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• 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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The qualitative research process, end-to-end

.css-1nrevy2{position:relative;display:inline-block;} The qualitative research process: step by step guide

Although research processes may vary by methodology or project team, some fundamentals exist across research projects. Below outlines the collective experience that qualitative researchers undertake to conduct research.

Step 1: Determine what to research

Once a researcher has determined a list of potential projects to tackle, they will prioritize projects based on the business's impact, available resourcing, timelines & dependencies to create a research roadmap. For each project, they will also identify the key questions they need to answer in the research.

The researcher should identify the participants they plan to research, and any key attributes that are a 'must-have' or 'nice to have' as these can be influential in determining the research approach (e.g. a niche group may require a longer timeline to recruit).

Researchers will generally aim for a mix of project types. Some may be more tactical or requests from stakeholders, and some will be projects that the researcher has proactively identified as opportunities for strategic research.

Step 2: Identify how to research it

Once the researcher has finalized the research project, they will need to figure out how they will do the work.

Firstly, the researcher will look through secondary data and research (e.g. analytics, previous research reports). Secondary analysis will help determine if there are existing answers to any of the open questions, ensuring that any net-new study doesn't duplicate current work (unless previous research is out of date).

After scoping the research, researchers will determine if the research input needs to be  attitudinal  (i.e. what someone says) or  behavioral  (i.e. what someone does); as well as if they need to  explore  a problem space or  evaluate  a product – these help determine the methodology to use. There are many methodologies out there, but the main ones you generally will find from a qualitative perspective are:

Interviews [Attitudinal / Exploratory]  – semi-structured conversation with a participant focused on a small set of topics. Runs for 30-60 minutes.

Contextual Inquiry [Behavioral / Exploratory]  – observation of a participant in their environment. Probing questions may be asked during the observation. Runs for 2-3 hours.

Survey [Attitudinal / Evaluative]  – gathering structured information from a sample of people, traditionally to generalize the results to a larger population. Surveys should generally not take participants more than 10 minutes to complete.

Usability Test [Behavioral / Evaluative]  – evaluating how representative users are, or are not, able to achieve critical tasks within an experience.

Check out these articles for more information about different methodologies:

When to Use Which User-Experience Research Methods

UX Research Cheat Sheet

Usability.gov

Design Research Kit

Step 3: Get buy-in and alignment from others

Once a researcher has determined what they will be researching and how they will research it, they will generally write up a research plan that includes additional information about the research goals, participant scope, timelines, and dependencies. The plan is typically either a document or presentation shared with stakeholders depending on the company and how they work.

After the research plan is complete, researchers will share the plan for feedback and input from their stakeholders to ensure that the stakeholders have the right expectations going into the research. Stakeholders may ask for additional question topics to be added, ensure that research will be executed against specific timelines, or provide recommendations on how the study will help make product decisions.

At organizations where there is a research team, researchers may also share their plan with other researchers informally or through a 'crit' process. Generally, researchers will provide feedback on the research craft, such as methodologies, participant mixes, and the research goals or questions.

Once the researcher feels confident in their plan, they will either begin to plan the research, or in the case of more junior researchers, get approval from their manager to begin the study.

Step 4: Prepare research

This step is where the researcher will get all of their ducks in a row to execute the research. Preparation activities include:

Equipment: Booking venues, labs, observation rooms, and procuring any appropriate equipment needed to run the study (e.g. cameras, mobile devices).

Participants: Sourcing participants from internal / external databases, reaching out/scheduling participants, managing schedule changes.

Incentives: Find budget, identify incentive type (e.g. Amazon gift card? customer credit? gift baskets), and purchasing.

Assets: Building relevant designs / prototypes (with design or design technologists), creating interview / observation guides and other research tools needed for sessions (e.g. physical cards for in-person card sorts).

Legal & Procurement: Participant waivers or NDAs preparation to ensure they are sent in advance of the research session to participants, vendor procurement, and management.

If Research Operations exists within an organization, they will generally take on most of the load in this area. The researcher will focus on assets required for executing research, such as interview guides.

In some cases, vendors may be engaged for some of these requirements (e.g. labs, participants, and incentive management) if resourcing is not available internally or if a researcher wants a blinded study (i.e. the participant doesn't know what company is running the research). In this case, additional time is incorporated to brief, onboard, and get approvals to work with the vendor.

Step 5: Execute research

Now the researcher gets to research!

Researchers will generally aim to execute research activities for 1–2 weeks, depending on the methodology to ensure they can be efficient in execution. In some more longitudinal methods (e.g., diary studies), or if a participant type is harder to recruit, it may take longer.

In consumer research, there will usually be back up participants available in case of no shows. However, in business or enterprise research, researchers will engage will all recruited participants as participants will generally have relationships with other parts of the company (e.g. sales). It is essential to maintain those relationships post-research.

During sessions, in a perfect world, there is one facilitator (principal researcher). In some cases, a secondary attendee who takes notes – this can be a stakeholder or a more junior researcher who can then learn soft skills from the primary researcher. By delegating note-taking, the principal researcher can focus on driving and managing the participant's conversation.

However, in most cases (especially if there is a "research team of one"), researchers will try to have to do both facilitation and documentation – this can lead to a clunkier conversation as the facilitator attempts to quickly write notes between trying to think of the next question. If a researcher decides to record a session instead, they will have to spend additional time after the research listening to the full recordings and writing notes.

In qualitative research, researchers may begin to  see patterns in the findings after five sessions . They may start to tailor the research questions to be more specific to gaps in their understanding.

Researchers may also set up an observation room for stakeholders (or share links to remote sessions) to attend live. Generally, researchers will have a backchannel (e.g., slack, chat, or SMS), so if a stakeholder has a follow-up question to an answer, the researcher can dig deeper. In some cases, researchers will give stakeholders an input form to take their notes that can be shared with the researcher afterward - this can be useful for the researcher to understand how the stakeholder views the research and what the stakeholder perceives as necessary to the research insights.

Step 6: Synthesize and find insights

Once the research capture is complete, the researcher will then aggregate findings to begin to look for common themes (in exploratory) or success rates (in evaluative). Both of these will then lead to insight generation that researchers will then look to tie back to the project's original research goals.

As analysis can be one of the most high-effort tasks in research, researchers will lean towards how to be efficient in their study, generally using digital tools, hacks, or workarounds. Researchers will usually create the analysis process they refine throughout their careers to help them become more efficient.

In cases where researchers are looking to get buy-in for research or capture stakeholder input, they may seem to more visual approaches (e.g. post-it affinity analysis) in war rooms. This process can take longer to process (especially if there is a high volume of data). Still, there can be a higher impact on analyzing research in this way – especially if the researcher is looking to get buy-in for future projects.

Step 7: Create research outputs

After a researcher identifies the key themes and insights, the researcher will reframe these findings to a relevant research output to ensure that stakeholders understand and buy-in to the outcomes. Outputs may include:

Report: Outlines vital findings from research in a document or presentation format. Will most likely include an executive summary, insight themes, and supporting evidence.

Videos: A highlight reel of supporting evidence from crucial findings. Generally seen as more useful and engaging compared to just a report. In most cases, the video will help the research report.

Personas : A written representation of a product's intended users to understand different types of user goals, needs, and behaviors. Also used to help stakeholders build empathy for the end-user of the product.

Journey Map : A visualization of the process that a person goes through to accomplish a goal. Generally created in conjunction with a persona.

Concepts / Wireframes / Designs: If research is evaluative, designs can visualize recommendations.

Storyboarding

Before a researcher makes the output, researchers will spend time planning the structure and storyboarding. Storyboarding is incredibly essential to help researchers define information requirements and ensure they present their findings in the most impactful way to stakeholders.

Having a point of view in outputs

Historically, researchers have tried to stay neutral to the data and not try to have a strong opinion or perspective to let the data speak. However, as researchers become more embedded in the industry, this has shifted to stakeholders wanting a strong point of view or recommendations from researchers that can help other stakeholders (especially product managers and designers) decide the knowledge captured as part of the research.

Having a strong perspective helps researchers have a seat at the table and appear as a trusted advisor/partner in cross-functional settings.

Step 8: Share and follow up on findings

After the research outputs are complete, some researchers will do a "pre-share" or walkthrough with key stakeholders or potential detractors to the research. The purpose of these meetings is to align with stakeholders' expectations and find potential 'watch-outs' (things that may derail a presentation).

Researchers will generally have to share their findings out multiple times to different stakeholder groups and tailor them for each audience. For example, executive meetings will be more higher level than a meeting with a product manager.

After sharing, researchers will follow up with key stakeholders (especially those who provided input to the research) to confirm they understand the findings and identify next steps. Next steps may include incorporating results in product strategy documents, proposals / PRDs, or user stories to ensure that the recommendations or findings have been reflected or sourced.

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7 Steps to Conducting Better Qualitative Research

While qualitative research is the collection and analysis of primarily non-numerical activities (words, pictures and actions), it doesn’t mean you can’t apply a structured approach to your research efforts.

Usability testing is often characterized as a qualitative activity. Summarizing findings from watching participants in a usability test generates a lot of utterances, actions and images.

In reality, usability testing is (or at least should be) a mixed-method approach: both qualitative and quantitative data are collected.

Here are seven steps to help structure your next qualitative research effort. These have been adapted from Johnson & Christensen 2012 to focus on the type of qualitative research more typical in User Experience such as usability tests or contextual inquires (where an interface is usually involved).

You don’t need to follow these steps linearly, or even include them all in your research, but having these steps should both help structure your next project and help focus the discussion the next time you’re working with someone who proposes a qualitative research approach.

• Determine Research Questions : Focused questions are at the heart of actionable qualitative research. In fact, they are at the heart of good quantitative research as well and play a key role in Lean UX thinking . Are users not using the mobile app because of usability, security concerns or something else? How do users make decisions about how to invest: do they ask a friend, use a financial advisor, or research on their own?
• Collect Data : The qualitative researcher should assume the role of an unobtrusive observer and have little impact on the settings being observed—whether it be watching participants use existing products at home or in a more controlled lab environment. Qualitative is often used synonymously with small samples, but one can take a qualitative approach to larger sample sizes (more than 50 participants) just as one can take a quantitative approach to small sample sizes (less than 10).
• Analyze Data : Most qualitative research studies generate a lot of data. Creating a system for coding actions and notable quotes helps speed through the process of turning utterances into actionable insights .
• Generate Findings : What was learned from engaging users? This step involves synthesizing the copious amount of notes, videos and artifacts.  As many of the responses from participants will be open-ended, there will be a need to identify patterns . For example, when we were interviewing users about why they didn’t pay their credit card bill on their mobile phone, we didn’t ask users if they had security concerns. Instead, many of them voiced the concern in their own words and stories.
• Validate findings : One of the best ways to validate findings is to triangulate using other methods , including surveys or additional sources. One weakness of qualitative research is that it is hard to establish external validity, that is, to provide corroborating evidence that the findings aren’t just the opinion of the researcher.  Every researcher, of course, does bring with her biases on the problems with a product or what deserves emphasis in the interview.One approach to minimize this researcher bias is to include a section on the interviewer or principal investigator’s background and how it might influence their conclusions. Having recordings of sessions and detailed notes helps other interested parties come to their own conclusions and can help validate findings. Including verbatims along with the interpretation also helps others see how the conclusions were drawn.
• Report :  We usually deliver a power point with backup notes or an appendix with more detailed findings and verbatims. While information comes in sequentially from each participant, we find reporting the data in an inverted pyramid by issue works best.  We start with the most important findings, and then note the number of participants that supported these findings and some good quotes to support what we concluded.  We also provide confidence intervals around the issue and insight frequency so readers have some idea about the prevalence of an issue in the larger user population.

You might also be interested in

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Introduction

How do you prepare for qualitative research?

Qualities of good study design, components of qualitative research, theoretical perspective, theoretical framework.

• Literature reviews

Research question

Conceptual framework.

• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Qualitative research preparation

Robust qualitative data collection and qualitative data analysis rely on thorough preparation, which can adequately guide your choice of theories and qualitative research methods . Conducting qualitative research requires consideration of important theoretical and methodological elements before the study can be considered rigorous and trustworthy. With that in mind, this section will preview the subsequent sections exploring the essential components of a qualitative study.

Quantitative research design is arguably more straightforward than qualitative research design. Studies that require statistical analyses, for example, can rely on clear pre-established processes and rely on statistical analysis programs to produce results. On the other hand, qualitative research methodology depends on what assumptions are made about the topic and object of inquiry. As a result, qualitative researchers have to deeply consider the epistemology informing the study design before deciding on qualitative research methods or what the qualitative research data will look like.

The traditional " paradigm wars " over whether quantitative research is better than qualitative research or vice versa presented numerous critiques that have, ironically, been good for qualitative researchers in the long run. These critiques have called on researchers to critically consider their theoretical assumptions and methodology in a manner that has allowed them and their peers to distinguish a rigorous study from a poorly designed one. More specifically, study design is important in qualitative research for several reasons:

Flexibility : Qualitative research often involves exploring complex, multifaceted phenomena that cannot be easily measured or quantified. As a result, the research design must be flexible enough to accommodate new insights or emerging themes as they arise during the data collection and data analysis .

Emphasis on context : Qualitative research often emphasizes the importance of context in shaping individual experiences and social phenomena. As a result, the research design must be sensitive to the specific cultural, social, and historical contexts in which the research is conducted.

Subjectivity : Qualitative research often acknowledges the role of the researcher's own subjectivity in shaping the research process and findings. As a result, the research design must account for the researcher's own biases and perspectives and strive to minimize their impact on the research process.

Richness and depth of data : Qualitative research often aims to produce rich, detailed data that can provide a deep understanding of the phenomenon under investigation. As a result, the research design must be tailored to produce data that is rich in detail and depth, such as through in-depth interviews or participant observation .

Importance of interpretation : Qualitative research often involves interpreting data and identifying patterns or themes that emerge from the data. As a result, the research design must be structured in a way that allows for systematic analysis and interpretation of data.

Overall, study design is important in qualitative research because it allows the researcher to tailor the research process to the unique characteristics of the phenomenon under investigation and to produce rich, detailed data that can provide a deep understanding of the phenomenon. By emphasizing context, subjectivity, and interpretation, qualitative research can produce nuanced, complex insights that may not be captured through quantitative research methods.

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Just as quantitative research methods commonly adhere to the scientific process, a rigorous qualitative research process has essential components that require consideration for scholars to think of a study as credible and trustworthy. These components guide a research project toward appropriate theories, methods, and analytical strategies. After all, you might like to conduct a focus group if it is your favorite qualitative method, but do your research questions call for using focus groups? Do the theoretical foundations embedded in your research make focus groups an appropriate data collection strategy? Do you have a comprehensive theoretical framework to adequately capture the insights you need from focus group data?

Knowing the answers to these questions is crucial for presenting a credible and persuasive study. With that in mind, here are the necessary elements typically found in qualitative research:

The theoretical perspective is the philosophical stance or worldview that underlies the researcher's approach to understanding the phenomenon of interest. This perspective can shape the entire course of the study, from defining the research question to selecting the methodology , analyzing the data , and interpreting the findings . It often involves broad assumptions about society and human behavior, and it can be informed by theories from a variety of disciplines, such as sociology, psychology, or anthropology. For example, a researcher with a constructivist perspective may seek to understand how individuals construct their own realities. In contrast, a researcher with a postmodern perspective may focus on the power dynamics and social constructions that shape people's experiences.

The theoretical framework is the lens through which the research question is viewed and examined. It is the application of a specific theoretical perspective to the phenomenon under investigation. It may involve one or multiple theories or models, which provide a conceptual structure for interpreting and understanding the research data. The theoretical framework guides the development of research questions and hypotheses, and it shapes the design of the study, including the data collection and analysis methods. Importantly, the theoretical framework also provides a basis for making logical inferences and generalizations from the research findings. For example, a researcher studying the impact of social media on body image might draw on social comparison theory to frame their investigation.

Literature review

The literature review is a rigorous and systematic examination of the body of knowledge that has been published on a particular topic. It provides an understanding of the current state of knowledge, identifies key concepts and variables relevant to the research question, and uncovers gaps or inconsistencies in the existing literature that the current research can address. It involves a careful selection and critical appraisal of studies, with the aim of understanding the strengths and limitations of previous research. In addition to summarizing and synthesizing the findings of previous studies, the literature review should also discuss how the current study fits within the broader research context. For instance, in the case of research on social media's impact on body image, the literature review might highlight different aspects of the issue that have been studied, such as the role of peer comparison, the impact of influencers, and the effect of different types of social media platforms.

The research question is a specific question or set of questions that the research aims to answer. The research question is typically informed by the theoretical perspective, theoretical framework, and literature review. The question you pursue will determine which qualitative methods are appropriate for your research, or it may even steer you toward quantitative methods. A study that investigates customer perspectives may warrant surveys or focus groups . An analysis of discourses may rely on a detailed coding process or content analysis methods to measure the use of certain expressions or words. An inquiry about cultural processes can call for an ethnographic research method for collecting qualitative data in the field for thorough understanding.

The conceptual framework serves as a roadmap for the research process, providing a clear picture of the proposed study. It's a researcher's individual interpretation of how the key parts of the study — the variables or concepts — are expected to relate to each other. This framework typically includes a set of concepts or variables that are directly related to the research question, as well as a set of propositions or hypotheses about how these concepts or variables are interconnected. While the conceptual framework may draw from multiple theoretical perspectives, it is tailored specifically to the research question at hand and the specific context of the study.

For example, in a study on the impact of online learning on student performance, the conceptual framework may include variables such as the frequency of online classes, student engagement, access to technology, and academic achievement. It might propose that a higher frequency of online classes and greater access to technology lead to increased student engagement, which in turn boosts academic performance. The conceptual framework also guides the data collection and analysis process, helping researchers to stay focused on the research objectives and to interpret their findings in a meaningful way. It ensures a systematic and rigorous approach to the research, and it provides a framework for reporting the research findings and for discussing their implications. Importantly, a well-developed conceptual framework can also make the research more credible and understandable to others, and it can help to identify potential limitations and areas for future research.

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Chapter 1. Introduction

“Science is in danger, and for that reason it is becoming dangerous” -Pierre Bourdieu, Science of Science and Reflexivity

Why an Open Access Textbook on Qualitative Research Methods?

I have been teaching qualitative research methods to both undergraduates and graduate students for many years.  Although there are some excellent textbooks out there, they are often costly, and none of them, to my mind, properly introduces qualitative research methods to the beginning student (whether undergraduate or graduate student).  In contrast, this open-access textbook is designed as a (free) true introduction to the subject, with helpful, practical pointers on how to conduct research and how to access more advanced instruction.  

Textbooks are typically arranged in one of two ways: (1) by technique (each chapter covers one method used in qualitative research); or (2) by process (chapters advance from research design through publication).  But both of these approaches are necessary for the beginner student.  This textbook will have sections dedicated to the process as well as the techniques of qualitative research.  This is a true “comprehensive” book for the beginning student.  In addition to covering techniques of data collection and data analysis, it provides a road map of how to get started and how to keep going and where to go for advanced instruction.  It covers aspects of research design and research communication as well as methods employed.  Along the way, it includes examples from many different disciplines in the social sciences.

The primary goal has been to create a useful, accessible, engaging textbook for use across many disciplines.  And, let’s face it.  Textbooks can be boring.  I hope readers find this to be a little different.  I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research.  Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.  These short accounts by practitioners should help inspire students.  So, let’s begin!

What is Research?

When we use the word research , what exactly do we mean by that?  This is one of those words that everyone thinks they understand, but it is worth beginning this textbook with a short explanation.  We use the term to refer to “empirical research,” which is actually a historically specific approach to understanding the world around us.  Think about how you know things about the world. [1] You might know your mother loves you because she’s told you she does.  Or because that is what “mothers” do by tradition.  Or you might know because you’ve looked for evidence that she does, like taking care of you when you are sick or reading to you in bed or working two jobs so you can have the things you need to do OK in life.  Maybe it seems churlish to look for evidence; you just take it “on faith” that you are loved.

Only one of the above comes close to what we mean by research.  Empirical research is research (investigation) based on evidence.  Conclusions can then be drawn from observable data.  This observable data can also be “tested” or checked.  If the data cannot be tested, that is a good indication that we are not doing research.  Note that we can never “prove” conclusively, through observable data, that our mothers love us.  We might have some “disconfirming evidence” (that time she didn’t show up to your graduation, for example) that could push you to question an original hypothesis , but no amount of “confirming evidence” will ever allow us to say with 100% certainty, “my mother loves me.”  Faith and tradition and authority work differently.  Our knowledge can be 100% certain using each of those alternative methods of knowledge, but our certainty in those cases will not be based on facts or evidence.

For many periods of history, those in power have been nervous about “science” because it uses evidence and facts as the primary source of understanding the world, and facts can be at odds with what power or authority or tradition want you to believe.  That is why I say that scientific empirical research is a historically specific approach to understand the world.  You are in college or university now partly to learn how to engage in this historically specific approach.

In the sixteenth and seventeenth centuries in Europe, there was a newfound respect for empirical research, some of which was seriously challenging to the established church.  Using observations and testing them, scientists found that the earth was not at the center of the universe, for example, but rather that it was but one planet of many which circled the sun. [2]   For the next two centuries, the science of astronomy, physics, biology, and chemistry emerged and became disciplines taught in universities.  All used the scientific method of observation and testing to advance knowledge.  Knowledge about people , however, and social institutions, however, was still left to faith, tradition, and authority.  Historians and philosophers and poets wrote about the human condition, but none of them used research to do so. [3]

It was not until the nineteenth century that “social science” really emerged, using the scientific method (empirical observation) to understand people and social institutions.  New fields of sociology, economics, political science, and anthropology emerged.  The first sociologists, people like Auguste Comte and Karl Marx, sought specifically to apply the scientific method of research to understand society, Engels famously claiming that Marx had done for the social world what Darwin did for the natural world, tracings its laws of development.  Today we tend to take for granted the naturalness of science here, but it is actually a pretty recent and radical development.

To return to the question, “does your mother love you?”  Well, this is actually not really how a researcher would frame the question, as it is too specific to your case.  It doesn’t tell us much about the world at large, even if it does tell us something about you and your relationship with your mother.  A social science researcher might ask, “do mothers love their children?”  Or maybe they would be more interested in how this loving relationship might change over time (e.g., “do mothers love their children more now than they did in the 18th century when so many children died before reaching adulthood?”) or perhaps they might be interested in measuring quality of love across cultures or time periods, or even establishing “what love looks like” using the mother/child relationship as a site of exploration.  All of these make good research questions because we can use observable data to answer them.

What is Qualitative Research?

“All we know is how to learn. How to study, how to listen, how to talk, how to tell.  If we don’t tell the world, we don’t know the world.  We’re lost in it, we die.” -Ursula LeGuin, The Telling

At its simplest, qualitative research is research about the social world that does not use numbers in its analyses.  All those who fear statistics can breathe a sigh of relief – there are no mathematical formulae or regression models in this book! But this definition is less about what qualitative research can be and more about what it is not.  To be honest, any simple statement will fail to capture the power and depth of qualitative research.  One way of contrasting qualitative research to quantitative research is to note that the focus of qualitative research is less about explaining and predicting relationships between variables and more about understanding the social world.  To use our mother love example, the question about “what love looks like” is a good question for the qualitative researcher while all questions measuring love or comparing incidences of love (both of which require measurement) are good questions for quantitative researchers. Patton writes,

Qualitative data describe.  They take us, as readers, into the time and place of the observation so that we know what it was like to have been there.  They capture and communicate someone else’s experience of the world in his or her own words.  Qualitative data tell a story. ( Patton 2002:47 )

Qualitative researchers are asking different questions about the world than their quantitative colleagues.  Even when researchers are employed in “mixed methods” research ( both quantitative and qualitative), they are using different methods to address different questions of the study.  I do a lot of research about first-generation and working-college college students.  Where a quantitative researcher might ask, how many first-generation college students graduate from college within four years? Or does first-generation college status predict high student debt loads?  A qualitative researcher might ask, how does the college experience differ for first-generation college students?  What is it like to carry a lot of debt, and how does this impact the ability to complete college on time?  Both sets of questions are important, but they can only be answered using specific tools tailored to those questions.  For the former, you need large numbers to make adequate comparisons.  For the latter, you need to talk to people, find out what they are thinking and feeling, and try to inhabit their shoes for a little while so you can make sense of their experiences and beliefs.

Examples of Qualitative Research

You have probably seen examples of qualitative research before, but you might not have paid particular attention to how they were produced or realized that the accounts you were reading were the result of hours, months, even years of research “in the field.”  A good qualitative researcher will present the product of their hours of work in such a way that it seems natural, even obvious, to the reader.  Because we are trying to convey what it is like answers, qualitative research is often presented as stories – stories about how people live their lives, go to work, raise their children, interact with one another.  In some ways, this can seem like reading particularly insightful novels.  But, unlike novels, there are very specific rules and guidelines that qualitative researchers follow to ensure that the “story” they are telling is accurate , a truthful rendition of what life is like for the people being studied.  Most of this textbook will be spent conveying those rules and guidelines.  Let’s take a look, first, however, at three examples of what the end product looks like.  I have chosen these three examples to showcase very different approaches to qualitative research, and I will return to these five examples throughout the book.  They were all published as whole books (not chapters or articles), and they are worth the long read, if you have the time.  I will also provide some information on how these books came to be and the length of time it takes to get them into book version.  It is important you know about this process, and the rest of this textbook will help explain why it takes so long to conduct good qualitative research!

Example 1 : The End Game (ethnography + interviews)

Corey Abramson is a sociologist who teaches at the University of Arizona.   In 2015 he published The End Game: How Inequality Shapes our Final Years ( 2015 ). This book was based on the research he did for his dissertation at the University of California-Berkeley in 2012.  Actually, the dissertation was completed in 2012 but the work that was produced that took several years.  The dissertation was entitled, “This is How We Live, This is How We Die: Social Stratification, Aging, and Health in Urban America” ( 2012 ).  You can see how the book version, which was written for a more general audience, has a more engaging sound to it, but that the dissertation version, which is what academic faculty read and evaluate, has a more descriptive title.  You can read the title and know that this is a study about aging and health and that the focus is going to be inequality and that the context (place) is going to be “urban America.”  It’s a study about “how” people do something – in this case, how they deal with aging and death.  This is the very first sentence of the dissertation, “From our first breath in the hospital to the day we die, we live in a society characterized by unequal opportunities for maintaining health and taking care of ourselves when ill.  These disparities reflect persistent racial, socio-economic, and gender-based inequalities and contribute to their persistence over time” ( 1 ).  What follows is a truthful account of how that is so.

Cory Abramson spent three years conducting his research in four different urban neighborhoods.  We call the type of research he conducted “comparative ethnographic” because he designed his study to compare groups of seniors as they went about their everyday business.  It’s comparative because he is comparing different groups (based on race, class, gender) and ethnographic because he is studying the culture/way of life of a group. [4]   He had an educated guess, rooted in what previous research had shown and what social theory would suggest, that people’s experiences of aging differ by race, class, and gender.  So, he set up a research design that would allow him to observe differences.  He chose two primarily middle-class (one was racially diverse and the other was predominantly White) and two primarily poor neighborhoods (one was racially diverse and the other was predominantly African American).  He hung out in senior centers and other places seniors congregated, watched them as they took the bus to get prescriptions filled, sat in doctor’s offices with them, and listened to their conversations with each other.  He also conducted more formal conversations, what we call in-depth interviews, with sixty seniors from each of the four neighborhoods.  As with a lot of fieldwork , as he got closer to the people involved, he both expanded and deepened his reach –

By the end of the project, I expanded my pool of general observations to include various settings frequented by seniors: apartment building common rooms, doctors’ offices, emergency rooms, pharmacies, senior centers, bars, parks, corner stores, shopping centers, pool halls, hair salons, coffee shops, and discount stores. Over the course of the three years of fieldwork, I observed hundreds of elders, and developed close relationships with a number of them. ( 2012:10 )

When Abramson rewrote the dissertation for a general audience and published his book in 2015, it got a lot of attention.  It is a beautifully written book and it provided insight into a common human experience that we surprisingly know very little about.  It won the Outstanding Publication Award by the American Sociological Association Section on Aging and the Life Course and was featured in the New York Times .  The book was about aging, and specifically how inequality shapes the aging process, but it was also about much more than that.  It helped show how inequality affects people’s everyday lives.  For example, by observing the difficulties the poor had in setting up appointments and getting to them using public transportation and then being made to wait to see a doctor, sometimes in standing-room-only situations, when they are unwell, and then being treated dismissively by hospital staff, Abramson allowed readers to feel the material reality of being poor in the US.  Comparing these examples with seniors with adequate supplemental insurance who have the resources to hire car services or have others assist them in arranging care when they need it, jolts the reader to understand and appreciate the difference money makes in the lives and circumstances of us all, and in a way that is different than simply reading a statistic (“80% of the poor do not keep regular doctor’s appointments”) does.  Qualitative research can reach into spaces and places that often go unexamined and then reports back to the rest of us what it is like in those spaces and places.

Example 2: Racing for Innocence (Interviews + Content Analysis + Fictional Stories)

Jennifer Pierce is a Professor of American Studies at the University of Minnesota.  Trained as a sociologist, she has written a number of books about gender, race, and power.  Her very first book, Gender Trials: Emotional Lives in Contemporary Law Firms, published in 1995, is a brilliant look at gender dynamics within two law firms.  Pierce was a participant observer, working as a paralegal, and she observed how female lawyers and female paralegals struggled to obtain parity with their male colleagues.

Fifteen years later, she reexamined the context of the law firm to include an examination of racial dynamics, particularly how elite white men working in these spaces created and maintained a culture that made it difficult for both female attorneys and attorneys of color to thrive. Her book, Racing for Innocence: Whiteness, Gender, and the Backlash Against Affirmative Action , published in 2012, is an interesting and creative blending of interviews with attorneys, content analyses of popular films during this period, and fictional accounts of racial discrimination and sexual harassment.  The law firm she chose to study had come under an affirmative action order and was in the process of implementing equitable policies and programs.  She wanted to understand how recipients of white privilege (the elite white male attorneys) come to deny the role they play in reproducing inequality.  Through interviews with attorneys who were present both before and during the affirmative action order, she creates a historical record of the “bad behavior” that necessitated new policies and procedures, but also, and more importantly , probed the participants ’ understanding of this behavior.  It should come as no surprise that most (but not all) of the white male attorneys saw little need for change, and that almost everyone else had accounts that were different if not sometimes downright harrowing.

I’ve used Pierce’s book in my qualitative research methods courses as an example of an interesting blend of techniques and presentation styles.  My students often have a very difficult time with the fictional accounts she includes.  But they serve an important communicative purpose here.  They are her attempts at presenting “both sides” to an objective reality – something happens (Pierce writes this something so it is very clear what it is), and the two participants to the thing that happened have very different understandings of what this means.  By including these stories, Pierce presents one of her key findings – people remember things differently and these different memories tend to support their own ideological positions.  I wonder what Pierce would have written had she studied the murder of George Floyd or the storming of the US Capitol on January 6 or any number of other historic events whose observers and participants record very different happenings.

This is not to say that qualitative researchers write fictional accounts.  In fact, the use of fiction in our work remains controversial.  When used, it must be clearly identified as a presentation device, as Pierce did.  I include Racing for Innocence here as an example of the multiple uses of methods and techniques and the way that these work together to produce better understandings by us, the readers, of what Pierce studied.  We readers come away with a better grasp of how and why advantaged people understate their own involvement in situations and structures that advantage them.  This is normal human behavior , in other words.  This case may have been about elite white men in law firms, but the general insights here can be transposed to other settings.  Indeed, Pierce argues that more research needs to be done about the role elites play in the reproduction of inequality in the workplace in general.

Example 3: Amplified Advantage (Mixed Methods: Survey Interviews + Focus Groups + Archives)

The final example comes from my own work with college students, particularly the ways in which class background affects the experience of college and outcomes for graduates.  I include it here as an example of mixed methods, and for the use of supplementary archival research.  I’ve done a lot of research over the years on first-generation, low-income, and working-class college students.  I am curious (and skeptical) about the possibility of social mobility today, particularly with the rising cost of college and growing inequality in general.  As one of the few people in my family to go to college, I didn’t grow up with a lot of examples of what college was like or how to make the most of it.  And when I entered graduate school, I realized with dismay that there were very few people like me there.  I worried about becoming too different from my family and friends back home.  And I wasn’t at all sure that I would ever be able to pay back the huge load of debt I was taking on.  And so I wrote my dissertation and first two books about working-class college students.  These books focused on experiences in college and the difficulties of navigating between family and school ( Hurst 2010a, 2012 ).  But even after all that research, I kept coming back to wondering if working-class students who made it through college had an equal chance at finding good jobs and happy lives,

What happens to students after college?  Do working-class students fare as well as their peers?  I knew from my own experience that barriers continued through graduate school and beyond, and that my debtload was higher than that of my peers, constraining some of the choices I made when I graduated.  To answer these questions, I designed a study of students attending small liberal arts colleges, the type of college that tried to equalize the experience of students by requiring all students to live on campus and offering small classes with lots of interaction with faculty.  These private colleges tend to have more money and resources so they can provide financial aid to low-income students.  They also attract some very wealthy students.  Because they enroll students across the class spectrum, I would be able to draw comparisons.  I ended up spending about four years collecting data, both a survey of more than 2000 students (which formed the basis for quantitative analyses) and qualitative data collection (interviews, focus groups, archival research, and participant observation).  This is what we call a “mixed methods” approach because we use both quantitative and qualitative data.  The survey gave me a large enough number of students that I could make comparisons of the how many kind, and to be able to say with some authority that there were in fact significant differences in experience and outcome by class (e.g., wealthier students earned more money and had little debt; working-class students often found jobs that were not in their chosen careers and were very affected by debt, upper-middle-class students were more likely to go to graduate school).  But the survey analyses could not explain why these differences existed.  For that, I needed to talk to people and ask them about their motivations and aspirations.  I needed to understand their perceptions of the world, and it is very hard to do this through a survey.

By interviewing students and recent graduates, I was able to discern particular patterns and pathways through college and beyond.  Specifically, I identified three versions of gameplay.  Upper-middle-class students, whose parents were themselves professionals (academics, lawyers, managers of non-profits), saw college as the first stage of their education and took classes and declared majors that would prepare them for graduate school.  They also spent a lot of time building their resumes, taking advantage of opportunities to help professors with their research, or study abroad.  This helped them gain admission to highly-ranked graduate schools and interesting jobs in the public sector.  In contrast, upper-class students, whose parents were wealthy and more likely to be engaged in business (as CEOs or other high-level directors), prioritized building social capital.  They did this by joining fraternities and sororities and playing club sports.  This helped them when they graduated as they called on friends and parents of friends to find them well-paying jobs.  Finally, low-income, first-generation, and working-class students were often adrift.  They took the classes that were recommended to them but without the knowledge of how to connect them to life beyond college.  They spent time working and studying rather than partying or building their resumes.  All three sets of students thought they were “doing college” the right way, the way that one was supposed to do college.   But these three versions of gameplay led to distinct outcomes that advantaged some students over others.  I titled my work “Amplified Advantage” to highlight this process.

These three examples, Cory Abramson’s The End Game , Jennifer Peirce’s Racing for Innocence, and my own Amplified Advantage, demonstrate the range of approaches and tools available to the qualitative researcher.  They also help explain why qualitative research is so important.  Numbers can tell us some things about the world, but they cannot get at the hearts and minds, motivations and beliefs of the people who make up the social worlds we inhabit.  For that, we need tools that allow us to listen and make sense of what people tell us and show us.  That is what good qualitative research offers us.

How Is This Book Organized?

This textbook is organized as a comprehensive introduction to the use of qualitative research methods.  The first half covers general topics (e.g., approaches to qualitative research, ethics) and research design (necessary steps for building a successful qualitative research study).  The second half reviews various data collection and data analysis techniques.  Of course, building a successful qualitative research study requires some knowledge of data collection and data analysis so the chapters in the first half and the chapters in the second half should be read in conversation with each other.  That said, each chapter can be read on its own for assistance with a particular narrow topic.  In addition to the chapters, a helpful glossary can be found in the back of the book.  Rummage around in the text as needed.

Chapter Descriptions

Chapter 2 provides an overview of the Research Design Process.  How does one begin a study? What is an appropriate research question?  How is the study to be done – with what methods ?  Involving what people and sites?  Although qualitative research studies can and often do change and develop over the course of data collection, it is important to have a good idea of what the aims and goals of your study are at the outset and a good plan of how to achieve those aims and goals.  Chapter 2 provides a road map of the process.

Chapter 3 describes and explains various ways of knowing the (social) world.  What is it possible for us to know about how other people think or why they behave the way they do?  What does it mean to say something is a “fact” or that it is “well-known” and understood?  Qualitative researchers are particularly interested in these questions because of the types of research questions we are interested in answering (the how questions rather than the how many questions of quantitative research).  Qualitative researchers have adopted various epistemological approaches.  Chapter 3 will explore these approaches, highlighting interpretivist approaches that acknowledge the subjective aspect of reality – in other words, reality and knowledge are not objective but rather influenced by (interpreted through) people.

Chapter 4 focuses on the practical matter of developing a research question and finding the right approach to data collection.  In any given study (think of Cory Abramson’s study of aging, for example), there may be years of collected data, thousands of observations , hundreds of pages of notes to read and review and make sense of.  If all you had was a general interest area (“aging”), it would be very difficult, nearly impossible, to make sense of all of that data.  The research question provides a helpful lens to refine and clarify (and simplify) everything you find and collect.  For that reason, it is important to pull out that lens (articulate the research question) before you get started.  In the case of the aging study, Cory Abramson was interested in how inequalities affected understandings and responses to aging.  It is for this reason he designed a study that would allow him to compare different groups of seniors (some middle-class, some poor).  Inevitably, he saw much more in the three years in the field than what made it into his book (or dissertation), but he was able to narrow down the complexity of the social world to provide us with this rich account linked to the original research question.  Developing a good research question is thus crucial to effective design and a successful outcome.  Chapter 4 will provide pointers on how to do this.  Chapter 4 also provides an overview of general approaches taken to doing qualitative research and various “traditions of inquiry.”

Chapter 5 explores sampling .  After you have developed a research question and have a general idea of how you will collect data (Observations?  Interviews?), how do you go about actually finding people and sites to study?  Although there is no “correct number” of people to interview , the sample should follow the research question and research design.  Unlike quantitative research, qualitative research involves nonprobability sampling.  Chapter 5 explains why this is so and what qualities instead make a good sample for qualitative research.

Chapter 6 addresses the importance of reflexivity in qualitative research.  Related to epistemological issues of how we know anything about the social world, qualitative researchers understand that we the researchers can never be truly neutral or outside the study we are conducting.  As observers, we see things that make sense to us and may entirely miss what is either too obvious to note or too different to comprehend.  As interviewers, as much as we would like to ask questions neutrally and remain in the background, interviews are a form of conversation, and the persons we interview are responding to us .  Therefore, it is important to reflect upon our social positions and the knowledges and expectations we bring to our work and to work through any blind spots that we may have.  Chapter 6 provides some examples of reflexivity in practice and exercises for thinking through one’s own biases.

Chapter 7 is a very important chapter and should not be overlooked.  As a practical matter, it should also be read closely with chapters 6 and 8.  Because qualitative researchers deal with people and the social world, it is imperative they develop and adhere to a strong ethical code for conducting research in a way that does not harm.  There are legal requirements and guidelines for doing so (see chapter 8), but these requirements should not be considered synonymous with the ethical code required of us.   Each researcher must constantly interrogate every aspect of their research, from research question to design to sample through analysis and presentation, to ensure that a minimum of harm (ideally, zero harm) is caused.  Because each research project is unique, the standards of care for each study are unique.  Part of being a professional researcher is carrying this code in one’s heart, being constantly attentive to what is required under particular circumstances.  Chapter 7 provides various research scenarios and asks readers to weigh in on the suitability and appropriateness of the research.  If done in a class setting, it will become obvious fairly quickly that there are often no absolutely correct answers, as different people find different aspects of the scenarios of greatest importance.  Minimizing the harm in one area may require possible harm in another.  Being attentive to all the ethical aspects of one’s research and making the best judgments one can, clearly and consciously, is an integral part of being a good researcher.

Chapter 8 , best to be read in conjunction with chapter 7, explains the role and importance of Institutional Review Boards (IRBs) .  Under federal guidelines, an IRB is an appropriately constituted group that has been formally designated to review and monitor research involving human subjects .  Every institution that receives funding from the federal government has an IRB.  IRBs have the authority to approve, require modifications to (to secure approval), or disapprove research.  This group review serves an important role in the protection of the rights and welfare of human research subjects.  Chapter 8 reviews the history of IRBs and the work they do but also argues that IRBs’ review of qualitative research is often both over-inclusive and under-inclusive.  Some aspects of qualitative research are not well understood by IRBs, given that they were developed to prevent abuses in biomedical research.  Thus, it is important not to rely on IRBs to identify all the potential ethical issues that emerge in our research (see chapter 7).

Chapter 9 provides help for getting started on formulating a research question based on gaps in the pre-existing literature.  Research is conducted as part of a community, even if particular studies are done by single individuals (or small teams).  What any of us finds and reports back becomes part of a much larger body of knowledge.  Thus, it is important that we look at the larger body of knowledge before we actually start our bit to see how we can best contribute.  When I first began interviewing working-class college students, there was only one other similar study I could find, and it hadn’t been published (it was a dissertation of students from poor backgrounds).  But there had been a lot published by professors who had grown up working class and made it through college despite the odds.  These accounts by “working-class academics” became an important inspiration for my study and helped me frame the questions I asked the students I interviewed.  Chapter 9 will provide some pointers on how to search for relevant literature and how to use this to refine your research question.

Chapter 10 serves as a bridge between the two parts of the textbook, by introducing techniques of data collection.  Qualitative research is often characterized by the form of data collection – for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos.  Techniques can be effectively combined, depending on the research question and the aims and goals of the study.   Chapter 10 provides a general overview of all the various techniques and how they can be combined.

The second part of the textbook moves into the doing part of qualitative research once the research question has been articulated and the study designed.  Chapters 11 through 17 cover various data collection techniques and approaches.  Chapters 18 and 19 provide a very simple overview of basic data analysis.  Chapter 20 covers communication of the data to various audiences, and in various formats.

Chapter 11 begins our overview of data collection techniques with a focus on interviewing , the true heart of qualitative research.  This technique can serve as the primary and exclusive form of data collection, or it can be used to supplement other forms (observation, archival).  An interview is distinct from a survey, where questions are asked in a specific order and often with a range of predetermined responses available.  Interviews can be conversational and unstructured or, more conventionally, semistructured , where a general set of interview questions “guides” the conversation.  Chapter 11 covers the basics of interviews: how to create interview guides, how many people to interview, where to conduct the interview, what to watch out for (how to prepare against things going wrong), and how to get the most out of your interviews.

Chapter 12 covers an important variant of interviewing, the focus group.  Focus groups are semistructured interviews with a group of people moderated by a facilitator (the researcher or researcher’s assistant).  Focus groups explicitly use group interaction to assist in the data collection.  They are best used to collect data on a specific topic that is non-personal and shared among the group.  For example, asking a group of college students about a common experience such as taking classes by remote delivery during the pandemic year of 2020.  Chapter 12 covers the basics of focus groups: when to use them, how to create interview guides for them, and how to run them effectively.

Chapter 13 moves away from interviewing to the second major form of data collection unique to qualitative researchers – observation .  Qualitative research that employs observation can best be understood as falling on a continuum of “fly on the wall” observation (e.g., observing how strangers interact in a doctor’s waiting room) to “participant” observation, where the researcher is also an active participant of the activity being observed.  For example, an activist in the Black Lives Matter movement might want to study the movement, using her inside position to gain access to observe key meetings and interactions.  Chapter  13 covers the basics of participant observation studies: advantages and disadvantages, gaining access, ethical concerns related to insider/outsider status and entanglement, and recording techniques.

Chapter 14 takes a closer look at “deep ethnography” – immersion in the field of a particularly long duration for the purpose of gaining a deeper understanding and appreciation of a particular culture or social world.  Clifford Geertz called this “deep hanging out.”  Whereas participant observation is often combined with semistructured interview techniques, deep ethnography’s commitment to “living the life” or experiencing the situation as it really is demands more conversational and natural interactions with people.  These interactions and conversations may take place over months or even years.  As can be expected, there are some costs to this technique, as well as some very large rewards when done competently.  Chapter 14 provides some examples of deep ethnographies that will inspire some beginning researchers and intimidate others.

Chapter 15 moves in the opposite direction of deep ethnography, a technique that is the least positivist of all those discussed here, to mixed methods , a set of techniques that is arguably the most positivist .  A mixed methods approach combines both qualitative data collection and quantitative data collection, commonly by combining a survey that is analyzed statistically (e.g., cross-tabs or regression analyses of large number probability samples) with semi-structured interviews.  Although it is somewhat unconventional to discuss mixed methods in textbooks on qualitative research, I think it is important to recognize this often-employed approach here.  There are several advantages and some disadvantages to taking this route.  Chapter 16 will describe those advantages and disadvantages and provide some particular guidance on how to design a mixed methods study for maximum effectiveness.

Chapter 16 covers data collection that does not involve live human subjects at all – archival and historical research (chapter 17 will also cover data that does not involve interacting with human subjects).  Sometimes people are unavailable to us, either because they do not wish to be interviewed or observed (as is the case with many “elites”) or because they are too far away, in both place and time.  Fortunately, humans leave many traces and we can often answer questions we have by examining those traces.  Special collections and archives can be goldmines for social science research.  This chapter will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Chapter 17 covers another data collection area that does not involve face-to-face interaction with humans: content analysis .  Although content analysis may be understood more properly as a data analysis technique, the term is often used for the entire approach, which will be the case here.  Content analysis involves interpreting meaning from a body of text.  This body of text might be something found in historical records (see chapter 16) or something collected by the researcher, as in the case of comment posts on a popular blog post.  I once used the stories told by student loan debtors on the website studentloanjustice.org as the content I analyzed.  Content analysis is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest.  In other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue.  This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis.

Where chapter 17 has pushed us towards data analysis, chapters 18 and 19 are all about what to do with the data collected, whether that data be in the form of interview transcripts or fieldnotes from observations.  Chapter 18 introduces the basics of coding , the iterative process of assigning meaning to the data in order to both simplify and identify patterns.  What is a code and how does it work?  What are the different ways of coding data, and when should you use them?  What is a codebook, and why do you need one?  What does the process of data analysis look like?

Chapter 19 goes further into detail on codes and how to use them, particularly the later stages of coding in which our codes are refined, simplified, combined, and organized.  These later rounds of coding are essential to getting the most out of the data we’ve collected.  As students are often overwhelmed with the amount of data (a corpus of interview transcripts typically runs into the hundreds of pages; fieldnotes can easily top that), this chapter will also address time management and provide suggestions for dealing with chaos and reminders that feeling overwhelmed at the analysis stage is part of the process.  By the end of the chapter, you should understand how “findings” are actually found.

The book concludes with a chapter dedicated to the effective presentation of data results.  Chapter 20 covers the many ways that researchers communicate their studies to various audiences (academic, personal, political), what elements must be included in these various publications, and the hallmarks of excellent qualitative research that various audiences will be expecting.  Because qualitative researchers are motivated by understanding and conveying meaning , effective communication is not only an essential skill but a fundamental facet of the entire research project.  Ethnographers must be able to convey a certain sense of verisimilitude , the appearance of true reality.  Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them.  And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important.

The book concludes with a short chapter ( chapter 21 ) discussing the value of qualitative research. At the very end of this book, you will find a glossary of terms. I recommend you make frequent use of the glossary and add to each entry as you find examples. Although the entries are meant to be simple and clear, you may also want to paraphrase the definition—make it “make sense” to you, in other words. In addition to the standard reference list (all works cited here), you will find various recommendations for further reading at the end of many chapters. Some of these recommendations will be examples of excellent qualitative research, indicated with an asterisk (*) at the end of the entry. As they say, a picture is worth a thousand words. A good example of qualitative research can teach you more about conducting research than any textbook can (this one included). I highly recommend you select one to three examples from these lists and read them along with the textbook.

A final note on the choice of examples – you will note that many of the examples used in the text come from research on college students.  This is for two reasons.  First, as most of my research falls in this area, I am most familiar with this literature and have contacts with those who do research here and can call upon them to share their stories with you.  Second, and more importantly, my hope is that this textbook reaches a wide audience of beginning researchers who study widely and deeply across the range of what can be known about the social world (from marine resources management to public policy to nursing to political science to sexuality studies and beyond).  It is sometimes difficult to find examples that speak to all those research interests, however. A focus on college students is something that all readers can understand and, hopefully, appreciate, as we are all now or have been at some point a college student.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

Patton’s Ten Suggestions for Doing Qualitative Research

The following ten suggestions were made by Michael Quinn Patton in his massive textbooks Qualitative Research and Evaluations Methods . This book is highly recommended for those of you who want more than an introduction to qualitative methods. It is the book I relied on heavily when I was a graduate student, although it is much easier to “dip into” when necessary than to read through as a whole. Patton is asked for “just one bit of advice” for a graduate student considering using qualitative research methods for their dissertation.  Here are his top ten responses, in short form, heavily paraphrased, and with additional comments and emphases from me:

• Make sure that a qualitative approach fits the research question. The following are the kinds of questions that call out for qualitative methods or where qualitative methods are particularly appropriate: questions about people’s experiences or how they make sense of those experiences; studying a person in their natural environment; researching a phenomenon so unknown that it would be impossible to study it with standardized instruments or other forms of quantitative data collection.
• Study qualitative research by going to the original sources for the design and analysis appropriate to the particular approach you want to take (e.g., read Glaser and Straus if you are using grounded theory )
• Find a dissertation adviser who understands or at least who will support your use of qualitative research methods. You are asking for trouble if your entire committee is populated by quantitative researchers, even if they are all very knowledgeable about the subject or focus of your study (maybe even more so if they are!)
• Really work on design. Doing qualitative research effectively takes a lot of planning.  Even if things are more flexible than in quantitative research, a good design is absolutely essential when starting out.
• Practice data collection techniques, particularly interviewing and observing. There is definitely a set of learned skills here!  Do not expect your first interview to be perfect.  You will continue to grow as a researcher the more interviews you conduct, and you will probably come to understand yourself a bit more in the process, too.  This is not easy, despite what others who don’t work with qualitative methods may assume (and tell you!)
• Have a plan for analysis before you begin data collection. This is often a requirement in IRB protocols , although you can get away with writing something fairly simple.  And even if you are taking an approach, such as grounded theory, that pushes you to remain fairly open-minded during the data collection process, you still want to know what you will be doing with all the data collected – creating a codebook? Writing analytical memos? Comparing cases?  Having a plan in hand will also help prevent you from collecting too much extraneous data.
• Be prepared to confront controversies both within the qualitative research community and between qualitative research and quantitative research. Don’t be naïve about this – qualitative research, particularly some approaches, will be derided by many more “positivist” researchers and audiences.  For example, is an “n” of 1 really sufficient?  Yes!  But not everyone will agree.
• Do not make the mistake of using qualitative research methods because someone told you it was easier, or because you are intimidated by the math required of statistical analyses. Qualitative research is difficult in its own way (and many would claim much more time-consuming than quantitative research).  Do it because you are convinced it is right for your goals, aims, and research questions.
• Find a good support network. This could be a research mentor, or it could be a group of friends or colleagues who are also using qualitative research, or it could be just someone who will listen to you work through all of the issues you will confront out in the field and during the writing process.  Even though qualitative research often involves human subjects, it can be pretty lonely.  A lot of times you will feel like you are working without a net.  You have to create one for yourself.  Take care of yourself.
• And, finally, in the words of Patton, “Prepare to be changed. Looking deeply at other people’s lives will force you to look deeply at yourself.”
• We will actually spend an entire chapter ( chapter 3 ) looking at this question in much more detail! ↵
• Note that this might have been news to Europeans at the time, but many other societies around the world had also come to this conclusion through observation.  There is often a tendency to equate “the scientific revolution” with the European world in which it took place, but this is somewhat misleading. ↵
• Historians are a special case here.  Historians have scrupulously and rigorously investigated the social world, but not for the purpose of understanding general laws about how things work, which is the point of scientific empirical research.  History is often referred to as an idiographic field of study, meaning that it studies things that happened or are happening in themselves and not for general observations or conclusions. ↵
• Don’t worry, we’ll spend more time later in this book unpacking the meaning of ethnography and other terms that are important here.  Note the available glossary ↵

An approach to research that 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 ). Contrast with quantitative research .

In contrast to methodology, methods are more simply the practices and tools used to collect and analyze data.  Examples of common methods in qualitative research are interviews , observations , and documentary analysis .  One’s methodology should connect to one’s choice of methods, of course, but they are distinguishable terms.  See also methodology .

A proposed explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.  The positing of a hypothesis is often the first step in quantitative research but not in qualitative research.  Even when qualitative researchers offer possible explanations in advance of conducting research, they will tend to not use the word “hypothesis” as it conjures up the kind of positivist research they are not conducting.

The foundational question to be addressed by the research study.  This will form the anchor of the research design, collection, and analysis.  Note that in qualitative research, the research question may, and probably will, alter or develop during the course of the research.

An approach to research that collects and analyzes numerical data for the purpose of finding patterns and averages, making predictions, testing causal relationships, and generalizing results to wider populations.  Contrast with qualitative research .

Data collection that takes place in real-world settings, referred to as “the field;” a key component of much Grounded Theory and ethnographic research.  Patton ( 2002 ) calls fieldwork “the central activity of qualitative inquiry” where “‘going into the field’ means having direct and personal contact with people under study in their own environments – getting close to people and situations being studied to personally understand the realities of minutiae of daily life” (48).

The people who are the subjects of a qualitative study.  In interview-based studies, they may be the respondents to the interviewer; for purposes of IRBs, they are often referred to as the human subjects of the research.

The branch of philosophy concerned with knowledge.  For researchers, it is important to recognize and adopt one of the many distinguishing epistemological perspectives as part of our understanding of what questions research can address or fully answer.  See, e.g., constructivism , subjectivism, and  objectivism .

An approach that refutes the possibility of neutrality in social science research.  All research is “guided by a set of beliefs and feelings about the world and how it should be understood and studied” (Denzin and Lincoln 2005: 13).  In contrast to positivism , interpretivism recognizes the social constructedness of reality, and researchers adopting this approach focus on capturing interpretations and understandings people have about the world rather than “the world” as it is (which is a chimera).

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Research based on data collected and analyzed by the research (in contrast to secondary “library” research).

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

The specific group of individuals that you will collect data from.  Contrast population.

The practice of being conscious of and reflective upon one’s own social location and presence when conducting research.  Because qualitative research often requires interaction with live humans, failing to take into account how one’s presence and prior expectations and social location affect the data collected and how analyzed may limit the reliability of the findings.  This remains true even when dealing with historical archives and other content.  Who we are matters when asking questions about how people experience the world because we, too, are a part of that world.

The science and practice of right conduct; in research, it is also the delineation of moral obligations towards research participants, communities to which we belong, and communities in which we conduct our research.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Research, according to US federal guidelines, that involves “a living individual about whom an investigator (whether professional or student) conducting research:  (1) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or  (2) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

A method of observational data collection taking place in a natural setting; a form of fieldwork .  The term encompasses a continuum of relative participation by the researcher (from full participant to “fly-on-the-wall” observer).  This is also sometimes referred to as ethnography , although the latter is characterized by a greater focus on the culture under observation.

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

An epistemological perspective that posits the existence of reality through sensory experience similar to empiricism but goes further in denying any non-sensory basis of thought or consciousness.  In the social sciences, the term has roots in the proto-sociologist August Comte, who believed he could discern “laws” of society similar to the laws of natural science (e.g., gravity).  The term has come to mean the kinds of measurable and verifiable science conducted by quantitative researchers and is thus used pejoratively by some qualitative researchers interested in interpretation, consciousness, and human understanding.  Calling someone a “positivist” is often intended as an insult.  See also empiricism and objectivism.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña 2021:5).

Usually a verbatim written record of an interview or focus group discussion.

The primary form of data for fieldwork , participant observation , and ethnography .  These notes, taken by the researcher either during the course of fieldwork or at day’s end, should include as many details as possible on what was observed and what was said.  They should include clear identifiers of date, time, setting, and names (or identifying characteristics) of participants.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

A detailed description of any proposed research that involves human subjects for review by IRB.  The protocol serves as the recipe for the conduct of the research activity.  It includes the scientific rationale to justify the conduct of the study, the information necessary to conduct the study, the plan for managing and analyzing the data, and a discussion of the research ethical issues relevant to the research.  Protocols for qualitative research often include interview guides, all documents related to recruitment, informed consent forms, very clear guidelines on the safekeeping of materials collected, and plans for de-identifying transcripts or other data that include personal identifying information.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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How to Do Qualitative Research

Last Updated: October 26, 2022 Fact Checked

This article was co-authored by Jeremiah Kaplan . Jeremiah Kaplan is a Research and Training Specialist at the Center for Applied Behavioral Health Policy at Arizona State University. He has extensive knowledge and experience in motivational interviewing. In addition, Jeremiah has worked in the mental health, youth engagement, and trauma-informed care fields. Using his expertise, Jeremiah supervises Arizona State University’s Motivational Interviewing Coding Lab. Jeremiah has also been internationally selected to participate in the Motivational Interviewing International Network of Trainers sponsored Train the Trainer event. Jeremiah holds a BS in Human Services with a concentration in Family and Children from The University of Phoenix. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 744,966 times.

Qualitative research is a broad field of inquiry that uses unstructured data collections methods, such as observations, interviews, surveys and documents, to find themes and meanings to inform our understanding of the world. [1] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source Qualitative research tends to try to cover the reasons for behaviors, attitudes and motivations, instead of just the details of what, where and when. Qualitative research can be done across many disciplines, such as social sciences, healthcare and businesses, and it is a common feature of nearly every single workplace and educational environment.

Preparing Your Research

• The research questions is one of the most important pieces of your research design. It determines what you want to learn or understand and also helps to focus the study, since you can't investigate everything at once. Your research question will also shape how you conduct your study since different questions require different methods of inquiry.
• You should start with a burning question and then narrow it down more to make it manageable enough to be researched effectively. For example, "what is the meaning of teachers' work to teachers" is too broad for a single research endeavor, but if that's what you're interested you could narrow it by limiting the type of teacher or focusing on one level of education. For example, "what is the meaning of teachers' work to second career teachers?" or "what is the meaning of teachers' work to junior high teachers?"

Tip: Find the balance between a burning question and a researchable question. The former is something you really want to know about and is often quite broad. The latter is one that can be directly investigated using available research methods and tools.

• For example, if your research question focuses on how second career teachers attribute meaning to their work, you would want to examine the literature on second career teaching - what motivates people to turn to teaching as a second career? How many teachers are in their second career? Where do most second career teachers work? Doing this reading and review of existing literature and research will help you refine your question and give you the base you need for your own research. It will also give you a sense of the variables that might impact your research (e.g., age, gender, class, etc.) and that you will need to take into consideration in your own study.
• A literature review will also help you to determine whether you are really interested and committed to the topic and research question and that there is a gap in the existing research that you want to fill by conducting your own investigation.

For example, if your research question is "what is the meaning of teachers' work to second career teachers?" , that is not a question that can be answered with a 'yes' or 'no'. Nor is there likely to be a single overarching answer. This means that qualitative research is the best route.

• Consider the possible outcomes. Because qualitative methodologies are generally quite broad, there is almost always the possibility that some useful data will come out of the research. This is different than in a quantitative experiment, where an unproven hypothesis can mean that a lot of time has been wasted.
• Your research budget and available financial resources should also be considered. Qualitative research is often cheaper and easier to plan and execute. For example, it is usually easier and cost-saving to gather a small number of people for interviews than it is to purchase a computer program that can do statistical analysis and hire the appropriate statisticians.

• Action Research – Action research focuses on solving an immediate problem or working with others to solve problem and address particular issues. [7] X Research source
• Ethnography – Ethnography is the study of human interaction in communities through direct participation and observation within the community you wish to study. Ethnographic research comes from the discipline of social and cultural anthropology but is now becoming more widely used. [8] X Research source
• Phenomenology – Phenomenology is the study of the subjective experiences of others. It researches the world through the eyes of another person by discovering how they interpret their experiences. [9] X Research source
• Grounded Theory – The purpose of grounded theory is to develop theory based on the data systematically collected and analyzed. It looks at specific information and derives theories and reasons for the phenomena.
• Case Study Research – This method of qualitative study is an in-depth study of a specific individual or phenomena in its existing context. [10] X Research source

Collecting and Analyzing Your Data

• Direct observation – Direct observation of a situation or your research subjects can occur through video tape playback or through live observation. In direct observation, you are making specific observations of a situation without influencing or participating in any way. [12] X Research source For example, perhaps you want to see how second career teachers go about their routines in and outside the classrooms and so you decide to observe them for a few days, being sure to get the requisite permission from the school, students and the teacher and taking careful notes along the way.
• Participant observation – Participant observation is the immersion of the researcher in the community or situation being studied. This form of data collection tends to be more time consuming, as you need to participate fully in the community in order to know whether your observations are valid. [13] X Research source
• Interviews – Qualitative interviewing is basically the process of gathering data by asking people questions. Interviewing can be very flexible - they can be on-on-one, but can also take place over the phone or Internet or in small groups called "focus groups". There are also different types of interviews. Structured interviews use pre-set questions, whereas unstructured interviews are more free-flowing conversations where the interviewer can probe and explore topics as they come up. Interviews are particularly useful if you want to know how people feel or react to something. For example, it would be very useful to sit down with second career teachers in either a structured or unstructured interview to gain information about how they represent and discuss their teaching careers.
• Surveys – Written questionnaires and open ended surveys about ideas, perceptions, and thoughts are other ways by which you can collect data for your qualitative research. For example, in your study of second career schoolteachers, perhaps you decide to do an anonymous survey of 100 teachers in the area because you're concerned that they may be less forthright in an interview situation than in a survey where their identity was anonymous.
• "Document analysis" – This involves examining written, visual, and audio documents that exist without any involvement of or instigation by the researcher. There are lots of different kinds of documents, including "official" documents produced by institutions and personal documents, like letters, memoirs, diaries and, in the 21st century, social media accounts and online blogs. For example, if studying education, institutions like public schools produce many different kinds of documents, including reports, flyers, handbooks, websites, curricula, etc. Maybe you can also see if any second career teachers have an online meet group or blog. Document analysis can often be useful to use in conjunction with another method, like interviewing.

• Coding – In coding, you assign a word, phrase, or number to each category. Start out with a pre-set list of codes that you derived from your prior knowledge of the subject. For example, "financial issues" or "community involvement" might be two codes you think of after having done your literature review of second career teachers. You then go through all of your data in a systematic way and "code" ideas, concepts and themes as they fit categories. You will also develop another set of codes that emerge from reading and analyzing the data. For example, you may see while coding your interviews, that "divorce" comes up frequently. You can add a code for this. Coding helps you organize your data and identify patterns and commonalities. [15] X Research source tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
• Descriptive Statistics – You can analyze your data using statistics. Descriptive statistics help describe, show or summarize the data to highlight patterns. For example, if you had 100 principal evaluations of teachers, you might be interested in the overall performance of those students. Descriptive statistics allow you to do that. Keep in mind, however, that descriptive statistics cannot be used to make conclusions and confirm/disprove hypotheses. [16] X Research source
• Narrative analysis – Narrative analysis focuses on speech and content, such as grammar, word usage, metaphors, story themes, meanings of situations, the social, cultural and political context of the narrative. [17] X Research source
• Hermeneutic Analysis – Hermeneutic analysis focuses on the meaning of a written or oral text. Essentially, you are trying to make sense of the object of study and bring to light some sort of underlying coherence. [18] X Research source
• Content analysis / Semiotic analysis – Content or semiotic analysis looks at texts or series of texts and looks for themes and meanings by looking at frequencies of words. Put differently, you try to identify structures and patterned regularities in the verbal or written text and then make inferences on the basis of these regularities. [19] X Research source For example, maybe you find the same words or phrases, like "second chance" or "make a difference," coming up in different interviews with second career teachers and decide to explore what this frequency might signify.

Community Q&A

• Qualitative research is often regarded as a precursor to quantitative research, which is a more logical and data-led approach which statistical, mathematical and/or computational techniques. Qualitative research is often used to generate possible leads and formulate a workable hypothesis that is then tested with quantitative methods. [20] X Research source Thanks Helpful 0 Not Helpful 0
• Try to remember the difference between qualitative and quantitative as each will give different data. Thanks Helpful 4 Not Helpful 0

You Might Also Like

• ↑ https://www.ncbi.nlm.nih.gov/books/NBK470395/
• ↑ https://owl.purdue.edu/owl/research_and_citation/conducting_research/writing_a_literature_review.html
• ↑ https://academic.oup.com/humrep/article/31/3/498/2384737?login=false
• ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4275140/
• ↑ http://www.qual.auckland.ac.nz/
• ↑ http://www.socialresearchmethods.net/kb/qualapp.php
• ↑ http://www.socialresearchmethods.net/kb/qualdata.php
• ↑ tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
• ↑ https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php
• ↑ https://explorable.com/qualitative-research-design

About This Article

To do qualitative research, start by deciding on a clear, specific question that you want to answer. Then, do a literature review to see what other experts are saying about the topic, and evaluate how you will best be able to answer your question. Choose an appropriate qualitative research method, such as action research, ethnology, phenomenology, grounded theory, or case study research. Collect and analyze data according to your chosen method, determine the answer to your question. For tips on performing a literature review and picking a method for collecting data, read on! Did this summary help you? Yes No

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5 Steps in Qualitative Research to Unlocking Insights

Qualitative research is a valuable approach that allows researchers to delve into the complexities of human behavior, perceptions, and experiences. It provides a deeper understanding of the “why” and “how” behind actions, making it an essential methodology in various fields such as social sciences, psychology, anthropology, and more. In this blog post, we will delve into the five essential steps in qualitative research that can help unlock profound insights.

What is qualitative research?

Qualitative research is a research approach or methodology that seeks to explore, understand, and interpret individuals’ or groups’ underlying meanings, motivations, attitudes, behaviors, and experiences within a specific context. This type of research aims to generate rich, non-numerical data and insights into the complexities of human behavior, social phenomena, and cultural practices. 

Qualitative research typically employs methods such as interviews, focus groups, participant observation, and open-ended surveys to collect data, and it emphasizes subjective analysis, context, and the development of theories or explanations to illuminate the studied phenomenon. 

It is often used in social sciences, psychology, anthropology, and other fields to understand various aspects of the human experience better.

Importance of qualitative research

Qualitative research is a valuable and important research methodology that provides in-depth insights into human behavior, experiences, and social phenomena. Its significance can be understood through several key aspects:

Understanding Human Behavior and Experiences

Qualitative research allows researchers to explore complex human behaviors, attitudes, and experiences. It delves deep into the “why” and “how” of people’s actions and perceptions, providing a rich understanding of the underlying factors.

Contextual Understanding

Qualitative research is well-suited for capturing the context in which phenomena occur. It helps researchers uncover the cultural, social, and historical factors that influence people’s thoughts and actions, providing a holistic view of the subject matter.

Generating Hypotheses

The qualitative analysis process often serves as an initial step in the research process, helping researchers generate hypotheses or refine research questions. It can guide the development of quantitative studies by uncovering relevant variables and relationships.

Exploring Complex Issues

Qualitative methods are particularly useful for studying complex or sensitive topics where quantitative data might not fully capture the nuances involved. It allows researchers to explore multifaceted issues in depth.

Theory Development

Qualitative research can contribute to theory development by generating concepts, categories, and theoretical frameworks grounded in empirical data. These insights can help build and refine theories in various fields.

Policy and Program Development

Qualitative research can inform the development and implementation of policies, programs, and interventions. It provides valuable information on the needs, preferences, and barriers faced by specific populations. 

Enhancing Quantitative Research

Qualitative research often complements quantitative research by providing a deeper understanding of the phenomena under investigation. It can help researchers design better surveys and experiments. 

Personalization and Customer Insights

Qualitative research is instrumental in understanding consumer preferences, attitudes, and behaviors in business and marketing. This information can guide product development, marketing strategies, and customer service improvements.

Healthcare and Patient-Centered Care

Qualitative research is essential in healthcare to understand patient experiences, preferences, and satisfaction. It informs patient-centered care approaches and can lead to better healthcare outcomes.

Social and Cultural Research

Qualitative research is indispensable for exploring social and cultural phenomena, such as identity, social norms, and cultural practices. It helps researchers appreciate the diversity and uniqueness of human societies.

5 steps in qualitative research for creating research study

Creating a qualitative research study involves careful planning and execution to gather in-depth insights into a specific research question or phenomenon.

In the early stages of conducting steps in qualitative research, gathering qualitative data through methods such as interviews and surveys is followed by the critical step of discourse analysis to unearth underlying themes and patterns in the collected information. Here are five essential steps in qualitative research:

1. Create a Project Layout with Objectives

When beginning a market research project, it is important to have a clear outline of the challenge you are hoping to find clarity on. There are a number of reasons why you would want to survey consumers, including:

• Brand awareness
• Product testing
• Customer satisfaction
• Price sensitivity

Next, know your objective.  Objectives are geared towards the end result and information you will obtain, not steps in a qualitative research project.  For example:

Ideal Objective

Understand what drives customer retention for my company vs. the direct competitor

Not an Ideal Objective

Recruit 20 participants for an in-depth interview

2. Create an outline of questions to ask

With your goals and objectives created, craft your survey questions.  Use QuestionPro’s survey platform to put together screener questions to filter the right participants from your community. The number one mistake when creating survey questions is asking leading questions .  They will unconsciously sway the participant towards one side of an argument. For example:

Leading question

Do you think the color red is the right color for this product?

Non-leading question

What are your thoughts on the color of this product?

Some additional best practices include:

• Not asking absolute questions (using always, never, all, every)
• When asking questions of a sensitive nature (such as race, sexual orientation, beliefs, etc.), ask near or at the end.
• Do not use jargon or abbreviations without explaining what each word means

3. Recruitment from your Community

Before collecting data, it is important to think about your sample size.   In a qualitative survey , 15-20 is typically the ideal number of participants to survey.  Your community will make it easy to view your member’s profiles to see past participation, so you aren’t surveying members too frequently.  

The community participant sample should be a general representation of your targeted demographic. Otherwise, your answers are going to miss the mark on who you are trying to reach.  Once your target sample from your community is set up, you are ready to conduct your research.

4. Conducting the Research

This is the time when you are administering your survey, conducting your qualitative interviews , and implementing your field test. These important steps can be time-consuming but are critical.  However, using a community makes it easy to set up questions and tasks without having to survey each individual in person.  

You can ask a question to either the entire group and have a discussion or pose questions to individuals separately. The answers, choices, and observations are collected and recorded so you can go back to review to prepare your report.

5. Crunch the Data and prepare your report

Reviewing and analyzing qualitative data analysis can be difficult, depending on the number of questions asked, how many participants were surveyed, and the number of team members reviewing the data.  Since you are asking open-ended questions, it’s important to try and structure the answers. You can explore our new blog, “ Examples of Qualitative Data in Education . “

Once you begin noticing phrases and subjects aligning, start adding them into word piles, and remember to keep track of who mentioned each comment.  Doing this can create a persona to delve even more into your target demographic.

Your deliverables matter when presenting your work.  The great thing about using qualitative data is that you can get creative with how you present.  Use a word cloud to see what keywords pop up frequently.

The most important step of all is after your presentation.  Make sure to go over actionable steps, create timelines to regroup, and bring in appropriate stakeholders to review your findings. Don’t let the data you worked hard to obtain not create value for your company.

Qualitative research methods

Qualitative research methods are a set of techniques used in social sciences, psychology, anthropology, and various other fields to gather and analyze non-numerical data. These methods are particularly useful when qualitative researchers aim to understand better human behavior, perceptions, experiences, and social phenomena. 

Qualitative research methodology typically involves collecting and interpreting textual or visual data, such as interviews, observations, surveys, or written documents. Here are some common qualitative research methods:

Qualitative interviews involve open-ended questions to elicit detailed responses from participants. These interviews can be structured (with a predefined set of questions) or unstructured (more like a conversation), depending on the research objectives.

Focus Groups

In focus groups, a small group of participants is brought together to discuss a particular topic or issue. The interaction among group members can provide insights into shared perspectives, differences, and consensus.

Observations

Researchers engage in direct or participant observations to gather data about people’s behaviors and interactions in their natural settings. Field notes and observations are recorded for analysis.

Content Analysis

Content analysis involves systematically analyzing written, spoken, or visual material, such as documents, speeches, videos, or social media posts. Researchers look for patterns, themes, and meanings within the content.

• Case Studies

Case studies involve in-depth exploration of a single individual, group, or organization. Researchers collect data from multiple sources (interviews, documents, observations) to understand a particular case in detail.

Grounded Theory

Grounded theory is a research approach that aims to develop theories or explanations from the data itself rather than testing pre-existing theories. It involves systematic coding and analysis of data to generate new theoretical insights.

Ethnography

Ethnographic research findings involve immersion in a particular cultural or social setting to understand a group’s behaviors, values, and norms. Researchers often participate in the community to gain a deeper perspective.

Narrative Analysis

Narrative research focuses on the stories people tell about their experiences. Researchers analyze these narratives’ structure, content, and themes to gain insights into personal experiences and meanings.

Document Analysis

Researchers examine existing written or visual documents, such as historical records, diaries, photographs, or artwork, to gain insights into past events, cultures, or social phenomena.

Surveys with Open-Ended Questions

While surveys are typically associated with quantitative research, adding open-ended questions allows participants to provide qualitative research approach responses, which can provide more context and depth to the data.

Additional tips:

• Maintain ethical considerations throughout your research, ensuring informed consent from participants and protecting their privacy.
• Consider data saturation, where you continue data collection until no new insights or themes emerge.
• Keep detailed records of your research process, including field notes, transcripts, and any changes made during the study.
• Engage in member checking, which involves sharing your findings with participants to validate the accuracy of your interpretations.
• Write a comprehensive research report presenting your findings, discussing their implications, and relating them to the existing literature.

Qualitative research is a valuable methodology that provides rich insights into human behavior, experiences, and social phenomena. By following the five essential steps in qualitative research outlined in this guide, you can create a well-structured interpreting qualitative data analysis research study that generates meaningful and actionable insights. 

Whether you are exploring customer preferences, evaluating policies, or understanding cultural practices, qualitative research can enhance your understanding and decision-making processes.

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• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

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

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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

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

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

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

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

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

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

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

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

The 5 steps to doing qualitative data analysis

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

What is Qualitative Data Analysis?

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

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

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

How is qualitative data analysis different from quantitative data analysis?

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

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

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

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

Qualitative Data Analysis methods

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

Content Analysis

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

Narrative Analysis

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

Discourse Analysis

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

Thematic Analysis

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

Grounded Theory

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

Challenges of Qualitative Data Analysis

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

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

Benefits of qualitative data analysis

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

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

How to do Qualitative Data Analysis: 5 steps

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

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

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

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

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

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

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

Classic methods of gathering qualitative data

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

Using your existing qualitative feedback

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

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

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

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

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

Utilize untapped qualitative data channels

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

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

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

Step 2: Connect & organize all your qualitative data

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

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

The manual approach to organizing your data

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

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

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

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

Computer-assisted qualitative data analysis software (CAQDAS)

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

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

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

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

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

Organizing your qualitative data in a feedback repository

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

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

Organizing your qualitative data in a feedback analytics platform

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

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

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

Step 3: Coding your qualitative data

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

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

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

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

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

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

How to manually code your qualitative data

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

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

Using software to speed up manual coding of qualitative data

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

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

Automating the qualitative coding process using thematic analysis software

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

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

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

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

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

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

The key benefits of using an automated coding solution

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

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

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

Step 4: Analyze your data: Find meaningful insights

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

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

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

Manually create sub-codes to improve the quality of insights

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

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

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

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

Correlate the frequency of codes to customer segments

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

Segments can be based on:

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

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

Manually visualizing coded qualitative data

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

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

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

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

You can then visualize this data using a bar chart.

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

Trends over time

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

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

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

The visualization could look like this:

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

Using a text analytics solution to automate analysis

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

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

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

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

Step 5: Report on your data: Tell the story

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

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

Creating graphs and reporting in Powerpoint

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

Using visualization software for reporting

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

Visualizing your insights inside a feedback analytics platform

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

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

Conclusion - Manual or Automated?

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

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

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

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

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

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

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

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

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

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• Open access
• Published: 27 March 2024

Exploring health and disease concepts in healthcare practice: an empirical philosophy of medicine study

• Rik R. van der Linden   ORCID: orcid.org/0000-0002-9642-3715 1 &
• Maartje H.N. Schermer   ORCID: orcid.org/0000-0003-4283-9659 1  

BMC Medical Ethics volume  25 , Article number:  38 ( 2024 ) Cite this article

Metrics details

In line with recent proposals for experimental philosophy and philosophy of science in practice, we propose that the philosophy of medicine could benefit from incorporating empirical research, just as bioethics has. In this paper, we therefore take first steps towards the development of an empirical philosophy of medicine, that includes investigating practical and moral dimensions. This qualitative study gives insight into the views and experiences of a group of various medical professionals and patient representatives regarding the conceptualization of health and disease concepts in practice and the possible problems that surround them. This includes clinical, epistemological, and ethical issues. We have conducted qualitative interviews with a broad range of participants ( n  = 17), working in various health-related disciplines, fields and organizations. From the interviews, we highlight several different practical functions of definitions of health and disease. Furthermore, we discuss 5 types of problematic situations that emerged from the interviews and analyze the underlying conceptual issues. By providing theoretical frameworks and conceptual tools, and by suggesting conceptual changes or adaptations, philosophers might be able to help solve some of these problems. This empirical-philosophical study contributes to a more pragmatic way of understanding the relevance of conceptualizing health and disease by connecting the participants’ views and experiences to the theoretical debate. Going back and forth between theory and practice will likely result in a more complex but hopefully also better and more fruitful understanding of health and disease concepts.

Peer Review reports

In the philosophy of medicine, scholars have primarily addressed ‘health’ and ‘disease’ as theoretical concepts without exploring their actual use in practice all too much. Yet, it has been argued that the way we conceptualize health and disease also affects the practical and moral dimension of medicine [ 1 , 2 ]. While many philosophers recognize the practical consequences of defining health and disease in certain ways, most still tend to depart from theory to determine how health and disease should be defined. In the traditional analytical debate, only limited attention has been paid to the ways in which these concepts are embedded in the various practices they are deployed in. In the medical-philosophical literature, the conceptual, epistemic and bioethical issues associated with proposed disease-definitions, such as medicalization and overdiagnosis, have been primarily addressed as theoretical problems, often lacking contextualization and empirical foundation. Consequently, it is often not clear to what extent such conceptual issues are in fact experienced as problematic in practice and for whom exactly this is a problem. While it is increasingly recognized that the traditional method of conceptual analysis is ill-equipped to answer the various normative, ontological and epistemological questions surrounding the conceptualization of health and disease [ 2 , 3 , 4 ], new philosophical perspectives and research methods have to yet to be explored.

In recent contributions to the debate, several promising proposals have been made for a new direction, in which health and disease are viewed as plural concepts that need to be specified [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Instead of formulating definitions on monistic grounds, it is proposed to continue the debate by philosophical explication (4, 10], and by developing precising definitions [ 12 ]. This is important as concepts may serve various practical functions and are deployed in diverse contexts. As different practices may have different values, goals, and priorities, different types of definitions may be needed [ 7 ]. Moreover, we have recently suggested that we should assess the successfulness of concept definitions in relation to the function they serve in the context they are deployed in [ 5 ]. This shift towards a pragmatist stance requires scholars to look beyond theoretical arguments and to explore the various practical motivations of defining health and disease. Hence, when explicating concepts, it seems important to complement the theoretical debate by empirically studying the use of concepts in practice.

In contrast to the field of bioethics where empirical methods are commonly used to research attitudes, beliefs and perspectives of certain groups of people, empirical research is only seldomly conducted in philosophical studies on health, disease, and related concepts. Adding these methods to our philosophical toolbox enables us to investigate more closely how concepts of health and disease operate in medical practice and to explore what kind of problems occur in relation to them. We could use existing socio-empirical studies that, for example, investigate psychosocial and cultural aspects of certain diseases (e.g., see [ 13 ]), that review definitions and meanings of certain medical or bioethical concepts (e.g., see [ 14 , 15 ]), or that explore patients’ and professionals’ views towards certain research programs or medical developments (e.g., see [ 16 ]). Both quantative and qualitative methods can be useful, depending on the research question at stake. However, as we propose in this paper, besides making use of existing empirical literature, we can also conduct empirical philosophy of medicine studies that aim to explore philosophical questions head-on.

Referring to debates on empirical ethics, Seidlein & Salloch [ 17 ] recently argued that the reconciliation of perspectives in the philosophy of medicine and socio-empirical research will lead to a more nuanced discussion that includes experiences of patients. Drawing on Alexander Kon’s [ 18 ] pragmatic classification of empirical methods, they argue that this approach may be used to investigate current practices (‘Lay of the Land’), revealing differences between illness conceptions in different groups of people, or between notions of ‘disease’ and ‘illness’. Such studies may improve patient-centered and shared decision-making, as it becomes clearer ‘what’ should be treated (cf. [ 19 ]). In addition to this, we argue that studying the views, attitudes and beliefs of medical researchers, clinicians and other healthcare stakeholders, seems important for obtaining a better and wider understanding of how health and disease concepts are used in actual practice and why they are conceptualized in certain ways. This proposal for incorporating tools and methods of the social sciences in philosophical work on health and disease concepts resonates with calls for experimental philosophy of medicine Footnote 1 [ 20 , 21 ], and for more ‘philosophy of science in practice’ [ 22 , 23 ].

While there have not been many studies focused particularly on health and disease concepts in which empirical methods are used, some exceptions should be mentioned here. In Hofmann [ 24 ], physicians were presented a list of different conditions and were asked to classify them as disease or non-disease. Hofmann demonstrated that there are disparities between what physicians consider diseases. In Stronks et al. [ 25 ], lay people, randomly recruited on the streets, were asked to define what ‘health’ means to them. The study resulted in an extensive overview of different aspects of health and disease, categorized into multiple clusters, with interesting differences between socio-economic classes. In Kohne et al. [ 26 ], clinicians, patients, and clinicians who have been patients themselves, were interviewed to explore their ideas regarding the ontology of mental disorders. They observed that the ‘ontological palette’ is more diverse than is commonly perceived within the dominant scientific and educational discourse. In Van Heteren et al. [ 27 ], frontline professionals were interviewed to investigate their conceptions of health in clients with psychosocial problems. They observed that professionals define health in different ways but that they also accommodate for the views of their patients and to the broader context care is provided in.

As we understand health and disease concepts to be context-dependent, we believe it is important to investigate their function and problems arising in relation to them in various contexts. Regarding the methodology and the type of inquiry, our pragmatist approach encourages us to look for problematic situations . The term ‘problematic situations’ originates from the work of pragmatist John Dewey (see [ 28 ]), who argued that academic inquiry must always start with (solving) actual problems. Here, we will use the term problematic situation to describe as a situation in which current conceptions/definitions of health and disease are no longer sufficient for the continuation of a certain health care (related) practice, or the achievement of a goal of the specific practice that is at stake. Thus, besides mapping different health and disease conceptualizations, we primarily explore what kind of problematic situations are experienced in practice and investigate possible underlying conceptual issues. In doing so, we aim to further elucidate the philosophical debate on conceptualization of health and disease and give it more practical relevance. In this study we have therefore conducted qualitative interviews with a broad range of professionals and patient representatives, working in various health-related disciplines, fields and organizations. We chose qualitative methods because these are considered the most suitable for investigating new and underexplored areas.

Methodology

We have designed a qualitative interview study with professionals working in various fields and organizations. Interviews were conducted by RL. As the sample included a broad range of professionals and patient representatives, a one-size-fits-all approach was not considered to be useful. We used a semi-structured interview guide that could be adjusted and specified to each of the interviews. This structure allowed us to explore context specific problems in more detail and to respond more extensively to issues participants mentioned during the interviews. Examples of interview questions include (for the complete guide, see appendix): ‘How would you describe ‘health’ and ‘disease’ yourself?’; ‘Would colleagues in your field agree with your definitions?’; ‘Are there any specific problematic situations that you encounter in practice that are related to definitions of health and disease?’; ‘Do you see any solutions to such problematic situations or have there been solutions brought forward to solve these issues?’. From these broader, more abstract questions, the interview was subsequently narrowed down to more specific questions, in response to the answers given by the participants. The interviews were conducted digitally, via Microsoft Teams, and took 46 min on average (ranging from 37 to 57 min). Audio recordings of the interviews were transcribed verbatim.

Setting and recruitment

This study was conducted in The Netherlands. All participants were Dutch speaking and all were highly educated. All participants were selected following the principle of purposeful sampling. The reason for choosing for purposeful sampling was that we wanted to study definitions of disease and health in relation to actual problems arising in health-related practices. We recruited professionals who have spoken out in public or professionally about problems in relation to health and disease definitions and/or who work in fields/organizations that we considered to be interesting because we expected such issues to arise. Moreover, we aimed to cover a broad range of healthcare practices. The participants were recruited by e-mail.

Participants

The sample details a broad range of professionals ( n  = 17), including doctors, policy makers, representatives of patient organizations, humanities experts, and medical professionals working in various advisory boards and governmental organizations (see appendix for a specified overview of participants their expertise). All participants were Dutch speaking, highly educated and experienced professionals. The representatives of the patient organizations that we included were interviewed in their professional role and not as patients (if applicable). One of the interviews had to be excluded from analysis because the recording was unusable due to a technological error, bringing down the total number of transcripts from 17 to 16.

Data analysis

The data was analyzed using NVivo software (11th edition). The first interview reports and transcripts were discussed among RL and MS. Based on these discussions, RL made a first coding-scheme and discussed this with MS, which resulted in some adaptations. To reduce ‘tunnel vision’, transcripts were then analyzed and coded by RL and MS separately and compared afterwards. The interviews were analyzed in a way that may be best described as a method in between ‘grounded theory’ [ 29 ] and ‘directed content analysis’ [ 30 ]. That is, we did not build a conceptual scheme completely bottom-up as one would do with grounded theory. However, it was also not the case that we already had a solid theoretical framework at the start of the analysis which we would use to frame the issues discussed in the interviews, as is common in directed content analysis. We have taken the answers given by participants as a point of departure, exploring what their views are regarding the function of health and disease concepts, and exploring what kind of problematic situations they experience in practice. Sometimes, participants would already refer to specific theories, approaches or models themselves However, for other parts of the analysis, we have made use of distinctions and concepts from the academic literature to make sense of the many issues that were brought forward by participants. For instance, some issues mentioned by participants could be viewed as being practical examples of what is called a ‘line-drawing problem’ in the theoretical debate [ 10 , 31 ]. Such categories appeared useful for analyzing and interpreting the data but where not selected prior to the analysis.

Defining health and disease

In the interviews, respondents have pointed to various important practical functions of health and disease concepts. In some interviews the influence of certain definitions/approaches was explicitly articulated by participants. Participants talked about practical problems that they experienced and were often able to link these with how health and disease are conceptualized in their fields. For instance, some participants described specific models or definitions, such as the Biopsychosocial model [ 32 , 33 , 34 ] and Positive Health [ 35 , 36 ] and talked about their significance for their professional fields. In other interviews, however, the link between conceptualizations of health and disease and practical issues was more implicit. Participants would, for example, speak more broadly about ‘biomedical’ and ‘holistic’ approaches, or discussed how thinking in terms of ‘Evidence Based Medicine’ (EBM) could (negatively) affect clinical practice.

While some of the respondents mentioned that it would be convenient to have general, all-encompassing definitions, none of them thought it would be possible to formulate them in a way that they are exhaustive and practically useful at the same time. Instead, in some interviews, viewing health and disease as plural concepts was discussed as being a possible alternative. HD01, says in this regard:

I’m not saying that one type of concept is primary or more legitimate than the other. But if you are talking about a health concept for the use in scientific research, then I would argue for a concept that is more clearly defined. If you’re talking about how people experience things or use, for example laymen, you could be talking about a simpler concept. And I think those things can coexist just fine.

At the same time, other participants were more hesitant when discussing the possibility of having multiple definitions of health and disease. Concerns were raised that such a situation may lead to problems of communication between institutions, (medical) disciplines, but possibly also between doctor and patient. As defining health and disease was viewed by many to be important to facilitate communication, for some participants it also seemed to be problematic to have a plurality of definitions. Furthermore, some participants would also critically question the endeavor of defining health and disease, questioning the goal of defining concepts itself. In several interviews, defining health and disease is described as a continuous process of reflection and adjustment, rather than a pursuit of finding ultimate answers. One participant, HD02, describes that how we define our concepts always have an effect on practice:

I think that every description is functional, in the sense that it always has an effect. Words aren’t neutral so it’s not- I don’t believe in that correspondence theory of there being something in reality that you just have to put the right term on. A word always does something. And I think that’s what it’s more about, so when use a certain view of health, for example, the absence of diagnosis. Then it is important to see, what effect does that have? Who or what is excluded? Or who benefits from this? Who gets worse from this?

Health and disease concepts in practice

One of the key aims of this study was to explore how health and disease are conceptualized, defined or approached, in actual practice. In particular, we were interested what kind of practical functions health and disease concepts have in various contexts. In our analysis of the interviews, we observed that respondents discuss different types of health and disease concepts, working on different levels and as used for various kinds of purposes. If we look at the different type of functions and contexts the concepts are deployed in, and the levels on which they ‘operate’, an interesting picture emerges. We have categorized them broadly into three types of practical functions: (1) a ‘strategic, political and policy-making function’, (2) an ‘institutional and social function’, and (3), ‘guiding clinical practice and medical research’.

Strategic, political and policy-making function

In the context of strategic development, political debates and higher-order policy-making, definitions of health and disease can stay relatively broad and vague. Their function is not, for example, to give clinicians clear thresholds for line-drawing between the normal and pathological. Rather, their function is to steer public health policy, to change current practice within a healthcare organization, or to facilitate cooperation between organizations and institutions. Within this context, health and disease concepts do not need to have the analytical or explanatory power as may be needed in, for example, medical research or clinical practice. The definitions at stake may be demanding and idealistic, as they are used for questioning and/or changing the current state of affairs. Participant HD09 says in this regard:

If you want to explain to a politician why we are going to deploy all kinds of healthcare resources that are not directly focused physically, somatically, then you have to be able to explain it in clearly defined goals, objectives, and health definitions. And in that sense, it is of course also very important for the WHO to adjust such a definition. Because that changes your entire health policy worldwide. For example, it has an effect on what you use for prevention, but it also has an effect on what you use for treatment.

Embedded in these (inter)national discussions on definitions, goals and policies, we may find related discussions in the context of policy on local or organizational levels. Participant HD03 explains why defining health and disease concepts are considered to be important for organizational strategy and policy-making within healthcare organizations:

In the academic hospitals, we are primarily using a biomedical approach towards disease. At the same time, we have the ambition to expand to preventive medicine and to strive for positive health, public health, global health, that are all approaches of health. However, as an academic hospital you are only specialized in thinking about disease in biomedical terms.’’ … “So that’s the problem. If you make a strategy, what are you going to focus on? And so, what I say is, the wish is to focus on prevention, public health, global health and to look more broadly at health and disease.

Although broad and vague definitions may be used successfully for the purpose of guiding or changing policy, more concrete definitions may be needed in other contexts and for other purposes.

Institutional and social function

Another practical function that participants ascribed to the disease concept, and more concretely, to medical diagnosis, is a ‘gatekeeper function’ for issues regarding assessing eligibility for reimbursement of treatment and other healthcare arrangements. Examples mentioned by participants include debates on the legitimacy of viewing clinical conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and chronic pain disorders as ‘genuine diseases’. What we consider to be diseases may therefore also be viewed to be a social and political agreement, some argue. Participant HD05 explains why ‘disease’ could be viewed as an institutional concept:

Who will be reimbursed for their medical treatment? That is decided on a political level.’’ … ‘‘And you can say that, at some point you have to say that someone has a disease, within the framework of a certain law, because that is how it has been agreed upon. And that is an institutional fact, because that is what has been agreed upon by various authorities.

What our institutions acknowledge as ‘genuine diseases’ does not only have impact within the medical realm, but also plays an important role in societal and personal debates. What we define as disease has also a social function. It creates a situation in which others take care of you as a patient, but it can also excuse responsibility from social tasks and duties, for example. In this regard, HD09 says the following:

And no matter how you look at it, sickness creates privileges. Because if you’re sick, people will bring you breakfast in bed, or not if you’re vomiting. And then you get get-well cards, people send flowers and you get time off. Then you are very pathetic and that comes with all kinds of perks. And I’m not saying that people get sick on purpose because of the perks, but that is an automatic consequence. Because my demented patients don’t get get-well cards and flowers and breakfast in bed at all, they are looked at strangely in the supermarket. And patients with psychiatric disorders, well, let’s say… they are usually not the most popular. And that has to do with the fact that we, I think, as a society have determined that being sick has to do with physical ailments…. There’s a discrepancy there. Physically ill: pathetic, perks. Not visibly ill: poser, difficult, hassle, hassle, hassle. That stings.

Guiding clinical practice and medical research

In a clinical context, health and disease can be approached in different ways depending on the type and level of care that is provided. For example, in emergency situations a medical doctor needs to focus on the direct biological problem, but when the patient is in a recovery phase they may have to ‘switch’ and take psychological and social aspects more into account. When caring for a patient suffering from a chronic condition a medical doctor may want to focus on aspects such as resilience and adaptation, and supporting the patient in what they consider to be meaningful. By going through these levels of care, health and disease may be approached differently. Here, HD06 explains this process of ‘shifting’ between models:

Of course, healthcare is very broad. The trauma surgeon and the emergency room doctor who provide acute care for a trauma patient, they are mainly focused on the biomedical model, their A, B, C, D, E, breathing, blood pressure, circulation, you name it. But then you end up in a rehabilitation process in which the biopsychosocial model is used. And then you come to an occupational doctor and an insurance doctor where I think it is very important to also use that model of Positive Health. Because there- Well, we see that with trauma patients too. In our research, independent of the seriousness of the injury, impediments to the ability to function were actually caused by all sorts of personal factors. So, you have to support people in finding their own direction and adaptability.

While taking account for ‘personal’ factors such as adaptability (or resilience) and societal participation may be of relevance for the treatment and revalidation of patients, and thus could be considered as being part of ‘health’, in context of medical research such factors are usually separated from health and disease outcomes and viewed as determinants instead. This allows researchers to measure causal relations between factors such as societal participation and health in a better way. Taking all kinds of (intra)personal and societal factors as being part of the health concept may result in problems for causal explanations in scientific research. Participant HD01 says the following regarding this tension:

The moment you use a broad concept of health, in which all these things are lumped together, you risk that the causality is not actually clear. So, in that sense, I’d like to stick to defining health as biomedical and mental functioning. And I would like to keep those other factors in their own place. And then you can look much better at, what causes what? Or how are things connected?

Problematic situations in practice

A second key aim of this study was to ask participants if they did experience problematic situations in practice that are caused by or related to conceptual issues. In the interviews, a large variety of problematic situations were discussed, including various clinical, epistemological, and ethical issues. Some participants described more abstract problems such as ‘medicalization’ or ‘healthism’ in a broad sense, while others described more concrete issues, such as social or bureaucratic problems in case of patients with medically unexplained symptoms (MUS). Because of the diversity of participants included in our study (i.e., people working in different fields and organizations), the answers to our questions were also diverse and related to their particular context. We have clustered the problematic situations which were brought up in the interviews into 5 types:

1) Illness without identifiable pathology

2) Biomedical versus holistic approaches

3) Line-drawing and threshold problems

4) Problems with translational medicine: from research to the clinic

5) Communication problems

Illness without identifiable pathology

One issue that was discussed in several interviews is the problem of patients suffering from illness without identifiable pathology (or, ‘disease’). This includes patients suffering from ME/CFS, functional neurological disorders, chronic pain disorders, and other conditions that are often described under the umbrella term ‘medically unexplained symptoms’ (MUS). As illness is often viewed to be secondary to disease, and as it is commonplace to think that in order to overcome the illness, one has to cure the underlying disease, it seems only logical to search for the causing pathology. However, in many cases this search does not lead to a clearcut answer. As a result of this, unfortunately, the suffering of the patient is sometimes not taken seriously by medical professionals.

Besides being taken serious by medical professionals and getting the care they need, patients suffering from illness without known pathology may also encounter other type of problems. For example, for patients who cannot work due to illness a medical diagnosis is a necessary criterium to be met for being excused from work and to gain access to certain social and financial resources Footnote 2 . HD07 explains the institutional aspect of medical diagnosis:

Well, in this sense, we are dealing with legal frameworks. The law prescribes that to be able to claim a sickness benefit, one must be diagnosed with a disease. If it stops there, then we do not need to test those other two criteria. And sometimes you will find yourself in a gray area. Because yes, for example, I am also thinking about an example that I have. Social problems can also often lead to dysfunction. In the case of a social problem, there is not by definition disease, but can become one. And we often have to deal with those kinds of dilemmas, that if you see someone with informal care, with a financial problem, just to name a few- Those people who are walking on eggshells at a given moment when they come to us. We establish that, legally, there is no disease. But it might turn into disease.

In line with the situation sketched by HD07, HD15 argues that this problem of not getting recognized by our institutions as having a genuine disease, is a terrible experience for patients. HD15 explains that this in matter of fact urges their organization, a patient organization, to ‘medicalize’ the condition:

Then it will get very bad for them. Because people have a disease on the one hand, on the other hand, they always have to prove that they have it, and then there is also a financial need. So, that’s really the crux of the story. And, of course, we try with our work to make it clear as much as possible, that it is a progressive, biological condition, biomedical condition and that just needs research.

On the other hand, negative aspects of medicalization were also mentioned throughout the interviews. Participant HD14 mentions that including a condition in the ICD should be done with precaution:

The bottom line is that I’m a huge proponent of including pain in the ICD-11, the way as it is now. But I also see that there, I also see that in that balance of those arguments, there are, well, let’s just call it dangers. And that is that you do indeed have things that are normal part of life, which we are going to call disease. And that medical procedures are set up by people, who say, ‘hey, come to me, because I can solve it’. And that is, we have to be very careful about that, in communication, on the one hand to recognize that pain that is there, et cetera, and to take it seriously and with all the benefits that entails. But at the same time to ensure that we do not make it too medical where it is not desired.

In the interviews, many participants argue that, in clinical practice, the illness-experience of the patient is most important and deserves recognition. HD10 argues:

I think a disease is largely about the experience of the patient. And again, of course there is a biological construct underneath, but not always, eh. There are also people with a disease without a biological construct. And just to say, those people are not sick, I think that is far too short-sighted.’’ …. ‘‘We relatively often see people with a functional disorder, something that used to be called conversion or functional neurological symptoms. Those people can suffer a lot from this, but there is no biologically identifiable cause. And I think you shouldn’t dismiss those people as posers or say, you have nothing. No, they do have something and they do suffer from it and that leads to hindrance in daily life. So, I think you can speak of disease.

Biomedical versus holistic approaches

A broader issue that came up in many of the interviews is one that may be best described as problems that are due to biomedical versus holistic approaches towards health and disease. Participants discussed that focusing treatment primarily on a biomedical parameter while paying less attention to the experience of the patient as a whole can be problematic for providing good clinical care. That is, patients may be treated for their medical condition without taking sufficient account of their personal circumstances and/or life goals. Participant HD11 said in this regard:

Of course, you can approach disease in many different ways. If you approach it cell-chemically, so to speak, disease is what damages, or attacks, or if you will, the biochemical integrity of your cell. But if you look from a patient’s perspective, or from a doctor’s perspective, then a disease is something that hurts, bothers, hinders that patient. And the perspective of the patient, but also the approach of society, of course, plays a very important role in this.

In some cases, the emphasis on the biomedical paradigm may even lead to instances of ‘treating’ biomarkers that may not have a clear clinical significance. HD11, discussing the implications of the new drug (aducanumab) for Alzheimer’s Disease, explains that:

The bottom line is, there is a new drug that, if you look at the cellular level, biochemical level, it absolutely does something. It does something to the proteins in your brain, period. However, if you look at the clinical effect on the patient, and what it can do for the patient, it does nothing. Patients don’t improve, we have no improvement, cognition does not improve, general daily activities neither, nothing. The FDA has approved it on the grounds that, despite the fact that it doesn’t do anything clinically, biochemically the evidence is so clear that it does something, it’s bound to do something clinically. While it just doesn’t.

Yet, also in cases where a biomedical treatment has proven to be clinically effective, it could be nevertheless problematic to forget about the patient’s personal circumstances. Sometimes it may be more important to help people with psychosocial issues, for example, than to direct attention to the medical problem. Participant HD10 discusses person-centered care for diabetes patients and argues that taking care of the patient - improving their health - includes more than treating the disease biomedically:

That also touches on the need for person-centered care, - that the care providers really can actually see from the patient’s eyes which approach they should take. Do they really have to focus on that disorder or do they indeed have to focus on the social realm?

Another related problem that was mentioned in the interviews is that of prioritizing biomedical diagnosis over other holistic aspects when assessing the prognosis. Although the diagnosis may give important information regarding the development of a disease and about chances for successful treatment, other non-medical factors may have an underestimated influence on the prognosis as well. In some instances, psychosocial aspects may even show a stronger correlation with prognosis and treatment than the medical diagnosis does, participant HD04 says in this regard:

The classic assumption is very much like, if you know a diagnosis, then you know the prognosis and then you know whether or not you need to do something to influence that prognosis. Whether or not you can do something to influence that prognosis. And what we are gradually noticing is that that prognosis may well be determined by many other factors and that the diagnosis is only a small part of it and therefore only partly determines what the prognosis is. The prognosis is also determined by all kinds of other factors. other variables, to put it in scientific terms.

According to HD04, it is common for medical professionals to focus too much on biomedical diagnosis and to underestimate the influence of ‘non-medical’ variables on the prognosis and the well-being of patients – which, she beliefs, should be the ultimate aim. This does not only go for patients with medical unexplained symptoms, for which finding the right diagnosis is considered to be very difficult. Also for diseases that can be diagnosed straightforwardly there seems to exist a disparity between a biomedical view of disease and more holistic ones. HD04 gives the following example:

Examples abound. People with rheumatoid arthritis, we can diagnose rheumatoid arthritis fairly well with lab tests, with clinical tests, with imaging tests. We have criteria, you can always argue about that, but we generally agree on that. And then we also have a measure of the disease activity. So, if you have a very high sedimentation rate, then you have a high disease activity, for example. And if you then look at the severity of the complaints and the disability that people have and relate that to disease activity, then that is not a nice linear relationship. So, then there are people with, if you would look at it as a rheumatologist, as a doctor, if you look at it as a doctor, then well, that disease is just well under control, hardly swollen joints, no increased sedimentation rate, goes well, but in fact people suffer very much.

Line-drawing and treatment threshold problems

In the interviews, problems with drawing the lines between states of health, disease, or ‘being at-risk’, and problems with determining the right thresholds for starting medical interventions, were considered important reasons for having clear definitions. Having clear cut-offs for diagnosing disease and for starting treatment is seen as convenient for clinical practice. Participants expressed a desire to have objective measures to decide whether we are talking about disease, and when to start treatment. Yet, they were also highly doubtful if such clear lines could be drawn. On the one hand, they said diagnostic tests are used to examine if a patient deviates from the (objective, biomedical) norm. On the other hand, participants also argued that patients’ symptoms should be viewed as central to drawing the line. This also seems to be problematic, however, as patients may sometimes deviate from the norm but do not experience symptoms, or vice versa, patients may experience symptoms but test results do not show significant abnormalities. HD08 talks about the challenges of the line-drawing problem for clinical-decision making:

Of course, it is difficult, because doctors like to work neatly, like to work according to scientific evidence, like to work according to guidelines. And a guideline only works well if you can make hard statements, otherwise you have a guideline that says about everything: you ‘may consider this’. And yes, that is how guidelines end relatively often, but then it is not very useful in practice, because you want such a guideline to guide you. And the surgeon, just to name one, who wants to determine whether he should operate. And it’s easy if that just has a cut-off point that says, you have to operate above 23 and not below, just to name something. So, whenever there’s a big gray area, it’s complicated and leads to subjectivity and also different doctors making different decisions.

This was also discussed in relation to prevention, when patients are ‘treated’ with medication to prevent future disease(s) while they do not experience symptoms at that point of time. In particular, participants pointed to the lowering of diagnostic and treatment standards for risk-factors such as high blood pressure and high cholesterol as examples in which it is difficult to draw the line. Participant HD09, who reflect on this problem, says the following:

But you can get quit some muscle cramps from cholesterol lowering drugs. Yes, so it may be that he has one in twenty, one in thirty less chance of that stroke, but in the meantime, he is no longer able to walk down the stairs and do his own shopping because of those muscle complaints and perhaps even take a fall. Well, and it’s not the case that everyone has muscle problems, so for the people who don’t get this it might be the best treatment. That is the way you have to look at it. And also evaluate, eh, and that’s about when you start something, you have to follow up what it does to someone, even if someone has been using it for some time, because that can change.

When participants were asked if they could identify reasons for this trend of lowering diagnostic and treatment thresholds, some suggest that cultural values and norms play an important role. Not only there is an increasing societal pressure on living a healthy life, health is also increasingly viewed as a moral good. It is this normative shift, in combination with ever growing technological possibilities, that is suggested to lead medicine to focusing on early detection and treatment of health risks more and more – even if chances of developing actual diseases are expected to be low. Patients may desire more diagnostic testing or more frequent health check-ups and medical professionals may feel obliged to grant their requests, since the technology is available. This is not without consequences, however. HD11, for example, explains that excessive diagnostic testing may lead to overdiagnosis. In particular, ‘incidental findings’ Footnote 3 are considered to be a problematic situation:

And that is, I think, also an ethical dilemma that doctors have, because then you find something and what do I do now? They have no complaints at the moment, so I don’t really have to do something with it now. But imagine that it is cancer, and in four months they will come in with metastatic disease, and then I could have prevented that. That’s difficult. And then the technology renders it unlikely that such a patient says, never mind, we’ll see how things will go. Because everyone says oh, yes, if something can be done about it, then let’s do that scan, then do that biopsy, then do that incredibly complicated procedure.

Incidental findings may be clear instances of pathology, and in these cases, it may be regarded as fortunate that the patient can be treated for a disease that may otherwise have gone undetected until it was too late. However, in other cases incidental findings may be benign deviations or anomalies and it is questionable if the patient will benefit from further diagnostic testing and/or medical intervening, as it is not clear if the anomaly will ever lead to clinical symptoms. Again, this begs the question where to draw the line between normal and abnormal, between health and disease.

Problems with translational medicine: from research to the clinic, and beyond

In the interviews, some participants also discussed problems regarding translating medical scientific findings from a research context into clinical practice. One approach that was mentioned by participants as particularly problematic was ‘evidence-based medicine’ (EMB) Footnote 4 . While medical professionals may be aware of the different aims and goals of medical research versus clinical medicine, and of the problems surrounding EBM, they may feel bounded by institutional agreements and regulations. For example, insurers may only reimburse treatments that are proven to be effective according to standards of EBM and therefore may not sufficiently allow for tailoring treatment to the personal needs of the patient. HD09 explains how the broad implementation of the EBM style of reasoning, from research to the clinic and beyond, to institutional arrangements, is not without danger:

Evidence-based medicine, with its mono-focus thinking, traditionally, it’s fortunately changing, can also bring real dangers, because what you see is that politics and insurers are very much steering policy and reimbursing on the basis of guideline indicators.

HD13 goes even a step further by provocatively referring to EBM as ‘pharmaceutical-based medicine’. He argues that medical professionals are restricted by the rules and regulations of the healthcare institutions such as the National Healthcare Institute (‘Zorginstituut’), which require treatments to be ‘evidence-based’ before they can be considered eligible for reimbursement. As a result, HD13 claims, we end up with suboptimal medical treatments:

The entire ‘pharmaceutical-based medicine’ is currently ‘the’ steering element of the National Healthcare Institute and of affordable care in the Netherlands, of reimbursed care. And it’s not the best treatment that gets reimbursed, but the treatment that has been the most researched; not the one with the best outcomes.

Another problem that was particularly mentioned in the interviews was that of generalizing medical knowledge from the research context to the clinical context. As diseases and their treatments are commonly researched in study populations that do not represent patient populations in clinical practice - e.g., age range between 18 and 50, mostly Western, male subjects, having only one disease instead of several - a rather homogenous picture of specific disease entities with specific treatments is generated that often does not match the heterogeneous reality in clinical practice. Moreover, while medical research is often focused on curing a disease, or at least reducing its symptoms, patients may in fact have different goals and wishes that need a different approach. Participant HD09 argues that the goals of medical research do not always match the goals of clinical medicine:

So, the average patient in a trial is a middle-aged man. The average user, who is treated according to the guideline based on those trials, is an old woman or one who has more medical conditions and uses several medications. And then it is also the case that those trials are aimed at preventing a new event or surviving. And, for example, not having a second heart attack, not having a stroke. Well, those may be things that are important to someone, but I just said that is often not the most important thing. Those people are not all at about living longer, they care about function preservation. And then it can still be important to prevent that stroke, but then you really have to look at it in a different way.

Especially in case of (chronic) multimorbidity, in which patients suffer from multiple diseases at the same time and also use multiple medications, it can become questionable what is treated, exactly. A set of separate diseases, or the combined physiological effects and symptoms of a multitude of underlying pathologies, or even of the medications used? As a consequence, ‘evidence-based’ treatment protocols could potentially harm patient populations that do not fit the assumptions on which the treatment is found to be efficacious. Furthermore, diseases and also the medications that are used may interact, resulting in a clinical picture that is very different from what is expected. We might describe this situation as one that is epistemologically opaque : it seems to get very difficult, if not impossible, to distinguish cause and effect. HD09 explains:

And then the question is whether it will work the same way with that woman with all those old age conditions compared to what happened with that fifty-year-old man. So, it probably reacts differently as well. It reacts differently, because there are multiple diseases, interaction with disease. And it reacts differently because there are a whole lot more medications, interacting with medication. And it reacts differently because the body is different.

So, while medical research tries to reduce complexity and look into single homogenous diseases and patient groups, in clinical practice disease often manifests very differently.

Communication problems

While participants were generally doubtful about arriving at univocal and all-encompassing definitions of health and disease and favored the idea of conceptual pluralism, some participants also expressed concerns with regard to communication. If we all use different definitions or different health and disease concepts, how do we know we are still speaking of the same thing? As clear-cut definitions are often desired precisely for the purpose of solving ongoing problematic situations in medicine, it may seem paradoxical to accept conceptual pluralism. In practice, having multiple ways to understand a disease can lead to communication problems, participants fear. For example, when medical specialists’ views differ so significantly that they almost literally speak about different diseases, it is questionable if they are still able to sufficiently communicate with each other and their patients.

In an interview with HD08, opposing views on Alzheimer’s Disease among medical specialists were discussed. Alzheimer’s Disease was originally diagnosed on the basis of clinical signs and symptoms, but in recent years a part of the neurologist community has switched to prioritizing biomarker testing (i.e., primarily the presence of beta-amyloid) over clinical presentation. However, the problem is that the group of patients with positive biomarker tests do not completely match the group of patients who get symptoms. Therefore, changing the way of diagnosing Alzheimer’s disease in patients also seem to imply changing the definition of Alzheimer’s disease. Hence, it becomes unclear if medical specialists are still discussing the ‘same’ disease. HD08 says the following about the opposing views:

Well, I think there’s- You could almost say, it’s kind of a clash of civilizations. You have the people who just want a hardcore biological substrate and then have little regard for other aspects. And you have people who say yes, maybe it is not possible to classify it exactly into careful categories, let’s also take into account the less ‘hard’, less definable aspects that are important for the functioning of a patient.

While acknowledging the challenges and pitfalls that come with speaking different ‘medical languages’, at the same time, participants also see benefits of having different approaches towards health and disease. Some of them note that we already are using different languages, scientific explanations and medical classifications, and that this could be viewed as something valuable. In a combined interview with HD13 and HD14, HD14 discusses the different classification systems that are being used for chronic pain patients among different (para)medical professionals:

No, I think you should cherish that, because an anesthesiologist can do things that a rehabilitation doctor cannot do, and vice versa. So, you really have to use each other for that and that also applies to all those other medical specialists and paramedical specialists. So that in itself is not a big deal. What- Or rather, that’s very functional, that’s excellent. At the same time, we must speak each other’s language and that must be the same language with each other, but we must certainly not forget the patient. And, because the patient must also be at the center of our interprofessional communication. And, but also the wishes and needs of the patient. So, if HD13 says ‘I’m good at ICD’, and I’m good at ICF, to put it very bluntly, that’s not going to work. I need to know about ICD, enough to talk to HD13. And HD13 needs to know about ICF, enough to talk with me. But really, we should all be able to know enough to be able to talk to the patient properly.

Thus, interestingly, the suffering of one patient could be classified in several different ways, depending on the classification system that is used. While recognizing the challenges this brings for medical professionals, HD13 and HD14 also see the benefits of looking through different lenses – as long there is sufficient common ground to communicate with each other and the patient. So, concepts of health and disease seems to be approached differently at different levels of care (i.e., primary, secondary, and tertiary lines of healthcare) and between different types of (para)medical professionals. The situation as sketched by HD13 and HD14 seems evident for healthcare as arranged in The Netherlands, where various classification systems are indeed being used in different levels and types of healthcare practices Footnote 5 . Every classification system has its strengths and weaknesses. An ongoing challenge seems to lie in being able to sufficiently understand each other’s ‘medical language’.

Philosophers can contribute to medicine by exploring, analyzing and articulating conceptual issues. However, as we take health and disease concepts to be context-dependent, it is crucial to study their meaning in context. Building on recent proposals for a pragmatist understanding of health and disease that embraces conceptual pluralism, investigating different perspectives is very important. As Veit argues: “Questions such as how medical practitioners see, use, and evaluate concepts like health, pathology, and disease are important to the philosophy of medicine. Yet, these questions cannot be answered through introspection alone. They require investigative empirical methods” [ 21 ] (p.183). In similar vein, Seidlein & Salloch [ 17 ] argue that empirical methods can be used to gain better understanding of the complex relationship between illness and disease, by reflecting upon patient and professional perspectives. Including qualitative methods and other types of empirical research to our toolbox can bring theory and practice closer together and stimulate new medical-philosophical and bioethical explorations.

The current study differs from previous empirical studies [ 24 , 25 , 26 , 27 ], in that it was specifically focused on exploring how health and disease concepts have a function in practice and how they may lead to problematic situations . The existing studies have already shown a palette of different conceptualizations, but did not interpret these in terms of their practical function and role in problematic situations. In our interviews, various important practical functions of health and disease concepts were discussed and our participants suggest that different contexts and purposes may require different types of definitions. We agree with Veit that finding such a lack of consensus and a pluralism of concepts and functions, strengthens the case against conceptual monism, and favors positions that “relativise the concept to human interests and cultural dynamics’’ [ 21 ] (p.178). Indeed, our study reveals that “the notion [of disease] serves a variety of purposes that perhaps cannot be accomplished using a single concept” [ 21 ] (p.180).

However, the plurality of functions and the definitions that are used to serve them, may not always be compatible with each other. A broad concept definition of health may work, for example, to steer healthcare policy in a certain direction on a political or organizational level, but may cause problems when it must be implemented in a research setting. Of course, different functions and definitions do not exist in a vacuum but also interact. Moreover, as is evident from the interviews, although the plurality of definitions may sometimes be problematic for reasons of communication, it is also a reality. Therefore, it may be more fruitful to acknowledge this and to elucidate and explain the differences; this may actually enhance communication and understanding across domains.

In this article, we have highlighted 5 types of problematic situations that were discussed in the interviews and that can be related to the conceptualization of health and disease. Some problems are already recognized in the medical-philosophical literature, such as problem of line-drawing. Others may offer new starting points for medical-philosophical and bioethical inquiry. Philosophy of medicine might help to analyze and elucidate the conceptual components of these problems and come up with suggestions of how conceptual work might help to find solutions. For example, the work that has already been done by Rogers and Walker [ 12 , 31 ] regarding the line-drawing problem might be useful for medical practitioners and medical guideline developers. They propose using context specific précising definitions that serve to prevent overdiagnosis; such an approach may also be useful to help solve the line-drawing and treatment threshold problems, and the risks of over or undertreatment, that we encountered in this study.

Furthermore, tensions between biomedical and holistic approaches of health and disease – that have led to major debates in the philosophy of medicine and has important ethical implications – were also described by participants as problematic in practice. However, there was also a hint of a solution in the interviews. As one participant explained, different contexts may benefit from different approaches. Strictly biomedical definitions may be more useful for the emergency care doctor while during rehabilitation a holistic normative biopsychosocial model is considered more helpful. Footnote 6 This idea is in line with the proposal by Haverkamp et al. [ 7 ], to consider using concepts that fit best with the purposes and values of a specific healthcare practice. Some of the problematic situations described in the interviews may also give new input for investigating these purposes and values in different contexts. For example, the changing conceptualization of Alzheimer’s disease and the use of biomarker diagnostic testing, that was mentioned in the interviews, is a current topic of medical-philosophical and bioethical debate (e.g., see [ 43 , 44 , 45 ]).

Another role for philosophy can be to help healthcare professionals and policy makers to better understand how some of their problematic situations arise. For example, some of the issues we identified could be understood in terms of a disconnect between the three spheres of the conceptual triad of ‘disease, illness and sickness’, as originally presented by Twaddle [ 46 ] and as later updated by Hofmann [ 47 ]. As Hofmann already noted, cases of non-health are generally considered to be less controversial when two or three of the spheres align. However, when only one or two of the are deemed applicable to a certain condition, it becomes epistemically and normatively challenging [ 47 ]. This conceptual triad may help patients, healthcare professionals and policymakers to better understand issues around the problem of medically unexplained symptoms, also in relation to the institutional and social function of the disease concept. At this point, it may also be significant to note that in the Dutch language, in which the interviews were conducted, the distinction between disease, illness and sickness is not available. A single word, ‘ziek’ or ziekte’, is used to cover all three notions, making the conceptual confusion perhaps even more salient than in the English-speaking community.

Some of the problematic situations that we have described may, at first glance, be viewed as practical problems with only little conceptual basis. For example, when discussing disease as an institutional and social concept, and describing problems that patients who suffer from medically unexplained symptoms may face (e.g., problems with accessing certain healthcare resources, or social and financial arrangements), one might question to what extent this is a problem with the conceptualization of disease.

One might argue, as Hesslow [ 48 ] did, that we have been misled by the idea that we need a concept of disease to make normative decisions on clinical, moral or socially important issues. However, from a pragmatist perspective, the theoretical, practical and normative dimensions of concepts are inherently related. As De Vreese argues: ‘‘it seems undeniable that the health/disease distinctions made on the basis of tacit understandings of the disease notion do play an important role in the background of health care-related research and decision-making processes (clinical, moral, legal, social, or otherwise), which might have important consequences in practice’’ [ 6 ] (p.429). Starting from this observation, we might consider adapting our concepts to better fit the social and institutional arrangements (cf. [ 49 , 50 ]). or we might propose better concepts or criteria to base these decisions on (e.g., see [ 51 ]). Both seem to be pre-eminently tasks for philosophers and ethicists to pursue. Additionally, empirical studies may help to further explore these ‘tacit understandings of the disease notion’ and investigate what these ‘important consequences in practice’ entail, as starting points for further philosophical and ethical reflection.

Limitations

As is common for qualitative research, results cannot be generalized and results may not represent the views, attitudes and beliefs of the whole community of medical professionals or patient organizations. As the sample of this study is relatively small and consisted of a broad range of professionals, the findings should be viewed as starting points for further investigation, not definitive answers. Moreover, as indicated in the methods section, the sample consisted of a group of highly educated and experienced professionals. Although there were good reasons to select them, it is important to remark that as a consequence, we did not study the views and experiences of other, more ‘ordinary’ healthcare workers and patients. Also, we did not include the views of different nationalities, cultures, and/or for example less educated or marginalized people. Indeed, we should ask: ‘who are the rightful owners of the concepts disease, illness and sickness’ [ 9 ]? If we view health and disease as plural concepts then an empirical philosophy of medicine should do justice to this plurality by including the views and experiences of these groups as well. Future studies may focus on investigating more specific groups (e.g., a specific medical specialist field or certain group of patients) and/or institutional contexts.

Furthermore, as we have learned from discussions on the empirical turn in medical ethics [ 52 ], one should be careful and considerate when making normative claims on basis of empirical data. However, given the explorative character of this study, this is not deemed a significant problem. Our aim was to explore the range of views regarding health and disease concepts, and the existence of problematic situations related to health and disease concepts, not to give an exhaustive or quantitative overview of such concepts and situations. Furthermore, in qualitative research, it is generally acknowledged that the researcher is not merely a ‘neutral observer’ but also an actor who actively engages with participants in the research process, and thus, is part of the data that is generated [ 53 ]. In this study in particular, with its aim of exploring how health and disease concepts function in practice and examining whether they could lead to problems, the interview guide was drafted from a specific theoretical angle. Moreover, the interviews were analyzed with existing theoretical discussions and frameworks in the back of our minds. By being open and reflexive about this process, and by making our interpretations as transparent as possible, we hope to have gained sufficient rigor.

The traditional debate on health and disease concepts commonly departs from theory rather than from practice. In line with recent calls for experimental philosophy of medicine and empirical philosophy of science, we suggest that theoretical work could benefit from incorporating empirical research. In this qualitative interview study, we have examined the relevance and significance of health and disease concepts, as experienced by participants in various healthcare practices. We found that there are three types of functions that health and disease concepts serve in practice: (1) ‘Strategic development, politics and policy-making’, (2) ‘Institutional and social function’, and (3), ‘Guiding clinical practice and medical research’. Being aware of these different purposes may prevent bluntly using concepts beyond their functional scope. We also explored what kind of difficulties participants experienced in relation to the conceptualization of health and disease in practice, and found five main types of problematic situations: (1) Illness without identifiable pathology, (2) Biomedical versus holistic approaches, (3) Line-drawing and treatment threshold problems, (4) Problems with translational medicine: from research to the clinic, and beyond, and (5), Communication problems.

This study demonstrates how concepts of health and disease can influence different aspects of healthcare and healthcare-related practices and may sometimes contribute to complex problematic situations. By analyzing these influences, by making underlying implicit assumptions explicit, giving further interpretation to the problems observed in practice, providing theoretical frameworks and conceptual tools, and by suggesting conceptual changes or adaptations, we might be able to help solve some of these problems. To do this in a proper way, we need both theoretical and empirical work. If we want our philosophical definitions to be a part of the solution for real-world problems, it is important to consider the intuitions and ideas of people working in different types of medical fields, patients, researchers, and all other stakeholders [ 20 ]. Paraphrasing Immanuel Kant, we may conclude that philosophy of medicine without empirical research risks being empty, while empirical research without philosophical theorizing will still leave us blind. Going back and forth between theory and practice will probably result in a more complex but hopefully also in a better and more fruitful understanding of concepts of health and disease.

Data availability

The data that support the findings of this study are available from the Erasmus Medical Center but GDPR restrictions apply to the availability of these data and are therefore not publicly available.

The notion of experimental philosophy is relatively new and its definition is therefore not yet solidified. Sometimes it is used broadly, including various kinds of empirical research methods. In other instances, it refers specifically to philosophical studies with an experimental design, in which one variable is changed in isolation to measure changes in a philosophically relevant outcome (e.g., moral judgement). We believe that the latter, more narrow definition is useful to distinguish between experimental and other empirical studies. Therefore, in the title of our study, we explicitly use the term empirical philosophy instead of experimental philosophy.

This is, at least, how things are arranged in the Netherlands. Similar arrangements are in place in many other countries worldwide.

Incidental findings are anomalies that are detected in clinical tests that were in fact aimed on testing something else. As the clinical significance of these findings is often not clear, clinicians and/or clinical researchers are confronted with ethical dilemmas [ 37 , 38 , 39 ].

EBM can be described as an approach towards medicine that takes scientific evidence as a central point for guiding clinical decision-making. Typically, in EBM meta-analyses and randomized clinical trials (RCTs) are considered to be the highest forms of scientific evidence. While these methods can indeed have strong benefits over other types of medical research, there is ample discussion about its down sides as well [ 40 , 41 , 42 ].

For instance, general physicians, who provide primary care, use a different classification system (International Classification of Primary Care; ICPC) than a medical specialist in a hospital (International Statistical Classification of Diseases and Related Health Problems; ICD), who provides secondary and tertiary care, uses. Physiotherapists (International Classification of Functioning, Disability and Health; ICF), and psychologists (Diagnostic and Statistical Manual of Mental Disorders; DSM), in turn, also use different types of classification systems.

Another way to frame this would be to say that in emergency care, only ‘disease’ may be relevant to provide proper medical care, whereas in a rehabilitation setting the whole triad of disease, illness and sickness is being addressed.

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Acknowledgements

The authors would like to thank all the participants of the interview study for their input.

This research is funded by the Dutch Scientific Organization (NWO), Project Number 406.18.FT.002. The funding body played no role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript.

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RL and MS were both involved in conceptualizing and designing the qualitative study, and both wrote the main manuscript. RL conducted the interviews with the participants. Both authors analyzed and interpreted the qualitative data. MS has secured the funding. Both authors read and approved the final manuscript.

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The research proposal was submitted for review by the research ethics review committee of the Erasmus Medical Center. A waiver was granted (MEC-2021-0584), as the study did not fall within the scope of the WMO (Dutch Medical Research Involving Human Subject Act). This study was carried out in accordance with General Data Protection Regulation (GDPR), and the Privacy Regulation of Erasmus Medical Center. Written and oral informed consent was obtained before the start of the interviews.

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van der Linden, R.R., Schermer, M.H. Exploring health and disease concepts in healthcare practice: an empirical philosophy of medicine study. BMC Med Ethics 25 , 38 (2024). https://doi.org/10.1186/s12910-024-01037-9

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
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• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

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Group Dynamics in Focus Groups

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Social Constructivism

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Mixed Methods

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Collecting Qualitative Data

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Constructivist Grounded Theory

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