qualitative research discussion guide

How to Write a Discussion Guide for Qualitative Research

• January 18, 2023

Article Summary:  Discussion guides are the “script” used by qualitative researchers when conducting interviews. Though they shouldn’t be read like a script (questions asked verbatim), they are fundamental when conducting interviews. Understanding the structure of the guide and how to frame the questions is key to a good guide.

In  qualitative research,  the discussion guide is the fundamental document that outlines the questions that the interviewer asks a participant or group of participants.

In this post, I’m going to focus on discussion guides that are used in interview-based research, and not on platforms (for example, mobile ethnography platforms, bulletin boards, or online diaries). Although, keep in mind that the best platform-research  ends  with an  in-depth interview or group discussion , so a discussion guide will come after the first phase.

Discussion guides are fundamental to good interviewing. Moderators often have various techniques with how they use guides (some digest the key questions they need to know and skip around, others follow the question outline closely), but most moderators will agree that setting up your questions first is the key to a good interview.

Before I get started and dive into the key components every discussion guide has, let me first say that discussion guides are  not a  script. They’re a guide – and the key to being a good moderator is to know how to let participants go on tangents and when to guide people back to the core questions. Rarely, though, are guides read through verbatim.

Step 1 to writing a good discussion guide: First, know the goal of the research and the essential question

There is a lot of pre-work that has to happen before a discussion guide ever gets written. This includes understanding the core goals of the research, defining the outputs, and aligning the stakeholders. Our process for this stage is to conduct workshops on Miro with stakeholders, but everyone has their own methods.

This initial stage is where the researcher will define what I like to call “the essential question.”

In other words, if you could only learn one thing from the research, what would it be?

Additionally, you’ll want to clearly label and record the various hypotheses that are being tested. Once you know this – and the team is aligned – you’ll be able to choose the methodology, define the participant criteria, and, once everyone has signed off, start on the guide. (Keep in mind this is a general description of qualitative projects, but of course the details will differ depending on the specific project goals.)

Step 2 to writing a good discussion guide: The introduction

When a moderator begins a research discussion, the introduction is critical. This is the part where the moderator builds rapport with the participant and sets the scene. Be sure to include the following in this stage:

–          Purpose of the study and length of the interview (be sure to keep the client name out if the study is being done blindly)

–          Confidentiality details: If it’s being recorded, how it will be used, and what information will be shared with whom

–          Length of the study

–          Ground rules (this is mostly used in  focus groups or co-creation groups) : Not trying to build consensus, letting everyone speak, participants can discuss ideas with each other as well as the moderator

Once the key expectations are covered, it’s then good to add in a sort of ice-breaker or non-study related question to get the group members or the individual participant to relax. For example, you can ask people what their dream car is or where they most want to travel. I typically try to tie the ice-breaker question to the study theme.

Step 3 to writing a good discussion guide: General questions about the topic

Discussion guides can be seen as an upside-down triangle: Start general at the top (broad at top) and get narrower as you go along.

In this second section, the next goal is to set the scene: Ask general questions about the topic and participant(s). This phase helps build empathy and also slowly invites the participant(s) into the topic. A key component here is that you want the participants to define and name their perceptions of the category before you name it. This is a great opportunity to add in  projective techniques . One favorite one that I typically do at this stage – if I’m leading groups – is to do an association exercise. I’ll write down a few words related to the topic on a board and have everyone write down all the associations they have with the category on sticky notes. They first write it down individually, so as not to bias each other – and then we collect the stickies and discuss as a group. This brings everyone in and sets the tone. Importantly, it also gives the moderator context and helps the moderator to be grounded in the category knowledge or opinions.

Step 4 to writing a good discussion guide: Specific questions and activities

Once the participants have defined the category and the researcher has “set the scene,” the discussion guide then moves into the next section: the specifics. If the study is a user test, this is where the moderator has the participant move through the product design. If it’s a focus group, the researcher will start to hone-in on the Essential Question that was defined at the outset of the study. This is where moderator training is so crucial: Good moderators know how to probe, guide, and ask non-leading questions – while still capturing how people think, feel, and do. Projective techniques and exercises are also commonly used in this phase.

Step 5 to writing a good discussion guide: Closing the interview

As the interview winds down, this is where the researcher has a chance to share the brand name (if the study is blind in the beginning but not 100% blind) to test perceptions. If it’s a completely blind study, this last phase of the discussion guide is to close-the-loop. For example, how would the participant rate the concepts? Where would the participant expect to purchase the product? What type of media outlets does the participant pay attention to (to test brand placement)? Or how is the decision-making done at an organization (to understand the buying process). The closing section is crucial as it allows the moderator to then capture more direct responses without leading the participant, since the categories and initial perceptions/ideas were captured organically – with the participant defining the terms – in the very beginning of the interview.

The discussion guide is crucial: Spend time on this step!

To close up, expect to spend 5-8 hours developing your discussion guide. How the questions are set up, the order of the questions, and, super important – the exercises included in the interview – require creativity and thought to put together.

Once the guide is together, practice and know it well – this will help you skip around if the participant brings up topics before you get to them. When appropriate, be able to skip around as well as probe on ideas that are the most pertinent to the study’s objectives.

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The five steps to writing a qualitative research discussion guide

Learn how to create an effective discussion guide for qualitative research by following these five steps.

Creating a qualitative research discussion guide

Editor’s note: Joanna Jones is the CEO and founder of InterQ and co-founder of InterQ Learning Labs. This is an edited version of an article that originally appeared under the title “ How to write a discussion guide for qualitative research .”

In qualitative research, the discussion guide is the fundamental document that outlines the questions that the interviewer asks a participant or group of participants. This article focuses on discussion guides that are used in interview-based research, not on platforms (for example, mobile ethnography platforms, bulletin boards or online diaries). Although, keep in mind that the best platform research ends with an in-depth interview or group discussion, so a discussion guide will come after the first phase.

Discussion guides are fundamental to good interviewing. Moderators often have various techniques with how they use guides (some digest the key questions they need to know and skip around, others follow the question outline closely), but most moderators will agree that setting up your questions first is the key to a good interview.

Before diving into the key components every discussion guide has, let me first say that discussion guides are not a script. They’re a guide – and the key to being a good moderator is to know how to let participants go on tangents and when to guide people back to the core questions. Rarely, though, are guides read through verbatim.

Step 1: Know the goal and the essential question

There is a lot of pre-work that must happen before writing a discussion guide. This includes understanding the core goals of the research, defining the outputs and aligning the stakeholders. Our process for this stage is to conduct workshops with stakeholders, but everyone has their own methods.

This initial stage is where the researcher will define what I like to call “the essential question.” In other words, if you could only learn one thing from the research, what would it be?

Additionally, you’ll want to clearly label and record the various hypotheses that are being tested. Once you know this – and the team is aligned – you’ll be able to choose the methodology, define the participant criteria and, once everyone has signed off, start on the guide. (Keep in mind this is a general description of qualitative projects and details will differ depending on the specific project goals.)

Step 2: The introduction

When a moderator begins a research discussion, the introduction is critical. This is the part where the moderator builds rapport with the participant and sets the scene. 

What to include in this stage:

• The purpose of the study and the length of the interview (be sure to keep the client name out if the study is being done blindly).
• Confidentiality details: If it’s being recorded, how it will be used and what information will be shared with whom.
• The length of the study.
• Ground rules (this is mostly used in focus groups or co-creation groups): Not trying to build consensus, letting everyone speak, participants can discuss ideas with each other as well as the moderator.

Once the key expectations are covered, it’s then good to add in a sort of icebreaker or non-study related question to get the group members or the individual participant to relax. For example, you can ask people what their dream car is or where they most want to travel. I typically try to tie the ice-breaker question to the study theme.

Step 3: Ask general questions about the topic

Discussion guides can be seen as an upside down triangle: Start general at the top and get narrower as you go along.

The next goal is to set the scene by asking general questions about the topic. This phase helps build empathy and slowly invites the participant(s) into the topic. A key component here is that you want the participants to define and name their perceptions of the category before you name it. This is a great opportunity to add in projective techniques. A favorite one that I typically do at this stage – if I’m leading groups – is an association exercise. I’ll write down a few words related to the topic on a board and have everyone write down all the associations they have with the category on sticky notes. They first write it down individually, so as not to bias each other – and then we collect the stickies and discuss as a group. This brings everyone in and sets the tone. It also gives the moderator context and helps them be grounded in the category knowledge or opinions.

Step 4: Ask specific questions and conduct activities

Once participants have defined the category and the researcher has set the scene, the discussion guide then moves into the next section: the specifics. If the study is a user test, this is where the moderator has the participant move through the product design. If it’s a focus group, the researcher will start to hone-in on the essential question that was defined at the outset of the study. This is where moderator training is so crucial. Good moderators know how to probe, guide and ask non-leading questions – while still capturing how people think, feel and do. Projective techniques and exercises are also commonly used in this phase. 

Step 5: Close the interview

As the interview winds down, this is where the researcher has a chance to share the brand name to test perceptions. If it’s a completely blind study, this last phase of the discussion guide is to close the loop. For example, how would the participant rate the concepts? Where would the participant expect to purchase the product? What type of media outlets does the participant pay attention to (to test brand placement)? How is the decision-making done at an organization (to understand the buying process)? The closing section is crucial as it allows the moderator to capture more direct responses without leading the participant, since the categories and initial perceptions/ideas were captured organically – with the participant defining the terms – in the very beginning of the interview.

Discussion guides are important

To close, expect to spend five to eight hours developing your discussion guide. How the questions are set up, the order of the questions and, super important – the exercises included in the interview – require creativity and thought to put together.

Once the guide is together, practice and know it well – this will help you skip around if the participant brings up topics before you get to them. When appropriate, be able to skip around as well as probe on ideas that are the most pertinent to the study’s objectives.

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The purpose of the discussion section is to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and to explain any new understanding or insights that emerged as a result of your research. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from where you left them at the end of your review of prior research.

Annesley, Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Peacock, Matthew. “Communicative Moves in the Discussion Section of Research Articles.” System 30 (December 2002): 479-497.

Importance of a Good Discussion

The discussion section is often considered the most important part of your research paper because it:

• Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
• Presents the underlying meaning of your research, notes possible implications in other areas of study, and explores possible improvements that can be made in order to further develop the concerns of your research;
• Highlights the importance of your study and how it can contribute to understanding the research problem within the field of study;
• Presents how the findings from your study revealed and helped fill gaps in the literature that had not been previously exposed or adequately described; and,
• Engages the reader in thinking critically about issues based on an evidence-based interpretation of findings; it is not governed strictly by objective reporting of information.

Annesley Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Bitchener, John and Helen Basturkmen. “Perceptions of the Difficulties of Postgraduate L2 Thesis Students Writing the Discussion Section.” Journal of English for Academic Purposes 5 (January 2006): 4-18; Kretchmer, Paul. Fourteen Steps to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive; be concise and make your points clearly
• Avoid the use of jargon or undefined technical language
• Follow a logical stream of thought; in general, interpret and discuss the significance of your findings in the same sequence you described them in your results section [a notable exception is to begin by highlighting an unexpected result or a finding that can grab the reader's attention]
• Use the present verb tense, especially for established facts; however, refer to specific works or prior studies in the past tense
• If needed, use subheadings to help organize your discussion or to categorize your interpretations into themes

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : Comment on whether or not the results were expected for each set of findings; go into greater depth to explain findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning in relation to the research problem.
• References to previous research : Either compare your results with the findings from other studies or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results instead of being a part of the general literature review of prior research used to provide context and background information. Note that you can make this decision to highlight specific studies after you have begun writing the discussion section.
• Deduction : A claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or highlighting best practices.
• Hypothesis : A more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research]. This can be framed as new research questions that emerged as a consequence of your analysis.

III.  Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, narrative style, and verb tense [present] that you used when describing the research problem in your introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequence of this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data [either within the text or as an appendix].
• Regardless of where it's mentioned, a good discussion section includes analysis of any unexpected findings. This part of the discussion should begin with a description of the unanticipated finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each of them in the order they appeared as you gathered or analyzed the data. As noted, the exception to discussing findings in the same order you described them in the results section would be to begin by highlighting the implications of a particularly unexpected or significant finding that emerged from the study, followed by a discussion of the remaining findings.
• Before concluding the discussion, identify potential limitations and weaknesses if you do not plan to do so in the conclusion of the paper. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of your findings. Avoid using an apologetic tone; however, be honest and self-critical [e.g., in retrospect, had you included a particular question in a survey instrument, additional data could have been revealed].
• The discussion section should end with a concise summary of the principal implications of the findings regardless of their significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This would demonstrate to the reader that you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results, usually in one paragraph.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the underlying meaning of your findings and state why you believe they are significant. After reading the discussion section, you want the reader to think critically about the results and why they are important. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. If applicable, begin this part of the section by repeating what you consider to be your most significant or unanticipated finding first, then systematically review each finding. Otherwise, follow the general order you reported the findings presented in the results section.

III.  Relate the Findings to Similar Studies

No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your results to those found in other studies, particularly if questions raised from prior studies served as the motivation for your research. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your study differs from other research about the topic. Note that any significant or unanticipated finding is often because there was no prior research to indicate the finding could occur. If there is prior research to indicate this, you need to explain why it was significant or unanticipated. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research in the social sciences is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your hypothesis or prior assumptions and biases. This is especially important when describing the discovery of significant or unanticipated findings.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Note any unanswered questions or issues your study could not address and describe the generalizability of your results to other situations. If a limitation is applicable to the method chosen to gather information, then describe in detail the problems you encountered and why. VI.  Make Suggestions for Further Research

You may choose to conclude the discussion section by making suggestions for further research [as opposed to offering suggestions in the conclusion of your paper]. Although your study can offer important insights about the research problem, this is where you can address other questions related to the problem that remain unanswered or highlight hidden issues that were revealed as a result of conducting your research. You should frame your suggestions by linking the need for further research to the limitations of your study [e.g., in future studies, the survey instrument should include more questions that ask..."] or linking to critical issues revealed from the data that were not considered initially in your research.

NOTE: Besides the literature review section, the preponderance of references to sources is usually found in the discussion section . A few historical references may be helpful for perspective, but most of the references should be relatively recent and included to aid in the interpretation of your results, to support the significance of a finding, and/or to place a finding within a particular context. If a study that you cited does not support your findings, don't ignore it--clearly explain why your research findings differ from theirs.

V.  Problems to Avoid

• Do not waste time restating your results . Should you need to remind the reader of a finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “In the case of determining available housing to single women with children in rural areas of Texas, the findings suggest that access to good schools is important...," then move on to further explaining this finding and its implications.
• As noted, recommendations for further research can be included in either the discussion or conclusion of your paper, but do not repeat your recommendations in the both sections. Think about the overall narrative flow of your paper to determine where best to locate this information. However, if your findings raise a lot of new questions or issues, consider including suggestions for further research in the discussion section.
• Do not introduce new results in the discussion section. Be wary of mistaking the reiteration of a specific finding for an interpretation because it may confuse the reader. The description of findings [results section] and the interpretation of their significance [discussion section] should be distinct parts of your paper. If you choose to combine the results section and the discussion section into a single narrative, you must be clear in how you report the information discovered and your own interpretation of each finding. This approach is not recommended if you lack experience writing college-level research papers.
• Use of the first person pronoun is generally acceptable. Using first person singular pronouns can help emphasize a point or illustrate a contrasting finding. However, keep in mind that too much use of the first person can actually distract the reader from the main points [i.e., I know you're telling me this--just tell me!].

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. "How to Write an Effective Discussion." Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008; The Lab Report. University College Writing Centre. University of Toronto; Sauaia, A. et al. "The Anatomy of an Article: The Discussion Section: "How Does the Article I Read Today Change What I Will Recommend to my Patients Tomorrow?” The Journal of Trauma and Acute Care Surgery 74 (June 2013): 1599-1602; Research Limitations & Future Research . Lund Research Ltd., 2012; Summary: Using it Wisely. The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion. Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide . Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Over-Interpret the Results!

Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.

MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287; Ward, Paulet al, editors. The Oxford Handbook of Expertise . Oxford, UK: Oxford University Press, 2018.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretation of those results and their significance in relation to the research problem, not the data itself.

Azar, Beth. "Discussing Your Findings."  American Psychological Association gradPSYCH Magazine (January 2006).

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if the purpose of your research was to measure the impact of foreign aid on increasing access to education among disadvantaged children in Bangladesh, it would not be appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim or if analysis of other countries was not a part of your original research design. If you feel compelled to speculate, do so in the form of describing possible implications or explaining possible impacts. Be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand your discussion of the results in this way, while others don’t care what your opinion is beyond your effort to interpret the data in relation to the research problem.

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Building Great Discussion Guides: How to Rethink the Most Important Part of Your Research Prep Process

Posted by Mitra Martin on Jan 19, 2021

Researchers, how much time are you setting aside to develop your discussion guide? In a busy world, it can easily become an afterthought. But, putting high-quality creative time into discussion guide development is one of the most high-leverage things you can do to contribute to high-quality insights. It can help you transform a pedestrian study into an experience that’s deeply connecting, satisfying, and illuminating — for stakeholders, participants, and yourself too. Here are a few things to keep in mind as you craft your next discussion guide. 

What is a discussion guide? Let’s define common terms.

A research study is a web of interconnecting questions that operate on different levels, directed to different audiences. We need to be clear about how our discussion guide fits into this web. A discussion guide is more than a list of questions. It's a specific instrument. Here are some useful distinctions: 

Research objectives:

Sometimes called “research questions,” research objectives represent the goals of the research. They frame the overall domain of insights you hope to uncover. Objectives may be written as questions (e.g., “What pain points do new users have when onboarding?”), or as statements (e.g., “To understand new users’ pain points when onboarding.”) 

Stakeholder questions:

When preparing for research as part of a cross-functional team, some researchers source questions from other team members. The result is a list of things those team members want to learn from the research. Sometimes this is referred to as “stakeholder questions” or, confusingly, “research questions.”

Research plan:

The term “research plan” is used in different ways by different organizations. Often, it refers to a sort of “primary document” that centralizes a variety of different pieces of information about the study, including research objectives, context from previous research, and the timeline — along with links to screeners, participant profiles, or other documentation. Often, the discussion guide is included or linked to in the research plan.

Discussion guide:

A discussion guide is an instrument designed to optimize the limited time we have with a participant, allowing for the exploration of all topics in a comfortable way. Pacing, timing, ordering, and craftsmanship of the guide must center the research objectives while also facilitating a warm, human experience for both the interviewer and the participant. It is a mix of scripted or semi-scripted statements or bullet point reminders, along with questions or question-like prompts offering areas to probe. It includes approximate and realistic time-allocations but always incorporates room for the unexpected. The guide helps the interviewer while they are moderating by providing an easy-to-use framework for the interview with visually scannable reminders about what is important.   

Here is a handy chart you can share with your stakeholders to show the distinction between these four instruments. 

Creating your discussion guide

The act of developing a discussion guide is essential preparation for the session itself. Imagine your participant and begin the process of attuning to them. Try to look at reality through their eyes, to think through their thoughts. Reach into their world and ask yourself: how is my participant going to experience the session? Am I speaking their language? What is their POV?

Writing the guide and crafting your questions helps you imagine the human you’ll be interviewing, prepare for the session, and get ready to improvise with the perfect probing questions and follow-ups that arise from the unexpected. It is an essential step in kindling a feeling for your participant that will energize the whole session and yield richer conversation and ultimately, deeper insights. Doing this with care and heart will also make you less likely to reproduce organizational blinders in your conversation, ensuring you sidestep preconceived assumptions and narratives.

It takes time, concentration, and imagination to transform objectives and stakeholder questions into questions that facilitate connection. Here are some examples of stakeholder questions transformed into participant-facing questions. 

Best practices: 

• Try to ensure the moderator develops the discussion guide themselves, based on a full and thorough understanding of context, research objectives, and stakeholder questions.
• When, as a moderator, you receive a pre-written discussion guide from someone else, take the time to write your own discussion guide based on your understanding of the objectives. Even if you don’t use it, it will help you moderate.

The flow of a discussion guide

Creating a comfortable experience for a participant is not only the right thing to do, it's the best way to really reach them and their experience to get valuable insights. Unfortunately, it's easy to overlook the importance of something as subtle and organic as “flow” in the often rushed process of pulling together a discussion guide, when the focus tends to be more on content. 

When studies have many objectives, there is a temptation to pack more and more content into the session than fits in the allotted time. Flow is the first thing out the window. As more gets packed into the session, it's easy to forget the need to save a few minutes to establish rapport, create transitions, and come to closure. And, for multi-topic studies there may be pressure to order topics based on criteria other than the participant's comfort, such as wanting a particular team's questions to be first. 

Don’t shortchange flow. It’s not worth it. A disjointed, mechanical, hurried flow doesn't support organic connection, can rattle a participant, and sacrifices the quality of the sessions, despite everyone's best interests. Engage your team with the following rules of thumb, reminding them that following them will serve the overall objectives:

• Don't limit the introduction. Make it spacious and include contextual questions. Use this time to honestly get to know the participant. 
• Move from the most abstract content to the least abstract, and from the most complex to the simplest. 
• Once rapport is built, move from the most emotional content to the least emotional.
• Embrace improvisation once the interview begins. 
• Do not ask or rely on participants to donate extra, unpaid time to your organization. 

In short, design for flow — and be willing to adjust the flow real-time, during the session. This provides a more comfortable, relevant, and often, more revealing interaction with the participant and whatever material you're exploring. To learn more about session flow and moderation techniques, check out our UX Research Moderator’s Rubric.

Criteria for a great discussion guide

At AnswerLab, we partner with teams who need insights on many, many topics very quickly. Team members have access to different sources of data, are curious about different things, have different needs and hopes. There is an art to compressing 13-page lists of 157 questions into a usable framework for a successful 60-minute interview that will please everyone. Fully understanding the purpose of the research enables us to (sometimes overnight!) transform incoming questions at a variety of different levels of abstraction into an experience that everyone will love — the stakeholders, researcher, and the participant. 

The session post-it

The structure of your research interview should fit on a single post-it, your "steering wheel" for the study, compressing all the preparation you've done into a clear plan: 4-5 areas of exploration with rough time allocations. If there are more sections than your post-it note can fit, consider how you might combine or adapt them — or shift them to other studies.

A concise outline

Your post-it should be built from a more detailed moderator-crafted discussion guide document, which often takes the form of an outline. Your outline is usually 2-3 pages long, with section headers and bullets with questions, and key prompts. This is crafted based on a thorough understanding of the research objectives, the stakeholder questions, and any other discussion guide drafts or skeletons that have been provided. 

Being disciplined about approaching a discussion guide this way supports reproducibility. When a guide is too complicated and unwieldy, it’s harder to ensure that the most important things will be consistently addressed across sessions. Boiling down a lengthy catalog of questions to a concise, human-centered outline makes it easier to stay connected with the core research goals from session to session. Although exact wording and even order will likely flex, the “steering wheel” post-it will ensure the session gets to its destination in time.

Take your discussion guides to a new level

To take your discussion guides to a new level, think carefully about fine-tuning your process for creating guides. Try some of these takeaways:

• Be sure you fully understand the research objectives. Ask clarifying questions. Re-read the objectives often, including several times immediately before the session and between sessions.
• Schedule high-quality, focused time to develop your discussion guide. 
• Give yourself time to synthesize stakeholder questions, acquaint yourself with the subject matter, use the product, and talk with others who know more. 
• Brainstorm on possible areas of inquiry and exercises. 
• Explore the system you're researching so you understand the user's flow through that interface. 
• When your session includes specific tasks, provide a larger context by offering a relatable real-world scenario for those tasks. This will help make the flow more meaningful for participants, and you'll get better results.
• While most discussion guides are not intended to be precisely scripted, you may need to include 'verbatim' questions or directions, depending on your objectives.
• Don't be afraid to push back. If you’re getting a lot of asks, facilitate conversations about the core objectives and tradeoffs that will enable you to deliver superb results. There may not be room for everything, and cramming too much in can compromise the outcome.
• Do mock interviews early in the process to refine your guide. Practicing with someone else gives you clear insight into whether your guide will facilitate flow. If this isn’t possible, minimally build in time for a thoughtful peer-review of your discussion guide.
• Champion the importance of space. A 60-minute session has at most 50-55 minutes of research content. Leave room for a spacious introduction, contextual getting-to-know-you questions, relaxed transitions and room for improvised probing, and a moment for reflection and closure.

Feeling confident in your discussion guides? Next, take your moderating skills to the next level with our UX Research Moderator's Rubric.

Mitra Martin

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How to write a topic guide

A guide can start as a shopping list for insights.

It’s useful if its a forum for you, colleagues and clients to discuss, prioritise, and create hypotheses. It shows everyone what will come out of the research.

Its not useful if it becomes a skip that everyone dumps a few questions into. Take charge and ensure you have enough time to cover all the main points. Allow time for new ideas and discussins to come through.

A guide helps you with managing time, keeping people engaged, and exploring the topics.

Guides are written with relationship building and group process in mind, as well asking questions.

It helps with consistency between interviews/groups or with other researchers. However guides can evolve  and change focus as the research progresses.

Ultimately a guide is to help the moderator/interviewer do their job, and it need to be in a suitable format for this.

Experienced researchers can work with bullet points and lists of probes. Newer researchers may need questions written out in full.

Over-long, detailed guides get in the way of the research by being too directive and stopping the researcher from focusing on the respondents.

Qualitative guides come to an understanding of the subject by exploring various aspects of it.

You may want to know would they open an account with a challenger bank, but you don’t ask directly.  You explore their beliefs, attitudes and behaviour about banking, what new banks they have heard of, who they perceive as users of challenger banks? 

What are the perceived advantages – what are the barriers to change?  What features convince them, what do they feel insecure about?

• Brainstorm the topic and context

Get background information from the internet, social media, trends

Use emotional, social, cultural perspectives

Explore AROUND the subject and then prioritise what is most useful.

2. Imagine the interview or groups from the participants’ point of view

What will they be able to remember/ tell you?

At what points might they get bored?

Will you need projective techniques or stimulus material?

3. Follow a logical flow from broad exploration to a narrower focus.

Remember you are building a relationship of trust and empathy with the participant/s.

Use an easy to read layout with open questions, and lists of prompts and probes where needed. Add in techniques and stimulus.

Include a full researcher’s introduction

Design an easy but effective warm up 

Work out how long various sections might take.

Imagine what it will be like for the  participant/s – would you enjoy that interview?

Download full instructions on how to write a topic guide, including two example guides.

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• Best practices in writing a discussion guide

Best practices for crafting a discussion guide that results in effective research.

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Discussion guides are essential if you want to generate powerful research outcomes. Whether you’re a moderator looking to effectively facilitate a focus group or an executive looking to deepen your understanding of market insights, your discussion guide is the roadmap that leads you to the golden nuggets — the rich insights that can help propel your business forward.What is a discussion guide?

A discussion guide is a script or set of questions/topics to guide participants through a conversation for research purposes. On the Remesh platform, discussion guides can have a variety of questions and elements that engage participants — polls, open-ended questions, rank questions, images, video, and more. 

Your participants’ time is valuable, and discussion guides assist you in making the most of your session. 

Why use a discussion guide?

Using a discussion guide keeps you focused on your research goals so you can maximize the productivity of your session. 

While it’s absolutely OK to veer from your script sometimes (we think it’s important to have this flexibility), it’s a great idea to have a strong discussion guide to work from that keeps you on track. A strong discussion guide is key to preparing for the most effective research study and for gathering quality insights from your audience.

Best practices: 3 steps to writing a discussion guide

Most of the work in writing the discussion guide isn’t in the writing at all; it’s in the prep work. Don’t take shortcuts here. Doing your due diligence upfront will reap the biggest reward. 

Follow these steps to write a discussion guide that will lead to actionable recommendations. 

1. Establish the parameters for your research

At its core, what is the purpose of your research? What overall question do you want to research and answer? Creating clear goals keeps your research focused and purposeful. 

2. Create your information wishlist

Make a list of the information you need to answer the question/goals you identified in step one. Data you might need could include annual reports/filings, financial reports, statistical data, consumer information, industry reports, among others. 

3. Start the search

Here’s the fun part. Find the information listed in step two. This will likely include both internal and external data. Internal data is owned by you or your client’s company — things like market trend reports, financial statements, and inventory records. External data includes secondary research that already exists by third parties.

The importance of secondary research

Using secondary research to create a discussion guide is becoming more popular. Secondary research (or secondary data) is data that has already been collected or research that has already been conducted. Secondary research is more accessible than ever, less time-consuming, and cost efficient when compared to primary research. 

You can use secondary research to:

Identify gaps in data that you want to dig into for your own research

Help you form your own hypothesis

Get familiar with your audience’s behavior/preferences before ever starting a conversation with them

Inform recruitment of research participants

Support your own research goals

Frame additional insights that are not tied to your company 

A word of caution: the relevance and accuracy of secondary research may be limited. It may also be inaccurate or outdated. While secondary research can be very valuable, it’s also important to check methodologies.

Crafting the perfect survey questions

The heavy lifting of your discussion guide is in the upfront planning and research — setting clear goals and gathering information. But the questions won’t write themselves, and there are some do’s and don’ts to consider. 

Start with onboarding questions to collect demographic information and warm up participants before moving on to the meat of the conversation

Ensure each question is within your research parameters and contributes to a goal

Ask open-ended questions to prevent bias and gather valuable qualitative data 

Restrict answers to poll questions alone

Force your preconceived notions and opinions on participants

Forget to thank your participants at the end of the conversation

 As a researcher, it’s vital to ask the right questions that uncover the best insights. The resource guide includes 200 open-ended survey questions to help you get started. Download the guide now to gain access to the question bank as well as a more in-depth look at discussion guides and market research best practices.

Alexandra Gendes

Alexandra is a dedicated Marketing Professional passionate about creating innovative marketing solutions. She shows excellent communication and leadership skills and is able to work with teams at all levels of an organization. She is a motivational leader who engages and supports all team members in alignment with projects and organizational goals. Her approachable style has enabled her to create relationships with team members and clients in a variety of work environments. Alexandra is known for taking the lead in turning ideas into actionable marketing initiatives and finds inspiration in creating a positive vision for a company’s long-term impact.

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The SAGE Handbook of Qualitative Research

• Norman K. Denzin - University of Illinois, Urbana - Champaign, USA
• Yvonna S. Lincoln - Texas A&M University, USA
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See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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• New contributors offer 19 completely new chapter topics , including indigenous methodologies, methodologies in an age of new technologies, queer/quare theory, ethnodrama, data and its problematics, triangulation, collaborative inquiry, digital ethnography, the global audit culture, and much more.
• Substantial revisions from returning authors provide reinvigorated content resulting in very different chapters.
• Content on a wide range of key topics, diverse perspectives, and current controversies derived from members of an international and interdisciplinary editorial board ensure the timeliest revision.
• Six classic chapters cover topics from paradigmatic controversies, contradictions, and emerging confluences, to performance ethnography, writing as a method of inquiry, strategies for composition, and creating narratives and research reports.
• Contributions from well-known international scholars allow readers to study the differences in approach among European, Australian, and American practitioners and theoreticians, as well as to hear the voices of non-Western authors.
• Coverage of state-of-the-art t opics include critical social science, critical pedagogy, mixed methods, narrative inquiry, qualitative research and technology, online ethnography, oral history, human rights, disability communities, queer theory, and performance ethnography.
• Discussions on a wide range of methods expand the reader's repertoire of methodologies, enlarging the range of data that can be brought to bear on social and educational issues. 

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While it is more common for Science, Technology, Engineering and Mathematics (STEM) researchers to write separate, distinct chapters for their data/ results and analysis/ discussion , the same sections can feel less clearly defined for a researcher in Social Sciences, Arts and Humanities (SSAH). This article will look specifically at some useful approaches to writing the analysis and discussion chapters in qualitative/SSAH research.

Note : Most of the differences in approaches to research, writing, analysis and discussion come down, ultimately, to differences in epistemology – how we approach, create and work with knowledge in our respective fields. However, this is a vast topic that deserves a separate discussion.

Look for emerging themes and patterns

The ‘results’ of qualitative research can sometimes be harder to pinpoint than in quantitative research. You’re not dealing with definitive numbers and results in the same way as, say, a scientist conducting experiments that produce measurable data. Instead, most qualitative researchers explore prominent, interesting themes and patterns emerging from their data – that could comprise interviews, textual material or participant observation, for example. 

You may find that your data presents a huge number of themes, issues and topics, all of which you might find equally significant and interesting. In fact, you might find yourself overwhelmed by the many directions that your research could take, depending on which themes you choose to study in further depth. You may even discover issues and patterns that you had not expected , that may necessitate having to change or expand the research focus you initially started off with.

It is crucial at this point not to panic. Instead, try to enjoy the many possibilities that your data is offering you. It can be useful to remind yourself at each stage of exactly what you are trying to find out through this research.

What exactly do you want to know? What knowledge do you want to generate and share within your field?

Then, spend some time reflecting upon each of the themes that seem most interesting and significant, and consider whether they are immediately relevant to your main, overarching research objectives and goals.

Suggestion: Don’t worry too much about structure and flow at the early stages of writing your discussion . It would be a more valuable use of your time to fully explore the themes and issues arising from your data first, while also reading widely alongside your writing (more on this below). As you work more intimately with the data and develop your ideas, the overarching narrative and connections between those ideas will begin to emerge. Trust that you’ll be able to draw those links and craft the structure organically as you write.

Let your data guide you

A key characteristic of qualitative research is that the researchers allow their data to ‘speak’ and guide their research and their writing. Instead of insisting too strongly upon the prominence of specific themes and issues and imposing their opinions and beliefs upon the data, a good qualitative researcher ‘listens’ to what the data has to tell them.

Again, you might find yourself having to address unexpected issues or your data may reveal things that seem completely contradictory to the ideas and theories you have worked with so far. Although this might seem worrying, discovering these unexpected new elements can actually make your research much richer and more interesting. 

Suggestion: Allow yourself to follow those leads and ask new questions as you work through your data. These new directions could help you to answer your research questions in more depth and with greater complexity; or they could even open up other avenues for further study, either in this or future research.

Work closely with the literature

As you analyse and discuss the prominent themes, arguments and findings arising from your data, it is very helpful to maintain a regular and consistent reading practice alongside your writing. Return to the literature that you’ve already been reading so far or begin to check out new texts, studies and theories that might be more appropriate for working with any new ideas and themes arising from your data.

Reading and incorporating relevant literature into your writing as you work through your analysis and discussion will help you to consistently contextualise your research within the larger body of knowledge. It will be easier to stay focused on what you are trying to say through your research if you can simultaneously show what has already been said on the subject and how your research and data supports, challenges or extends those debates. By drawing from existing literature , you are setting up a dialogue between your research and prior work, and highlighting what this research has to add to the conversation.

Suggestion : Although it might sometimes feel tedious to have to blend others’ writing in with yours, this is ultimately the best way to showcase the specialness of your own data, findings and research . Remember that it is more difficult to highlight the significance and relevance of your original work without first showing how that work fits into or responds to existing studies. 

In conclusion

The discussion chapters form the heart of your thesis and this is where your unique contribution comes to the forefront. This is where your data takes centre-stage and where you get to showcase your original arguments, perspectives and knowledge. To do this effectively needs you to explore the original themes and issues arising from and within the data, while simultaneously contextualising these findings within the larger, existing body of knowledge of your specialising field. By striking this balance, you prove the two most important qualities of excellent qualitative research : keen awareness of your field and a firm understanding of your place in it.

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Focus Groups

Writing your First Qualitative Market Research Interview Discussion Guide

Discussion guides are supposed to enhance interviews. Our first tip: use methods, formats, and symbols that work for the moderator or whoever else in conducting the research. Going with a full blown script is tempting, particularly if new to interviewing. If so, then avoid reading it verbatim: doing so not only ruins the interview’s flow, but can easily alienate the participant. After all, no one really appreciates the sensation they are being “talked at” rather than being included in the conversation. Dave Kreimer has a particularly useful post on his blog the Qualitative Query .

Along with formatting, include all pertinent information at the very top of the discussion guide. A schedule complete with timetables is particularly useful for busy interviewers. First, it provides a quick and easy reference in case answers become overlong. Second, it prevents time from running to a close. Spell out all elements that the interview will entail, including the objectives, stimulus, and reminders for setup requirements.

Far as the actual script, make sure there’s an introduction that explains the interview process, objectives, and expectations. Doing so emphasizes trust and inclusiveness with the participant. That mentioned, never let the intro sound too procedural. Use simple metaphor, humor, and personal introductions to inform the participant and ease them into the conversation. Above all else, of course, make sure to retain clear phrasing throughout the interview. Data sometimes includes participant interpretations, sure, but actual preference matters. A matter-of-fact question typically encourages a matter-of-fact answer.

Interviews should always include structure, but make sure to explore different avenues of input. A reactive system, particularly if interviewing multiple participants or panels, is particularly useful when tracking reactions to peripheral elements or possible changes. Of course, exploring such avenues is an opportunity cost too. Keep off-topic information from becoming the focal point, as that can quickly shift the interview anywhere. Also never force progression too directly, as it is also important for participants to explore their own tangents. The truly effective interview discussion guide provokes questions and guides discourse.

Even if a tad more time-consuming than expected, however, always make sure to also add wiggle room to include longer answers or conversational tidbits. A participants’ reaction unto itself is valuable, and seeing where their reaction goes can provide a wealth of information about their consumer habits and identity. An interview guide may be the skeleton of an IDI or focus group, but responses consist of everything else.

Trust and rapport aside, there are other critical elements to a successful interview beside the guide. Technology, for one, needs to be stable, consistent, and offer tools that augment qualitative researchers’ efforts and objectives. Panel and participant quality also matters a great deal. Of course, our bandwidth-efficient HD platform is built from the ground up to realize such needs.

Discuss is made by researchers for researchers. Offering the collective experience of over half a century, we bring proactive consultation that maximizes your results while minimizing turnaround time and logistical headaches. Such qualitative research goes a long way with the right discussion guide, so start prepping yours well in advance of the actual webcam session. Have more in-depth questions? Just ask . We’re happy to answer!

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Thematic Analysis for Case Studies

Thematic analysis and case study research are widely used qualitative methods , each offering distinct ways to gather and interpret qualitative data . Thematic analysis allows researchers to identify patterns and themes within data sets, providing insight into shared experiences or perspectives. On the other hand, case study research focuses on in-depth analysis of a particular instance or case, offering detailed understanding of complex issues in real-life contexts. Combining these two methods can yield comprehensive insights, enabling researchers to analyze specific cases with a nuanced understanding of broader themes. This article provides a guide on conducting thematic analysis within the framework of case study research, outlining key steps and considerations to ensure rigorous and insightful outcomes to address your research objective.

A case study is a research strategy that involves an in-depth investigation of a single case or a number of cases within their real-life context. Unlike quantitative research , which seeks to quantify data and generalize results from a sample to a population, a case study approach allows for a more detailed and nuanced exploration of complex phenomena. This method is particularly useful in fields such as psychology, sociology, education, and business, where understanding the specifics of a single situation can require qualitative analysis to provide insights into broader patterns and issues.

Case studies can be based on various sources of evidence, including documents, archival records, interviews , direct observation , participant-observation, and physical artifacts. By employing multiple sources of data, case study research enhances the robustness of the findings, offering a more comprehensive view of the subject under study.

There are several types of case studies, each serving different purposes in research. These include exploratory, explanatory, and descriptive case studies. Exploratory case studies are often used as a prelude to further, more detailed research, allowing expert and novice researchers to gather initial insights and formulate hypotheses or propositions. Explanatory case studies are utilized to explain the mechanisms behind a particular phenomenon, often in response to theory-driven questions. Descriptive case studies, on the other hand, aim to provide a detailed account of the case within its context, without necessarily aiming to answer 'why' or 'how' questions.

One of the key strengths of case study research is its ability to provide insight into the context in which the case operates, which is often lost in larger-scale quantitative studies. This context can include social, economic, cultural, and other factors that significantly influence the case. Understanding these contextual factors is crucial for interpreting the findings accurately and can help to identify how the results of a case study might (or might not) be applicable in similar situations.

However, case study research is not without its challenges. The in-depth nature of the investigation often requires a significant amount of time and resources. Additionally, the findings from a case study are sometimes viewed as having limited generalizability due to the focus on a specific case or a small number of cases. To address this concern, researchers can employ a technique known as 'theoretical generalization,' where findings are related back to existing theories, contributing to a broader understanding of the phenomenon.

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Thematic analysis is a method for identifying, analyzing, and reporting patterns (themes) within data. It provides a flexible and useful tool for qualitative research , especially within the context of case study research . This section outlines the steps for conducting a thematic analysis in a case study research project after data collection , ensuring a systematic and rigorous approach to data analysis . The process is divided into three key subsections: preparing your data, identifying themes, and reviewing and defining themes.

Preparing qualitative data

The first step in thematic analysis is to become familiar with your data. Usually this is textual data that can help you name relevant themes later on. This involves a detailed and immersive reading of the data collected from your case study. Data can include interview transcripts , observation notes , documents , and other relevant materials. During this phase, it's crucial to start making initial notes and marking ideas for coding. Remember to refer to important theories from your literature review to inform your subsequent analyses. Organizing your data systematically is also essential; this could mean arranging data into different types based on the source or nature of the information. This preparatory work lays the foundation for a more focused and efficient analysis process.

Identifying themes

After familiarizing yourself with the data, you can code the data by selecting interesting segments of data and attaching a code (or label) to capture the essence of each data segment and how it relates to your research question. After this initial coding, the next step is to begin theme development. This involves collating all the codes and the relevant data to identify themes that emerge across the dataset. A theme captures something important about the data in relation to the research question and represents some level of patterned response or underlying meaning within the data set. During this phase, it's important to be flexible - themes may evolve or merge as you refine your analysis and gain a deeper understanding of the data.

Reviewing and defining themes

Once potential themes have been identified from your qualitative study, the next step is to review and refine them. This involves a two-level review process: first, reviewing the themes identified in relation to the coded extracts, and then reviewing these themes in relation to the entire dataset. This step ensures that each theme is coherent, consistent, and distinct. It also involves determining the "story" that each theme tells about the data, which is critical for the next steps of analysis and for writing up the findings. Finally, it is necessary to define and name the themes, which involves a careful consideration of what each theme captures about the data and how it relates to the research questions and objectives.

Turn your data into critical insights with ATLAS.ti

Qualitative researchers turn to ATLAS.ti for analyzing qualitative data. See why with a free trial.

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This book is an accessible introduction to doing qualitative inquiry. Through exploration of research ethics, study design, data collection, data analysis, writing, and other topics, students learn how to conduct their own qualitative research. Although students of any discipline will find this text helpful, it specifically will be of interest to students in health-related fields, including nursing, health studies, kinesiology, public health, physical/occupational therapy, health care administration, nutrition, medical sociology, and medical anthropology. The book can be used at both the upper level undergraduate and the graduate level. This book can also be used by health care practitioners and faculty interested in learning how to conduct qualitative research.

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• Open access
• Published: 02 May 2024

A stakeholder analysis to prepare for real-world evaluation of integrating artificial intelligent algorithms into breast screening (PREP-AIR study): a qualitative study using the WHO guide

• Rumana Newlands 1 ,
• Hanne Bruhn 1 ,
• Magdalena Rzewuska Díaz 1 ,
• Gerald Lip 2 ,
• Lesley A. Anderson 3 &
• Craig Ramsay 1  

BMC Health Services Research volume  24 , Article number:  569 ( 2024 ) Cite this article

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The national breast screening programme in the United Kingdom is under pressure due to workforce shortages and having been paused during the COVID-19 pandemic. Artificial intelligence has the potential to transform how healthcare is delivered by improving care processes and patient outcomes. Research on the clinical and organisational benefits of artificial intelligence is still at an early stage, and numerous concerns have been raised around its implications, including patient safety, acceptance, and accountability for decisions. Reforming the breast screening programme to include artificial intelligence is a complex endeavour because numerous stakeholders influence it. Therefore, a stakeholder analysis was conducted to identify relevant stakeholders, explore their views on the proposed reform (i.e., integrating artificial intelligence algorithms into the Scottish National Breast Screening Service for breast cancer detection) and develop strategies for managing ‘important’ stakeholders.

A qualitative study (i.e., focus groups and interviews, March-November 2021) was conducted using the stakeholder analysis guide provided by the World Health Organisation and involving three Scottish health boards: NHS Greater Glasgow & Clyde, NHS Grampian and NHS Lothian. The objectives included: (A) Identify possible stakeholders (B) Explore stakeholders’ perspectives and describe their characteristics (C) Prioritise stakeholders in terms of importance and (D) Develop strategies to manage ‘important’ stakeholders. Seven stakeholder characteristics were assessed: their knowledge of the targeted reform, position, interest, alliances, resources, power and leadership.

Thirty-two participants took part from 14 (out of 17 identified) sub-groups of stakeholders. While they were generally supportive of using artificial intelligence in breast screening programmes, some concerns were raised. Stakeholder knowledge, influence and interests in the reform varied. Key advantages mentioned include service efficiency, quicker results and reduced work pressure. Disadvantages included overdiagnosis or misdiagnosis of cancer, inequalities in detection and the self-learning capacity of the algorithms. Five strategies (with considerations suggested by stakeholders) were developed to maintain and improve the support of ‘important’ stakeholders.

Conclusions

Health services worldwide face similar challenges of workforce issues to provide patient care. The findings of this study will help others to learn from Scottish experiences and provide guidance to conduct similar studies targeting healthcare reform.

Study registration

researchregistry6579, date of registration: 16/02/2021.

Peer Review reports

Introduction

The National Breast Screening Programme (NHSBSP) in the United Kingdom (UK) invites women aged 50–70 years every three years for X-ray mammography [ 1 , 2 ]. The mammograms are digital images assessed independently by two readers (usually a radiologist and/or advanced radiography practitioner) for signs of cancer [ 3 , 4 ]. Each reader decides whether the image appears normal or needs a recall for further assessment. In case of disagreement, arbitration involves a third reader [ 5 , 6 ]. The NHSBSP detects between 17,000 and 20,000 breast cancers annually [ 3 , 5 ]. Ongoing reader shortages may affect patient care by delaying breast cancer diagnosis and treatment [ 7 ]. With the NHSBSP being temporarily paused in 2020 because of the Covid-19 pandemic, the service is under additional pressure to deal with backlogs [ 8 ]. Evidence suggests that artificial intelligence (AI) can potentially transform healthcare delivery by improving care processes and patient outcomes [ 9 , 10 , 11 , 12 , 13 ]. Several studies have demonstrated positive outcomes from the application of AI in breast screening, particularly for image reading and triage [ 6 , 13 , 14 , 15 ]. Research on the clinical and organisational benefits of AI is still at an early stage, with limited evidence to demonstrate how AI algorithms could benefit breast screening programmes [ 6 , 12 ]. Numerous concerns have been raised around the possible implications of AI for patient safety, data security, public acceptance and trust, accountability for decisions and impact in the broader healthcare system [ 12 , 16 ].

The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD, https://icaird.com/about/ ), a pan-Scotland multistakeholder programme, sought to evaluate an AI system targeting breast cancer detection in the real-world setting of the Scottish National Breast Screening Service. Modifying usual care through reform (e.g., integrating technologies to improve services) is a complex endeavour because the health care system is shaped and influenced by a diverse set of stakeholders [ 17 , 18 ]. Stakeholder engagement at the early stages of system reform could help increase support for the proposed reform [ 18 , 19 , 20 ]. Stakeholder analysis is a practical tool to identify the key stakeholders (who may have a vested interest) in a reform process and develop strategies to engage with them early to mitigate issues targeting its successful implementation [ 17 ]. It can be defined as a process of systematically gathering and analysing qualitative information to determine whose interests should be considered when developing and/or implementing a policy or program [ 20 ]. As part of the iCAIRD programme, a stakeholder analysis was conducted to systematically identify stakeholders, explore their views around the reform (i.e., integrating AI algorithm into the Scottish National Breast Screening Service for breast cancer detection) and develop strategies for managing ‘important’ stakeholders [ 18 , 20 ]. We defined ‘stakeholders’ as a person representing a group of individuals or an organisation who have a direct or indirect interest in, or the potential to influence or be affected by, the reform. This paper explains the detailed process and findings of our stakeholder analysis.

The World Health Organisation (WHO) guide was followed for conducting the stakeholder analysis [ 20 ]. The objectives of the study included: (A) Identify possible stakeholders (B) Explore stakeholders’ perspectives and describe their characteristics (C) Prioritise stakeholders in terms of importance and (D) Develop strategies to manage ‘important’ stakeholders (see Additional file 1 for steps involved). Seven stakeholder characteristics were assessed based on the guide: their knowledge of the targeted reform, position, interest, alliances, resources, power and leadership (see Additional file 2 for description, rating and interpretation).

The study setting involved three Scottish health boards: NHS Greater Glasgow & Clyde, NHS Grampian and NHS Lothian. Due to the time of the study (the COVID pandemic) and limited resources and time, we approached these three health boards, which cover a wide geographical area and a large number of the Scottish population. These health boards were also involved in developing and training AI technologies and testing patient data. Ethical approval was received from the University of Aberdeen School of Medicine, Medical Sciences and Nutrition Ethics Review Board (Ref: CERB/2020/11/1997) and approved by each health board’s NHS Research and Development (R&D) office: NHS Grampian (Ref no: 282,136), NHS Greater Glasgow & Clyde Health Board (Ref no: GN20ON578) and NHS Lothian (Ref no: 2020/0234).

A list of possible stakeholders was generated (objective A) and finalised after discussion with the iCAIRD team. Three questions were considered during this task (as criteria to identify and select stakeholders): ‘ Who may affect or be affected by the reform?’ , ‘Who has the power to make/stop it from happening?’ and ‘Who are the potential allies/opponents of the targeted reform?’ The final list was grouped as: (a) women, (b) patient and public representatives (e.g., Scotland-based patient advocacy groups and cancer charities) and (c) professionals [e.g., clinical (e.g., readers), non-clinical staff (e.g., hospital board members, procurement), private companies (e.g., AI developer)]. Various subgroups or stakeholder types were then listed under these three main groups. Stakeholders were further categorised as internal (if working within the NHS organisation) or external (all other stakeholders). Stakeholders from the final list were then prioritised (i.e., most to least ranked based on their roles, experiences and positions per organisation and the three health boards) and contacted to explore their perspectives on the proposed reform (objective B).

Focus groups (with women) and semi-structured interviews (with professionals and patient and public representatives) were conducted (March-November 2021) via Microsoft Teams® or telephone. All data collection tools (provided by the WHO guide e.g., topic guide, ) were adapted, piloted and finalised to meet the study aim. Additional file 3 and 4 presents the final versions of the topic guides (targeting professionals, and for women and patient and public representatives) and Additional file 5 presents the vignettes (an additional tool developed). Additional questions (e.g., concerns, possible solutions, challenges towards or expectations following the reform), beyond the WHO guide, were added to capture a more comprehensive view of the proposed reform based on stakeholder role and the organisation they represented. Prioritised stakeholders (from objective A) were approached in March 2021 with a recruitment target of approximately 40 participants from the three groups. Professionals and patient and public representatives were invited via email by the clinical director (GL) of the North-East Scotland Breast Screening Programme, including an invitation letter and participant information leaflet. Radiology unit leads circulated the invitation among clinical staff across each health board. Various patient representative groups (e.g., cancer charities) were approached to recruit patient and public representatives. Women from the Aberdeen Children of the 1950s cohort study (aged 64–70 years) were invited through a Facebook page advert. The study advert was shared further in the social media by cohort members and hence, additional women outside the target group participated in the study. Additional participants, suggested by study interviewees, mainly in the professional group, were also approached. For stakeholders representing more than one NHS organisational role, their main role was considered at the interview and analysis. Only focus group participants and patient and public representatives were reimbursed for their time (£15 retail e-vouchers). Informed verbal consent (via TEAMS or telephone, by going through the consent form and asking to agree to each point) was obtained from each participant and recorded (using an audio recorder) before data collection, with sessions recorded and transcribed by a third party verbatim. All anonymised transcripts were imported into NVivo (version 12), content analysed by generating themes using the WHO guide [ 21 ]. For consistency, 10% of the transcripts ( n  = 3) were double-coded (by RN and HB, 98% concordance). All findings were presented (February 2022) to the iCAIRD team with the aim of finalising the ‘important’ stakeholders’ list (objective C). According to the WHO guide, stakeholders are ‘important’ if they have the power to influence the reform. In our study, we defined ‘important’ as stakeholders (no matter how much influence they have), whose wants, and needs should be considered, prioritised and addressed to maintain and/or increase support towards the proposed reform.

Finally, prototype strategies and their possible actions were generated targeting all or specific sub-groups of ‘important’ stakeholders. These strategies were finalised (objective D) along with considerations after discussion (via Microsoft TEAMS® by RN during April, 2022) with the iCAIRD and research teams. It should be noted that these strategies need to be co-produced (e.g., the contents, topics, when, how and by whom should be implemented) in further detail and validated involving targeted stakeholders before implementation (which was not part of this study) [ 22 ].

Seventeen stakeholder subgroups were included in the stakeholder list. Thirty-two participants, who took part in the study, represented 14 (out of seventeen) subgroups. Five stakeholder sub-groups were external, and the remainder were internal. See Table  1 for the types of stakeholders who took part in the study and findings of their characteristics. To preserve anonymity names, gender, positions, and health boards are not disclosed.

Focus group meetings (women, n  = 14) lasted two hours; participants were aged 59–74 years (mean 65 years). The interviews ( n  = 18) lasted between 26 and 89 min (mean 57 min). Stakeholders’ perspectives were hypothetical and discussed below.

Influence of stakeholders

Six stakeholder sub-groups had high potential to influence the reform including clinical directors of the Scottish Breast Screening Programme, members of the Scottish Breast Screening Programme Board, hospital management group (e.g., workforce, strategic planning), National Services Scotland, National Screening Oversight Function Board and AI developing company. There were five stakeholder sub-groups with medium potential to influence including readers, members of the National Services Division, hospital management group (e.g., performance, procurement), the Health Technology Assessment group and the Scottish AI Alliance. The remaining four stakeholder sub-groups had low potential to influence including radiographers, innovation research group, women, and patient and public representatives.

Clinical directors had the highest influence on other stakeholders within, and outside the breast screening programme. The Readers (e.g., radiologists) described themselves as ‘AI users’ where AI is implemented rather than as someone with the power to influence the reform.

‘… the clinical directors of all the centres in Scotland have got the same power, and I think if wanted to bring it (AI) in then we would bring it in,…’ (ID 1). ‘… we could talk to the government at meetings, but most of us don’t have that power to do so.’ (ID 6).

The members of the hospital management group (high to moderate influence) had several roles including maintaining security, connecting digital equipment and national networks of the entire system; and contributing towards a decision-making process on whether to support the reform or not. As a result, they could influence others (e.g., external stakeholders) and a business case supporting the reform.

‘If they make it a bigger issue and make it organisational wide my influence would be much, much bigger.… I can absolutely be the executive sponsor for it and such like and absolutely get people rallying behind it.’ (ID 3). ‘I think I might have some influence in that or be able to bring that (evidence) to the right people’s attention.’ (ID 12).

The National Services Division’s influence involved recommending the reform through their governance channels (e.g., Scottish Breast Screening Programme board, National Screening Oversight Function Board, National Screening Committee and UK National Screening Committee) to the Scottish Government for approval and funding. The National Services Scotland’s influence involved evidence synthesis and communication with other stakeholders based on the evidence. The AI-developing company during this study was already engaged in evaluating an AI system in collaboration with the NHSBSP and had various communications with a range of stakeholders to influence the reform process.

‘… if the evidence comes through and that’s what we’re looking towards and can fund and can get the business case to support that then yes, we’ll be looking to push it forward’. (ID 4)

The Health Technology review group had influence in terms of generating evidence (e.g., evaluation of AI in a prospective study) via independent reviews and dissemination of findings for the decision-making process. The influence of the Scottish AI alliance was mentioned as communication of evidence (e.g., AI’s performance in Breast Screening Programme) to other stakeholders via networking and distribution of funds towards generating and testing AI systems. Women and a patient and public representative believed that they could influence other women to attend screening appointments once AI is integrated. However, they thought they had a low influence on the decision of the government on the reform, suggesting the underlying feeling of powerlessness. They were happy to utilise public money or taxes to support the reform. Radiographers and the research project manager also perceived their influence to be very low to impact the reform.

‘ I think our ability to influence is mixed.… aim is to improve the consistency of how these (technology)things are considered across the service’ (ID 18). ‘We’re the customer so to go back on your point, we should have a voice.’ (Focus group 1). ‘I suppose as an ordinary person, you obviously feel quite small in it, and I don’t think you’re going to have much influence. But if you’re particularly interested or passionate about it, you could probably find your way into somewhere to have your influence passed on your behalf’. (ID 19, patient and public rep)

Stakeholders’ knowledge varied between ‘none’ to ‘a lot’. Most participants with high-medium influence had a lot of knowledge of AI in health (in general) or the proposed reform and had gained it mainly via conference attendance and work-related meetings where AI in health and/or breast screening were discussed.

‘What I understand is that through technology and through programming algorithms… it can make an accurate diagnosis based on pre-determined criteria… and potentially more accurately than it can be done by a single individual’. (ID 7)

Knowledge of stakeholders who had low potential to influence varied widely (e.g., a lot to none). Participants in the women’s group had heard of AI but some of this knowledge was not directly related to health or medicine. Their knowledge was mainly based on reading documents and/or online information after receiving our study invitation. We noted that the members of the patient and public representative struggled to understand the underlying concepts (i.e., utilisation of AI in the screening programme) behind the reform.

‘I think that the AI term is slightly concerning or could be’. (Focus group 1) ‘… to be honest I don’t really know how the computer would look at an image and determine if something look suspicious or malignant (ID 19, public and patients’ rep).

Position of stakeholders

Most stakeholders were supporters or moderate supporters of AI. They preferred scenario one (i.e., AI would substitute one of the readers, see Additional file 5 ) because they either felt it would be easy to implement or they would feel more comfortable if humans were involved in screening.

I think that’s (Scenario 1) quite useful because if the radiologist agreed, then you wouldn’t need a second radiologist, and if the radiologist disagreed, it could go to arbitration, so it’s very similar to the system that we’ve got at the moment, and it would be interesting to see what the AI and you see exactly. I thought would be easiest to implement. (ID 6). I still think you need to have that adjudicator, that human element — any conflict. Even if it’s just completely AI, there still needs to be that human element of checking it and saying, “Wait a minute, let’s double-check it.” (ID 20).

Moderate supporters wanted to see evidence of AI’s performance (e.g., as good as or better than human radiologists) in the Scottish National Breast Screening Service before they would support the reform strongly.

‘From a patient perspective, I would almost feel more concerned because there is the potential that a computer could stop my scan ever getting near a radiologist (referring to scenario 2), albeit the prioritisation, they’re kind of wholly reliant on AI. If AI is one of the two sign-offs, so scenario one, I would feel more comfortable with that.… I strongly support it if there’s a robust evidence base, i.e. it’s been proven to operate at least or higher than a consultant… thenaye, yeah, I really support it’. (ID 16) I am getting towards retirement, so I would like to think I keep up with things, and I’m not opposed to change at all. I do think it’s the way forward, but I think we have to do it cautiously until we know where we’re at. (ID 1) In five years’ time ask me again and I’ll maybe have a very different opinion. But I think it’s just… to me it’s not as proven as I would like to say, “Actually yeah, this is brilliant”, but I think it’s so worth exploring… (ID 12) .

One stakeholder (a radiographer, low potential to influence) self-rated as moderate opponent based on the evidence of AI’s performance in banking, marketing and in the criminal justice system. However, this particular person talked about changing their mind and supporting the proposed reform in the future based on the evidence from prospective studies.

‘I’m not a fan of AI because… I think when people hear the word, “Intelligence” they assume a level of intelligence but it’s not intelligent in any way… If for example, in the financial world a young couple go for a mortgage but the algorithm, the artificial intelligence and it hasn’t ticked all the boxes then they get, “No”, but actually a human bank manager sitting down and discussing with them knowing them would say, “Yes, we can give you this because we know that…” (ID 13).

Most stakeholders reported willingness to work collaboratively with other stakeholders (e.g., patients, private companies, and NHS professionals) targeting successful integration of AI into practice. They mentioned about contributing and collaborating during the planning stage of the reform; evidence synthesis in a real setting and/or re-evaluation to judge its impact on the service.

‘… we work with a lot of industry, we work with lots of different health boards and universities, academia, there’s public sector and Scottish government, we’ll work with anybody. If we can help and make a difference then we’re happy to help’. (ID 2)

Most of our focus group participants expressed some concerns around working with commercial companies such as AI developers. They believed that to avoid any confusions and bias in findings future prospective studies should be conducted and reviewed by independent researchers.

‘… when commercial side gets involved and competition and things like that there’s a tendency for shortcuts and maybe even yeah, cutting corners for costs and stuff like that.… I’m talking about the developers, the developers themselves, those who have the control…’ (Focus group 1).

Interests of stakeholders

All stakeholders were interested in the proposed reform. Advantages and disadvantages of the reform were discussed in relation to its impact on health and the NHSBSP or the entire NHS. Advantages discussed included service efficiency (a) by filling in readers vacancies and reducing work pressure as AI tools would read a large number of mammograms in a short period time; (b) reducing waiting time for receiving mammography results and further assessments (e.g., biopsy) which would further reduce patients’ anxiety and stress; and (c) reducing recall rates (including technical/ ’unnecessary’ recalls) which would reduce pressure and stress on the system and patients too.

‘ AI can speed up the screening programme, reduce the load,… so it’s kind of dealing with waiting times et cetera, and it can provide a second opinion when a second opinion might not be available’. (ID 2) ‘I think, cutting down recall numbers certainly would save a lot of worry and anxiety amongst the women’. (Focus group 1)

Other advantages mentioned were that AI would reduce resource use and save money in the future (a) by replacing at least one radiologist with AI would cost less in the long run compared to a human reader; (b) by early diagnosis of cancer and saving lives; and (c) by better managing the overall radiology workflow such as allowing readers to do clinical tasks e.g., seeing patients face-to-face and cancer diagnosis.

‘… Again costs might come into it I suppose, but I don’t expect it’s going to be as expensive as employing people. (ID 1) ‘I think it’s using the manpower better and perhaps with less stress on the system’. (ID 19)

The main disadvantages mentioned were: (a) AI might be seen as a threat to people’s professional identities and skill sets as humans find change challenging and hence, they might disagree and over-ride AI’s assessment; (b) it might indirectly lead to deskilling human readers by limiting their opportunities to practice reading normal images (as they may focus on abnormal images suggested by AI).

‘I think it’s probably the fear of the unknown and concern about new things and change and taking a person out of the setup even though there are still humans within the setup.’ (ID 19, public and patients’ rep). ‘I think there’s a danger well if the radiologist is led by the AI that they become de-skilled actually’. (ID 12)

Some legal/ethical issues around the reform mentioned were: (a) Public or patients might not accept AI’s errors and then who would take the blame or how to solve these issues; (b) AI might over-diagnose (e.g., recognise lesions at a much earlier stage or lesions of insignificance) or misdiagnose (e.g., misinterprets scars as cancers); (c) there might be some inequalities in detection if AI is not trained appropriately using a wide range of population data (e.g., different ethnic communities, population of different age) and (d) AI might self-regulate by changing the algorithm as it is designed to learn overtime.

‘… a radiologist can miss something if the machine misses something it would have a very different public perception’. (ID 3) ‘If the algorithms are setting the criteria, who takes responsibility and can explain it if there’s a problem?’ (Focus group 2).

Others mentioned that AI could not perform work without human support such as (a) putting parts of a mammogram together might not be possible by AI for a complete assessment or it would not recognise technical issues and hence, might miss some cancers or recall patients unnecessarily; (b) AI don’t have emotions and hence, could not communicate with patients/readers to justify any assessments; and (c) projections of health might not be possible as it could not review the entire health or previous mammograms along with the recent ones. To address these issues all participants suggested keeping humans in the loop at least during the early stages of AI’s integration. Finally, it was believed that AI would read and provide results quicker than the current service, but it might cause pressure on other departments (e.g., surgery) of the NHS for patients’ follow-on treatments.

‘I think it’s just making sure that we’ve got the whole process there and the timing of the process. We need to make sure we can assess the ladies and obviously then those that are needing treatment can go forward and get the treatment’. (ID 4)

Most stakeholders believed that AI would bring greater benefits than disadvantages if trained and regulated properly. However, one (moderate opponent) participant compared AI’s integration with ‘Pandora’s box’ and stated that it might create more complications to the service than is anticipated.

‘In any form of radiology AI is very good at reducing variation because you can ask three radiologists for their perspective on one scan and all three of them can give you entirely different answers, whereas an AI won’t do that’. (ID 2) ‘I think it’s Pandora’s box to be honest and I think once it’s opened it’s opened and I don’t think we understand or I don’t think we’re aware of all the ramifications that could come from the use of AI as we bring it in’. (ID 13)

The important stakeholders

Thirteen stakeholders were identified as ‘important’ (Table  2 and see methods section for the definition). Three additional stakeholders were included in the list and their views were not explored in the study either because they declined participation or were not approached (e.g., Scottish Government) to take part in this study. Study findings suggested that funding is required from the Scottish Government for the success of the targeted reform and therefore, this stakeholder was added to the list. The UK NSC (see Table  1 for their role) was also added because their approval is needed first for any service reforms involving screening programme in the UK. The NSC and the Scottish Government also rely on their advice in deciding funding and national approval. The NSC should be added because they have influence on other stakeholders on a national (i.e., Scotland) level. Women (low potential to influence) were considered as ‘important’ because these stakeholders are the main users of the breast screening service. Moreover, most stakeholders mentioned that any reform proposals must be communicated to, and approved by, the women as they might, otherwise, not turn up for their mammography.

Strategies to improve future support

Most of our stakeholders were in support of the proposed reform. Hence, five strategies were generated to maintain their support and increase their power and leadership targeting the reform. Below is a summary of the proposed strategies with a full description available in Additional file 6 .

Strategy 1: improve knowledge of AI for all stakeholders

Most stakeholders (including internal and those with low to high potential to influence) did not fully understand the process and impacts of the proposed reform or sometimes found it controversial. They believed further information (e.g., based on evidence from prospective studies) would improve their confidence and support of the reform. Some requested a lot of information (e.g., facts and figures about AI’s performance, and consequences). Others (e.g., women) requested ‘little’ and ‘balanced’ information as they believed that their capacity to understand the entire reform process was limited, or they had the trust that the best service would be recommended by the NHS. Lastly, this information should be available to all stakeholders including patients, to assist them in making an informed choice when AI is integrated into practice.

Strategy 2: improve skills and ability of stakeholders directly involved in delivering the service

Stakeholders (e.g., readers, NSD; high and medium potential to influence) who provided direct services as part of the Scottish Breast Screening Programme also requested further information related to their roles to increase their confidence, ability and support of the reform: what AI can or can not do, how AI works and its impact on the system; how to interpret AI’s assessment of mammograms and what responsibilities they or readers must have for ‘safeguarding the system’ to make sure the service is running as intended and safe for all.

Some suggested that future staff training would be required to improve their ability to successfully embrace AI into practice. However, they were unsure about the types of training and believed that during a prospective study any gaps in their skills and training needs could be identified. However, user-friendly training modules (e.g., video or demonstration by a colleague) were suggested for an easy transition.

Strategy 3: empower women to influence the screening service

The majority of the Scottish Breast Screening Programme’s service users are women who had low potential to influence the proposed reform. Women and the public and patient representative thought that they had a voice and power to influence other women to attend (or not) their screening appointments. Therefore, it was believed that empowering these stakeholders further by turning their knowledge into action (e.g., using the media, posters) or experience into voice (e.g., showcasing personal testimonies through cancer charities’ websites) might be useful for influencing other service users or decision-makers. Generating and promoting such communication using case studies, television interviews, and podcasts were suggested. Social media was proposed as a major platform for these activities. However, a few stakeholders had some reservations about the use of social media because they believed that negative comments or blogs could damage the public perception of the reform and reduce support.

Strategy 4: use a collaborative team-based approach involving stakeholders to address challenges and improve support

Various context and deployment-related challenges were mentioned by out study participants. One of the main challenges suggested was having a wide range of stakeholders involved in the reform process and that lack of communication across health boards and different partners. As a result, reform proposals often take long to get approved or sometimes approval is not given because all stakeholders were not involved at the planning stage or were unaware of the entire process. Other challenges mentioned were around the AI-related IT and emergency services: no nationwide IT infrastructure is available to integrate and connect different software or digital equipment and mobile units, and who/how (e.g., ‘ an AI guru’ ) should be contacted to address technical problems. Therefore, a collaborative and team approach were proposed to engage relevant stakeholders (i.e., who would involve deploying and sustaining related tasks) from the beginning to the entire cycle of the reform process to improve its acceptance and continuous support. It was further suggested to use a visual model to explain and discuss the reform process with relevant stakeholders so that they could think through the process and identify potential issues and solutions. A need for a ‘champion’ was also highlighted to lead the teamwork targeting a successful reform.

Strategy 5: improve real-world evaluations of AI

All stakeholders suggested seeing evidence of AI’s performance from a prospective study (e.g., a ‘blinded randomised’ trial with long-term follow-up) and comparing it to the current service of the Scottish National Breast Screening Service and/or NHSBSP would be beneficial. Stakeholders’ views were captured further regarding what sort of evidence would satisfy them to continue and increase their support in the future (Additional file 6 ).

This, to our knowledge, is the first published study that conducted a stakeholder analysis targeting a real-world evaluation of integrating AI algorithms into a breast screening service. Worldwide health services are facing workforce issues to provide patient care like the NHSBSP in the UK. While the specific stakeholders may not always be directly comparable across regions or countries, limiting the generalisability of this work, the types of issues identified are likely to be broadly similar across breast screening programmes, especially concerning attitudinal aspects.

We captured a range of views from a wide variety of stakeholders. Most stakeholders were in favour of the reform (i.e., integrating AI algorithms into the Scottish National Breast Screening Service for breast cancer detection) but their knowledge and interests varied. They were keen to collaborate with other stakeholders for AI’s successful integration into the screening service. They expressed some concerns towards using AI and hence, suggested keeping human readers involved until they are confident that AI’s performance is satisfactory in the real-world setting of the NHSBSP. Stakeholders’ importance and influence were also assessed to guide future activities in this area.

The stakeholders (e.g., professionals) who were approached and/or took part in this study were mostly the leads of relevant organisations. Some frontline implementers (from the professional group) were unable to participate despite repeated efforts to reach them and of whom, some provided reasons for not taking part such as lack of time due to other commitments.

Our study captured stakeholders’ views of what evidence should be generated and what should be considered when evaluating AI in prospective studies. At the time of this study, no published prospective study was reported with long-term follow-up nor any evaluation framework for AI systems was available. The National Institute for Health and Care Excellence (NICE) subsequently published a medtech innovation briefing (MIB) on AI in mammography to highlight the importance of assessing impact of such reform on potential patients and the system [ 4 ]. It also emphasised the importance of generating guidance for the evaluation of components and the processes to be used during such assessments. In addition to this briefing, eight objectives towards evaluation were suggested by the AI in Health and Care Award playbook [ 23 ]: including establishing the accuracy, safety, effectiveness, value, fit with site, feasibility and suitability of scale-up, and implementation considerations. On comparison, the findings of our study are in line with these proposed objectives.

Most studies in the literature conducted surveys and/or interviews to explore views of the patient and public on the use of AI in mammography or medical care or radiology in general [ 24 , 25 , 26 , 27 ]. Findings from our focus group study are comparable with these studies highlighting that the screening population approves the introduction of AI systems for disease detection as long as there is some human involvement throughout the reading (e.g., 1st or 2nd reader) and/or diagnosis process.

Stakeholder analysis has been conducted worldwide [ 28 , 29 , 30 , 31 , 32 , 33 ] as part of health reform activities, but the methods and steps used are found to be heterogeneous [ 17 , 18 ]. Most studies discussed the implications of the analysis they conducted and related findings, and only a few publications explained future actions such as strategies to engage with stakeholders [ 34 , 35 , 36 , 37 , 38 ] or recommendations to improve their support [ 39 , 40 ]. Our study was drawn on WHO provided instructions and tools supported by academic theory and real-world application [ 20 ]. This practical tool helped us to identify and assess the stakeholders and their complex characteristics in a rigorous, transparent, and systematic manner. This study article contributes to the literature of stakeholder analysis and scholarly research by advancing the knowledge of the theory and analysis processes by providing the first detailed description of the methodology for every step of the WHO guidelines. This article therefore presents practical guidance on how we adopted the tools provided by the guide, how we scored and/or interpreted the findings with examples and finally, how we used the overall findings to inform strategies for managing ‘important’ stakeholders.

Some of the strengths of this study were that we used a systematic approach from the pre-selection of relevant stakeholders to data collection, analysis, interpretation and development of strategies to manage ‘important’ stakeholders. The research team managed to successfully engage with the majority of the targeted stakeholders during the post-covid time period and generated rich data. Several measures were taken to enhance the trustworthiness of the data. For example, the topic guides, scenarios and checklist and analysis tools were pilot tested before data collection. Interviews and focus group meetings were transcribed verbatim by an authorised and expert external transcriber company and were double checked by a researcher before the coding process began. Two researchers double coded 10% of the transcripts to inform and finalise the coding guide for the purpose of consistency and replication of the process. An analysis guide was developed, and we explicitly stated the criteria used for assessing characteristics to minimise bias and reduce ambiguity.

Some limitations include limiting the data collection to three geographical areas of Scotland. However, these areas cover large mixture of urban and rural population in relation to the breast cancer screening programme in Scotland. Due to the nature of the study (i.e., hypothetical and qualitative) it might be that some views were not captured or only people who are in support of the reform agreed to take part in the study. Efforts were made to identify and recruit relevant stakeholders during the entire cycle of the study. For example, we used snowballing technique during the data collection process to identify stakeholders.

who were not in our list and who might support or oppose the reform. As a result of this, one moderate opponent was recruited in this study. Perspectives of some stakeholders such as government officials were not captured. As per example, members of Scottish Government were not invited to take part in this study because of the hypothetical nature of this study and researcher’s limited access to them during the COVID pandemic. Women aged 59–74 year took part in the focus groups and hence, views of younger women (e.g., less than 50 year old) were not captured. Future studies should consider capturing younger women’s views and empower them to influence the future reform. The analysis was conducted by one researcher only, but the analysis tables and findings were reviewed by the working group and agreed by the workshop participants.

In our study, stakeholders were multiple, and they showed interest in various activities targeting the reform (i.e., integrating AI algorithm into the Scottish National Breast Screening Service for breast cancer detection). Besides some concerns were raised, they were mostly supportive of using AI in the breast screening programme. Five strategies were developed to maintain and improve the support of ‘important’ stakeholders. Findings of this study might contribute to shape the AI integrated future breast screening service.

In the next stage of the study, the proposed strategies must be co-produced with relevant stakeholder groups. Challenges related to implementing the strategies may include allocating resources such as time, funding, and staff, the organisational structure and individuals involved, and collaboration within the organisations (e.g., mutual communication and multidisciplinary codesign). Therefore, perceived challenges must be identified through discussion with the working group and relevant stakeholder groups and tackled throughout the implementation process to ensure its compelling design and effective delivery targeting the proposed reform. Future research also needs to evaluate the effectiveness and impact of the strategies once implemented, which would further contribute to advance the field of health reform policy implementation work. This article provides guidance for a novel approach to aid future researchers, policymakers or health planners to conduct similar studies targeting healthcare reform.

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Abbreviations

Artificial Intelligence

The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics

National Health Services

National Breast Screening Programme

National (Scottish) Screening Council

National Services Scotland

National Screening oversight Function Board

National Services Division

National Institute for Health and Care

United Kingdom

UK National Screening council/committee

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Acknowledgements

This work is supported by the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690]. We thank all participants in the study. We thank all co-authors for their contributions. We also thank Graham Scotland for interpreting findings around economic evaluation and the rest of the iCAIRD team (including Moragh Boyle, Roger Staff) for contributing towards Phases 3 and 4 of this study.

This project was funded by the Innovate UK - part of UK Research and Innovation (Project No: 20378). The funders had no role in the study design, data collection and analysis, publication decision, or manuscript preparation.

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Rumana Newlands, Hanne Bruhn, Magdalena Rzewuska Díaz & Craig Ramsay

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R.N: Conceptualization, Methodology, Data collection, Formal analysis, Interpretation of data, Writing - original draft, Writing - review & editing, Validation, Project administration. H.B: Data collection, Formal analysis, Writing - review & editing, Project administration. M.D: Conceptualization, Data collection, Formal analysis, Writing - Review & editing, Validation. G.L: Overview of the work, Writing - Review & editing, Validation. L.A: Writing - Review & editing, Validation. C.R: Conceptualization, Methodology, Investigation, Writing - review & editing, Validation, Supervision.

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Correspondence to Rumana Newlands .

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Newlands, R., Bruhn, H., Díaz, M.R. et al. A stakeholder analysis to prepare for real-world evaluation of integrating artificial intelligent algorithms into breast screening (PREP-AIR study): a qualitative study using the WHO guide. BMC Health Serv Res 24 , 569 (2024). https://doi.org/10.1186/s12913-024-10926-z

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