5 qualitative research designs

5 Types of Qualitative Methods

But just as with quantitative methods, there are actually many varieties of qualitative methods.

Similar to the way you can group usability testing methods , there are also a number of ways to segment qualitative methods.

A popular and helpful categorization separate qualitative methods into five groups: ethnography, narrative, phenomenological, grounded theory, and case study. John Creswell outlines these five methods in Qualitative Inquiry and Research Design .

While the five methods generally use similar data collection techniques (observation, interviews, and reviewing text), the purpose of the study differentiates them—something similar with different types of usability tests . And like classifying different usability studies, the differences between the methods can be a bit blurry. Here are the five qualitative methods in more detail.

1. Ethnography

Ethnographic research is probably the most familiar and applicable type of qualitative method to UX professionals. In ethnography, you immerse yourself in the target participants’ environment to understand the goals, cultures, challenges, motivations, and themes that emerge. Ethnography has its roots in cultural anthropology where researchers immerse themselves within a culture, often for years! Rather than relying on interviews or surveys, you experience the environment first hand, and sometimes as a “participant observer.”

For example, one way of uncovering the unmet needs of customers is to “ follow them home ” and observe them as they interact with the product. You don’t come armed with any hypotheses to necessarily test; rather, you’re looking to find out how a product is used.

2. Narrative

The narrative approach weaves together a sequence of events, usually from just one or two individuals to form a cohesive story. You conduct in-depth interviews, read documents, and look for themes; in other words, how does an individual story illustrate the larger life influences that created it. Often interviews are conducted over weeks, months, or even years, but the final narrative doesn’t need to be in chronological order. Rather it can be presented as a story (or narrative) with themes, and can reconcile conflicting stories and highlight tensions and challenges which can be opportunities for innovation.

For example, a narrative approach can be an appropriate method for building a persona . While a persona should be built using a mix of methods—including segmentation analysis from surveys—in-depth interviews with individuals in an identified persona can provide the details that help describe the culture, whether it’s a person living with Multiple Sclerosis, a prospective student applying for college, or a working mom.

3. Phenomenological

When you want to describe an event, activity, or phenomenon, the aptly named phenomenological study is an appropriate qualitative method. In a phenomenological study, you use a combination of methods, such as conducting interviews, reading documents, watching videos, or visiting places and events, to understand the meaning participants place on whatever’s being examined. You rely on the participants’ own perspectives to provide insight into their motivations.

Like other qualitative methods, you don’t start with a well-formed hypothesis. In a phenomenological study, you often conduct a lot of interviews, usually between 5 and 25 for common themes , to build a sufficient dataset to look for emerging themes and to use other participants to validate your findings.

For example, there’s been an explosion in the last 5 years in online courses and training. But how do students engage with these courses? While you can examine time spent and content accessed using log data and even assess student achievement vis-a-vis in-person courses, a phenomenological study would aim to better understand the students experience and how that may impact comprehension of the material.

4. Grounded Theory

Whereas a phenomenological study looks to describe the essence of an activity or event, grounded theory looks to provide an explanation or theory behind the events. You use primarily interviews and existing documents to build a theory based on the data. You go through a series of open and axial coding techniques to identify themes and build the theory. Sample sizes are often also larger—between 20 to 60—with these studies to better establish a theory. Grounded theory can help inform design decisions by better understanding how a community of users currently use a product or perform tasks.

For example, a grounded theory study could involve understanding how software developers use portals to communicate and write code or how small retail merchants approve or decline customers for credit.

5. Case Study

Made famous by the Harvard Business School, even mainly quantitative researchers can relate to the value of the case study in explaining an organization, entity, company, or event. A case study involves a deep understanding through multiple types of data sources. Case studies can be explanatory, exploratory, or describing an event. The annual CHI conference has a peer-reviewed track dedicated to case studies.

For example, a case study of how a large multi-national company introduced UX methods into an agile development environment would be informative to many organizations.

The table below summarizes the differences between the five qualitative methods.

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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 organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• Types of qualitative research designs

Last updated

20 February 2023

Reviewed by

Jean Kaluza

Researchers often conduct these studies to gain a detailed understanding of a particular topic through a small, focused sample. Qualitative research methods delve into understanding why something is happening in a larger quantitative study. 

To determine whether qualitative research is the best choice for your study, let’s look at the different types of qualitative research design.

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• What are qualitative research designs?

Qualitative research designs are research methods that collect and analyze non-numerical data. The research uncovers why or how a particular behavior or occurrence takes place. The information is usually subjective and in a written format instead of numerical.

Researchers may use interviews, focus groups , case studies , journaling, and open-ended questions to gather in-depth information. Qualitative research designs can determine users' concepts, develop a hypothesis , or add context to data from a quantitative study.

• Characteristics of qualitative research design

Most often, qualitative data answers how or why something occurs. Certain characteristics are usually present in all qualitative research designs to ensure accurate data. 

The most common characteristics of qualitative research design include the following:

Natural environment

It’s best to collect qualitative research as close to the subject’s original environment as possible to encourage natural behavior and accurate insights.

Empathy is key

Qualitative researchers collect the best data when they’re in sync with their users’ concerns and motivations. They can play into natural human psychology by combining open-ended questioning and subtle cues.

They may mimic body language, adopt the users’ terminology, and use pauses or trailing sentences to encourage their participants to fill in the blanks. The more empathic the interviewer, the purer the data.

Participant selection

Qualitative research depends on the meaning obtained from participants instead of the meaning conveyed in similar research or studies. To increase research accuracy, you choose participants randomly from carefully chosen groups of potential participants.

Different research methods or multiple data sources

To gain in-depth knowledge, qualitative research designs often rely on multiple research methods within the same group. 

Emergent design

Qualitative research constantly evolves, meaning the initial study plan might change after you collect data. This evolution might result in changes in research methods or the introduction of a new research problem.

Inductive reasoning

Since qualitative research seeks in-depth meaning, you need complex reasoning to get the right results. Qualitative researchers build categories, patterns, and themes from separate data sets to form a complete conclusion.

Interpretive data

Once you collect the data, you need to read between the lines rather than just noting what your participant said. Qualitative research is unique as we can attach actions to feedback. 

If a user says they love the look of your design but haven’t completed any tasks, it’s up to you to interpret this as a failed test, even with their positive sentiments.  

Holistic account

To paint a large picture of an issue and potential solutions, a qualitative researcher works to develop a complex description of the research problem. You can avoid a narrow cause-and-effect perspective by describing the problem’s wider perspectives. 

• When to use qualitative research design

Qualitative research aims to get a detailed understanding of a particular topic. To accomplish this, you’ll typically use small focus groups to gather in-depth data from varied perspectives. 

This approach is only effective for some types of study. For instance, a qualitative approach wouldn’t work for a study that seeks to understand a statistically relevant finding.

When determining if a qualitative research design is appropriate, remember the goal of qualitative research is understanding the “ why .” 

Qualitative research design gathers in-depth information that stands on its own. It can also answer the “why” of a quantitative study or be a precursor to forming a hypothesis. 

You can use qualitative research in these situations:

Developing a hypothesis for testing in a quantitative study

Identifying customer needs

Developing a new feature

Adding context to the results of a quantitative study

Understanding the motivations, values, and pain points that guide behavior

Difference between qualitative and quantitative research design

Qualitative and quantitative research designs gather data, but that's where the similarities end. Consider the difference between quality and quantity. Both are useful in different ways.

Qualitative research gathers in-depth information to answer how or why . It uses subjective data from detailed interviews, observations, and open-ended questions. Most often, qualitative data is thoughts, experiences, and concepts.

In contrast, quantitative research designs gather large amounts of objective data that you can quantify mathematically. You typically express quantitative data in numbers or graphs, and you use it to test or confirm hypotheses.

Qualitative research designs generally have the same goals. However, there are various ways to achieve these goals. Researchers may use one or more of these approaches in qualitative research.

Historical study

This is where you use extensive information about people and events in the past to draw conclusions about the present and future.

Phenomenology

Phenomenology investigates a phenomenon, activity, or event using data from participants' perspectives. Often, researchers use a combination of methods.

Grounded theory

Grounded theory uses interviews and existing data to build a theory inductively.

Ethnography

Researchers immerse themselves in the target participant's environments to understand goals, cultures, challenges, and themes with ethnography .

A case study is where you use multiple data sources to examine a person, group, community, or institution. Participants must share a connection to the research question you’re studying.

• Advantages and disadvantages of qualitative research

All qualitative research design types share the common goal of obtaining in-depth information. Achieving this goal generally requires extensive data collection methods that can be time-consuming. As such, qualitative research has advantages and disadvantages. 

Natural settings

Since you can collect data closer to an authentic environment, it offers more accurate results.  

The ability to paint a picture with data

Quantitative studies don't always reveal the full picture. With multiple data collection methods, you can expose the motivations and reasons behind data.

Flexibility

Analysis processes aren't set in stone, so you can adapt the process as ideas or patterns emerge.

Generation of new ideas

Using open-ended responses can uncover new opportunities or solutions that weren't part of your original research plan.

Small sample sizes

You can generate meaningful results with small groups.

Disadvantages

Potentially unreliable.

A natural setting can be a double-edged sword. The inability to attach findings to anything statistically relevant can make data more difficult to quantify. 

Subjectivity

Since the researcher plays a vital role in collecting and interpreting data, qualitative research is subject to the researcher's skills. For example, they may miss a cue that changes some of the context of the quotes they collected.

Labor-intensive

You generally collect qualitative data through manual processes like extensive interviews, open-ended questions, and case studies.

Qualitative research designs allow researchers to provide an in-depth analysis of why specific behavior or events occur. It can offer fresh insights, generate new ideas, or add context to statistics from quantitative studies. Depending on your needs, qualitative data might be a great way to gain the information your organization needs to move forward.

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Qualitative Research Design: Start

Qualitative Research Design

What is Qualitative research design?

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypotheses as well as 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 stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are 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 that there is 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 together.

Research Paradigms 

• Positivist versus Post-Positivist
• Social Constructivist (this paradigm/ideology mostly birth qualitative studies)

Events Relating to the Qualitative Research and Community Engagement Workshops @ CMU Libraries

CMU Libraries is committed to helping members of our community become data experts. To that end, CMU is offering public facing workshops that discuss Qualitative Research, Coding, and Community Engagement best practices.

The following workshops are a part of a broader series on using data. Please follow the links to register for the events. 

Qualitative Coding

Using Community Data to improve Outcome (Grant Writing)

Survey Design  

Upcoming Event: March 21st, 2024 (12:00pm -1:00 pm)

Community Engagement and Collaboration Event 

Join us for an event to improve, build on and expand the connections between Carnegie Mellon University resources and the Pittsburgh community. CMU resources such as the Libraries and Sustainability Initiative can be leveraged by users not affiliated with the university, but barriers can prevent them from fully engaging.

The conversation features representatives from CMU departments and local organizations about the community engagement efforts currently underway at CMU and opportunities to improve upon them. Speakers will highlight current and ongoing projects and share resources to support future collaboration.

Event Moderators:

Taiwo Lasisi, CLIR Postdoctoral Fellow in Community Data Literacy,  Carnegie Mellon University Libraries

Emma Slayton, Data Curation, Visualization, & GIS Specialist,  Carnegie Mellon University Libraries

Nicky Agate , Associate Dean for Academic Engagement, Carnegie Mellon University Libraries

Chelsea Cohen , The University’s Executive fellow for community engagement, Carnegie Mellon University

Sarah Ceurvorst , Academic Pathways Manager, Program Director, LEAP (Leadership, Excellence, Access, Persistence) Carnegie Mellon University

Julia Poeppibg , Associate Director of Partnership Development, Information Systems, Carnegie Mellon University 

Scott Wolovich , Director of New Sun Rising, Pittsburgh 

Additional workshops and events will be forthcoming. Watch this space for updates. 

Workshop Organizer

Qualitative Research Methods

What are Qualitative Research methods?

Qualitative research adopts numerous methods or techniques including interviews, focus groups, and observation. 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 is 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-participant or detached observers.

What constitutes a good research question? Does the question drive research design choices?

According to Doody and Bailey (2014);

 We can only develop a good research question by consulting relevant literature, colleagues, and supervisors experienced in the area of research. (inductive interactions).

Helps to have a directed research aim and objective.

Researchers should not be “ research trendy” and have enough evidence. This is why research objectives are important. It helps to take time, and resources into consideration.

Research questions can be developed from theoretical knowledge, previous research or experience, or a practical need at work (Parahoo 2014). They have numerous roles, such as identifying the importance of the research and providing clarity of purpose for the research, in terms of what the research intends to achieve in the end.

Qualitative Research Questions

What constitutes a good Qualitative research question?

A good qualitative question answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. Qualitative research gathers participants' experiences, perceptions and behavior.

Examples of good Qualitative Research Questions:

What are people's thoughts on the new library? 

How does it feel to be a first-generation student attending college?

Difference example (between Qualitative and Quantitative research questions):

How many college students signed up for the new semester? (Quan) 

How do college students feel about the new semester? What are their experiences so far? (Qual)

• Qualitative Research Design Workshop Powerpoint

Foley G, Timonen V. Using Grounded Theory Method to Capture and Analyze Health Care Experiences. Health Serv Res. 2015 Aug;50(4):1195-210. [ PMC free article: PMC4545354 ] [ PubMed: 25523315 ]

Devers KJ. How will we know "good" qualitative research when we see it? Beginning the dialogue in health services research. Health Serv Res. 1999 Dec;34(5 Pt 2):1153-88. [ PMC free article: PMC1089058 ] [ PubMed: 10591278 ]

Huston P, Rowan M. Qualitative studies. Their role in medical research. Can Fam Physician. 1998 Nov;44:2453-8. [ PMC free article: PMC2277956 ] [ PubMed: 9839063 ]

Corner EJ, Murray EJ, Brett SJ. Qualitative, grounded theory exploration of patients' experience of early mobilisation, rehabilitation and recovery after critical illness. BMJ Open. 2019 Feb 24;9(2):e026348. [ PMC free article: PMC6443050 ] [ PubMed: 30804034 ]

Moser A, Korstjens I. Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. Eur J Gen Pract. 2018 Dec;24(1):9-18. [ PMC free article: PMC5774281 ] [ PubMed: 29199486 ]

Houghton C, Murphy K, Meehan B, Thomas J, Brooker D, Casey D. From screening to synthesis: using nvivo to enhance transparency in qualitative evidence synthesis. J Clin Nurs. 2017 Mar;26(5-6):873-881. [ PubMed: 27324875 ]

Soratto J, Pires DEP, Friese S. Thematic content analysis using ATLAS.ti software: Potentialities for researchs in health. Rev Bras Enferm. 2020;73(3):e20190250. [ PubMed: 32321144 ]

Zamawe FC. The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections. Malawi Med J. 2015 Mar;27(1):13-5. [ PMC free article: PMC4478399 ] [ PubMed: 26137192 ]

Korstjens I, Moser A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur J Gen Pract. 2018 Dec;24(1):120-124. [ PMC free article: PMC8816392 ] [ PubMed: 29202616 ]

Saldaña, J. (2021). The coding manual for qualitative researchers. The coding manual for qualitative researchers, 1-440.

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245-51. [ PubMed: 24979285 ]

Palermo C, King O, Brock T, Brown T, Crampton P, Hall H, Macaulay J, Morphet J, Mundy M, Oliaro L, Paynter S, Williams B, Wright C, E Rees C. Setting priorities for health education research: A mixed methods study. Med Teach. 2019 Sep;41(9):1029-1038. [ PubMed: 31141390 ]

• Last Updated: Feb 14, 2024 4:25 PM
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Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

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MSc, Health Psychology, University of Nottingham

Lauren obtained an MSc in Health Psychology from The University of Nottingham with a distinction classification.

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On This Page:

Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

• Gain deep contextual understandings of the subjective social reality of individuals
• To answer questions about experience and meaning from the participant’s perspective
• To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin. 

Examples of qualitative research questions include: 

• How does stress influence young adults’ behavior?
• What factors influence students’ school attendance rates in developed countries?
• How do adults interpret binge drinking in the UK?
• What are the psychological impacts of cervical cancer screening in women?
• How can mental health lessons be integrated into the school curriculum? 

Characteristics 

Naturalistic setting.

Individuals are studied in their natural setting to gain a deeper understanding of how people experience the world. This enables the researcher to understand a phenomenon close to how participants experience it. 

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

Reality is socially constructed

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.

Observations

This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.

Ethnography

Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.

• Define a Research Question : To start content analysis, a clear research question should be developed.
• Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
• Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
• Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
• Code the Data : Follow the coding rules to categorize data into themes.
• Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.

• Define the Research Question : Develop a relevant research question to frame the analysis.
• Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
• Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
• Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.

• Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
• Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
• Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
• Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
• Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
• Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.

• Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
• Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
• Indexing : Apply the framework systematically to the entire study data.
• Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
• Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 

Co-coding 

Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

1. Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

2. Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

3. Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

4. Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

The exploratory nature of qualitative research helps generate hypotheses that can be tested quantitatively (Busetto et al., 2020).

Flexibility

Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 

Limitations

Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 

Subjectivity 

The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

While individual qualitative studies are often limited in their generalizability due to factors such as sample size and context, metasynthesis enables researchers to synthesize findings from multiple studies, potentially leading to more generalizable conclusions.

By integrating findings from studies conducted in diverse settings and with different populations, metasynthesis can provide broader insights into the phenomenon of interest.

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

• Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
• Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
• Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

What is narrative analysis?

Narrative analysis is a qualitative research method used to understand how individuals create stories from their personal experiences.

There is an emphasis on understanding the context in which a narrative is constructed, recognizing the influence of historical, cultural, and social factors on storytelling.

Researchers can use different methods together to explore a research question.

Some narrative researchers focus on the content of what is said, using thematic narrative analysis, while others focus on the structure, such as holistic-form or categorical-form structural narrative analysis. Others focus on how the narrative is produced and performed.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Comprehensive guide to the 5 types of qualitative research design.

Home » Comprehensive guide to the 5 types of qualitative research design

Qualitative research designs offer unique pathways to understanding human behavior, emotions, and experiences. Unlike quantitative approaches that focus on numbers, these designs delve deeper into the subtleties of context and perspective, allowing researchers to explore complex issues. This insightful method is particularly useful for those aiming to grasp the intricate narratives that shape individuals' lives.

In this guide, we will explore five primary types of qualitative research designs. Each type serves distinct purposes and provides valuable frameworks for gathering and analyzing qualitative data. By understanding these designs, researchers can select the best approaches that align with their goals and the specific questions they wish to answer.

Understanding Qualitative Research

Qualitative research is a powerful tool that focuses on understanding human experiences, emotions, and motivations through non-numerical data. This methodology emphasizes the richness of the data collected, making it invaluable for exploring complex social phenomena. By capturing responses in formats like interviews, open-ended surveys, and discussions, qualitative research goes beyond mere numbers, delving into the “why” behind actions and preferences.

An effective understanding of qualitative research designs involves several key aspects: the context of the study, the participants' perspectives, and the interaction between the researcher and subjects. These elements collectively shape the findings and conclusions drawn from the research. Each design type—like interviews, focus groups, and case studies—offers unique insights and methodologies that can illuminate different aspects of human behavior. By recognizing and applying these designs, researchers can better grasp the intricacies of human experiences, paving the way for deeper understanding and improved decision-making.

Definition and Importance of Qualitative Research

Qualitative research is a methodological approach focused on understanding human behavior and social phenomena through in-depth analysis. It goes beyond numbers and statistics, seeking to capture the richness of individual experiences. This type of research often involves various qualitative research designs, including interviews, focus groups, and case studies. By prioritizing participant perspectives, researchers can uncover deep insights into motivations, attitudes, and emotions.

The importance of qualitative research lies in its ability to provide a nuanced understanding of complex issues. It allows researchers to explore topics that are difficult to quantify, revealing underlying themes and patterns. Furthermore, qualitative research plays a significant role in informing product development and improving user experiences. By tapping into the voices of participants, researchers can identify critical market needs, leading to more effective solutions and enhanced decision-making processes. As a result, qualitative research is essential for organizations looking to connect authentically with their audience.

Key Differences Between Qualitative and Quantitative Research

Qualitative research designs focus on collecting in-depth insights through observational or participatory methods. In contrast, quantitative research relies on numerical data and statistical analysis to identify trends and patterns. The primary aim of qualitative research is to explore complex phenomena and understand participants' perspectives, whereas quantitative research seeks to quantify problems and predict outcomes based on measurable variables.

One key difference lies in the data collection process. Qualitative research often uses interviews, focus groups, or open-ended surveys to gather rich contextual data. Quantitative methods, on the other hand, utilize structured surveys, experiments, and existing data to generate figures that can be statistically analyzed. Another important distinction is in data analysis; qualitative research identifies themes and narratives, while quantitative research relies on statistical tools to analyze relationships and derive conclusive results. Understanding these differences can enhance the application of qualitative research designs in uncovering nuanced insights.

The Five Types of Qualitative Research Designs

Qualitative research designs offer a variety of approaches to understanding human behavior and experiences. Among the five main types, the first is phenomenology , which focuses on the lived experiences of individuals. This design seeks to understand how people perceive and make sense of their situations, emphasizing their subjective experiences.

Next is grounded theory , which aims to generate a theory based on data collected in the field. Researchers gather qualitative data, analyze it, and derive theories that emerge from the participants' realities. Then, we have ethnography , where researchers immerse themselves in a community to observe and interact with participants in their natural environment. This approach enhances the understanding of cultural contexts.

Another design is case studies , which provide an in-depth exploration of individual cases within their real-life context. Finally, narrative research focuses on the stories that people tell about their lives, allowing insights into their identities and how they interpret experiences. Each design serves distinct purposes, catering to different research questions within qualitative methodologies.

Ethnography: Exploring Cultures

Ethnography serves as a nuanced approach within qualitative research designs, focusing on the rich tapestry of human cultures. By immersing oneself in the everyday lives of participants, researchers gain insights that are not easily captured through traditional methods. This in-depth exploration allows for the discovery of social norms, values, and behaviors that define a culture, leading to a profound understanding of the subject matter.

Conducting ethnographic research involves key steps that enhance the quality of the inquiry. First, establishing rapport is essential for trust and openness. Second, participant observation enables researchers to gather authentic data within natural settings. Lastly, in-depth interviews provide personal narratives that further illuminate the cultural context under study. Through these methods, ethnography not only gathers data but also portrays the complexity of human experiences, adding significant depth to qualitative research designs. This approach enriches our appreciation of diversity and the shared human condition.

Phenomenology: Understanding Lived Experiences

Phenomenology in qualitative research focuses on understanding the essence of lived experiences. Researchers immerse themselves in individuals' stories, aiming to capture the feelings, perceptions, and meanings that shape their realities. This approach allows a deep exploration of subjective experiences, revealing nuances often overlooked by other methods.

Within phenomenology, several key principles emerge. First, bracketing involves setting aside preconceived notions to approach participants' insights with an open mind. Second, in-depth interviewing is crucial for collecting rich, descriptive data that reflects participants' experiences. Third, thematic analysis helps identify patterns that reveal commonalities across different narratives. By emphasizing these elements, phenomenology fosters a comprehensive understanding of personal experiences, making it an invaluable qualitative research design.

Overall, employing phenomenology provides researchers with profound insights into how individuals interpret their experiences and navigate their world. This understanding can inform practices, policies, and theories across various fields, enhancing empathy and responsiveness to human needs.

Case Study: In-depth Analysis

Case studies are pivotal in qualitative research designs as they provide an in-depth analysis of a specific phenomenon or situation. This research method involves examining a particular case, allowing researchers to gain detailed insights that broader studies might overlook. The case study approach typically focuses on understanding the nuances, complexities, and unique aspects of the case, which might be a person, group, event, or organization.

To effectively conduct a case study, researchers often follow key steps. First, they identify the case to analyze and decide on the research questions guiding the study. Next, they collect data through interviews, observations, and document reviews. After gathering data, researchers analyze it to draw meaningful conclusions and offer contextual insights. Lastly, the findings are documented, highlighting the implications within the realm of qualitative research designs. This method ultimately enriches understanding and generates new knowledge in the field.

Grounded Theory: Building Theories from Data

Grounded Theory offers a structured approach to developing theories from qualitative data. Researchers gather extensive information through interviews, observations, and focus groups, then analyze the data iteratively. This process involves coding the data and identifying patterns, which helps in formulating concepts that explain the phenomenon under study. The dynamic nature of qualitative research designs allows researchers to modify their focus based on emerging findings, leading to richer insights.

In essence, Grounded Theory emphasizes theory building as an ongoing process. This approach provides flexibility, enabling the incorporation of new data that may challenge or expand the initial understanding. It encourages researchers to be open to the complexities of human behavior and social phenomena, fostering a deeper analysis that can yield impactful theories. By systematically analyzing data, researchers can take significant strides towards understanding and interpreting qualitative research designs effectively.

Narrative Research: Telling Stories

Narrative research allows for an exploration of personal experiences and historical contexts through storytelling, making it a rich avenue within qualitative research designs. By collecting individual stories, researchers gain insights into how participants interpret their realities, providing meaningful understanding that quantitative data often misses. This method values the human experience, revealing the complexities of individual perspectives.

In narrative research, the stories unfold through interviews, personal reflections, and written accounts. These narratives not only document experiences but also help identify themes and patterns that reflect broader societal issues. The process highlights the voice and authenticity of participants, allowing researchers to connect with the emotional elements of the subjects’ lives. Through narrative research, the intricacies of human experiences come to life, illustrating the power of storytelling in qualitative inquiry.

Conclusion: Mastering Qualitative Research Designs

Mastering qualitative research designs involves understanding various methodologies and how they can be applied effectively. Each type of design offers unique insights into human behavior and experiences. By familiarizing yourself with these qualitative research designs, you will enhance your ability to gather meaningful data that impacts decision-making and strategy development.

The journey through qualitative research is not merely academic; it is about engaging with real-world complexities. This mastery will empower you to navigate challenges, enabling you to draw valid conclusions that resonate deeply within your research context. As you develop your skills in qualitative research designs, you will find yourself more adept at uncovering the nuanced stories that shape our understanding of diverse topics.

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9.4 Types of qualitative research designs

Learning objectives.

• Define focus groups and outline how they differ from one-on-one interviews
• Describe how to determine the best size for focus groups
• Identify the important considerations in focus group composition
• Discuss how to moderate focus groups
• Identify the strengths and weaknesses of focus group methodology
• Describe case study research, ethnography, and phenomenology.

There are various types of approaches to qualitative research.  This chapter presents information about focus groups, which are often used in social work research.  It also introduces case studies, ethnography, and phenomenology.

Focus Groups

Focus groups resemble qualitative interviews in that a researcher may prepare a guide in advance and interact with participants by asking them questions. But anyone who has conducted both one-on-one interviews and focus groups knows that each is unique. In an interview, usually one member (the research participant) is most active while the other (the researcher) plays the role of listener, conversation guider, and question-asker. Focus groups , on the other hand, are planned discussions designed to elicit group interaction and “obtain perceptions on a defined area of interest in a permissive, nonthreatening environment” (Krueger & Casey, 2000, p. 5).  In focus groups, the researcher play a different role than in a one-on-one interview. The researcher’s aim is to get participants talking to each other,  to observe interactions among participants, and moderate the discussion.

There are numerous examples of focus group research. In their 2008 study, for example, Amy Slater and Marika Tiggemann (2010) conducted six focus groups with 49 adolescent girls between the ages of 13 and 15 to learn more about girls’ attitudes towards’ participation in sports. In order to get focus group participants to speak with one another rather than with the group facilitator, the focus group interview guide contained just two questions: “Can you tell me some of the reasons that girls stop playing sports or other physical activities?” and “Why do you think girls don’t play as much sport/physical activity as boys?” In another focus group study, Virpi Ylanne and Angie Williams (2009) held nine focus group sessions with adults of different ages to gauge their perceptions of how older characters are represented in television commercials. Among other considerations, the researchers were interested in discovering how focus group participants position themselves and others in terms of age stereotypes and identities during the group discussion. In both examples, the researchers’ core interest in group interaction could not have been assessed had interviews been conducted on a one-on-one basis, making the focus group method an ideal choice.

Who should be in your focus group?

In some ways, focus groups require more planning than other qualitative methods of data collection, such as one-on-one interviews in which a researcher may be better able to the dialogue. Researchers must take care to form focus groups with members who will want to interact with one another and to control the timing of the event so that participants are not asked nor expected to stay for a longer time than they’ve agreed to participate. The researcher should also be prepared to inform focus group participants of their responsibility to maintain the confidentiality of what is said in the group. But while the researcher can and should encourage all focus group members to maintain confidentiality, she should also clarify to participants that the unique nature of the group setting prevents her from being able to promise that confidentiality will be maintained by other participants. Once focus group members leave the research setting, researchers cannot control what they say to other people.

Group size should be determined in part by the topic of the interview and your sense of the likelihood that participants will have much to say without much prompting. If the topic is one about which you think participants feel passionately and will have much to say, a group of 3–5 could make sense. Groups larger than that, especially for heated topics, can easily become unmanageable. Some researchers say that a group of about 6–10 participants is the ideal size for focus group research (Morgan, 1997); others recommend that groups should include 3–12 participants (Adler & Clark, 2008).  The size of the focus group is ultimately the decision of the researcher. When forming groups and deciding how large or small to make them, take into consideration what you know about the topic and participants’ potential interest in, passion for, and feelings about the topic. Also consider your comfort level and experience in conducting focus groups. These factors will help you decide which size is right in your particular case.

It may seem counterintuitive, but in general, it is better to form focus groups consisting of participants who do not know one another than to create groups consisting of friends, relatives, or acquaintances (Agar & MacDonald, 1995).  The reason is that group members who know each other may not share some taken-for-granted knowledge or assumptions. In research, it is precisely the  taken-for-granted knowledge that is often of interest; thus, the focus group researcher should avoid setting up interactions where participants may be discouraged to question or raise issues that they take for granted. However, group members should not be so different from one another that participants will be unlikely to feel comfortable talking with one another.

Focus group researchers must carefully consider the composition of the groups they put together. In his text on conducting focus groups, Morgan (1997) suggests that “homogeneity in background and not homogeneity in attitudes” (p. 36) should be the goal, since participants must feel comfortable speaking up but must also have enough differences to facilitate a productive discussion.  Whatever composition a researcher designs for her focus groups, the important point to keep in mind is that focus group dynamics are shaped by multiple social contexts (Hollander, 2004). Participants’ silences as well as their speech may be shaped by gender, race, class, sexuality, age, or other background characteristics or social dynamics—all of which might be suppressed or exacerbated depending on the composition of the group. Hollander (2004) suggests that researchers must pay careful attention to group composition, must be attentive to group dynamics during the focus group discussion, and should use multiple methods of data collection in order to “untangle participants’ responses and their relationship to the social contexts of the focus group” (p. 632).

The role of the moderator

In addition to the importance of group composition, focus groups also require skillful moderation. A moderator is the researcher tasked with facilitating the conversation in the focus group. Participants may ask each other follow-up questions, agree or disagree with one another, display body language that tells us something about their feelings about the conversation, or even come up with questions not previously conceived of by the researcher. It is just these sorts of interactions and displays that are of interest to the researcher. A researcher conducting focus groups collects data on more than people’s direct responses to her question, as in interviews.

The moderator’s job is not to ask questions to each person individually, but to stimulate conversation between participants. It is important to set ground rules for focus groups at the outset of the discussion. Remind participants you’ve invited them to participate because you want to hear from all of them. Therefore, the group should aim to let just one person speak at a time and avoid letting just a couple of participants dominate the conversation. One way to do this is to begin the discussion by asking participants to briefly introduce themselves or to provide a brief response to an opening question. This will help set the tone of having all group members participate. Also, ask participants to avoid having side conversations; thoughts or reactions to what is said in the group are important and should be shared with everyone.

As the focus group gets rolling, the moderator will play a less active role as participants talk to one another. There may be times when the conversation stagnates or when you, as moderator, wish to guide the conversation in another direction. In these instances, it is important to demonstrate that you’ve been paying attention to what participants have said. Being prepared to interject statements or questions such as “I’d really like to hear more about what Sunil and Joe think about what Dominick and Jae have been saying” or “Several of you have mentioned X. What do others think about this?” will be important for keeping the conversation going. It can also help redirect the conversation, shift the focus to participants who have been less active in the group, and serve as a cue to those who may be dominating the conversation that it is time to allow others to speak. Researchers may choose to use multiple moderators to make managing these various tasks easier.

Moderators are often too busy working with participants to take diligent notes during a focus group. It is helpful to have a note-taker who can record participants’ responses (Liamputtong, 2011). The note-taker creates, in essence, the first draft of interpretation for the data in the study. They note themes in responses, nonverbal cues, and other information to be included in the analysis later on. Focus groups are analyzed in a similar way as interviews; however, the interactive dimension between participants adds another element to the analytical process. Researchers must attend to the group dynamics of each focus group, as “verbal and nonverbal expressions, the tactical use of humour, interruptions in interaction, and disagreement between participants” are all data that are vital to include in analysis (Liamputtong, 2011, p. 175). Note-takers record these elements in field notes, which allows moderators to focus on the conversation.

Strengths and weaknesses of focus groups

Focus groups share many of the strengths and weaknesses of one-on-one qualitative interviews. Both methods can yield very detailed, in-depth information; are excellent for studying social processes; and provide researchers with an opportunity not only to hear what participants say but also to observe what they do in terms of their body language. Focus groups offer the added benefit of giving researchers a chance to collect data on human interaction by observing how group participants respond and react to one another. Like one-on-one qualitative interviews, focus groups can also be quite expensive and time-consuming. However, there may be some savings with focus groups as it takes fewer group events than one-on-one interviews to gather data from the same number of people. Another potential drawback of focus groups, which is not a concern for one-on-one interviews, is that one or two participants might dominate the group, silencing other participants. Careful planning and skillful moderation on the part of the researcher are crucial for avoiding, or at least dealing with, such possibilities. The various strengths and weaknesses of focus group research are summarized in Table 91.

Grounded Theory

Grounded theory has been widely used since its development in the late 1960s (Glaser & Strauss, 1967). Largely derived from schools of sociology, grounded theory involves emersion of the researcher in the field and in the data. Researchers follow a systematic set of procedures and a simultaneous approach to data collection and analysis. Grounded theory is most often used to generate rich explanations of complex actions, processes, and transitions. The primary mode of data collection is one-on-one participant interviews. Sample sizes tend to range from 20 to 30 individuals, sampled purposively (Padgett, 2016). However, sample sizes can be larger or smaller, depending on data saturation. Data saturation is the point in the qualitative research data collection process when no new information is being discovered. Researchers use a constant comparative approach in which previously collected data are analyzed during the same time frame as new data are being collected.  This allows the researchers to determine when new information is no longer being gleaned from data collection and analysis — that data saturation has been reached — in order to conclude the data collection phase.

Rather than apply or test existing grand theories, or “Big T” theories, grounded theory focuses on “small t” theories (Padgett, 2016). Grand theories, or “Big T” theories, are systems of principles, ideas, and concepts used to predict phenomena. These theories are backed up by facts and tested hypotheses. “Small t” theories are speculative and contingent upon specific contexts. In grounded theory, these “small t” theories are grounded in events and experiences and emerge from the analysis of the data collected.

One notable application of grounded theory produced a “small t” theory of acceptance following cancer diagnoses (Jakobsson, Horvath, & Ahlberg, 2005). Using grounded theory, the researchers interviewed nine patients in western Sweden. Data collection and analysis stopped when saturation was reached. The researchers found that action and knowledge, given with respect and continuity led to confidence which led to acceptance. This “small t” theory continues to be applied and further explored in other contexts.

Case study research

Case study research is an intensive longitudinal study of a phenomenon at one or more research sites for the purpose of deriving detailed, contextualized inferences and understanding the dynamic process underlying a phenomenon of interest. Case research is a unique research design in that it can be used in an interpretive manner to build theories or in a positivist manner to test theories. The previous chapter on case research discusses both techniques in depth and provides illustrative exemplars. Furthermore, the case researcher is a neutral observer (direct observation) in the social setting rather than an active participant (participant observation). As with any other interpretive approach, drawing meaningful inferences from case research depends heavily on the observational skills and integrative abilities of the researcher.

Ethnography

The ethnographic research method, derived largely from the field of anthropology, emphasizes studying a phenomenon within the context of its culture. The researcher must be deeply immersed in the social culture over an extended period of time (usually 8 months to 2 years) and should engage, observe, and record the daily life of the studied culture and its social participants within their natural setting. The primary mode of data collection is participant observation, and data analysis involves a “sense-making” approach. In addition, the researcher must take extensive field notes, and narrate her experience in descriptive detail so that readers may experience the same culture as the researcher. In this method, the researcher has two roles: rely on her unique knowledge and engagement to generate insights (theory), and convince the scientific community of the trans-situational nature of the studied phenomenon.

The classic example of ethnographic research is Jane Goodall’s study of primate behaviors, where she lived with chimpanzees in their natural habitat at Gombe National Park in Tanzania, observed their behaviors, interacted with them, and shared their lives. During that process, she learnt and chronicled how chimpanzees seek food and shelter, how they socialize with each other, their communication patterns, their mating behaviors, and so forth. A more contemporary example of ethnographic research is Myra Bluebond-Langer’s (1996)14 study of decision making in families with children suffering from life-threatening illnesses, and the physical, psychological, environmental, ethical, legal, and cultural issues that influence such decision-making. The researcher followed the experiences of approximately 80 children with incurable illnesses and their families for a period of over two years. Data collection involved participant observation and formal/informal conversations with children, their parents and relatives, and health care providers to document their lived experience.

Phenomenology

Phenomenology is a research method that emphasizes the study of conscious experiences as a way of understanding the reality around us. Phenomenology is concerned with the systematic reflection and analysis of phenomena associated with conscious experiences, such as human judgment, perceptions, and actions, with the goal of (1) appreciating and describing social reality from the diverse subjective perspectives of the participants involved, and (2) understanding the symbolic meanings (“deep structure”) underlying these subjective experiences. Phenomenological inquiry requires that researchers eliminate any prior assumptions and personal biases, empathize with the participant’s situation, and tune into existential dimensions of that situation, so that they can fully understand the deep structures that drives the conscious thinking, feeling, and behavior of the studied participants.

Some researchers view phenomenology as a philosophy rather than as a research method. In response to this criticism, Giorgi and Giorgi (2003) developed an existential phenomenological research method to guide studies in this area. This method can be grouped into data collection and data analysis phases. In the data collection phase, participants embedded in a social phenomenon are interviewed to capture their subjective experiences and perspectives regarding the phenomenon under investigation. Examples of questions that may be asked include “can you describe a typical day” or “can you describe that particular incident in more detail?” These interviews are recorded and transcribed for further analysis. During data analysis, the researcher reads the transcripts to: (1) get a sense of the whole, and (2) establish “units of significance” that can faithfully represent participants’ subjective experiences. Examples of such units of significance are concepts such as “felt space” and “felt time,” which are then used to document participants’ psychological experiences. For instance, did participants feel safe, free, trapped, or joyous when experiencing a phenomenon (“felt-space”)? Did they feel that their experience was pressured, slow, or discontinuous (“felt-time”)? Phenomenological analysis should take into account the participants’ temporal landscape (i.e., their sense of past, present, and future), and the researcher must transpose herself in an imaginary sense in the participant’s situation (i.e., temporarily live the participant’s life). The participants’ lived experience is described in form of a narrative or using emergent themes. The analysis then delves into these themes to identify multiple layers of meaning while retaining the fragility and ambiguity of subjects’ lived experiences.

Key Takeaways

• In terms of focus group composition, homogeneity of background among participants is recommended while diverse attitudes within the group are ideal.
• The goal of a focus group is to get participants to talk with one another rather than the researcher.
• Like one-on-one qualitative interviews, focus groups can yield very detailed information, are excellent for studying social processes, and provide researchers with an opportunity to observe participants’ body language; they also allow researchers to observe social interaction.
• Focus groups can be expensive and time-consuming, as are one-on-one interviews; there is also the possibility that a few participants will dominate the group and silence others in the group.
• Other types of qualitative research include case studies, ethnography, and phenomenology.
• Data saturation – the point in the qualitative research data collection process when no new information is being discovered
• Focus groups- planned discussions designed to elicit group interaction and “obtain perceptions on a defined area of interest in a permissive, nonthreatening environment” (Krueger & Casey, 2000, p. 5)
• Moderator- the researcher tasked with facilitating the conversation in the focus group

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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

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Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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What is Qualitative Research Design? Definition, Types, Examples and Best Practices

What is Qualitative Research Design?

Qualitative research design is defined as a systematic and flexible approach to conducting research that focuses on understanding and interpreting the complexity of human phenomena. 

Unlike quantitative research, which seeks to measure and quantify variables, qualitative research is concerned with exploring the underlying meanings, patterns, and perspectives that shape individuals’ experiences and behaviors. This type of research design is particularly useful when studying social and cultural phenomena, as it allows researchers to delve deeply into the context and nuances of a particular subject.

In qualitative research, data is often collected through methods such as interviews, focus groups, participant observation, and document analysis. These methods aim to gather rich, detailed information that can provide insights into the subjective experiences of individuals or groups. 

Researchers employing qualitative design are often interested in exploring social processes, cultural norms, and the lived experiences of participants. The emphasis is on understanding the depth and context of the phenomena under investigation, rather than generating statistical generalizations.

One key characteristic of qualitative research design is its iterative nature. The research process is dynamic and may evolve as new insights emerge. Researchers continually engage with the data, refining their questions and methods based on ongoing analysis. 

This flexibility allows for a more organic and responsive exploration of the research topic, making it well-suited for complex and multifaceted inquiries.

Qualitative research design also involves careful consideration of ethical concerns, as researchers often work closely with participants to gather personal and sensitive information. 

Establishing trust, maintaining confidentiality, and ensuring participants’ autonomy are critical aspects of ethical practice in qualitative research. In summary, qualitative research design is a holistic and interpretive approach that prioritizes understanding the intricacies of human experience, offering depth and context to our comprehension of social and cultural phenomena.

Key Characteristics of Qualitative Research Design

Qualitative research design is characterized by several key features that distinguish it from quantitative approaches. Here are some of the essential characteristics:

• Open-ended Nature: Qualitative research is open-ended and flexible, allowing for the exploration of complex social phenomena without preconceived hypotheses. Researchers often start with broad questions and adapt their focus based on emerging insights.
• Rich Descriptions: Qualitative research emphasizes rich and detailed descriptions of the subject under investigation. This depth helps capture the context, nuances, and subtleties of human experiences, behaviors, and social phenomena.
• Subjective Understanding: Qualitative researchers acknowledge the role of the researcher in shaping the study. The subjective interpretations and perspectives of both researchers and participants are considered valuable for understanding the phenomena being studied.
• Interpretive Approach: Rather than seeking universal laws or generalizations, qualitative research aims to interpret and make sense of the meanings and patterns inherent in the data. Interpretation is often context-dependent and involves understanding the social and cultural context in which the study takes place.
• Non-probability Sampling: Qualitative studies typically use non-probability sampling methods, such as purposeful or snowball sampling, to select participants deliberately chosen for their relevance to the research question. Sample sizes are often small but information-rich, allowing for a deep understanding of the selected cases.
• Inductive Reasoning: Qualitative data analysis is often inductive, meaning that it involves identifying patterns, themes, and categories that emerge from the data itself. Researchers let the data shape the analysis, rather than fitting it into preconceived categories.
• Coding and Categorization: Researchers use coding techniques to systematically organize and categorize data. This involves assigning labels or codes to segments of data based on recurring themes or patterns.
• Flexible Design: Qualitative research design is adaptable and allows for changes in research questions, methods, and strategies as the study progresses. This flexibility accommodates the evolving nature of the research process.
• Iterative Nature: Researchers engage in an iterative process of data collection, analysis, and refinement. As new insights emerge, researchers may revisit previous stages of the research, leading to a deeper and more nuanced understanding of the subject.

By embracing these key characteristics, qualitative research design offers a holistic and contextualized approach to studying the complexities of human behavior, culture, and social phenomena.

Key Components of Qualitative Research Design

Qualitative research design involves several key components that shape the overall framework and methodology of the study. These components help guide researchers in conducting in-depth investigations into the complexities of human experiences, behaviors, and social phenomena. Here are the key components of qualitative research design:

• Central Inquiry: Qualitative research begins with a well-defined central research question or objective. This question guides the entire study and determines the focus of data collection and analysis. The question is often broad and open-ended to allow for exploration and discovery.
• Rationale: Researchers provide a clear rationale for why the study is being conducted, outlining its significance and relevance. This may involve identifying gaps in existing literature, addressing practical problems, or contributing to theoretical debates.
• Theoretical Framework: Qualitative studies often draw on existing theories or conceptual frameworks to guide their inquiry. The theoretical lens helps shape the research design and provides a basis for interpreting findings.
• Study Design: Researchers decide on the overall approach to the study, whether it’s a case study, ethnography, grounded theory, phenomenology, or another qualitative design. The choice depends on the research question and the nature of the phenomenon under investigation.
• Sampling Strategy: Qualitative research employs purposeful or theoretical sampling to select participants who can provide rich and relevant information related to the research question. Sampling decisions are made to ensure diversity and depth in the data.
• Interviews: In-depth interviews are a common method in qualitative research. These interviews are typically semi-structured, allowing for flexibility while ensuring key topics are covered.
• Observation: Researchers may engage in direct observation of participants in natural settings. This can involve participant observation, where the researcher becomes part of the environment, or non-participant observation, where the researcher remains separate.
• Document Analysis: Researchers analyze existing documents, artifacts, or texts relevant to the study, such as diaries, letters, organizational records, or media content.
• Thematic Analysis: Researchers identify and analyze recurring themes or patterns in the data. This involves coding and categorizing data to uncover underlying meanings and concepts.
• Constant Comparative Analysis: Common in grounded theory, this method involves comparing data as it is collected, allowing researchers to refine categories and theories iteratively.
• Narrative Analysis: Focuses on the stories people tell, examining the structure and content of narratives to understand the meaning-making process.
• Informed Consent: Researchers obtain informed consent from participants, explaining the purpose of the study, potential risks, and ensuring participants have the right to withdraw at any time.
• Confidentiality and Anonymity: Researchers take measures to protect the privacy of participants by ensuring that their identities and personal information are kept confidential or anonymized.
• Credibility: Establishing credibility involves demonstrating that the study accurately represents participants’ perspectives. Techniques such as member checking, peer debriefing, and prolonged engagement contribute to credibility.
• Transferability: Researchers aim to make the study findings applicable to similar contexts. Detailed descriptions and thick descriptions enhance the transferability of qualitative research.
• Dependability and Confirmability: Ensuring dependability involves maintaining consistency in data collection and analysis, while confirmability ensures that findings are rooted in the data rather than researcher bias.
• Reflexivity: Researchers acknowledge their role in shaping the study and consider how their background, experiences, and biases may influence the research process and interpretation of findings. Reflexivity enhances transparency and the researcher’s self-awareness.

By carefully considering and integrating these key components, qualitative researchers can design studies that yield rich, contextually grounded insights into the social phenomena they aim to explore.

Types of Qualitative Research Design

Qualitative research design encompasses various approaches, each suited to different research questions and objectives. Here are some common types of qualitative research designs:

• Focus: Ethnography involves immersing the researcher in the natural environment of the participants to observe and understand their behaviors, practices, and cultural context.
• Data Collection: Researchers often use participant observation, interviews, and document analysis to gather data.
• Example: An anthropologist immersed in a remote tribe might live with the community for an extended period, participating in their daily activities, conducting interviews, and documenting observations. By doing so, the researcher gains a deep understanding of the tribe’s cultural practices, social relationships, and the significance of rituals in their way of life.
• Focus: Phenomenology explores the lived experiences of individuals to uncover the essence of a phenomenon.
• Data Collection: In-depth interviews and sometimes participant observation are common methods.
• Purpose: It seeks to understand the subjective meaning individuals attribute to an experience.
• In a study on the lived experiences of cancer survivors, researchers might conduct in-depth interviews to explore the subjective meaning individuals attach to their diagnosis, treatment, and recovery. Phenomenology seeks to uncover the essence of these experiences, capturing the emotional, psychological, and social dimensions that shape survivors’ perspectives on their journey through cancer.
• Focus: Grounded theory aims to develop a theory grounded in the data, allowing patterns and concepts to emerge organically.
• Data Collection: It involves constant comparative analysis of interviews or observations, with coding and categorization.
• Purpose: This approach is used when researchers want to generate theories or concepts based on the data itself.
• Research on retirement transitions using grounded theory might involve interviewing retirees from various backgrounds. Through constant comparison and iterative analysis, researchers may identify emerging themes and categories, ultimately developing a theory that explains the commonalities and variations in retirees’ experiences as they navigate this life stage.
• Focus: Case studies delve deeply into a specific case or context to understand it in detail.
• Data Collection: Multiple sources of data, such as interviews, observations, and documents, are used to provide a comprehensive view.
• Purpose: Case studies are useful for exploring complex phenomena within their real-life context.
• A case study on a company’s crisis response could involve a detailed examination of communication strategies, decision-making processes, and the organizational dynamics during a specific crisis. By analyzing the case in-depth, researchers gain insights into how the company’s actions and decisions influenced the outcome of the crisis and what lessons can be learned for future situations.
• Focus: Narrative research examines the stories people tell to understand how individuals construct meaning and identity.
• Data Collection: It involves collecting and analyzing narratives through interviews, personal accounts, or written documents.
• Purpose: Narrative research is often used to explore personal or cultural stories and their implications.
• Examining the life stories of refugees may involve collecting and analyzing personal narratives through interviews or written accounts. Researchers explore how displacement has shaped the refugees’ identities, relationships, and perceptions of home, providing a nuanced understanding of their experiences through the lens of storytelling.
• Focus: Action research involves collaboration between researchers and participants to identify and solve practical problems.
• Data Collection: Researchers collect data through cycles of planning, acting, observing, and reflecting.
• Purpose: It is geared towards facilitating positive change in a particular context or community.
• In an educational setting, action research might involve teachers and researchers collaborating to address a specific classroom challenge. Through cycles of planning, implementing interventions, and reflecting, the aim is to improve teaching practices and student learning outcomes, with the findings contributing to both practical solutions and the broader understanding of effective pedagogy.
• Focus: Content analysis examines the content of written, visual, or audio materials to identify patterns or themes.
• Data Collection: Researchers systematically analyze texts, images, or media content using coding and categorization.
• Purpose: It is often used to study communication, media, or cultural artifacts.
• A content analysis of news articles covering a specific social issue, such as climate change, could involve systematically coding and categorizing language and themes. This approach allows researchers to identify patterns in media discourse, explore public perceptions, and understand how the issue is framed in the media.
• Focus: Critical ethnography combines ethnographic methods with a critical perspective to examine power structures and social inequalities.
• Data Collection: Researchers engage in participant observation, interviews, and document analysis with a focus on social justice issues.
• Purpose: This approach aims to explore and challenge existing power dynamics and social structures.
• A critical ethnography examining gender dynamics in a workplace might involve observing daily interactions, conducting interviews, and analyzing policies. Researchers, guided by a critical perspective, aim to uncover power imbalances, stereotypes, and systemic inequalities within the organizational culture, contributing to a deeper understanding of gender dynamics in the workplace.
• Focus: Similar to grounded theory, constructivist grounded theory acknowledges the role of the researcher in shaping interpretations.
• Data Collection: It involves a flexible approach to data collection, including interviews, observations, or documents.
• Purpose: This approach recognizes the co-construction of meaning between researchers and participants.
• In a study on the experiences of individuals with chronic illness, researchers employing constructivist grounded theory might engage in open-ended interviews and data collection. The focus is on co-constructing meanings with participants, acknowledging the dynamic relationship between the researcher and those being studied, ultimately leading to a theory that reflects the collaborative nature of knowledge creation.

These qualitative research designs offer diverse methods for exploring and understanding the complexities of human experiences, behaviors, and social phenomena. The choice of design depends on the research question, the context of the study, and the desired depth of understanding.

Best practices for Qualitative Research Design

Qualitative research design requires careful planning and execution to ensure the credibility, reliability, and richness of the findings. Here are some best practices to consider when designing qualitative research:

• Clearly articulate the research questions or objectives to guide the study. Ensure they are specific, open-ended, and aligned with the qualitative research approach.
• Select a qualitative research design that aligns with the research questions and objectives. Consider approaches such as ethnography, phenomenology, grounded theory, or case study based on the nature of the study.
• Conduct a comprehensive literature review to understand existing theories, concepts, and research related to the study. This helps situate the research within the broader scholarly context.
• Use purposeful or theoretical sampling to select participants who can provide rich information related to the research questions. Aim for diversity in participants to capture a range of perspectives.
• Clearly outline the data collection methods, such as interviews, observations, or document analysis. Develop detailed protocols, guides, or questionnaires to maintain consistency across data collection sessions.
• Prioritize building trust and rapport with participants. Clearly communicate the study’s purpose, obtain informed consent, and establish a comfortable environment for open and honest discussions.
• Adhere to ethical guidelines throughout the research process. Protect participant confidentiality, respect their autonomy, and obtain ethical approval from relevant review boards.
• Pilot the data collection instruments and procedures with a small sample to identify and address any ambiguities, refine questions, and enhance the overall quality of data collection.
• Use a systematic approach to analyze data, such as thematic analysis, constant comparison, or narrative analysis. Maintain transparency in the coding process, and consider inter-coder reliability if multiple researchers are involved.
• Acknowledge and document the researcher’s background, biases, and perspectives. Practice reflexivity by continually reflecting on how the researcher’s positionality may influence the study.
• Enhance the credibility of findings by using multiple data sources and methods. Triangulation helps validate results and provides a more comprehensive understanding of the research topic.
• Consider member checking, where researchers share preliminary findings with participants to validate interpretations. This process enhances the credibility and trustworthiness of the study.
• Keep a detailed journal documenting decisions, reflections, and insights throughout the research process. This journal helps provide transparency and can contribute to the rigor of the study.
• Aim for data saturation, the point at which new data no longer provide additional insights. Saturation ensures thorough exploration of the research questions and increases the robustness of the findings.
• Clearly document the research process, from design to findings. Provide a detailed and transparent account of the study methodology, facilitating the reproducibility and evaluation of the research.

By incorporating these best practices, qualitative researchers can enhance the rigor, credibility, and relevance of their studies, ultimately contributing valuable insights to the field.

Interested in learning more about the fields of product, research, and design? Search our articles here for helpful information spanning a wide range of topics!

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Types Of Qualitative Research Designs And Methods

Qualitative research design comes in many forms. Understanding what qualitative research is and the various methods that fall under its…

Qualitative research design comes in many forms. Understanding what qualitative research is and the various methods that fall under its umbrella can help determine which method or design to use. Various techniques can achieve results, depending on the subject of study.

Types of qualitative research to explore social behavior or understand interactions within specific contexts include interviews, focus groups, observations and surveys. These identify concepts and relationships that aren’t easily observed through quantitative methods. Figuring out what to explore through qualitative research is the first step in picking the right study design.

Let’s look at the most common types of qualitative methods.

What Is Qualitative Research Design?

Types of qualitative research designs, how are qualitative answers analyzed, qualitative research design in business.

There are several types of qualitative research. The term refers to in-depth, exploratory studies that discover what people think, how they behave and the reasons behind their behavior. The qualitative researcher believes that to best understand human behavior, they need to know the context in which people are acting and making decisions.

Let’s define some basic terms.

Qualitative Method

A group of techniques that allow the researcher to gather information from participants to learn about their experiences, behaviors or beliefs. The types of qualitative research methods used in a specific study should be chosen as dictated by the data being gathered. For instance, to study how employers rate the skills of the engineering students they hired, qualitative research would be appropriate.

Quantitative Method

A group of techniques that allows the researcher to gather information from participants to measure variables. The data is numerical in nature. For instance, quantitative research can be used to study how many engineering students enroll in an MBA program.

Research Design

A plan or outline of how the researcher will proceed with the proposed research project. This defines the sample, the scope of work, the goals and objectives. It may also lay out a hypothesis to be tested. Research design could also combine qualitative and quantitative techniques.

Both qualitative and quantitative research are significant. Depending on the subject and the goals of the study, researchers choose one or the other or a combination of the two. This is all part of the qualitative research design process.

Before we look at some different types of qualitative research, it’s important to note that there’s no one correct approach to qualitative research design. No matter what the type of study, it’s important to carefully consider the design to ensure the method is suitable to the research question. Here are the types of qualitative research methods to choose from:

Cluster Sampling

This technique involves selecting participants from specific locations or teams (clusters). A researcher may set out to observe, interview, or create a focus group with participants linked by location, organization or some other commonality. For example, the researcher might select the top five teams that produce an organization’s finest work. The same can be done by looking at locations (stores in a geographic region). The benefit of this design is that it’s efficient in collecting opinions from specific working groups or areas. However, this limits the sample size to only those people who work within the cluster.

Random Sampling

This design involves randomly assigning participants into groups based on a set of variables (location, gender, race, occupation). In this design, each participant is assigned an equal chance of being selected into a particular group. For example, if the researcher wants to study how students from different colleges differ from one another in terms of workplace habits and friendships, a random sample could be chosen from the student population at these colleges. The purpose of this design is to create a more even distribution of participants across all groups. The researcher will need to choose which groups to include in the study.

Focus Groups

A focus group is a small group that meets to discuss specific issues. Participants are usually recruited randomly, although sometimes they might be recruited because of personal relationships with each other or because they represent part of a certain demographic (age, location). Focus groups are one of the most popular styles of qualitative research because they allow for individual views and opinions to be shared without introducing bias. Researchers gather data through face-to-face conversation or recorded observation.

Observation

This technique involves observing the interaction patterns in a particular situation. Researchers collect data by closely watching the behaviors of others. This method can only be used in certain settings, such as in the workplace or homes.

An interview is an open-ended conversation between a researcher and a participant in which the researcher asks predetermined questions. Successful interviews require careful preparation to ensure that participants are able to give accurate answers. This method allows researchers to collect specific information about their research topic, and participants are more likely to be honest when telling their stories. However, there’s no way to control the number of unique answers, and certain participants may feel uncomfortable sharing their personal details with a stranger.

A survey is a questionnaire used to gather information from a pool of people to get a large sample of responses. This study design allows researchers to collect more data than they would with individual interviews and observations. Depending on the nature of the survey, it may also not require participants to disclose sensitive information or details. On the flip side, it’s time-consuming and may not yield the answers researchers were looking for. It’s also difficult to collect and analyze answers from larger groups.

A large study can combine several of these methods. For instance, it can involve a survey to better understand which kind of organic produce consumers are looking for. It may also include questions on the frequency of such purchases—a numerical data point—alongside their views on the legitimacy of the organic tag, which is an open-ended qualitative question.

Knowledge of the types of qualitative research designs will help you achieve the results you desire.

With quantitative research, analysis of results is fairly straightforward. But, the nature of qualitative research design is such that turning the information collected into usable data can be a challenge. To do this, researchers have to code the non-numerical data for comparison and analysis.

The researcher goes through all their notes and recordings and codes them using a predetermined scheme. Codes are created by ‘stripping out’ words or phrases that seem to answer the questions posed. The researcher will need to decide which categories to code for. Sometimes this process can be time-consuming and difficult to do during the first few passes through the data. So, it’s a good idea to start off by coding a small amount of the data and conducting a thematic analysis to get a better understanding of how to proceed.

The data collected must be organized and analyzed to answer the research questions. There are three approaches to analyzing the data: exploratory, confirmatory and descriptive.

Explanatory Data Analysis

This approach involves looking for relationships within the data to make sense of it. This design can be useful if the research question is ambiguous or open-ended. Exploratory analysis is very flexible and can be used in a number of settings. But, it generally looks at the relationship between variables while the researcher is working with the data.

Confirmatory Data Analysis

This design is used when there’s a hypothesis or theory to be tested. Confirmatory research seeks to test how well past findings apply to new observations by comparing them to statistical tests that quantify relationships between variables. It can also use prior research findings to predict new results.

Descriptive Data Analysis

In this design, the researcher will describe patterns that can be observed from the data. The researcher will take raw data and interpret it with an eye for patterns to formulate a theory that can eventually be tested with quantitative data. The qualitative design is ideal for exploring events that can’t be observed (such as people’s thoughts) or when a process is being evaluated.

With careful planning and insightful analysis, qualitative research is a versatile and useful tool in business, public policy and social studies. In the workplace, managers can use it to understand markets and consumers better or to study the health of an organization.

Businesses conduct qualitative research for many reasons. Harappa’s Thinking Critically course prepares professionals to use such data to understand their work better. Driven by experienced faculty with real-world experience, the course equips employees on a growth trajectory with frameworks and skills to use their reasoning abilities to build better arguments. It’s possible to build more effective teams. Find out how with Harappa.

Explore Harappa Diaries to learn more about topics such as What is Qualitative Research , Quantitative Vs Qualitative Research , Examples of Phenomenological Research and Tips For Studying Online to upgrade your knowledge and skills.

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Aug 19, 2024 • 17 minutes read

Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

Qualitative UX research made easy

Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.

7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

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Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

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Open Access

Peer-reviewed

Research Article

Optimal truss design with MOHO: A multi-objective optimization perspective

Roles Formal analysis, Methodology, Validation, Visualization, Writing – original draft

Affiliation Department of Mechanical Engineering, Faculty of Engineering and Technology, Marwadi University, Rajkot, Gujarat, India

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Writing – original draft

Affiliation Ethics Infotech, Vadodara, Gujarat, India

Roles Investigation, Methodology, Validation, Visualization, Writing – original draft

Roles Methodology, Validation, Visualization, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran, Innovation Center for Artificial Intelligence Applications, Yuan Ze University, Taoyuan City, Taiwan, Applied Science Research Center, Applied Science Private University, Amman, Jordan

• Nikunj Mashru, 
• Ghanshyam G. Tejani, 
• Pinank Patel, 
• Mohammad Khishe

• Published: August 19, 2024
• https://doi.org/10.1371/journal.pone.0308474
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This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo’s behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used for this comparison and qualitative examination of the finest Pareto-front plots generated by each algorithm. The average values obtained by the Friedman rank test and comparison analysis unequivocally showed that MOHO outperformed other methods in resolving significant structure optimization problems quickly. In addition to finding and preserving more Pareto-optimal sets, the recommended algorithm produced excellent convergence and variance in the objective and decision fields. MOHO demonstrated its potential for navigating competing objectives through diversity analysis. Additionally, the swarm plots effectively visualize MOHO’s solution distribution of MOHO across iterations, highlighting its superior convergence behaviour. Consequently, MOHO exhibits promise as a valuable method for tackling complex multi-objective structure optimization issues.

Citation: Mashru N, Tejani GG, Patel P, Khishe M (2024) Optimal truss design with MOHO: A multi-objective optimization perspective. PLoS ONE 19(8): e0308474. https://doi.org/10.1371/journal.pone.0308474

Editor: Salar Farahmand-Tabar, University of Zanjan, ISLAMIC REPUBLIC OF IRAN

Received: April 28, 2024; Accepted: July 22, 2024; Published: August 19, 2024

Copyright: © 2024 Mashru et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: https://github.com/p-shyam23/MOHO

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Engineers dealing with structural design often have competing objectives that can adversely affect each other. For instance, they might attempt to reduce the structure’s weight for economic purposes, even if it would increase safety by strengthening the structure. MO optimization problems are characterized by multiple objectives, requiring mathematical optimization strategies to deal with them effectively. Multi-objective optimization problems typically need more than simple solutions. Instead, their responses usually manifest as a series of optimal solutions embodying a balance between the conflicting objectives being optimized. This trade-off guarantees that achieving a particular purpose will inevitably result in the degradation of other objectives [ 1 , 2 ]. The Pareto front provides the designer with various feasible alternatives by meeting all the constraints; among these, the final design can be selected. Applying metaheuristic optimization methods has become an increasingly popular research direction for multi-objective structural design. When dealing with difficult circumstances, metaheuristic algorithms have become highly sophisticated techniques concentrating on minimizing or maximizing an objective function to arrive at an optimal solution. These algorithms efficiently traverse the solution space by utilizing upper-level searching techniques.

Recently developed new metaheuristics such as the Synergistic Swarm Optimization algorithm. [ 3 ], Geyser Inspired Algorithm [ 4 ], Zebra Optimization Algorithm [ 5 ], Quadratic Interpolation Optimization [ 6 ], Serval Algorithm [ 7 ], Egret Swarm Optimization Algorithms [ 8 ], Waterwheel Plant Algorithm [ 9 ], Propagation Search Algorithm [ 10 ], Mantis Search Algorithm [ 11 ], Komodo Mlipir Algorithm [ 12 ], Eik herd optimizer [ 13 ], Crayfish optimization algorithm [ 14 ], Kepler optimization algorithm [ 15 ], Light Spectrum Optimization [ 16 ], Circle Search Algorithm [ 17 ], Electric eel foraging optimization [ 18 ], Puma optimizer [ 19 ], Partial reinforcement optimizer [ 20 ], The coronavirus search optimizer [ 21 ], geometric mean optimizer [ 22 ], Fick’s law algorithm [ 23 ], Prairie dog optimization algorithm [ 24 ]. The Arithmetic optimization algorithm [ 25 ], Grasshopper optimization algorithm [ 26 ], inspired by natural mechanisms, including evolutionary processes, physical lows, Mathematical theories, and behaviour observed in animals.

Numerous researchers have introduced innovative optimization algorithms across various applications, prioritizing statistical validation and rigorous experimentation to enhance the convergence time and solution quality compared to existing methods. These algorithms address complex optimization problems in domains such as engineering design. [ 27 ], metaheuristic approaches, and medical diagnostics [ 28 ], demonstrating encouraging developments in effectively resolving practical issues [ 29 ] and different strategies [ 30 , 31 ], such as boundary update techniques, multi-hybrid approaches [ 32 ], multi-strategy infused metaheuristics [ 33 – 35 ]. Each algorithm incorporates new metaheuristic methodologies to handle specific optimization goals and enhance overall performance.

These approximate optimization methods provide sufficiently good solutions for complex situations within a reasonable time frame. Population-based metaheuristics are particularly noteworthy since they are effective tools that may be used to solve MO optimization problems [ 36 ]. Some notable multi-objective algorithms include the MO version of Atomic Orbital Search. [ 37 ], MO version of the Material Generation Algorithm [ 38 ], MO Crystal Structure Algorithm [ 39 ], MO Chaos Game Optimization [ 40 ], NSGA-2 [ 41 ], MO Arithmetic Optimization Algorithm [ 42 ], MO Thermal Exchange Optimization [ 36 ], MO Passing Vehicle Search Algorithm [ 43 ], MO symbiotic organism search [ 44 ], and MO heat transfer search [ 45 ]. Many researchers have Improved MO optimization algorithms with unique approaches, such as decomposition-based MO heat transfer search. [ 46 ], improved MO particle swarm optimization [ 47 ], an indicator-based multi-SSM algorithm [ 48 ], MO improved marine predators algorithm [ 49 ], MO structural optimization using improved heat transfer search [ 50 ] Enhanced MO GWO with levy flight and mutation operators for feature selection [ 51 ], and a two-archive MO multi-verse optimizer for truss design [ 52 ].

The primary approach to estimating a Pareto front combines a non-dominated (ND) sorting technique with the population-based concept of meta-heuristics in MO optimization. The Pareto archive is continually refined by updating it with data from the current population and the archive of the previous iteration through the iterative reproduction of design solutions. This process was repeated until the Pareto front of the design problem was reached. Critical performance factors are optimizing the parameter settings using a self-adaptive method and preserving the search diversity through efficient clustering approaches. The main intention is to significantly increase the search intensity and variety because MO metaheuristics require extensive design space exploration while maintaining a high convergence rate, which is a more difficult challenge than single-objective metaheuristics.

The "No Free Lunch" (NFL) [ 53 ] theorem serves as a reminder that no individual metaheuristic is universally capable of solving all real-world problems. This understanding has catalyzed the advancement and enhancement of diverse metaheuristic methods. This research introduces an MO version of the Hippopotamus Optimization algorithm (HO) [ 54 ], drawing inspiration from the observed behaviors in hippos. Emphasizing traits and actions, such as their semi-aquatic lifestyle, herbivorous diet, and defensive strategies against predators, the algorithm incorporates their formidable jaws and warning vocalizations. Furthermore, it explores the adaptability of protective behaviors and social dynamics, aiming to understand their applicability in various contexts through insights from the hippopotamus behavior.

It is interesting to evaluate the performance of a newly created multi-objective metaheuristic in various engineering design challenges. This study applies MOHO to different five-truss structures: 10-bar truss, 25-bar truss, 60-bar ring truss, 72-bar truss, and 942-bar tower truss. The design challenge is decreasing the structure’s mass while decreasing the maximum nodal displacement, a structural stiffness indicator. The above objectives were pursued within the limitations of area and stress. HO’s mathematical model for organizing hippopotamuses within herds considers factors such as dominance and proximity to optimize their positioning in aquatic environments. defensive strategies for hippos against predators, including vocalizations and tactical movements, to ensure herd safety. Additionally, it outlines a behavior in which hippos escape predators by seeking refuge in water bodies, enhancing their survival prospects. Overall, this study explored methods to improve the safety and survival of hippopotamus herds through mathematical modeling and behavioral analysis. The primary progress of this investigation and its evolution, which surpasses the present contemporary, are outlined as follows.

• A multi-objective version of the unique hippopotamus optimization algorithm (MOHO) was applied to five planar and spatial truss structures to minimize the maximum nodal displacement and structural mass subjected to area and stress constraints.
• The performance of MOHO for different truss structures was compared with six well-known and efficient optimization algorithms, viz., MO Ant System (MOAS) [ 55 ], MO Ant colony system (MOACS) [ 56 ], MO differential evolution (DEMO) [ 57 ], NSGA-2 [ 41 ], MO ant lion optimizer (MOALO) [ 58 , 59 ], and MO moth flame optimizer (MOMFO) [ 60 ].
• Four commonly used performance metrics were used to assess the algorithms’ efficacy statistically: the Hypervolume Index (HV), Generational Distance (GD), Inverted Generational Difference (IGD), and spacing-to-extent (STE). In addition, each algorithm’s best Pareto-front plots were closely examined to evaluate the qualitative behavior. In addition, the algorithms were ranked for a thorough study using Friedman’s test at the 95% significance level.
• The results presented a fresh outlook on the benefits and drawbacks of MO optimization techniques in addressing diverse contradicting objectives in structural optimization.

The outline of the paper is as follows:

•  ▪ The 2 nd section presents a description and mathematical modeling of the elementary Hippopotamus Optimization Algorithm (HO).
•  ▪ 3 rd section elaborates on the proposed MOHO and outlines the formulations of MO structure optimization problems.
•  ▪ In the 4 th section, an experimental assessment of the MOHO optimizer and a comparison with other prominent algorithms for addressing truss bar problems are presented.
•  ▪ Section 5 discusses the performance metrics and enhanced Pareto fronts using diversity curves and swarm plots.
•  ▪ Finally, Section 6 concludes the study and discusses future work to explore the capability of MO algorithms.

2. Hippopotamus optimization algorithm

The hippopotamus, a captivating African vertebrate mammal, thrives in semi-aquatic habitats, such as rivers and ponds, displaying social behaviors within pods. Despite the challenges in gender determination, their herbivorous diet and exploratory nature drive them to investigate alternative food sources [ 61 ]. They are deemed one of the most dangerous mammals with immense strength and territorial behaviour, yet predators avoid confronting adult hippos because of their size. Defensive strategies include aggressive postures, loud vocalizations, and the rapid retreatment of water bodies. Inspired by the observed behavioral patterns, hippopotamus groups consist of various members, with calves prone to wandering and becoming prey [ 62 ]. Defensive behaviors include rotation toward predators, employing powerful jaws, and fleeing toward water sources for safety [ 63 ]. This algorithm uses two exploration stages and one exploitation phase after initial random solutions are produced. These phases are perceived to be superior to other algorithms for truss design problems.

2.1 Exploration phase-1 (position update in river or pond)

A herd of hippos consists of adult females, calves, adult males, and a dominant male leader. A value iteration process establishes dominance. While females encircle the dominant male, they defend the herd and its area. When a male reaches adulthood, the dominant male drives him out, forcing him to fight for supremacy elsewhere. Table 1 shows the mathematical positions of the hippos and their updates in the herd’s habitat.

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Stage 1 in Table 1 represents the population of hippos after initialization within the upper and lower bounds of the lake or pond. Stage 2 shows five different scenarios based on random numbers h 1 and h 2 . Stages 3 and 4 demonstrate the position of immature hippos from their mothers within the herd, otherwise separated based on the T value. Finally, in Stage 5, the position is updated based on its objective value.

2.2 Exploration phase-2 (defense against predators)

In this phase, hippopotamuses find safety in their herds, deterring predators due to their large size and collective presence. However, young and sick individuals are more susceptible to predator attacks. When threatened, hippopotamuses emit loud vocalizations and may approach predators to avoid potential threats. Table 2 demonstrates a mathematical representation of half of the population of hippos with exploration phase 2.

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In the second phase of exploration, which spans stages 6 to 11, the algorithm simulates the defense mechanisms of hippos against predators.

In Stage 6, the positions of the predators were determined within the search space. At stage 7, the algorithm evaluates the position of each predator relative to each hippo, triggering the corresponding reactions from the hippos. Stage 9 involves random movements of hippos, influenced by a Levy distribution, allowing search space exploration. Stages 10 and 11 determine whether predators hunt the hippos or if they successfully escape from predators. Overall, this phase simulates a dynamic interaction between predators and hippos, guiding the exploration process and helping to prevent the algorithm from becoming trapped in local minima.

2.3 Exploitation phase (escaping from the predator)

In this exploitation phase, as shown in Table 3 , hippos typically run to the closest body of water for safety when separated from the herd and attacked by lions or spotted hyenas. This strategy improves local search capabilities by simulating the habit of seeking refuge nearby. This behavior involves creating a random site close to the hippopotamus’s location to increase the cost function value. Iteratively, the hippopotamus adjusts its position to guarantee its proximity to safety.

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According to Table 3 , during the exploitation phase, hippos can use the value of S from Stage 14 to determine a safe location near where they are right now to protect themselves from predators. Finally, a position update for the hippos was made using their objective values.

3. Multi-objective (MO) problem formulation

3.1 mo optimization definitions.

In optimizing truss structures with multiple objectives, the focus is on simultaneously achieving optimization across various goals. While the term "multi-objective" typically addresses problems involving up to three objectives, the emergence of "many-objective optimization" many-objective optimization tackles the challenges posed by numerous objectives. Resolving conflicts between objectives is a significant challenge in multi-objective optimization, requiring specific approaches. The traditional relational comparisons between solutions are inadequate when there are several criteria. Therefore, alternative operators, such as the Pareto dominance operator, are essential to assess the relative superiority. From a mathematical point of view, Pareto optimality defines a set of solutions deemed nondominated and optimal for a given problem, particularly for MO optimization. These solutions constitute the MO optimization solution set, which thoroughly depicts realistic trade-offs between several objectives. This set, which illustrates the optimal solutions possible within the objective domain, is frequently represented and visualized as a Pareto optimal front (PF). Further clarification regarding the principles of domination and associated ideas can be obtained from Fig 1 , which graphically presents these concepts.

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3.2 Multi-Objective Hippopotamus Optimization algorithm (MOHO)

The MOHO algorithm starts with a random population (hippos) size N . It generates solutions X 1 , X 2 , … , X N within the specified search space defined by the lower and upper bounds of the problem for each dimension ( D ). The fitness function evaluates and assigns a fitness score to each candidate solution, guiding the selection process toward the optimal solutions. The fitness of each candidate solution was assessed using the fitness function provided for all initial solutions. The main loop of the HO algorithm starts with the first iteration and continues up to a specified number of generations. The best candidate solution (Xbest) and its corresponding fitness (fbest) were updated for each iteration. The loop was divided into three stages (two exploration phases and one exploitation phase).

Half of the total population explores the search space in exploration phase 1; each individual calculates two potential positions ( X_P 1 and X_P 2 ) based on its current position, the best solution found so far ( X best ), and a randomly selected mean group ( MeanGroup ). The selection of potential positions involves randomness, which is affected by various parameters. Individuals update their positions based on their potential positions if their fitness is improved. The remaining individuals in the population defend themselves against potential threats from predators. Each individual calculates a potential position ( X_P 3 ) based on its current position, randomly generated predator position, and Levy flight. The decision to update positions is based on whether the new position improves fitness compared to the current position. All individuals perform a localized search to escape potential predators and exploit promising regions. Each individual calculates a potential position ( X_P 4 ) based on its current position and randomly selected direction ( D ). Positions are updated if the new position improves fitness. The best solution found thus far, along with its corresponding fitness value, was stored for M objective functions as Pareto fronts. For every iteration, all fronts’ non-domination (ND) sorting process helps achieve superior ND fronts.

Fig 2 shows a flow chart of MOHO with two exploration phases, dividing the total population by half size for phase 1 (position update in rivers or ponds) and half size for phase 2 (defense against predators). After passing through any of the exploration phases, phase 3 (escaping from predators), the exploitation phase, will be activated, and the positions of hippos will be updated accordingly. The evaluated solution at the end of this phase is the non-dominated Pareto optimal front.

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4. Formulation of the truss design problem

Here, Ax is a vector for the cross-sectional area; ρ i and Ln i are the mass density and the length of an element, respectively; E and σ are the Modulus of elasticity and stress, respectively.

The first objective is the mass of the structures and the movement of the nodes, or connection points, throughout the truss construction, and is called nodal displacement, which is the secondary objective. We can ensure that the structure will deflect sufficiently under loads without exceeding the critical limits by optimizing the nodal displacement.

4.1 Truss structural problems

Five standard benchmark truss problems—the 10-bar, 25-bar, 60-bar, 72-bar, and 942-bar trusses—are used to evaluate the performance of the studied algorithms. Furthermore, the published results and those from previous studies are compared [ 56 ]. The following sections provide details of these five common benchmark truss problems. Figs 3 – 7 show the investigated truss constructions and their loading circumstances, with geometrical dimensions used for computational evaluations. Tables 4 – 8 summarize the truss design considerations.

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The first structural problem is a 10-bar truss, depicted in Fig 3 . Angelo et al. have extensively studied this 2-D truss [ 56 , 65 – 68 ]. Ten design variables were used to solve MO problems. As depicted in Fig 3 , a vertical downside load of 100 kips is applied at nodes 2 and 4.

Fig 4 shows the 25-bar truss used in the second truss problem. Academic research regularly utilizes this 3-D truss [ 56 , 66 – 68 ]. It is symmetric about the x-z and y-z planes, and its twenty-five elements are arranged into eight groups. Table 5 shows the density and Yong’s modulus of the truss material with loading conditions at different nodes.

The third benchmark truss is a Ring truss with 60 bars. [ 56 , 66 – 68 ], as seen in Fig 5 . Table 6 illustrates how the truss is divided into 25 parts, each representing a symmetry. This truss is subjected to three load cases with size variable (cross-sections) values ranging from 0.5 in2 to 4.9 in2. Material properties are displayed in Table 6 .

The 4 th benchmark, which is a 72-bar 3-D truss [ 56 , 66 – 68 ], is presented in Fig 6 . As shown in Table 7 , the truss is made up of 72 elemental cross-sections that are arranged into 16 segments. Two different load cases are considered as per Table 7 . The maximum allowable stress is 25 ksi as a constraint to prevent structure failure. Material properties are as per Table 7 for the 3-D 72-bar truss structures. Here, 72-bar elements are grouped into 16 members as design variables. Size variables represent possible cross-sectional areas that can be assigned to truss structures ranging from 0.1 in 2 to 2.5 in 2 .

As seen in Fig 7 , the fifth and last truss is a large tower truss with 942 bars [ 44 , 56 , 67 ] To preserve symmetry, the 942 members of the truss geometry are divided into 52 members, representing design variables as per Table 8 . Two loading conditions are given: nodal conditions and lateral loading, which are applied at different truss portions. Size variables for this huge tower range from 1 in2 to 200 in2. Material properties are density and Young’s modulus, as per Table 8 .

This study rigorously tested six different algorithms, with each approach undergoing one hundred runs for each studied truss problem. These evaluations were conducted meticulously, with each test encompassing 50,000 functional evaluations, ensuring a comprehensive analysis of algorithmic performance across various scenarios and challenges inherent in truss design problems.

4.2 Empirical assessment

MOHO was applied to evaluate all considered truss problems and ascertain the effectiveness of the approximate Pareto-optimal solutions generated by the MO optimization algorithm. The results obtained from MOHO were compared with those from MOAS, MOACS, DEMO, NSGA-2, MOALO, and MOMFO.

•  ▪ The hyper-volume (HV) index measures the percentage of target space that members of the ND solution occupy set S. It provides information about the S set’s convergence and diversity. A hypercube vi is created for every solution i in S by a collection of reference points. A higher HV value denotes an algorithm that performs better. A visual representation of HV for MO problems is shown in Fig 8 .

•  ▪ The gap between the actual Pareto-optimal front and the estimated Pareto-optimal front found during the search process is measured by the Generational Distance (GD). The actual and approximate Pareto-optimal fronts coincide when the GD value is zero.

•  ▪ The distance between each reference point and the resulting Pareto-optimal front is compared and evaluated using the Inverted Generational Difference (IGD). A closer distance between the obtained front and the reference locations is indicated by lower values of both GD and IGD, which suggests that the algorithm performed well.

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In the given equation, |P| denotes the count of outcomes in the Pareto front, where di represents the Euclidean distance across the nearest solution from the reference front and the objective function vector of the ith solution in the acquired front. Conversely, |P’| signifies the quantity of the solutions for the reference plane. This metric is utilized to measure both front expansions and progression. Pictorial representation of GD and IGD matrices are shown in Fig 9 which assesses the proximity of solutions in the approximation set to the true Pareto front.

•  ▪ Together, the metrics for extent (ET) and spacing (SP) create a new evaluation matrix called the spacing to extent (STE) ratio. This matrix allows one to examine the extent and spacing aspects simultaneously. A more efficient and non-dominated Pareto front usually has a lower STE value.

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5. Results-discussions and comparative study

The outcomes of the algorithms that are being examined are shown in below Tables 9 – 12 , which are elucidated as follows:

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Table 9 shows the hypervolume (HV) measures, which indicate how the algorithms’ ND sorting capability has evolved and changed over time. Greater HV values are associated with superior non-dominated fronts. Out of all the problems examined, MOHO had the highest HV values, followed by MOMFO and DEMO, which suggests that it performed better in reaching a reasonable convergence rate.

•  ▪ Variation in Hypervolume (HV): For all five considered truss constructions, MOHO shows significantly better average, maximum, and minimum HV values than MOMFO, MOALO, DEMO, NSGA-2, MOACS, and MOAS. Furthermore, MOHO’s standard (std) value from the HV test is excellent for all the algorithms considered for all truss constructions, indicating better search consistency.
•  ▪ Performance Comparison: Among all the average HV values of the algorithms considered, MOHO exhibits the highest values for truss structures, with respective averages of 53518.79, 1894.07, 4087.08, 2317.36, and 75288799 for 10-bar, 25-bar, 60-bar, 72-bar and 942-bar truss respectively. Algorithms with higher average HV values are more effective at exploring the solution space and identifying Pareto-optimal solutions.
•  ▪ MO algorithm effectiveness: Comparing the HV values for each MO algorithm across the different truss structures allows us to identify which algorithms consistently perform well across various optimization problems.
•  ▪ Standard deviation analysis: Std value by the MOHO for all considered trusses are less compared with other optimizers with Friedman values of 1.10, 1, 1, 1.03, and 1 for 10-bar, 25-bar, 60-bar, 72-bar and 942-bar truss respectively. The stability and consistency of algorithm performance can be inferred from the standard deviation of HV values. While higher values might suggest variability or sensitivity to specific problem instances, lower standard deviation values indicate more consistent performance.
•  ▪ Sensitivity of truss structures: The MOHO framework balances exploitation, prioritizing feasible solutions, and exploration, which involves seeking diverse solution options, and offering a well-rounded optimization approach. With these capabilities, MOHO holds promise in systematically navigating the vast solution space of larger structures, potentially approaching optimal designs.

Rigorous comparisons using Friedman’s rank test rank algorithms, with MOHO emerging with the top score of 1.03 among all algorithms with conflicting objectives, indicating superior Pareto front quality compared to alternatives. Analyzing the HV values in the table enables a comprehensive assessment of multi-objective optimisation algorithms’ effectiveness, robustness, and sensitivity across a range of truss structures, facilitating informed decision-making in algorithm selection and application.

Figs 11 – 15 depict the Pareto fronts for truss problems across seven algorithms. They reveal the correlation between mass and maximum displacement, which provides visual insights into the trade-offs between these objectives. Each figure represents a scatter plot showing the distribution of solutions along the Pareto front, where each point represents a unique solution generated by the optimization algorithm.

•  ▪ Truss Structures: The figures display Pareto fronts for different truss structures, such as 10-bar, 25-bar, 60-bar, 72-bar, and 942-bar, allowing for comparisons across various design complexities and sizes.
•  ▪ MO Optimization Algorithms: The plots include Pareto fronts generated by seven multi-objective optimization algorithms, such as MOAS, MOACS, DEMO, NSGA-2, MOALO, MOMFO, and MOHO. Each algorithm employs distinct optimization strategies and techniques to explore the solution space and identify Pareto-optimal solutions.
•  ▪ Mass vs. Displacement: The scatter plots’ axes represent the two objectives of interest: mass (structural weight) and displacement (structural deflection). The position of each point on the plot indicates the corresponding values of mass and displacement for a particular design solution.
•  ▪ Trade-Off Analysis: The researcher can analyse the trade-offs between both objectives by examining the distribution of points along the Pareto front. Solutions located closer to the Pareto front represent superior trade-offs, where improvements in one objective (e.g., reducing mass) come at the expense of the other (e.g., increasing displacement).
•  ▪ Algorithm Performance: The distribution and spread of points along the Pareto front provide insights into the performance of each optimization algorithm. Algorithms that generate solutions distributed across a wide range of Pareto front regions demonstrate better exploration capabilities and versatility in identifying diverse trade-off solutions.

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This observation underscores MOHO’s superior performance. Its Pareto fronts are characterized by smoothness and even distribution, in stark contrast to the fragmented and discontinuous fronts generated by other multi-objective algorithms. This distinction highlights MOHO’s prowess in optimizing truss structures more efficiently and effectively across various design objectives.

The hypervolume generation process with the function evaluation of different truss constructions is seen in Figs 16 – 20 . The hypervolume metric’s evolution during the optimization process is depicted graphically in these figures, which also highlight the various algorithms’ abilities to explore and converge towards the Pareto front. By scrutinizing these figures, it becomes apparent how effectively each algorithm performs concerning coverage and convergence toward optimal trade-off solutions. Also, the figures illustrating hypervolume through function evaluations for all MO optimization algorithms across various truss structures provide insights into the performance of each algorithm over time. By plotting the hypervolume values against the number of function evaluations, these figures showcase how efficiently each algorithm converges towards the Pareto-optimal front. A steeper increase in hypervolume indicates faster convergence towards a better Pareto front. Additionally, comparing the hypervolume curves of different algorithms allows for assessing their relative efficiency and effectiveness in exploring the solution space and identifying high-quality solutions. These figures serve as a visual representation of the optimization process’s progress and enable researchers to evaluate the algorithm’s performance dynamically. This empirical analysis helps to clarify how different optimization approaches compare in terms of their ability to solve multi-objective truss design problems.

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The findings of the Generational Distance (GD) metric are shown in Table 10 , which is an essential metric for evaluating the differences between the Pareto optimal front and ND solutions in various truss configurations. A lower GD score indicates an exceptional, non-dominated front. For the 10-bar truss average GD value is 4.1908 with a standard deviation (std) of 0.5805, for the 25-bar it is 0.4071 with a std 0.1666, for the 60-bar it is 0.6826 with a std of 0.3439, for the 72-bar it is 1.4976 with a std of 1.0042 and for the 942-bar truss structure average GD is 1609.70 with a std of 230.60 for MOHO. The GD measure results reveal that MOALO, MOACS, MOMFO, and MOHO have excellent non-dominated fronts and perform extraordinarily well, which means they can generate solutions closer to the true Pareto front and well-distributed across the objective space. On the other hand, MOAS, DEMO, and NSGA-2 perform worse when evaluated using the GD metric. These comparative analyses help to understand the performance of all considered MO algorithms for all considered five truss structures in terms of their ability to explore the solution space effectively and produce high-quality Pareto fronts.

A lower value of the Inverted Generational Distance (IGD) metric shows an improved ND front, which provides an all-encompassing assessment of convergence and diversity among Pareto fronts. The IGD measurements’ results are shown in Table 11 , which includes information on how different algorithms perform when compared to various truss structures. Regarding IGD values, MOHO leads to the front, followed by DEMO and MOMFO, all exhibiting better convergence and diversity in their Pareto fronts. The mean Euclidean distance between every point in the obtained front and the closest point in the actual Pareto front is measured. It assesses how close an algorithm’s solutions are to the true Pareto front. Additionally, MOHO is at the top, with a first rank at a 95% significant level in Friedman’s test, highlighting their efficacy in producing high-quality Pareto fronts. These results shed light on the relative merits and drawbacks of different methods in achieving convergence and diversity in MO optimization for truss design. They offer insightful information about the optimization procedure, which helps to clarify how various algorithms function when dealing with truss design difficulties.

Diversity curves illustrating the evolution of the diversity of solutions concerning function evaluation for all algorithms used for the truss structures under consideration are shown in Figs 21 – 25 . These curves illustrate each algorithm’s performance in exploring the solution space and preventing early convergence to poor solutions. They offer essential insights into how each approach maintains diversity throughout optimization. A higher diversity indicates a broader range of solutions the algorithm explores, potentially leading to a more comprehensive solution space exploration. Fluctuations or plateaus in the diversity curve may indicate transitions between exploration and exploitation phases or convergence to suboptimal solutions. Overall, these curves provide valuable insights into the algorithm’s ability to explore diverse solution alternatives and avoid premature convergence to local optima. When choosing algorithms for MO optimization problems in truss design, researchers can make well-informed decisions with the help of these figures, which are crucial tools for comprehending the dynamic behavior of optimization algorithms.

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Spacing represents a good distribution of solutions with evenly spaced points, indicating that the MO algorithm has explored the objective space effectively and generated the most diverse solution set. Extent is a range of solutions along the objective space; a higher value means the solutions cover a wide range of objectives, indicating diversity. By simultaneously evaluating both spacing and extent, the Spacing-To-Extent (STE) metric offers essential information about the quality of non-dominated fronts. A lower STE score indicates a superior, ND front because it matches the spacing-to-extent ratio better. Table 12 presents the STE findings for the MO algorithms under consideration in the study. For 10-bar, 25-bar, and 72-bar trusses, the average STE value of MOHO is 0.0110, less than MOAS, MOACS, MOALO, and NSGA-2. For a 60-bar ring truss, an average STE value from MOHO is 0.0077 with the least std of 0.0089, the third best compared with other MO algorithms. Same for the large tower truss 942-bar, an average STE measure is 0.0088 with a std of 0.0017 with a Friedman value of 2.3. As can be seen from the STE findings, DEMO performs best, demonstrating its ability to create comprehensive, evenly spaced, non-dominated fronts. As the runner-up, MOMFO and MOHO exhibit STE results similar to DEMO’s. According to Friedman’s test at a 95% significance threshold, while other metrics may yield comparable findings, the overall Friedman test positions DEMO, MOMFO, and MOHO as the top three algorithms, reaffirming their effectiveness in generating high-quality non-dominated fronts with favourable spacing to extent ratios.

Figs 26 – 30 depict swarm plots representing the performance of optimization algorithms across all the truss structures, respectively. Swarm plots visually represent the distribution of objective function values or performance metrics obtained by different algorithms for each truss structure. These swarm plots provide information about how the solutions produced by each algorithm are distributed among the various truss structures. This analysis aids in identifying any potential outliers or patterns in the optimization process and understanding algorithm behaviour regarding solution quality and variety. These plots offer insights into how the population evolves, showing trends such as convergence towards some areas of the objective space or the distribution of solutions across different regions. By visualizing the swarm plots, researchers can observe the algorithm’s ability to explore the solution space effectively and maintain diversity within the population. Variations in the density or dispersion of points in the swarm plot can indicate the algorithm’s performance in balancing exploration and exploitation. Additionally, researchers can analyze how the swarm evolves in response to changes in algorithm parameters or problem settings. Overall, swarm plots provide a dynamic representation of the optimization process, allowing researchers to monitor and interpret the behaviour of MO optimization algorithms in the truss structure design. MOHO has a superior quality of swarm generation for all considered truss structures.

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Box plots provide a visual summary of the distribution of objective function (HV) values or performance metrics obtained by different algorithms for each truss structure. These box plots evaluate the central tendency, variability, and dispersion of each method’s solution quality for various truss structures. A higher median hypervolume value and a narrower IQR (interquartile range) indicate better overall performance and consistency of an algorithm in generating Pareto-optimal solutions. Conversely, a wider IQR and a more dispersed distribution of hypervolume values may suggest more significant variability or instability in the algorithm’s performance. Box plots can be utilized to compare the overall potential distribution of different algorithms, spot any performance disparities, and learn more about how different algorithms behave when faced with varying levels of task complexity. Overall, Figs 31 – 35 are valuable tools that help assess and choose suitable algorithms for specific optimization problems by visually comparing the effectiveness of optimization algorithms over various truss structures.

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An overview of the comprehensive Friedman rank for all truss structures achieved by the techniques under consideration is shown in Table 13 . The average Friedman’s score for MOHO is 2.2850, which is the lowest and has the first rank compared with MOMFO, DEMO, MOALO, MOACS, MOAS, and NSGA-2. MOHO has an excellent convergence rate compared with the other prominent MO optimization algorithms. This dominance of MOHO is statistically significant, as indicated by Friedman’s rank test at a 95% level, further underscoring its better performance than other algorithms assessed in the research. Overall, MOHO has the most excellent HV values, indicating that it explores well and comes up with various solutions. The results of MOHO, MOALO, MOACS, and MOMFO are near the optimal Pareto front, as indicated by their lowest GD values. In most instances, MOHO also had the lowest IGD, indicating a decent mix of convergence and diversity. MOHO was the most effective algorithm for these truss structure problems when ranked according to all three measures. Put more, MOHO is the best preference for these challenging engineering investigations because it finds a good distribution of near-ideal, well-balanced solutions.

https://doi.org/10.1371/journal.pone.0308474.t013

6. Conclusion

This article presents the MO version of the novel hippopotamus optimization algorithm for solving five structural truss problems. Minimization of both objective function, structural mass, and maximum nodal displacement is subject to stress and area constraints. This algorithm’s two decisive exploration phases and one exploitation phase generate excellent results for the truss optimization problems to examine its exploratory, exploitative, local optima evasion, and convergence properties. Through quantitative and qualitative analyses, comparing MOHO with six prominent algorithms based on four significantly recognized performance measures, we demonstrated its effectiveness in handling real-world truss structure optimization problems. Our results show that MOHO ranks first in the average Friedman rank test and outperforms alternative optimizers on all structural issues. MOHO demonstrated significant advantages concerning coverage, convergence, and solution diversification.

More research into how it performs on higher-dimensional engineering design challenges is essential to evaluate MOHO’s potential thoroughly. Further studies could examine how MOHO can be applied to multi-modal and multi-dimensional technological problems with conflicting objectives. Additionally, the research can be expanded to investigate methods for enhancing performance and carrying out evaluations compared to other well-known optimization techniques. Further advances in efficiency and scalability will enable the MOHO for truss structures to manage more prominent and intricate structural systems. Promising directions include investigating its adaptability to different structural types beyond trusses, integration with advanced analysis techniques, and resilience in managing unknown parameters. The applicability and relevance of MOHO are further expanded in various engineering areas by hybridization with complementary optimization approaches and application to real-world engineering challenges. Enhancements in the future could involve extending MOHO to handle more extensive and more intricate truss structures containing increased design variables and constraints and additionally, adapting to dynamic environmental changes in design requirements using adaptive parameters. Overall, MOHO exhibits potential as a proficient and successful method for multi-objective optimization in truss-bar design issues, and its broader applicability to optimization contexts demands more research and analysis.

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• 60. J. (Telecommunications engineer) Wu, LNM Institute of Information Technology, IEEE Communications Society, M. IEEE Systems, and Institute of Electrical and Electronics Engineers, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI): September 21–24, 2016, the LNM Institute of Information Technology (LNMIT), Jaipur, India.

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• Published: 20 August 2024

An evaluation of a multi-partner approach to increase routine immunization coverage in six northern Nigerian States

• Leanne Dougherty   ORCID: orcid.org/0000-0002-5702-9328 1 , 2 ,
• Mayokun Adediran 1 ,
• Akinwumi Akinola 1 ,
• Matthew Alabi 1 ,
• Eno-Obong Etim 1 ,
• Jane Ohioghame 1 &
• Adebola Adedimeji 1  

BMC Health Services Research volume  24 , Article number:  951 ( 2024 ) Cite this article

Metrics details

Global health partnerships are increasingly being used to improve coordination, strengthen health systems, and incentivize government commitment for public health programs. From 2012 to 2022, the Bill & Melinda Gates Foundation (BMGF) and Aliko Dangote Foundation (ADF) forged Memorandum of Understanding (MoU) partnership agreements with six northern state governments to strengthen routine immunization (RI) systems and sustainably increase immunization coverage. This mixed methods evaluation describes the RI MoUs contribution to improving program performance, strengthening capacity and government financial commitment as well as towards increasing immunization coverage.

Drawing from stakeholder interviews and a desk review, we describe the MoU inputs and processes and adherence to design. We assess the extent to which the program achieved its objectives as well as the benefits and challenges by drawing from a health facility assessment, client exit interview and qualitative interviews with service providers, community leaders and program participants. Finally, we assess the overall impact of the MoU by evaluating trends in immunization coverage rates.

We found the RI MoUs across the six states to be mostly successful in strengthening health systems, improving accountability and coordination, and increasing the utilization of services and financing for RI. Across all six states, pentavalent 3 vaccine coverage increased from 2011 to 2021 and in some states, the gains were substantial. For example, in Yobe, vaccination coverage increased from 10% in 2011 to nearly 60% in 2021. However, in Sokoto, the change was minimal increasing from only 4% in 2011 to nearly 8% in 2021. However, evaluation findings indicate that issues pertaining to human resources for health, insecurity that inhibits supportive supervision and vaccine logistics as well as harmful socio-cultural norms remain a persistent challenge in the states. There is also a need for a rigorous monitoring and evaluation plan with well-defined measures collected prior to and throughout implementation.

Introducing a multi-partner approach grounded in a MoU agreement provides a promising approach to addressing health system challenges that confront RI programs.

Peer Review reports

Introduction

Routine Immunization (RI) is the backbone of all national immunization programs and disease control efforts. The Government of Nigeria has faced persistent challenges in addressing low immunization coverage rates in the northern region. According to the 2015 National Nutrition and Health Surveys, children receiving the pentavalent vaccine in six northern states ranged from 4% in Sokoto to 33% in Kaduna [ 1 ]. Between 2008 and 2018, Demographic and Health Surveys (DHS) reported that the country’s RI coverage had stagnated nationally and deteriorated in the northern regions. The 2018 DHS estimated full immunization coverage to be only 31% nationally, and 20–23% in the northwestern and northeastern regions respectively [ 2 ]. These low coverage rates have been driven by several challenges such as, shortage of vaccines and supplies, poor community engagement, weak human resource system and harmonization of stakeholder efforts, inadequate ownership; systemic bottlenecks including sub-optimal funding by many state governments; weak cold-chain and vaccine logistics systems; and ineffective supportive supervision [ 3 , 4 , 5 , 6 ]. In the northern states specifically, the RI program and polio eradication campaign have faced historical boycotts at the local level driven by rumors (e.g., vaccinations causes HIV or sterility in young Muslim girls) and amplified by the political context [ 7 ]. One significant determinant of the poor performance and underlying restraints included a need for political commitment and accountability that contributed to weak financial support [ 8 ]. While the government and several development partners have deployed significant resources to improve RI coverage in Nigeria, the results often fell short of expectations due to weak harmonization of stakeholder efforts [ 9 ].

Over the last twenty years, global health partnerships have emerged as an important resource for health system strengthening and addressing public health challenges [ 10 , 11 , 12 ]. These partnerships bring together two or more organizations toward a common goal and often engage in advocacy, provide financing, and support technical or capacity strengthening efforts [ 13 ]. Previous studies have identified a number of benefits from these partnerships including increased coordination, reduced duplication of investments and activities, knowledge sharing and increased funding due to the establishment of a common platform that gains legitimacy and support [ 14 , 15 ]. Despite these benefits, a number of criticisms have also been made about global health partnerships, including that they impose external priorities through the introduction of vertical disease programs that distract countries from focusing on health system strengthening, limit stakeholders’ voices in decision making, provide insufficient resources, and promote poor governance practices [ 16 , 17 , 18 ].

Intervention description

In response to the challenges with the routine immunization program in northern Nigeria, the Bill & Melinda Gates Foundation (BMGF) and Aliko Dangote Foundation (ADF) forged a partnership with six northern state governments (Bauchi, Borno, Kaduna, Kano, Sokoto and Yobe) in a multi-year Memorandum of Understanding (MoU) partnership that aimed to strengthen RI systems and sustainably increase its immunization coverage. Unlike previous global health partnerships that operated in multiple countries or at a national level, these RI MoUs created six state-level public-private platforms on which to establish sustainable financing for RI, improve partner coordination and accountability, strengthen RI systems and, ultimately, increase vaccination coverage. Ultimately, the MoU aimed to increase vaccination coverage for DPT3 to 80% by the end of the agreement which, is the immunization coverage rate needed to achieve herd immunity to prevent the spread of the poliovirus. The first partnership in collaboration with the Kano state government was introduced in 2012 and later expanded between 2014 and 2016, as the governments of Bauchi, Borno, Yobe, Kaduna, and Sokoto states negotiated and signed RI MoUs with the foundations. The United States Agency for International Development (USAID) joined as a technical partner in Bauchi and Sokoto states. An important component of the RI MoU was the establishment of state managed RI program bank accounts (referred to as basket funds) whereby partners could contribute to the program. BMGF also provided technical assistance through its local partners, Solina Health, Chigari Foundation, and others. Key resources and sample documents for the MoU approach including a sample MoU, workplan and costing model are publicly available [ 19 ].

RI MoU logic model

The MoU approach was developed with the primary outcome of increasing immunization coverage in six northern states of Nigeria by improving program performance and increasing capacity of the State Primary Health Care Development Agency (SPHCDA) and its staff to manage the program. To achieve these outcomes, inputs, as shown in Fig.  1 , were focused on (a) the creation of a basket fund where resources from donors and government could be pooled together in a regressive funding model (i.e. BMGF and ADF contributed approximately 70% of RI program funds in the first year and then the proportion of funding declined each year as the state increased their contribution to fully fund the program by the end of the agreement), (b) establishment of meetings with key stakeholders and government officials to ensure high level government engagement, and (c) provision of technical assistance to support implementation of the program. The inputs aimed to facilitate processes related to governance, financial management, vaccine supply chain, service delivery, and monitoring and evaluation and community engagement that would ultimately improve (1) coordination and management, (2) financial accountability and transparency, (3) vaccine availability, (4) equitable access to quality services, (5) availability of quality administrative data for action, and (6) demand for RI services.

The RI MoU logic model

While there is some evidence on the benefits and challenges in implementing institutional health partnerships, there is limited evidence on their effectiveness [ 20 ]. In this paper, we contribute to the existing evidence base to evaluate the RI MoU partnership. We describe the extent to which the MoU was successful in achieving the desired objectives outlined in the RI MoU logic model and describe governments, donors and other stakeholders adherence to their financial commitments.

Materials and methods

Study setting.

The MoU approach was implemented at the state, local government area (LGA) (i.e. an administrative subdivision of the state government) and health facility levels in six northern states. Population size across the six states ranges from approximately 13 million in Kano to three million in Yobe [ 21 ]. Several states (i.e., Borno, Kaduna and Sokoto) experienced insecurity during implementation.

Study design

We conducted a mixed methods study to evaluate the effectiveness of the RI MoU approach in the six northern states. The rationale for the study approach was to (1) strengthen the level of inference for key findings by triangulating multiple data sources given the lack of baseline and comparison data values and; (2) provide a holistic understanding by capturing perspectives from multiple levels (e.g. participants, service providers, community structures, partners, donors and government). The evaluation was commissioned by the BMGF and conducted by the Population Council; an organization external to the MoU approach. First, to describe the MoU inputs and processes and adherence to design, the study team reviewed existing program documents through a desk review and conducted Key Informant (KI) interviews with stakeholders and state program implementers. Second, the study team conducted a quantitative health facility assessment and client exit interview and qualitative interviews with service providers, community leaders and program participants to assess the extent to which the approach achieved the objectives outlined in the RI MoU logic model and to understand the benefits and challenges of the approach. Finally, to assess the overall impact of the MoU in achieving program outcomes, we assessed data from household surveys and the District Health Information System 2 (DHIS2). The household survey data provided an objective assessment of RI program performance while the DHIS2 figures informed how well the system monitored and evaluated program performance. A summary of measures based on programmatic outputs and outcomes is described in Table  1 .

Desk review

Documents included in the desk review addressed all stages of the MoU design, start-up, and implementation. To inform analysis on the design of the MoU, the team reviewed the diagnostic reports, MoU agreements and case studies. To assess fidelity to implementation design, the team reviewed MoU related meeting summaries and presentations, workplans and strategies such as the community engagement strategy. To assess the extent to which the MoU achieved its objectives, the team reviewed findings from the national Primary Healthcare Under One Roof (PHCUOR) Implementation Scorecard, state financial management trackers, vaccine dashboards, routine immunization supportive supervision (RISS) monitoring, DHIS2 data to review fixed and outreach session completion, and AFeNET variance assessments between survey and administrative data. And, to assess impact of the MoU approach, the team reviewed National Indicator Cluster Survey/ Multiple indicator cluster survey (NICS/MICS) data from 2011, 2016, and 2021 to assess the proportion of children 12–23 months who received the pentavalent 3 vaccine.

Quantitative data

A health facility assessment of Primary Health Centers (PHC) was conducted in March-April 2022 in selected local LGAs of the six implementation states (Supplementary file 1). We used a two-stage stratified sampling procedure in selecting health facilities. We generated a list of all the LGAs in each of the three senatorial districts and selected 50% of the total number of LGAs in each state with the exception of Borno and Kaduna due to security concerns as described in Table  2 and shown in Fig.  2 . In Borno, the study team selected five LGA’s and oversampled health facilities within a secured area around the capital of Borno instead of the 14 LGAs as planned. In Kaduna, four LGAs near the capital of Kaduna were over sampled to replace LGAs in the western part of the state which could not be accessed due to security concerns. In each of the LGAs, approximately two PHCs were randomly selected. The targeted number of health facilities across all the LGAs was 156, at two health facilities per LGA. A total of 163 Health Facility Assessments (HFA) were conducted across all six MoU states. The additional seven health facilities were because of oversampling in Borno state due to the inability to access some LGAs because of security reasons. Client Exit Interviews (CEI) ( N  = 1,093) were conducted in the sampled health facilities to assess client satisfaction with RI services provided (Supplementary file 2). The study team interviewed on average 3–7 clients per facility except for in Yobe where immunization days were taking place resulting in a higher volume of clients referred for interviews. Clients were selected to participate in exit interviews if they were a primary caregiver (at least 18 years or older) of a child under the age of two years, accessing vaccination service.

Map of Nigeria and study sites

Qualitative data

The qualitative data was collected in three separate data gathering activities between March and June, 2022. We conducted KI interviews with BMGF staff, government stakeholders, and implementers on the program model, execution, impact, and opportunities to be leveraged for future programs (Supplementary file 6). Second, In-depth Interviews (IDIs) with health workers were conducted to assess the extent to which the MoU achieved its objectives including benefits and challenges and lessons learned from the health worker perspective (Supplementary file 5). Lastly, Focus Group Discussions (FGDs) were conducted with community leaders (e.g. traditional, religious leaders) and program participants to assess benefits and challenges of the MoU and lessons learned from the community and program participants’ perspective (Supplementary files 3 and 4). KIs lasted approximately two hours while IDIs lasted approximately one hour in duration, both were conducted in English. FGDs lasted approximately 1–1.5 h in duration and were conducted in Hausa. Table  3 provides a summary of the interviews conducted by qualitative method and study respondent.

For the quantitative data sources, we computed counts with percentages for categorical variables and medians with standard deviations for continuous variables. Data cleaning and analysis were performed using Stata version 14 software. For all of the qualitative data sources, the study team determined deductive codes prior to analysis based on the RI MoU logic model which draws from the World Health Organization health systems building blocks framework [ 22 ] and then generated subcodes inductively by reviewing transcripts line by line. Inconsistencies and questions that arose during coding were discussed through reoccurring meetings and resolved by consensus as a team to ensure inter-rater reliability. Sub-codes were further grouped during analysis to address research questions which included lessons learned, areas of improvement, recommendations, what worked well and what did not work well and sustainability. Additional codes were generated for the KIs conducted with national and international respondents and covered areas related to design, implementation, and transition. Codes for all of the qualitative data were analyzed thematically by state.

Study team members were responsible for managing specific data sources from data collection through analysis and used a convergence model of triangulation to bring together the complementary data sources during an analysis workshop conducted in June 2022. Study authors attended the analysis workshop. The objective of the workshop was to provide team members leading specific aspects of the study (e.g., qualitative interviews, desk reviews and quantitative surveys) an opportunity to present their respective findings by evaluation question and to discuss how each data source responded to the study questions and contributed to the overall evaluation findings.

The workshop was followed by regularly scheduled meetings where the team came together to discuss how each source responded to the study questions. We compared findings on similar topics and identified where different data sources worked to explain pathways outlined in the RI MoU logic model [ 23 ].

Ethical approval

The study received approval from the Population Council Institutional Review Board (Protocol number 992). In Nigeria, ethical approval to conduct the study was obtained at national and state levels. At the national level, approval was granted by the National Health Research Ethics Committee with approval number NHREC/01/01/2007-17/01/2022. At the state level, ethics applications were submitted, and approval obtained from individual State Health and Research Ethics Committee before the commencement of field activity. Bauchi (NREC/03/11/19B/2021/10); Borno (073/2021); Kaduna (MOH/ADM/744/VOL.1/1171); Kano (SHREC/2022/3078); Sokoto (SKHREC/016/2022) and Yobe (MOH/GEN/747). The relevant ethical approval and consent details were received and are available on request by the editor or editorial office. Study participants provided informed consent by using their signature. In addition, all methods were carried out in accordance with relevant guidelines and regulations and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

We present findings for each output considered in the RI MoU logic model specifically related to governance, financial management, vaccine supply chain, service delivery, and monitoring and evaluation and community engagement. Next, we consider outcome measures including the extent to which each partner met their financial contribution to the RI MoU and an assessment of immunization coverage rates over the period of implementation. A summary of the RI MoU performance measures by state is presented in Table  4 .

Governance: improved program coordination and management

The PHCUOR policy, enacted in June 2014 and considered a precondition for the MoU, called for states to consolidate planning and management around all PHC services and resources “under one roof,” the SPHCDA. The RI MoU aimed to support improved program coordination and management by encouraging adoption and implementation of the PHCUOR and its principles including the establishment of one annual workplan supported by the basket fund and one coordinating mechanism. The PHCUOR scorecard provides evidence of the state’s average performance across the nine PHCUOR pillars as shown in Table  4 under Governance. The nine pillars assessed governance and ownership, legislation, adoption of a minimum service package, repositioning, system development, operational guidelines, human resources, funding sources and structure and office setup. All six state governments improved their overall performance from 2015 to 2019 with a percentage point increase ranging from 19 to 51%.

The RI MoU also supported the establishment of functional thematic area working groups led by government employees with defined membership and clear terms of reference. The working groups addressed specific areas that needed strengthening, including finance, community engagement/social mobilization, monitoring and evaluation/supportive supervision, logistics, and training. This structure strengthened government ownership of the program contributing to strong political will for RI funding and creating partnerships to support program delivery.

“Although it took time for everyone to come together and put it into one single workplan , but it has helped us to be operating as a single team. The spirit of teamwork was strengthened and visibility. So , even a new partner that comes , will have to now look at it and see where does your work key into this… So , [there is] integration in Yobe and [a] single work plan.” Government stakeholder , KI , Yobe .

However, several challenges contributed to some delays in implementation including competing priorities between the national and state government as well as state government and partners.

“The challenge sometimes is the approach from the partners. Sometimes what we want to do or intend to do is different from what the partners want us to do. although it is our own decision. We talked about this ownership , but sometimes this doesn’t exist when you look at it deeply. This is a challenge , really.” Government stakeholder , KI , Borno .

In addition, political differences and interference in program implementation remains a challenge.

“A politician … will just come and tell you , ‘Sir , appoint so person.’… If you didn’t appoint him then he will go back to the community and sabotage all your effort. And if you appoint him he may not be able to do the work you assign him to.” Government stakeholder , KI Sokoto .

Financial Management: improved financial accountability and transparency across all levels

The RI MoU instituted several processes to improve financial accountability and transparency across all levels of the health system. These processes included (1) instituting the no-objection process for program spending which stipulates that the state must seek approval from the MoU signatories to spend funds above a specified threshold; (2) instituting direct disbursements of program funds to end users through dedicated RI accounts and development of predefined disbursement schedules; (3) ensuring prompt retirement and submission, analysis and validation of funds; (4) instituting a system for recouping unspent/unretired MoU funds; and (5) conducting internal and external audits.

With the introduction of the MoU, bank accounts were opened at the state, LGA, and health facility level and the quarterly release and direct disbursement of funds was initiated. After the introduction of the MoU, the percentage of health facilities assessed which reported that funds were disbursed from state to health facility bank accounts at least quarterly ranged from 67% in Kaduna to 92% in Sokoto as shown in Table  4 under Financial management. Diagnostic reports conducted to inform the MoU design found that prior to the RI MoU funds disbursed were not regularly accounted for, and financial audits were not conducted on a regular basis. The MoU introduced financial management procedures including internal and external audits and aimed for all facilities to participate in a financial audit each year. As a result, the percentage of facilities assessed reported completing an audit in the 12 months preceding the assessment ranged from 50% in Sokoto to 94% in Yobe.

MoU partners considered the establishment of the basket fund to pool resources an effective mechanism that supported program coordination by eliminating multiple implementation silos and reducing duplication.

“Having the funds in one basket has made it very nice… You have one channel of getting the resources , one channel again to implement all the programs…. unlike other ones when everybody is running his own parallel programs.” Government stakeholder , KI Kaduna .

Through the introduction of the new MoU financial systems and processes, stakeholders also reflected on improved financial accountability and transparency.

“We have introduced electronic digital financial tracking tool to ensure financial accountability , because you give people money to conduct an activity , if they don’t conduct the activity , they need to refund it. So , the executive secretary has made that every activity that has not been reported or that has been confirmed not to be conducted , the money must be refunded , and we have a lot of cases where people are refunding the money.” –Government stakeholder , KI , Yobe .

However, some challenges still exist in the release of partners funds to support activities in the workplan due to conflicting fiscal years which has resulted in delays including quarterly disbursement from state to health facility bank accounts.

“Although there are clear guidelines on how this funding should come but sometimes there is delay in the release of the funds. You have a beautiful plan , and it is time bound. You need to do this in January , you need to do 1 , 2 , 3 , activities in the first week of February and the activities are costed and time bound. If the funds are not available as at when due , the activities may not take place.” Government stakeholder , KI Kano .

Vaccine supply Chain: availability of sufficient, potent vaccines at all service delivery points

The RI MoU implemented several measures aimed to redesign and institutionalize a Direct Vaccine Delivery system to ensure last mile delivery of vaccines. This included setting up stock data management systems to ensure visibility into stock data and procuring, installing, and routinely maintaining solar cold chain equipment (CCE) across all wards.

Prior to the MoU, there were routine stock outs and the diagnostic reports found that the method used to forecast vaccines which was based on target population and coverage was underestimating potential demand because LGAs were running out of stock by the end of the month. Following introduction of the MoU, the proportion of all antigens below the minimum level of adequate stock or stocked out at the apex health facilities ranged from 32% in Sokoto to 41% in Kano and Yobe in 2021 as shown in Table  4 under Vaccine supply chain. Several service providers noted that improved stock management further strengthened the state’s ability to make available sufficient, potent vaccines at all service delivery points.

“I told you that we used to plan for how many vaccines we want , right? So , if … I didn’t plan for it , I didn’t know how many vaccines , how many doses of vaccines do I need… people will come waiting for me and at the end of the day I will say I didn’t have the vaccine or the vaccine has finished…There is one key form that we use to fill; that one you’ll fill it based on your vaccine consumption… They will not … just give me [vaccines] off head [without vaccine use data].” - Service provider , IDI , Kano .

Adequate CCE was also procured through the MoU to ensure a more consistent supply of appropriately stored vaccines and fewer stockouts. The percentage of wards with functional cold chain equipment according to government managed vaccine dashboards increased across all six states. For example, in Bauchi, the percentage of wards with functional cold chain equipment was 96% in Bauchi in 2021 up from 28% in 2014–2015 at the time of MoU diagnostic assessments. Similarly, the percentage of wards with functional cold chain equipment was 93% in Sokoto in 2021 up from 29% in 2014–2015.

The increased availability of functional solar drives and other CCE strengthened the states’ ability to make available sufficient, potent vaccines at all service delivery points, as well as the use of third-party vendors for vaccine delivery in some states.

“In every ward , we also have cold chain equipment. It’s also maintained by solar so there will be no wahala (problem) … We can keep [vaccines] at the local government level or at the facility level… if you go to anyone , they all have solar for maintenance of all our vaccines.” Government stakeholder , KI Bauchi .

Initially, a push system for direct delivery of vaccines was introduced to apex health facilities through a private distributor to improve the reliability of vaccine delivery. With this approach, vaccines were distributed to bigger PHCs with Solar direct drive (apex facilities) in the community, from which smaller facilities or those without CCE – which rely on apex facilities for weekly vaccine supplies - may “pull” their supply. This eliminated high costs and long travel for personnel, as well as concerns about dangers associated with travel to collect vaccines. In Borno and Kaduna states, direct vaccine delivery through a third-party vendor alleviated pre-MoU challenges. However, in other states such as Bauchi, the government realized they had the capacity and means to effectively deliver vaccines and did not need to rely on a third-party distributor which was more expensive. In the end, facility readiness for CCE due to inadequate refurbishment by government, or theft, vandalism, or destruction of installed CCE, led most states to pursue a hybrid push-pull system for vaccine delivery despite initial focus on push system to apex facilities.

“Previously , there were transportation issues around how we went to get the vaccines from the state’s cold chain officer… But presently it is easier because it is directly delivered by the vendors… making the routine immunization easier for us.” Service provider , IDI , Borno .

However, insecurity, poor terrain, delays from the national level in vaccine delivery, and insufficient resources continued to pose problems in the timely delivery of vaccines to service delivery points.

“During the rainy season , there are some areas you cannot go. We have hard to reach areas , no matter whatever the strategy you apply , you cannot get to that place. So , it’s a serious challenge.” Government stakeholder , KI Bauchi .

Service delivery: improved equitable access to quality immunization services for all eligible children

The RI MoU aimed to improve the equitable access to quality immunization services for all eligible children by fully scaling up service availability across all health facilities in the state; developing and updating comprehensive health facility reaching every ward (REW) microplans and session plans and funding and monitoring implementation of fixed and outreach sessions and supportive supervisory visits to health facilities. The investments contributed to high levels of planned fixed and outreach sessions conducted between 2017 and 2020 (data not shown). For example, planned supportive supervision visits that were conducted also increased in Kaduna from 55% in 2018 to 82% in 2020 as observed in the RI supportive supervision dashboard. Increases in planned supportive supervision visits conducted in Yobe also increased from 70% in 2018 to 84% in 2020.

The RI MOU also aimed to improve the quality of immunization services provided. Client exit interviews assessed the quality of provider-client interactions by asking if providers provided information on four important RI counseling points. In three states (Bauchi, Kaduna, and Sokoto), the percentage of clients who said providers shared information on the four RI counseling was over 85% as shown in Table  4 under service delivery. However, more variation was observed in Borno where the percentage of clients who said providers shared information on the four RI counseling was less than 70% for three of the four components.

Monitoring and Evaluation: Improved availability and use of complete and quality administrative data for action

The RI MoU worked to institutionalize the use of DHIS2 as the primary source of administrative data by providing tools to ensure timeliness and completeness of reporting. Additional efforts included instituting data quality interventions to improve the integrity of the data and establishing platforms for data review, feedback, and continuous monitoring.

Evidence of improvements in data quality was identified in Bauchi where the variance between survey and administrative immunization coverage rates declined from 67% in 2017 for pentavalent 3 to 59% in 2020 as shown in Table  4 under Monitoring and evaluation. Similar trends were observed in other states such as Kaduna where the variance declined from 72% in 2017 for pentavalent 3 to 38% in 2020. The establishment of frequent data review meetings has contributed to the improvements in data quality as well as digitized supportive supervision improved monitoring efforts.

“What worked well is that we’ve already digitalized our supervision because they are in the same group. Now we are using digital devices to do supervision , it’s faster , easier and it has the coordinates , unlike before where somebody will sit down on his bed and fill a form that he has gone to supervision. Now when you send our report it will show the coordinates where you sent [it from]. – Government stakeholder , KI Kano.

Despite these improvements, challenges remain with data reporting including falsification of monitoring data as described by one government stakeholder, an overburdened workforce, and issues with security.

“The challenges we are facing is that health workers are … overstretched with a lot of activities. And they tend to see the data reporting as not important as the rendering of the services. So…in reporting , they tend to be negligent in some of the activities.” Government stakeholder , KI Yobe.

Community Engagement: improved community demand for routine immunization services

The RI MoU aimed to improve community demand for RI services by implementing a name-based community engagement strategy including identification and tracking of all eligible children led by a traditional system. All states adopted the use of line listing for newborn, as well as defaulter tracking to improve community use of vaccination services. States worked with community actors (Mai Unguwas) and created defined roles and plans to support the work. This engagement with community actors was seen as an important contribution to the RI program.

“We used to call the community stakeholders , telling them the importance of immunization , and we use to tell them the importance of their participation for these services. So that helps us a lot. They used to go for community mobilization. They are having meeting within themselves to mobilize their people that they should come and take this vaccine because the vaccine is very important.” Service provider , IDI Bauchi.

Based on client exit interviews, mothers are not the primary decision maker regarding the child’s vaccination status in a number of states. For example, in Kano, 62% of fathers are the primary decision makers regarding whether the child goes for vaccinations as shown in Table  4 under community engagement. This challenge was reflected in the qualitative interviews, where a program participant noted that women cannot access services without the husband’s approval.

“The woman cannot do anything about [service uptake] if her husband is against it.” Program participant , FGD Bauchi .

However, there is some evidence that religious leaders are engaging with both men and women to encourage the use of vaccination services.

“We both know we’re in the North. I mean not just even in the North , Nigeria as a whole , we tend to really listen to our religious and traditional leaders… Carrying along those institutions that we knew that could have some influence on the people was also something that went well.” Partner , KI Kaduna .

While community leaders played an important role, the lack of incentives for community volunteers and influencers resulted in poor motivation to conduct activities.

“They [volunteers] are not on a pay roll. And you know the economic situation in…not only the state , in the country… Some are still volunteering to do the job , but some are saying since there is no pay , we are not continuing.” Government stakeholder , KI Kaduna .

This is further supported with qualitative evidence where spousal refusal from poor sensitization on adverse events following immunization, contributed to vaccine hesitancy and refusal.

“The children get fever when we return. When he [husband] asked why and I told him that I collected an injection for them , he asked why? I should not go again; that on the quest of getting drugs for catarrh , I have brought something new [and more serious] upon him.” Non-beneficiary , FGD Borno .

Improved Financing for RI

The MoU approach was developed with the goal of creating sustained financing for the RI program. We assessed MoU funds contributed between 2013 and 2022 by contributor. Overall, BMGF and ADF paid the full amount of their committed funds over the course of the MoU period for all six states. However, each state made varied progress towards their commitment of assuming the full program costs as shown in Table  5 . Bauchi state paid $3.8 million of the $6.5 million U.S. Dollar (USD) committed from 2015 to 2018 while Kaduna state paid $3.5 million of the $4.6 million USD committed from 2016 to 2021. Kano paid $7.8 million of the $12.5 million USD committed from 2013 to 2021 while Yobe paid $2.8 million of the $4 million USD committed from 2016 to 2021. Borno paid $1.8 million of the $2.6 million USD committed while Sokoto paid $2.6 million of the $3.8 million USD committed both from 2016 to 2021.

Improved capacity of the SPHCDA and its staff to manage the RI program efficiently and independently and with clear accountability

The MoU aimed to improve the capacity of the SPHCDA and its staff to manage the RI program. Management capacity to implement the MoU was built through trainings and learning visits to Kano. Cascade trainings were implemented and built capacity to deliver RI services, manage cold chain, improve monitoring and evaluation, etc. and, the Basic Guide for RI Service Providers was introduced and used to train staff and serve as a reference document.

The HFA found that in most states, the National Primary Health Care Development Agency (NPHCDA) minimum standard number of at least two community health officers (CHO) and five community health extension workers (CHEWs) were not available. For example, in Bauchi, there was a median of two CHOs at the facility and there were no full-time nurses or midwife assigned to the facility as shown in Table  4 under Staff capacity. Community health worker motivation was another challenge reflected through the qualitative interviews.

“The challenge is that they need some incentives , then some support either financially or what have you , most especially those volunteers. They are not having salaries; their work is voluntary. So sometimes , they may demand some assistance , and we use to take it into consideration , but the authority concerned cannot do it.” Service provider , IDI Bauchi .

However, efforts to improve health worker performance through appraisals, rewards, and annual recognitions was seen as a promising approach.

Outside urban areas, insufficient numbers of skilled health workers to meet rising demand for services, and dwindling state resources to hire health workers and pay their salaries was mentioned as a challenge.

“There [are] dwindling resources to employ. Even when you want to employ the satisfactory number of health workers into the facilities…sometimes their salaries and wages is something …we’ve been having these challenges of resources.” Government stakeholder , KI Kaduna .

Sustained increase in immunization coverage

The MoU approach was developed with the primary outcome of increasing immunization coverage. Figure  3 presents the proportion of children 12–23 months who were fully vaccinated. Across all six states, vaccination coverage increased from 2011 to 2021. In some states, the gains were substantial. For example, in Yobe, vaccination coverage increased from 10% in 2011 to nearly 60% in 2021. However, in Sokoto, the change was minimal increasing from only 4% in 2011 to nearly 8% in 2021.

Proportion of children 12–23 months who fully vaccinated by MoU State, 2011, 2016, 2021

This evaluation explains the extent to which the RI MoU contributed to improved program performance including increased immunization coverage through strengthened health system capacity and increased government financial commitment across six states in northern Nigeria. Our findings, organized by RI MoU logic model, contribute to the growing body of literature exploring how global health partnerships can be used to strengthen public health programs. Drawing from multiple data sources over the course of implementation, we assessed measures of program performance and identified benefits and challenges associated with implementation.

Several notable achievements associated with RI MoU investments were observed. First, we found the RI MoU was successful in instituting mechanisms that improved coordination across partners and increased government ownership of decisions which was consistent with previous research [ 24 ]. There was also evidence from the PHCUOR scorecard assessment of progress in addressing the PHCUOR pillars. However, state governments addressed specific pillars to varying degrees which may be consistent with previous findings that state governments are more interested in executing aspects of the PHCUOR that are easy to achieve rather than address the more challenging human resource management and funding requirements [ 25 , 26 ]. The RI MoU was also successful in improving financial accountability and transparency. Previous research has found a lack of accountability and widespread corruption to be a barrier to high RI program performance in Nigeria [ 27 , 28 ]. However, evaluation findings found evidence that investments led to improvements in accountability and transparency because of the introduction of electronic mechanisms for validating expenditures as well as establishing routine audits. The significant investment in upgrading the vaccine supply chain including the procurement of solar refrigerators and direct to facility deliveries of vaccine supplies has also contributed to a reduction in stock out rates [ 29 ]. However, insecurity remains a challenge in some areas resulting in the destruction of installed CCE in some wards and more effort is required to work directly with community leaders to protect installed CCE.

RI MoU states showed improvement in vaccination coverage rates from 2011. However, there was variation between states suggesting that challenges remain. First, the RI MoU community engagement efforts focused largely on line listing approaches to encourage uptake of vaccination which may not have been adequate to address the numerous challenges at the community level. The approach required strong support from community leaders and the use of community volunteers who were not compensated for their services. Introducing non-monetary incentives may be an effective option to increase community based participation and motivation for the RI program [ 30 ]. While the use of community leaders is important in addressing community level barriers [ 31 ], the approach did not focus on the individual behavioral barriers that may require efforts to address knowledge, attitudes, beliefs, social norms, and self-efficacy. Efforts such as SMS reminders may be one way to raise awareness about vaccination services [ 32 ]. In addition, efforts to engage directly with fathers who were often the primary decision maker of whether a child was vaccinated through traditional channels such as Wanzams may also address barriers to vaccination coverage [ 33 , 34 ].

In addition to challenges at the community level, a number of challenges were noted relating to service delivery and staff capacity. Data from the HFA found that the median number of health workers was below the recommended number at PHCs in all states. Given the limitations of the public sector to provide services due to insufficient staff as well as challenges in reaching insecure areas, it may be beneficial to consider a public-private partnership model to expand service delivery [ 35 ] to hard-to-reach areas or consider redistributing and/or incentivizing staff to work in hard to reach areas. In addition, given the frequent migration and political insecurity, it may be necessary to adopt new methods such as applying satellite derived maps to identify vulnerable populations that are not being reached through traditional RI microplan approaches [ 36 , 37 , 38 ]. Finally, training health providers on how to address vaccine hesitancy and concerns related to adverse events following immunization may also be required [ 39 ].

Several efforts were made to improve routine monitoring of the RI program including the incorporation of RI module in DHIS2 which provided information to inform planning [ 40 , 41 , 42 ]. However, insecurity remained a challenge in some areas compromising monitoring and supportive supervision visits in some local governments, which has led to poor data reporting. Consequently, the state must explore innovative approaches to retrieve program data from high-risk security areas [ 43 ]. Finally, the RI MoU logic model provided a structure for assessing the RI MoU performance, however a theory driven model including a clear monitoring and evaluation plan with indicators and targets established prior to implementation of the MoU is required in order to provide a more rigorous assessment of the RI MoU approach [ 44 , 45 ]. This approach would also help to provide a better understanding of why some states such as Yobe have been effective in increasing immunization coverages while others such as Borno and Sokoto continue to struggle.

Limitations

There are several limitations associated with this study. First, each state included in the evaluation initiated implementation at different time points and progressed to more comprehensive PHC MoU models at staggered times. In addition, while the states completed a comprehensive diagnostic assessment prior to implementation, comparable baseline measures were not collected. Next, due to the complexity of the approach, no single data source could be used to measure the full influence of the approach. This coupled with the lack of a comparison area made it impossible to control for the effect of individual inputs or how the MoU states performed relative to other states in the region who did not benefit from the MoU investment [ 20 ]. Third, a comprehensive monitoring and evaluation plan was not established prior to implementation with clearly defined indicators and data sources. As such, limited data were available following several years of implementation and at irregular intervals. Finally, details on programs not operating under the purview of the RI MoU approach as well as contextual factors likely influenced the variable outcomes observed across the six states [ 46 ]. Specifically, Gavi’s national level investments to strengthen the health system including cold chain equipment investment despite focusing on non-MoU states may have reached to a limited extent MoU states. And, the Covid-19 pandemic while not assessed in this evaluation contributed to a lack of transport and limited outreach visits [ 47 ]. Despite these challenges, the evaluation did endeavor to achieve a holistic understanding of the RI MoU approach by gathering perspectives from multiple levels (e.g. participants, service providers, community structures, partners, donors and government stakeholders).

Consistent with previous research on the advantages of partnership models, we found the RI MoUs across the six states to be mostly successful in strengthening health systems, improving accountability and coordination, and increasing the utilization of services and financing for RI which serves as an important foundation as the country transitions to sector wide approaches [ 14 , 15 ]. However, evaluation findings indicate that issues pertaining to human resources for health, insecurity that inhibits supportive supervision and vaccine logistics as well as harmful socio-cultural norms remain a persistent challenge in the states suggest that the RI MoU approach would benefit from increased technical assistance and capacity building to address these limitations [ 13 ]. Attention to numbers, capacity, and distribution of frontline health providers will be an important component for health system strengthening moving forward. Furthermore, addressing cultural norms received minimal consideration throughout the MoU design. If interventions to address socio-cultural norms are not incorporated into the program, service uptake may remain low.

Data availability

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

Abbreviations

Aliko Dangote Foundation

Bill and Melinda Gates Foundation

Cold Chain Equipment

Community Health Extension Worker

Community Health Officer

Client Exit Interview

District Health Information System 2

Demographic and Health Survey

Focus Group Discussion

Health Facility Assessment

In depth Interview

Key Informant

Local Government Area

Memorandum of Understanding

National Primary Health Care Development Agency

Primary Health Care

Primary Health Care Under One Roof

Reaching every ward

Routine immunization

Routine Immunization Supportive Supervision

Solar Direct Drive

State Primary Health Care Development Agency

U.S Agency for International Development

United States Dollar

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Acknowledgements

The authors would like to thank the Bill and Melinda Gates Foundation in commissioning and funding this study. We would also like to thank the field staff who supported the data collection efforts.

The Bill and Melinda Gates Foundation provided funding and reviewed the draft manuscript. The contents are the responsibility of the authors and do not necessarily reflect the views of the foundation.

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LD conceptualized the study. LD, MA, AA, MA, EE, JO, AA contributed to data collection, analysis and reporting. LD drafted the manuscript. All authors reviewed and provided critical revision into the final version of the manuscript as well as final approval of the version to be submitted. The manuscript is not under consideration for publication elsewhere and if accepted will not be published elsewhere.

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Dougherty, L., Adediran, M., Akinola, A. et al. An evaluation of a multi-partner approach to increase routine immunization coverage in six northern Nigerian States. BMC Health Serv Res 24 , 951 (2024). https://doi.org/10.1186/s12913-024-11403-3

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DOI : https://doi.org/10.1186/s12913-024-11403-3

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