualitative research and evaluation methods: integrating theory and practice . 4th ed. Thousand Oaks, CA: Sage; 2015.
Hennink M, Hutter I, Bailey A. Qualitative research methods . Thousand Oaks, CA: Sage; 2010.
Lincoln YS, Guba EG. The constructivist credo . Walnut Creek, CA: Left Coast Press; 2013.
Mack N, Woodsong C, MacQueen KM, Guest G, Namey E. Qualitative research methods: a data collectors field guide. https://www.fhi360.org/sites/default/files/media/documents/Qualitative%20Research%20Methods%20-%20A%20Data%20Collector%27s%20Field%20Guide.pdf
Kvale S, Brinkmann S. Interviews: learning the craft of qualitative research . Thousand Oaks, CA: Sage; 2009:230–43.
Krueger RA, Casey MA. Focus groups: a practical guide for applied research . Thousand Oaks, CA: Sage; 2014.
Margolis E, Pauwels L. The Sage handbook of visual research methods . Thousand Oaks, CA: Sage; 2011.
Mason M. Sample size and saturation in PhD studies using qualitative interviews. Forum : Qualitative Social Research/Sozialforschung. 2010;11(3).
Miles MB, Huberman AM, Saldana J. Qualitative data analysis: a methods sourcebook . 3rd ed. Thousand Oaks, CA: Sage; 2014.
Silver C, Lewins A. Using software in qualitative research: a step-by-step guide . Thousand Oaks, CA; Sage: 2014.

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Qualitative Data Collection Methods

Qualitative data collection methods are exploratory in nature and are mainly concerned with gaining insights and understanding on underlying reasons and motivations. Qualitative data is a linguistic or visual material. Qualitative data collection methods emerged after it became evident that traditional quantitative data collection methods were unable to express human feelings and emotions.

Monette et al (2010) [1] credit qualitative methods with the acknowledgement of abstraction and generalisation. Polonsky and Waller (2011) [2] categorize vision, images, forms and structures in various media, as well as, spoken and printed word and recorded sound into qualitative data collection methods.

Qualitative data collection methods are used in order to examine the following phenomenon:

Human feelings and experiences
Meanings and relationships
Social norms and cultural practices.

It is noted that “qualitative methods are often regarded as providing rich data about real life people and situations and being more able to make sense of behaviour and to understand behaviour within its wider context. However, qualitative research is often criticised for lacking generalizability, being too reliant on the subjective interpretations by researchers and being incapable of replication by other researchers.” [3]

Popular qualitative data collection methods used in business studies include interviews , focus groups , observation and action research . Moreover, grounded theory and document analysis can be also used as data collection method in qualitative studies. The main sources and procedures associated with the most popular qualitative methods are presented on Table 1 below as proposed by Yamagata-Lynch (2010) [4] :

Table 1 Sources and procedures associated with qualitative data collection

Table 2 below illustrates strength and weaknesses associated with qualitative research and data collection methods

Table 2 Strengths and weaknesses associated with qualitative data collection methods and qualitative research [5]

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of qualitative data collecton methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words.

John Dudovskiy

[1] Monette, D.R., Gullivan, T.J. & DeJong, C.R. (2010) “Applied Social Research: A Tool for the Human Resources” Cengage Learning

[2] Polonsky, M.J. & Waller, D.S. (2011) “Designing and Managing a Research Project: A Business Student’s Guide” 2 nd edition, SAGE

[3] Vaus, D. (2002) “Surveys in Social Research” Taylor and Francis, p.5

[4] Yamagata-Lynch, L.C. (2010) “Activity Systems Analysis Methods: Understanding Complex Learning Environments” Springer Publications

[5] Source: Albery, I. & Munafo, M. (2008) “Key Concepts in Health Psychology” SAGE Publications

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Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

The aim of the research
The type of data that you will collect
The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

Experimental research is primarily a quantitative method.
Interviews , focus groups , and ethnographies are qualitative methods.
Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
Double-check manual data entry for errors.
If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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Qualitative Data Analysis Methodologies and Methods

Qualitative data analysis involves interpreting non-numerical data to identify patterns, themes, and insights. There are several methodologies and methods used in qualitative data analysis.

In this article, we will explore qualitative data analysis techniques in great detail, with each method providing a different perspective on how to interpret qualitative data.

Table of Content

Types of Qualitative Data Analysis Methodologies

1. content analysis, 2. thematic analysis, 3. narrative analysis, 4. discourse analysis, 5. grounded theory analysis, 6. text analysis, 7. ethnographic analysis, advantages and disadvantages of different qualitative data analysis methodologies, best practices for qualitative data analysis, qualitative data analysis methods- faq’s.

Lets weigh the benefits and disadvantages of each:

Content analysis involves systematically reading textual content or other types of communication to perceive patterns, themes, and meanings within the content. It provides a dependent technique to inspecting huge volumes of records to discover insights or trends. Researchers categorize and code the content material based on predetermined criteria or emergent themes, taking into consideration quantitative and qualitative interpretation of the facts. Content analysis is regularly an iterative procedure, with researchers revisiting and refining the coding scheme, collecting additional facts, or accomplishing in addition analysis as needed to deepen know-how or cope with new studies questions.

There are 3 fundamental techniques to content analysis:

Conventional Content Analysis : In conventional content analysis, researchers technique the records with out preconceived categories or theoretical frameworks. Instead, they allow classes and themes to emerge evidently from the statistics through an iterative system of coding and analysis. This technique is exploratory and bendy, allowing for the discovery of latest insights and styles inside the content material.
Directed Content Analysis : Directed content material analysis entails studying the statistics based totally on existing theories or principles. Researchers start with predefined categories or subject matters derived from theoretical frameworks or previous research findings. The analysis is focused on confirming, refining, or extending present theories in place of coming across new ones. Directed content analysis is specifically beneficial whilst researchers intention to test hypotheses or explore particular concepts in the statistics.
Summative Content Analysis : Summative content material analysis focuses on quantifying the presence or frequency of precise content within the information. Researchers expand predetermined classes or coding schemes primarily based on predefined criteria, after which systematically code the statistics in line with those classes. The emphasis is on counting occurrences of predefined attributes or topics to provide a numerical summary of the content. Summative content material analysis is frequently used to track modifications over time, examine unique assets of content material, or verify the superiority of specific subject matters inside a dataset.

When to Use Content Analysis?

Exploratory Research : Content analysis is appropriate for exploratory research in which the goal is to uncover new insights, discover emerging developments, or recognize the breadth of communique on a particular subject matter.
Comparative Analysis: It is useful for comparative analysis, permitting researchers to compare conversation throughout extraordinary sources, time periods, or cultural contexts.
Historical Analysis : Content analysis can be carried out to historical research, allowing researchers to analyze ancient files, media content, or archival substances to apprehend conversation styles over the years.
Policy Analysis: It is valuable for policy analysis, supporting researchers look at the portrayal of problems in media or public discourse and informing coverage-making methods.
Market Research: Content analysis is usually utilized in market research to investigate advertising and marketing substances, social media content, and customer critiques, presenting insights into patron perceptions and possibilities.

Thematic analysis is a method for identifying, analyzing, and reporting styles or topics within qualitative records. It entails systematically coding and categorizing information to become aware of not unusual issues, styles, or ideas that emerge from the dataset. Researchers interact in a method of inductive reasoning to generate topics that capture the essence of the facts, making an allowance for interpretation and exploration of underlying meanings.

Thematic analysis is appropriate when researchers are seeking for to become aware of, analyze, and document patterns or issues inside qualitative records. It is especially beneficial for exploratory studies where the intention is to find new insights or recognize the breadth of studies and views associated with a specific phenomenon.

Thematic analysis offers a bendy and systematic approach for identifying and reading styles or topics within qualitative statistics, making it a treasured method for exploring complex phenomena and producing insights that inform concept, exercise, and policy.

When to use Thematic analysis?

Psychology : Thematic analysis is used to explore mental phenomena, which include coping mechanisms in reaction to strain, attitudes towards mental fitness, or stories of trauma.
Education : Researchers practice thematic analysis to apprehend student perceptions of getting to know environments, teaching methods, or academic interventions.
Healthcare : Thematic analysis enables take a look at affected person reports with healthcare offerings, attitudes towards treatment alternatives, or obstacles to gaining access to healthcare.
Market Research: Thematic analysis is applied to research purchaser remarks, perceive product options, or recognize emblem perceptions in marketplace research research.

Narrative analysis entails analyzing and interpreting the memories or narratives that people use to make feel of their stories. It focuses on the shape, content, and which means of narratives to apprehend how people construct and speak their identities, values, and ideals via storytelling. It is especially beneficial for exploring how people assemble and communicate their identities, values, and beliefs through storytelling.

When to use Narrative Analysis?

It’s extensively used throughout numerous disciplines, which includes sociology, psychology, anthropology, literary research, and verbal exchange studies. Some applications of narrative analysis in qualitative statistics analysis methodologies are:

Understanding Identity Construction : Narrative analysis can be used to explore how people construct their identities through the tales they tell approximately themselves. Researchers can examine the issues, plot systems, and language utilized in narratives to uncover how individuals perceive themselves and their place inside the world.
Exploring Life Experiences : Researchers frequently use narrative analysis to research the lived reports of people or groups. By inspecting the narratives shared by using members, researchers can advantage insights into the demanding situations, triumphs, and extensive events that shape people’s lives.
Examining Cultural Meanings and Practices: Narrative analysis can provide treasured insights into cultural meanings and practices. By studying the stories shared within a selected cultural context, researchers can find shared values, ideals, and norms that influence behavior and social interactions.
Exploring Trauma and Healing : Narrative analysis is usually utilized in studies on trauma and restoration tactics. By studying narratives of trauma survivors, researchers can explore how individuals make experience of their studies, deal with adversity, and embark on trips of restoration and resilience.
Analyzing Media and Popular Culture : Narrative analysis also can be applied to analyze media texts, inclusive of films, tv suggests, and literature. Researchers can have a look at the narratives constructed within these texts to understand how they reflect and shape cultural beliefs, ideologies, and norms.

Narrative analysis offers a powerful technique for exploring the structure, content, and that means of narratives or stories instructed by people, providing insights into their lived reports, identities, and perspectives. However, researchers need to navigate the interpretive subjectivity, time-extensive nature, and moral concerns related to reading narratives in qualitative studies.

Discourse analysis examines the approaches wherein language is used to construct that means, form social interactions, and reproduce electricity members of the family inside society. It makes a speciality of studying spoken or written texts, in addition to the wider social and cultural contexts in which communique happens. Researchers explore how language displays and shapes social norms, ideologies, and power dynamics.

Discourse analysis is employed when researchers are seeking to investigate social interactions, power dynamics, and identity creation through language. It is applied to take a look at how language shapes social relations, constructs identities, and reflects cultural norms and values.

When to use Discourse Analysis?

Linguistics and Language Studies : Discourse analysis is foundational to linguistics and language research, where it’s miles used to study language use, communique patterns, and discourse structures. Linguists behavior discourse analysis to investigate how language shapes social interactions, constructs identities, and reflects cultural norms. Discourse analysis facilitates uncover the underlying meanings, ideologies, and energy dynamics embedded in language.
Media and Communication : Discourse analysis is applied in media and conversation research to have a look at media representations, discursive practices, and ideological frameworks. Researchers conduct discourse analysis to analyze media texts, information coverage, and political speeches, exploring how language constructs and disseminates social meanings and values. Discourse analysis informs media literacy efforts, media grievance, and media coverage debates.
Political Science : Discourse analysis is applied in political science to look at political rhetoric, public discourse, and policymaking tactics. Researchers behavior discourse analysis to research political speeches, party manifestos, and coverage files, analyzing how language constructs political identities, legitimizes authority, and shapes public opinion. Discourse analysis informs political verbal exchange techniques, political campaigning, and policy advocacy.

Grounded theory analysis is an inductive studies approach used to broaden theories or causes based on empirical data. It includes systematically studying qualitative information to perceive ideas, categories, and relationships that emerge from the statistics itself, rather than testing preconceived hypotheses. Researchers have interaction in a procedure of constant assessment and theoretical sampling to refine and increase theoretical insights.

Grounded theory analysis is hired whilst researchers are seeking for to find styles, relationships, and tactics that emerge from the records itself, with out implementing preconceived hypotheses or theoretical assumptions.

When to use Grounded Theory Analysis?

Grounded concept analysis is applied throughout various disciplines and studies contexts, such as:

Social Sciences Research : Grounded Theory Analysis is significantly used in sociology, anthropology, psychology, and related disciplines to discover diverse social phenomena together with organization dynamics, social interactions, cultural practices, and societal structures.
Healthcare Research : In healthcare, Grounded Theory can be implemented to apprehend affected person reviews, healthcare provider-patient interactions, healthcare delivery procedures, and the impact of healthcare guidelines on individuals and communities.
Organizational Studies : Researchers use Grounded Theory to examine organizational conduct, leadership, place of work subculture, and worker dynamics. It enables in knowledge how groups function and the way they may be advanced.
Educational Research : In training, Grounded Theory Analysis can be used to discover teaching and getting to know processes, scholar studies, educational regulations, and the effectiveness of educational interventions.

Text analysis involves examining written or verbal communique to extract meaningful insights or styles. It encompasses numerous techniques which includes sentiment analysis, subject matter modeling, and keyword extraction. For instance, in a have a look at on patron opinions of a eating place, textual content analysis is probably used to become aware of established topics along with food first-class, service enjoy, and atmosphere. Key additives and strategies worried in text analysis:

Sentiment Analysis : This approach includes determining the sentiment expressed in a piece of textual content, whether or not it is high quality, bad, or impartial. Sentiment analysis algorithms use natural language processing (NLP) to analyze the words, phrases, and context within the text to deduce the overall sentiment. For instance, in customer reviews of a eating place, sentiment analysis could be used to gauge purchaser delight levels based totally on the emotions expressed within the critiques.
Topic Modeling : Topic modeling is a statistical technique used to become aware of the underlying topics or issues present within a group of documents or text statistics. It entails uncovering the latent patterns of co-occurring phrases or terms that constitute awesome topics. Techniques like Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) are normally used for topic modeling. In the context of eating place opinions, subject matter modeling should assist identify not unusual subject matters inclusive of meals excellent, provider revel in, cleanliness, etc., across a large corpus of opinions.
Keyword Extraction : Keyword extraction includes figuring out and extracting the most applicable phrases or phrases from a bit of text that seize its essence or major topics. This technique enables to summarize the important thing content material or subjects mentioned within the textual content. For instance, in eating place analysiss, key-word extraction ought to identify often referred to terms like “scrumptious meals,” “friendly group of workers,” “lengthy wait times,” etc., presenting a quick analysis of customer sentiments and concerns.

When to use Text Analysis?

Text analysis has numerous programs throughout diverse domain names, including:

Business and Marketing: Analyzing purchaser remarks, sentiment analysis of social media posts, brand monitoring, and market fashion analysis.
Healthcare: Extracting scientific statistics from scientific notes, analyzing patient comments, and detecting unfavorable drug reactions from textual content information.
Social Sciences: Studying public discourse, political communique, opinion mining, and discourse analysis in social media.
Academic Research: Conducting literature analysiss, analyzing studies articles, and identifying rising studies topics and trends.
Customer Experience : Understanding purchaser sentiments, identifying product or service problems, and improving client satisfaction via text-based totally comments analysis.

Ethnographic analysis involves immersing in a selected cultural or social setting to understand the views, behaviors, and interactions of the human beings within that context. Researchers conduct observations, interviews, and participant observations to gain insights into the culture, practices, and social dynamics of the community under study. It is is suitable when researchers aim to gain an in-depth understanding of a particular cultural or social setting, including the perspectives, behaviors, and interactions of the people within that context. Particularly beneficial for reading complex social phenomena of their natural environment, wherein observations and interactions arise organically.

When to use Ethnographic Analysis?

Cultural Understanding : Ethnographic analysis is right whilst researchers goal to gain deep insights into the lifestyle, ideals, and social practices of a selected institution or community.
Behavioral Observation : It is beneficial while researchers want to observe and apprehend the behaviors, interactions, and each day activities of individuals within their natural surroundings.
Contextual Exploration : Ethnographic analysis is valuable for exploring the context and lived stories of individuals, presenting wealthy, exact descriptions of their social and cultural worlds.
Complex Social Dynamics: It is suitable whilst analyzing complex social phenomena or phenomena which might be deeply embedded within social contexts, including rituals, traditions, or network dynamics.
Qualitative Inquiry: Ethnographic analysis is desired while researchers are seeking for to conduct qualitative inquiry targeted on know-how the subjective meanings and perspectives of individuals inside their cultural context.

Ethnographic analysis gives a effective method for analyzing complex social phenomena of their herbal context, offering rich and nuanced insights into the cultural practices, social dynamics, and lived experiences of individuals inside a particular community. However, researchers need to cautiously bear in mind the time commitment, ethical considerations, and potential biases associated with ethnographic studies.

Clearly Defined Research Question : Ground analysis in a clear and targeted research question. This will manual for information series and preserve you on the right track at some point of analysis.
Systematic Coding : Develop a coding scheme to categorize facts into significant topics or concepts. Use software gear to assist in organizing and dealing with codes.
Constant Comparison : Continuously examine new facts with current codes and subject matters to refine interpretations and make sure consistency.
Triangulation : Validate findings by the use of a couple of records sources, strategies, or researchers to corroborate consequences and beautify credibility.

Refine subject matters and interpretations through engaging in repeated cycles of gathering, coding, and analysis.

Qualitative data analysis techniques are effective means of revealing deep insights and comprehending intricate phenomena in both practice and study. Through the use of rigorous analytical approaches, researchers may convert qualitative data into significant ideas, interpretations, and narratives that further knowledge and support evidence-based decision-making.

Is it possible to mix quantitative and qualitative methodologies for data analysis?

A: In order to triangulate results and get a thorough grasp of study concerns, researchers do, in fact, often use mixed methods techniques.

How can I choose the best approach for analyzing qualitative data for my study?

A: To choose the best approach, take the research topic, the properties of the data, and the theoretical framework into consideration.

What are some tactics I might do to improve the reliability and validity of my qualitative data analysis?

Aim for peer debriefing and member verification to improve validity, and maintain transparency, reflexivity, and methodological coherence throughout the analytic process.

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Qualitative study.

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Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." [2] Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Qualitative Research Approaches

Ethnography

Ethnography as a research design originates in social and cultural anthropology and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc, through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded theory

Grounded Theory is the "generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior." [5] Unlike quantitative research, which is deductive and tests or verifies an existing theory, grounded theory research is inductive and, therefore, lends itself to research aimed at social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is the "study of the meaning of phenomena or the study of the particular.” [5] At first glance, it might seem that Grounded Theory and Phenomenology are pretty similar, but the differences can be seen upon careful examination. At its core, phenomenology looks to investigate experiences from the individual's perspective. [2] Phenomenology is essentially looking into the "lived experiences" of the participants and aims to examine how and why participants behaved a certain way from their perspective. Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources. In contrast, Phenomenology focuses on describing and explaining an event or phenomenon from the perspective of those who have experienced it.

Narrative research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called a "thick" or "rich" description and is a strength of qualitative research. Narrative research is rife with the possibilities of "thick" description as this approach weaves together a sequence of events, usually from just one or two individuals, hoping to create a cohesive story or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be "opportunities for innovation." [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards underpinning different research approaches. Essentially, research paradigms are the "worldviews" that inform research. [4] It is valuable for qualitative and quantitative researchers to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontologies and epistemologies. Ontology is defined as the "assumptions about the nature of reality,” whereas epistemology is defined as the "assumptions about the nature of knowledge" that inform researchers' work. [2] It is essential to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a complete understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, researchers must understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist versus postpositivist

To further understand qualitative research, we must discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social and natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in their research. It stems from positivist ontology, that there is an objective reality that exists that is wholly independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained, but could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world,” and therefore, postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are also constructivist, meaning they think there is no objective external reality that exists but instead that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. "Constructivism contends that individuals' views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality.” [6] constructivist thought focuses on how "reality" is not a fixed certainty and how experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike positivist views, that there is not necessarily an "objective"reality we all experience. This is the ‘relativist’ ontological view that reality and our world are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have. It can even change the role of the researchers. [2] For example, is the researcher an "objective" observer, such as in positivist quantitative work? Or is the researcher an active participant in the research, as in postpositivist qualitative work? Understanding the philosophical base of the study undertaken allows researchers to fully understand the implications of their work and their role within the research and reflect on their positionality and bias as it pertains to the research they are conducting.

Data Sampling

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors. The following are examples of participant sampling and selection: [7]

Purposive sampling- selection based on the researcher’s rationale for being the most informative.
Criterion sampling selection based on pre-identified factors.
Convenience sampling- selection based on availability.
Snowball sampling- the selection is by referral from other participants or people who know potential participants.
Extreme case sampling- targeted selection of rare cases.
Typical case sampling selection based on regular or average participants.

Data Collection and Analysis

Qualitative research uses several techniques, including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic, and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant-observers to share the experiences of the subject or non-participants or detached observers.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or the participants' environment, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed, which may then be coded manually or using computer-assisted qualitative data analysis software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results could also be in the form of themes and theory or model development.

Dissemination

The healthcare team can use two reporting standards to standardize and facilitate the dissemination of qualitative research outcomes. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a more comprehensive range of qualitative research. [13]

Applications

Many times, a research question will start with qualitative research. The qualitative research will help generate the research hypothesis, which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data to better understand what the numbers truly mean and their implications. The qualitative techniques can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research, researchers can explore poorly studied subjects with quantitative methods. These include opinions, individual actions, and social science research.

An excellent qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure no omissions of part of the target population. A proper collection method should be selected that will help obtain the desired information without overly limiting the collected data because, often, the information sought is not well categorized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of why teens start to smoke and factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered "cool," and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current nonsmokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the survey results to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the primary factor that keeps teens from starting to smoke, and peer pressure was the primary factor that contributed to teens starting smoking. The researcher can go back to qualitative research methods to dive deeper into these for more information. The researcher wants to focus on keeping teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly in the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure to smoke. The researcher finds a local park where many local teenagers hang out and sees that the smokers tend to hang out in a shady, overgrown area of the park. The researcher notes that smoking teenagers buy their cigarettes from a local convenience store adjacent to the park, where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk populations their perceptions of the changes and what factors are still at play, and quantitative research that includes teen smoking rates in the community and the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or combined with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation not only to help generate hypotheses that can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are. Qualitative research allows researchers to understand what is going on, especially when things are not easily categorized. [16]

Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many ways, including the evaluation criteria. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. Credibility, transferability, dependability, and confirmability are the correlating concepts in qualitative research. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept on the left and the qualitative concept on the right:

Internal validity: Credibility
External validity: Transferability
Reliability: Dependability
Objectivity: Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid, qualitative researchers should ensure that their work has credibility.

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

Triangulation: Triangulation involves using multiple data collection methods to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable if we interviewed the magician, backstage hand, and the person who "vanished." In qualitative research, triangulation can include telephone surveys, in-person surveys, focus groups, and interviews and surveying an adequate cross-section of the target demographic.
Peer examination: A peer can review results to ensure the data is consistent with the findings.

A "thick" or "rich" description can be used to evaluate the transferability of qualitative research, whereas an indicator such as an audit trail might help evaluate the dependability and confirmability.

Thick or rich description: This is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was conducted. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data, which can help with transferability and replicability.
Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original information records should also be kept (eg, surveys, notes, recordings).

One issue of concern that qualitative researchers should consider is observation bias. Here are a few examples:

Hawthorne effect: The effect is the change in participant behavior when they know they are being observed. Suppose a researcher wanted to identify factors that contribute to employee theft and tell the employees they will watch them to see what factors affect employee theft. In that case, one would suspect employee behavior would change when they know they are being protected.
Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens unconsciously for the participant, so it is essential to eliminate or limit the transmission of the researcher's views.
Artificial scenario effect: Some qualitative research occurs in contrived scenarios with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
Clinical Significance

Qualitative or quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research is not an island apart from quantitative research but an integral part of research methods to understand the world around us. [17]

Enhancing Healthcare Team Outcomes

Qualitative research is essential for all healthcare team members as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research. Much of the qualitative research data acquisition is completed by numerous team members, including social workers, scientists, nurses, etc. Within each area of the medical field, there is copious ongoing qualitative research, including physician-patient interactions, nursing-patient interactions, patient-environment interactions, healthcare team function, patient information delivery, etc.

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

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Published: 09 May 2024

Exploring factors affecting the unsafe behavior of health care workers’ in using respiratory masks during COVID-19 pandemic in Iran: a qualitative study

Azadeh Tahernejad 1 ,
Sanaz Sohrabizadeh ORCID: orcid.org/0000-0002-9170-178X 1 &
Somayeh Tahernejad 2

BMC Health Services Research volume 24 , Article number: 608 ( 2024 ) Cite this article

175 Accesses

Metrics details

The use of respiratory masks has been one of the most important measures to prevent the spread of COVID-19 among health care workers during the COVID-19 pandemic. Therefore, correct and safe use of breathing masks is vital. The purpose of this study was to exploring factors affecting the unsafe behavior of health care workers’ in using respiratory masks during the COVID-19 pandemic in Iran.

This study was carried out using the conventional qualitative content analysis. Participants were the number of 26 health care workers selected by purposive sampling method. Data collection was conducted through in-depth semi-structured interviews. Data analysis was done using the content analysis approach of Graneheim and Lundman. This study aligns with the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist and was conducted between December 2021 and April 2022.

The factors affecting the unsafe behavior of health care workers while using respiratory masks were divided into 3 main categories and 8 sub-categories. Categories included discomfort and pain (four sub-categories of headache and dizziness, skin discomfort, respiratory discomfort, feeling hot and thirsty), negative effect on performance (four sub-categories of effect on physical function, effect on cognitive function, system function vision, and hearing), and a negative effect on the mental state (two subcategories of anxiety and depression).

The findings can help identify and analyze possible scenarios to reduce unsafe behaviors at the time of using breathing masks. The necessary therapeutic and preventive interventions regarding the complications of using masks, as well as planning to train personnel for the correct use of masks with minimal health effects are suggested.

Peer Review reports

The COVID-19 pandemic has brought unprecedented challenges to healthcare systems worldwide, requiring Health Care Workers (HCWs) to adopt strict infection control measures to protect themselves [ 1 ]. Among these measures, the proper use of respiratory masks plays a crucial role in preventing the transmission of the virus [ 2 ]. Iran was among the initial countries impacted by COVID-19. In Iran, as in many other countries, HCWs have been at the forefront of the battle against COVID-19, facing various challenges in utilizing respiratory masks effectively [ 3 ]. Over 7.6 million Iranians have been infected by the SARS-CoV-2 virus, with more than 146,480 reported deaths as of August 2023 [ 4 ]. Amid the COVID-19 pandemic, Iran’s healthcare system experienced significant impacts as well [ 5 ].

Despite the passage of several years since the onset of the COVID-19 pandemic, new variant of the virus continues to emerge worldwide. It is crucial to be prepared for future pandemics and similar biological disasters.

Due to the SARS-CoV-2 virus transmission via respiratory droplets, the use of masks and personal protective equipment is essential [ 6 ]. The World Health Organization recommended the use of medical masks, such as surgical masks, for HCWs during the COVID-19 pandemic [ 7 ]. These masks are designed to provide a barrier to respiratory droplets and help reduce the transmission of the virus [ 8 ].

Few studies have been devoted to negative aspects of using respiratory masks in human being. The physiological and adverse effects of using PPE have been investigated in a systematic review study [ 9 ]. In another review study, of skin problems related to the use of respiratory masks were studied [ 10 ]. Also, in some studies, a significant relationship has been found between the time of using masks and the severity of the adverse effects of using masks [ 11 ]. In all the above studies, questionnaires have been used to check the prevalence of these adverse effects among HCWs.

Incorrect use of masks is considered as the unsafe behaviors of HCWs. In some studies, unsafe behaviors are defined as disobeying an accepted safe method while working with the capability of causing an accident [ 12 ]. Since the reasons for unsafe behavior are complex and multifaceted, their prevention requires a clear understanding of important and influential factors. In various studies about the prevalence of unsafe behaviors in work environments, several factors such as individual characteristics, psychological aspects, safety conditions, perceived risk, and stress have been introduced as effective factors in demonstrating the unsafe behaviors [ 12 , 13 , 14 ]. However, the findings are still unable to provide a deep understanding of the underlying causes and motivations contributing to unsafe behaviors.

In the present study, unsafe behaviors while using respiratory masks is defined as the behaviors that are seen by some HCWs, which reduce the effectiveness of respiratory masks due to improper placement on the face or hand contact with the mask [ 15 ]. Some researchers in their studies indicated that other unknown factors are also effective in the unsafe behaviors [ 14 ]. However, the findings are still unable to provide a deep understanding of the underlying causes and motivations contributing to unsafe behaviors. Qualitative studies are needed to answer these questions and determine its causes. Hence, the present study is aimed to explore the factors affecting the unsafe behavior of HCWs while using respiratory masks during the COVID-19 pandemic through a qualitative study.

Study design

This study was carried out using conventional qualitative content analysis (item 9 in COREQ checklist). The interviews explored HCWs’ experiences regarding factors affecting the unsafe behavior in using respiratory masks during covid-19 pandemic in Iran. This research adheres to the guidelines outlined in the Consolidated Criteria for Reporting Qualitative Research (COREQ).

This study was conducted in government and non-government hospitals in Tehran, Mashhad and Rafsanjan that admitted patients with COVID-19 disease. The authors’ place of work and access to participants were important causes of choosing the settings. Moreover, these hospitals experienced a large amount of patients seeking healthcare during the Covid-19 pandemic. This study was performed between December 2021 and April 2022.

Participants

In this study, interviews were performed with healthcare workers (HCWs) including nurses, physicians and hospital workers who had direct contact with patients that used masks for more than 4 h in each work shift. Also, participants frequently utilized surgical masks. Among them, few employed filter masks or a combination of both types. The inclusion criteria were people with experience of using respiratory masks for more than one year and the ability to express their experiences and point of views. The sole exclusion criterion of the current study was a lack of interest in further participation. The participants were selected using purposive sampling method (item 10 in COREQ checklist) in which the researcher selected the most informed people who could explain their experiences regarding the research topic [ 16 ]. The number of participants was determined based on the data saturation principle in which no new concepts were obtained. Data saturation was achieved after 24 interviews, and to ensure saturation, two more interviews were also performed. Finally, the total number of participants was 26 people (items 12–13 in COREQ checklist).

Data gathering

Data collection was performed through in-depth face to face (item 11 in COREQ checklist) semi-structured interviews. The first author, who received training in qualitative research methods, conducted all the interviews (items 1–5 in COREQ checklist). The participants were presented with information about the research topic, objectives, and the researchers’ identities. The researcher thoroughly described the study procedure to those who consented to participate, and written informed consent was obtained from all participants (items 6–8 in COREQ checklist). The data was gathered in the workplace of the participants. Additionally, demographic data of the participants was documented (items 14–16 in COREQ checklist). At first, 5 unstructured interviews were done to extract the primary concept, and then, 21 semi-structured interviews were conducted using the interview guide. The interviews were done in a quiet and comfortable place. The interviews started with simple and general topics and were gradually directed to specific questions based on the answers. Some of the questions were: Based on your experience, what factors are effective in not using your mask safely?

New concepts were extracted from each interview, and this process continued until data saturation was reached. After obtaining permission from the participants to record the interviews, the implementation of the interviews was done immediately after the completion of each interview to increase the accuracy of the obtained data. The duration of the interviews was between 15 and 40 min (30 min on average). Field notes were made during or after the interview and transcripts were returned to participants for the comments and corrections (items 17–23 in COREQ checklist).

Data analysis

Data analysis was done using the five-step content analysis approach of Graneheim and Lundman [ 17 ]. Immediately after conducting each interview, the recorded file of the interview was transcribed in Word software. The interview text was read several times and based on the research question, all the content related to the participants’ experiences were extracted in the form of meaning units. In addition, notes were written in the margins of the text and then, the abstracted meaning units were designated as the code. Subsequently, the compiled codes were categorized into subcategories according to similarities. This process was repeated for all transcribed interviews until the main categories were established. The whole data analysis process was carried out by the researchers. Direct quotes from the interviews included in the results section to elucidate the codes, categories, and themes. (items 24–32 in COREQ checklist).

Trustworthiness

The strategies of transferability, dependability, credibility outlined by Lincoln and Guba were employed to achieve data trustworthiness [ 18 ]. Credibility and dependability were established through data triangulation approach, which involved interviews and field notes. Furthermore, peer check and member check were applied for ensuring credibility. To obtain member check, the transcribed interviews and codes were shared with some participants to receive their feedbacks. In the case of peer check, the research team and independent experts were verified the extracted codes and sub-categories. Data transferability and Confirmability were met through the detailed explanation of the research stages and process.

Women were 50% of all participants and the highest frequency of education was bachelor’s degree ( n = 17). Furthermore, the highest amount of work experience was 22 years (Table 1 ).

In the present study, 689 initial codes were identified in the initial writing, and after removing duplicate codes and cleaning, the number of final codes included 132 codes. After reviewing and analyzing the data, the factors affecting the unsafe behavior of HCWs while using respiratory masks were divided into 3 main categories and 8 sub-categories (Table 2 ). Categories included discomfort and pain (four sub-categories of headache and dizziness, skin discomfort, respiratory discomfort, feeling hot and thirsty), negative effect on performance (four sub-categories of effect on physical function, effect on cognitive function, system function vision and hearing), and a negative effect on the mental state (two subcategories of anxiety and depression).

Pain and discomfort

Some of the participants reported that the reason for improper and unsafe use of the mask is feeling pain and discomfort, and the reasons include the four subcategories of headache and dizziness, skin discomfort, respiratory discomfort, discomfort caused by heat and thirst.

Skin disorders

The side effects of the mask on the skin are of the important factors in this category. Thus, some participants, due effects of the mask to their skin, limited the use of the mask or did not use it correctly. Among the skin problems experienced by the participants were acne and skin sensitivities, which in some cases required drug treatments. The subcategory of skin sensitivities such as itching and burning was mentioned by more than 70% of the samples as the most important cause of discomfort.

“…I can’t help touching my mask. After half an hour when I put on the new mask, my face, especially my nose, starts to itch badly and I often have to blow my nose from under the mask or over the mask with my fingers, palm or the back of my hand…” (P1)

Respiratory disorders

Most of the participants in the study noted to problems such as difficulty in breathing, heart palpitations, carbon dioxide and unpleasant smell inside the mask as the most important respiratory problems. Therefore, it can be one of the important reasons for removing the mask and unsafe behavior in using the mask.

“… at any opportunity, I remove my mask to take a breath…” (P15)

Feeling hot and thirsty

Temperature discomfort, especially in long-term use and when people had to use two masks, was mentioned as an annoying factor.

“… the heat inside the mask bothers me a lot, I sweat and the mask gets wet… no matter how much water I drink, I still feel thirsty…” (P6)

Unfitness of mask with the individual’s face

Another important point extracted from the interviews was the importance of when to use the mask. In this way, as the time of using the mask increased, the person’s feeling of discomfort due to the mismatch between the belt and the mask increased, because the feeling of pressure and pain on the nose, behind the ears, and the face usually occurs several hours after wearing the mask. Several participants reported experiencing discomfort and headaches after wearing the mask. Although These headaches were often short-term and didn’t have long-term complications according to the participants’ reports, they could affect the work performance of HCWs and their behavior in the correct use of respiratory masks.

“…. After a while, the mask puts pressure on my nose and parts of my head and face. Sometimes I touch and move it unintentionally…” (P3) “… if I don’t move the mask on my face, I get a headache because the mask strap puts pressure on my head and nose…” (P21)

Effects on performance

The participants reported that wearing a mask for a long time is one of their important problems in performing their duties, and one of the main categories extracted from this study is the effects on performance, which includes the physical, cognitive, vision and hearing performance.

Effects on physical performance

The effect on the physical performance of HCWs had less effect on their unsafe behavior in using masks than other cases. But when masks were used for a long time and people were more physically tired, sometimes people removed the mask to increase their ability to perform physical work.

“…when I wear a mask, it becomes difficult for me to walk and do physical work, as if I am short of breath…” (P17)

Effects on cognitive function

It was the most frequent subcategory. Because when people feel uncomfortable, their attention decreases and part of the working memory is involved in feeling uncomfortable. Of course, it should be noted that many of the participants in the present study reported the decrease in alertness to be an effective factor in reducing their cognitive performance.

“…When I take off the mask, I can focus better on my work. Especially when I wear it in longer times, I get tired. Many times, I move the mask to finish my job faster…” (P8)

Based on the participants’ point of views, data perception (understanding information through the visual and auditory systems) decreases while using the mask. However, the negative effect of mask on the visual performance affects the unsafe behavior of the HCWs in the incorrect use of the mask and moving it on the face more than other cases. Most of the people who used glasses reported the steam condensation under the glasses as an important cause of discomfort and interference of the mask with their work duties.

“…Using glasses with a mask is really annoying. I have eye pain and burning, and there is always a fog in front of my eyes…” (P2)

Effects on mental status

Among the other main categories extracted in this study is the effects on mental status, which includes the subcategories of depression and anxiety. The negative effect of the mask on the mental state unconsciously affects the person’s behavior in using the respiratory mask.

Some of the participants in this study reported feeling anxious while wearing the mask for various reasons. Therefore, they refuse to wear masks, although they have no justification for doing so. In many cases, the participants in this study expressed that during higher psychological stress, they suffer more from wearing masks and tend to wear them improperly.

“… Sometimes I distractedly take off my mask so that the other person hears my voice better. However, there are many patients, So I am afraid of getting infected. Sometimes I have to speak loudly and this makes me furious … I worry about making a mistake or misunderstanding the conversation, and …” (P4)

One of the most important factors mentioned as a cause of depression was harder communication with colleagues and patients while wearing a mask. This occurs by increasing the physical and mental workload and placing people in social isolation. In this situation, HCWs sometimes consciously take off their masks, so that they can communicate with each other more conveniently.

“…When I wear a mask, I get tired when talking to others. I prefer not to talk to my colleague. Sometimes I don’t pay attention, I take the mask down so they can understand me …” (P5)

To the best of our knowledge, this research is one of the first qualitative studies to extract the experiences of HCWs for explaining the factors affecting the unsafe behavior of HCWs in using respiratory masks during the COVID-19 pandemic in Iran. Although many reasons can cause the unsafe behavior of HCWs in the correct use of respiratory masks in the hospital, according to the present results, three main categories include discomfort and pain, effects on performance, effects on mental status. Skin and respiratory discomforts and the negative effect of the mask on cognitive functions are among the most important factors affecting the unsafe behavior of HCWs in the field of correct use of respiratory masks.

Based on the present study, the participants experienced discomfort and pain while using the mask, and this was one of the important factors of unsafe use of respiratory masks. Discomfort while wearing masks has been confirmed in several studies [ 19 ]. Additionally, in a similar study, researchers found that wearing face masks during the COVID-19 era heightens the discomfort experienced by HCWs [ 20 ]. Some studies have delved into these discomforts in greater detail. For example, the prevalence of skin disorders among HCWs using PPE during the COVID-19 pandemic was reported to be significant [ 21 ]. Some researchers also reported significant prevalence of respiratory disorders and headaches when using PPE [ 22 ]. The findings of a study suggested that a novel form of headache has emerged among HCWs when using a mask during the COVID-19 pandemic. Both exacerbation of existing headaches and the onset of new headaches have been observed to rise with mask usage, irrespective of the use duration [ 23 ]. In some studies, a significant percentage of people reported feeling thirsty and dehydrated after long-term use of respiratory masks [ 24 ]. Several studies reported disturbing rates of perspiration from prolonged use of respiratory masks [ 25 , 26 , 27 ]. A similar study reported that prolonged exposure to masks and protective gear, especially among HCWs, can lead to various issues such as acne, skin irritation, cognitive impairment, and headaches [ 28 ]. According to the results of the present study, discomfort often causes HCWs to move the mask and disturb the correct fitness of the mask on their face.

The results of the present study indicated that respiratory masks have the ability to hinder the work performance of their users. Various studies have confirmed the adverse effect of respiratory masks on HCWs performance. A similar research indicated that respiratory masks reduce physical performance [ 29 ]. Several studies have highlighted the issue of mask users’ ability to see and read being hindered by fogging of glasses [ 22 , 27 , 30 ]. The feel of weakness to perform cognitive tasks has also been reported in various studies [ 31 , 32 ]. An increase in physical fatigue has been mentioned in some studies as an adverse effect of respiratory masks [ 27 , 31 ]. A research showed the effect of respiratory mask on hearing and visual performance [ 33 ]. Another study reported that high-protection respiratory masks reduced physiological and psychological ability, especially if the workers perform physical work [ 34 ].

The third category is related to the negative impact on the psychological state of HCWs. Some studies noted the use of some PPE, including respiratory masks, as one of the possible reasons for the increase of mental health problems among HCWs [ 35 , 36 ]. Before the prevalence of the COVID-19 virus, the hypothesis of the negative effect of respiratory masks on the mental state of people was investigated and confirmed by some studies [ 37 ]. Furthermore, one study reported that wearing respiratory masks leads to an increase in anxiety [ 38 ].

The non-ergonomic nature of respiratory masks (the lack of suitability of masks for people for long-term use) can affect the effectiveness of respiratory masks by encouraging people to perform unsafe behaviors in using respiratory masks [ 39 ]. An important point was that the attitude and knowledge of health care works regarding the use of respiratory masks were not identified as the cause of unsafe behavior of HCWs. However, this factor has been reported in some previous studies as a reason for people not using PPE properly [ 40 ]. The COVID-19 pandemic situation and the extensive information collected about this pandemic may improve the level of awareness and the attitude of the HCWs.

The escalation in infection rates among HCWs, despite receiving training and utilizing personal protective equipment, served as a catalyst for this research endeavor. So far, there has been a deficiency in the context-specific research that could offer a more profound understanding of this issue. Therefore, the outcomes of this qualitative study may prove beneficial in enhancing the design and execution of respiratory protection programs for HCWs in infectious hospital departments or during similar pandemics.

Implications for nursing practice

It is expected that the findings of this study can provide a better understanding of the factors influencing the unsafe behavior of HCWs while using masks. Furthermore, it can be used as a preliminary study to evaluate the effectiveness of safety and infection control programs in hospitals in the COVID-19 pandemic and similar disasters in the future.

Discomfort and pain, effects on performance, and effects on mental status are important factors for unsafe behavior of HCWs’ in using respiratory masks. Our results could contribute to the identification and analysis of possible scenarios to reduce unsafe behaviors in the use of respiratory masks. Accordingly, it is recommended to provide the necessary therapeutic and preventive interventions regarding the complications of using masks. Planning to reduce the side effects of masks and training personnel on the correct use of masks with minimal health effects are recommended as well.

Limitations

The physical and cognitive workload of HCWs which increased during the COVID-19 pandemic [ 41 ], had possible impacts on the work ability of the staff [ 42 ]. Therefore, their explanation about the negative effects of wearing masks may be affected by their specific working conditions.

Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

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Tahernejad, A., Sohrabizadeh, S. & Tahernejad, S. Exploring factors affecting the unsafe behavior of health care workers’ in using respiratory masks during COVID-19 pandemic in Iran: a qualitative study. BMC Health Serv Res 24 , 608 (2024). https://doi.org/10.1186/s12913-024-11000-4

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Background: Bipolar disorder is a serious mental illness, which requires new strategies for prevention and management. Recent evidence suggests that a ketogenic diet may be an effective intervention. This research aimed to explore the feasibility and acceptability of a ketogenic diet intervention for bipolar disorder, fidelity to its behavioural components and the experiences of the participants and research clinicians involved. Methods: A mixed-methods process evaluation was conducted. Semi-structured telephone interviews were carried out with 15 participants 1-2 months after completing a 6-8 week modified ketogenic diet intervention, and 4 research clinicians from the study team following the completion of data collection. Data were thematically analysed. Fidelity checklists completed by research dietitians were analysed using descriptive count and percentage statistics. Findings are reported post-hoc, following the analysis and publication of the main pilot study findings. Results: Qualitative data indicated that participants had various motives for taking part in the study, including weight loss. It was important to support people's motives while facilitating clear and realistic expectations. Despite the challenges of initiating and maintaining a ketogenic diet, including for some its disruptive effects on daily living, many participants perceived physical and psychological benefits (e.g. significant weight loss, mood stability and an enhanced ability to focus). A range of behavioural (e.g. goal setting), social (e.g. family and dietitians) and technological (e.g. apps for monitoring) support mechanisms were generally considered key facilitating factors. Meanwhile, dietary preferences, concerns about the diet and its impact, the testing burden and capacity of the delivery team were perceived as barriers for some. The importance of wider contextual influences (e.g. the cost of living and sociocultural expectations) were highlighted. Overall, descriptive analyses indicated moderate-to-good fidelity to the behaviour change components of the study. Conclusion: We provide novel insight into the experiences of people living with bipolar disorder initiating and following a ketogenic diet, as well as those of research clinicians who support the intervention. Future trials may benefit from increased clinical research capacity, better-defined entry and exit routes, additional interpersonal support, and greater understanding of how social and societal factors impact participation.

Competing Interest Statement

IHC has a diagnosis of bipolar disorder and follows a ketogenic diet to manage his symptoms. His current fellowship is funded by the Baszucki Research Fund.

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This study was funded by the Baszucki Brain Research Fund. BPR and SAS acknowledge funding from the UKRI Medical Research Council (MC_UU_00022/1) and the Chief Scientist Office of the Scottish Government Health Directorates (SPHSU16). MJT acknowledges funding from the Chief Scientist Office of the Scottish Government Health Directorates through the NHS Lothian Research and Development Office.

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Types of Qualitative Data Analysis Methodologies. Systematically analyzing textual, visual, or auditory content to identify patterns, themes, and meanings. Includes conventional, directed, and summative approaches. Identifying, analyzing, and reporting patterns or themes within qualitative data. Offers a systematic approach to coding and ...
Qualitative Study
Qualitative research is a type of research that explores and provides deeper insights into real-world problems.[1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences ...
Exploring factors affecting the unsafe behavior of health care workers
This study was carried out using the conventional qualitative content analysis. Participants were the number of 26 health care workers selected by purposive sampling method. Data collection was conducted through in-depth semi-structured interviews. Data analysis was done using the content analysis approach of Graneheim and Lundman.
Pilot study of a ketogenic diet in bipolar disorder: a process
Methods: A mixed-methods process evaluation was conducted. Semi-structured telephone interviews were carried out with 15 participants 1-2 months after completing a 6-8 week modified ketogenic diet intervention, and 4 research clinicians from the study team following the completion of data collection. Data were thematically analysed.

Qualitative Data Collection: What it is + Methods to do it

What is Qualitative Data?

Individual interview

Qualitative surveys

Paper surveys

Record keeping

Case studies

Observations

Step 1: Arrange your Data

Step 2: Organize all your Data

Step 3: Set a Code to the Data Collected

Step 4: Validate your Data

Step 5: Concluding the Analysis Process

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Chapter 10. Introduction to Data Collection Techniques

Revisiting Approaches

Overview of the Four Primary Approaches

Triangulation and Mixed Methods

Advanced Reading: Triangulation

Further Readings

8 Essential Qualitative Data Collection Methods

What is Qualitative Data?

Qualitative vs. Quantitative Data

8 Types of Qualitative Data Collection Methods

1. Interviews

2. Focus Groups

3. Observation

4. Open-Ended Surveys and Questionnaires

5. Case Studies

6. Text Analysis

7. Audio and Video Recordings

8. Hybrid Methodologies

Collect and Collate Actionable Data with UpMetrics

Disrupting Tradition

Collecting and Analyzing Qualitative Data

Semi-Structured Interviews

Visualization Methods

Selecting a Sample of Study Participants

Determining Sample Size

Recruiting Study Participants

Managing Qualitative Data

Condensing Qualitative Data

Displaying Qualitative Data

Drawing and Verifying Conclusions

Exit Notification / Disclaimer Policy

Qualitative Data Collection Methods

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Data Collection Methods | Step-by-Step Guide & Examples

Table of contents

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Operationalisation

Standardising procedures

Creating a data management plan

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Pritha Bhandari

Qualitative Data Analysis Methodologies and Methods

Types of Qualitative Data Analysis Methodologies

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