qualitative research design and methodology

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Shona McCombes

Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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The Oxford Handbook of Qualitative Research (2nd edn)

Patricia Leavy Independent Scholar Kennebunk, ME, USA

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The Oxford Handbook of Qualitative Research, second edition, presents a comprehensive retrospective and prospective review of the field of qualitative research. Original, accessible chapters written by interdisciplinary leaders in the field make this a critical reference work. Filled with robust examples from real-world research; ample discussion of the historical, theoretical, and methodological foundations of the field; and coverage of key issues including data collection, interpretation, representation, assessment, and teaching, this handbook aims to be a valuable text for students, professors, and researchers. This newly revised and expanded edition features up-to-date examples and topics, including seven new chapters on duoethnography, team research, writing ethnographically, creative approaches to writing, writing for performance, writing for the public, and teaching qualitative research.

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Chapter 2. Research Design

Getting started.

When I teach undergraduates qualitative research methods, the final product of the course is a “research proposal” that incorporates all they have learned and enlists the knowledge they have learned about qualitative research methods in an original design that addresses a particular research question. I highly recommend you think about designing your own research study as you progress through this textbook. Even if you don’t have a study in mind yet, it can be a helpful exercise as you progress through the course. But how to start? How can one design a research study before they even know what research looks like? This chapter will serve as a brief overview of the research design process to orient you to what will be coming in later chapters. Think of it as a “skeleton” of what you will read in more detail in later chapters. Ideally, you will read this chapter both now (in sequence) and later during your reading of the remainder of the text. Do not worry if you have questions the first time you read this chapter. Many things will become clearer as the text advances and as you gain a deeper understanding of all the components of good qualitative research. This is just a preliminary map to get you on the right road.

Research Design Steps

Before you even get started, you will need to have a broad topic of interest in mind. [1] . In my experience, students can confuse this broad topic with the actual research question, so it is important to clearly distinguish the two. And the place to start is the broad topic. It might be, as was the case with me, working-class college students. But what about working-class college students? What’s it like to be one? Why are there so few compared to others? How do colleges assist (or fail to assist) them? What interested me was something I could barely articulate at first and went something like this: “Why was it so difficult and lonely to be me?” And by extension, “Did others share this experience?”

Once you have a general topic, reflect on why this is important to you. Sometimes we connect with a topic and we don’t really know why. Even if you are not willing to share the real underlying reason you are interested in a topic, it is important that you know the deeper reasons that motivate you. Otherwise, it is quite possible that at some point during the research, you will find yourself turned around facing the wrong direction. I have seen it happen many times. The reason is that the research question is not the same thing as the general topic of interest, and if you don’t know the reasons for your interest, you are likely to design a study answering a research question that is beside the point—to you, at least. And this means you will be much less motivated to carry your research to completion.

Researcher Note

Why do you employ qualitative research methods in your area of study? What are the advantages of qualitative research methods for studying mentorship?

Qualitative research methods are a huge opportunity to increase access, equity, inclusion, and social justice. Qualitative research allows us to engage and examine the uniquenesses/nuances within minoritized and dominant identities and our experiences with these identities. Qualitative research allows us to explore a specific topic, and through that exploration, we can link history to experiences and look for patterns or offer up a unique phenomenon. There’s such beauty in being able to tell a particular story, and qualitative research is a great mode for that! For our work, we examined the relationships we typically use the term mentorship for but didn’t feel that was quite the right word. Qualitative research allowed us to pick apart what we did and how we engaged in our relationships, which then allowed us to more accurately describe what was unique about our mentorship relationships, which we ultimately named liberationships ( McAloney and Long 2021) . Qualitative research gave us the means to explore, process, and name our experiences; what a powerful tool!

How do you come up with ideas for what to study (and how to study it)? Where did you get the idea for studying mentorship?

Coming up with ideas for research, for me, is kind of like Googling a question I have, not finding enough information, and then deciding to dig a little deeper to get the answer. The idea to study mentorship actually came up in conversation with my mentorship triad. We were talking in one of our meetings about our relationship—kind of meta, huh? We discussed how we felt that mentorship was not quite the right term for the relationships we had built. One of us asked what was different about our relationships and mentorship. This all happened when I was taking an ethnography course. During the next session of class, we were discussing auto- and duoethnography, and it hit me—let’s explore our version of mentorship, which we later went on to name liberationships ( McAloney and Long 2021 ). The idea and questions came out of being curious and wanting to find an answer. As I continue to research, I see opportunities in questions I have about my work or during conversations that, in our search for answers, end up exposing gaps in the literature. If I can’t find the answer already out there, I can study it.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

When you have a better idea of why you are interested in what it is that interests you, you may be surprised to learn that the obvious approaches to the topic are not the only ones. For example, let’s say you think you are interested in preserving coastal wildlife. And as a social scientist, you are interested in policies and practices that affect the long-term viability of coastal wildlife, especially around fishing communities. It would be natural then to consider designing a research study around fishing communities and how they manage their ecosystems. But when you really think about it, you realize that what interests you the most is how people whose livelihoods depend on a particular resource act in ways that deplete that resource. Or, even deeper, you contemplate the puzzle, “How do people justify actions that damage their surroundings?” Now, there are many ways to design a study that gets at that broader question, and not all of them are about fishing communities, although that is certainly one way to go. Maybe you could design an interview-based study that includes and compares loggers, fishers, and desert golfers (those who golf in arid lands that require a great deal of wasteful irrigation). Or design a case study around one particular example where resources were completely used up by a community. Without knowing what it is you are really interested in, what motivates your interest in a surface phenomenon, you are unlikely to come up with the appropriate research design.

These first stages of research design are often the most difficult, but have patience . Taking the time to consider why you are going to go through a lot of trouble to get answers will prevent a lot of wasted energy in the future.

There are distinct reasons for pursuing particular research questions, and it is helpful to distinguish between them.  First, you may be personally motivated.  This is probably the most important and the most often overlooked.   What is it about the social world that sparks your curiosity? What bothers you? What answers do you need in order to keep living? For me, I knew I needed to get a handle on what higher education was for before I kept going at it. I needed to understand why I felt so different from my peers and whether this whole “higher education” thing was “for the likes of me” before I could complete my degree. That is the personal motivation question. Your personal motivation might also be political in nature, in that you want to change the world in a particular way. It’s all right to acknowledge this. In fact, it is better to acknowledge it than to hide it.

There are also academic and professional motivations for a particular study.  If you are an absolute beginner, these may be difficult to find. We’ll talk more about this when we discuss reviewing the literature. Simply put, you are probably not the only person in the world to have thought about this question or issue and those related to it. So how does your interest area fit into what others have studied? Perhaps there is a good study out there of fishing communities, but no one has quite asked the “justification” question. You are motivated to address this to “fill the gap” in our collective knowledge. And maybe you are really not at all sure of what interests you, but you do know that [insert your topic] interests a lot of people, so you would like to work in this area too. You want to be involved in the academic conversation. That is a professional motivation and a very important one to articulate.

Practical and strategic motivations are a third kind. Perhaps you want to encourage people to take better care of the natural resources around them. If this is also part of your motivation, you will want to design your research project in a way that might have an impact on how people behave in the future. There are many ways to do this, one of which is using qualitative research methods rather than quantitative research methods, as the findings of qualitative research are often easier to communicate to a broader audience than the results of quantitative research. You might even be able to engage the community you are studying in the collecting and analyzing of data, something taboo in quantitative research but actively embraced and encouraged by qualitative researchers. But there are other practical reasons, such as getting “done” with your research in a certain amount of time or having access (or no access) to certain information. There is nothing wrong with considering constraints and opportunities when designing your study. Or maybe one of the practical or strategic goals is about learning competence in this area so that you can demonstrate the ability to conduct interviews and focus groups with future employers. Keeping that in mind will help shape your study and prevent you from getting sidetracked using a technique that you are less invested in learning about.

STOP HERE for a moment

I recommend you write a paragraph (at least) explaining your aims and goals. Include a sentence about each of the following: personal/political goals, practical or professional/academic goals, and practical/strategic goals. Think through how all of the goals are related and can be achieved by this particular research study . If they can’t, have a rethink. Perhaps this is not the best way to go about it.

You will also want to be clear about the purpose of your study. “Wait, didn’t we just do this?” you might ask. No! Your goals are not the same as the purpose of the study, although they are related. You can think about purpose lying on a continuum from “ theory ” to “action” (figure 2.1). Sometimes you are doing research to discover new knowledge about the world, while other times you are doing a study because you want to measure an impact or make a difference in the world.

Basic research involves research that is done for the sake of “pure” knowledge—that is, knowledge that, at least at this moment in time, may not have any apparent use or application. Often, and this is very important, knowledge of this kind is later found to be extremely helpful in solving problems. So one way of thinking about basic research is that it is knowledge for which no use is yet known but will probably one day prove to be extremely useful. If you are doing basic research, you do not need to argue its usefulness, as the whole point is that we just don’t know yet what this might be.

Researchers engaged in basic research want to understand how the world operates. They are interested in investigating a phenomenon to get at the nature of reality with regard to that phenomenon. The basic researcher’s purpose is to understand and explain ( Patton 2002:215 ).

Basic research is interested in generating and testing hypotheses about how the world works. Grounded Theory is one approach to qualitative research methods that exemplifies basic research (see chapter 4). Most academic journal articles publish basic research findings. If you are working in academia (e.g., writing your dissertation), the default expectation is that you are conducting basic research.

Applied research in the social sciences is research that addresses human and social problems. Unlike basic research, the researcher has expectations that the research will help contribute to resolving a problem, if only by identifying its contours, history, or context. From my experience, most students have this as their baseline assumption about research. Why do a study if not to make things better? But this is a common mistake. Students and their committee members are often working with default assumptions here—the former thinking about applied research as their purpose, the latter thinking about basic research: “The purpose of applied research is to contribute knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment. While in basic research the source of questions is the tradition within a scholarly discipline, in applied research the source of questions is in the problems and concerns experienced by people and by policymakers” ( Patton 2002:217 ).

Applied research is less geared toward theory in two ways. First, its questions do not derive from previous literature. For this reason, applied research studies have much more limited literature reviews than those found in basic research (although they make up for this by having much more “background” about the problem). Second, it does not generate theory in the same way as basic research does. The findings of an applied research project may not be generalizable beyond the boundaries of this particular problem or context. The findings are more limited. They are useful now but may be less useful later. This is why basic research remains the default “gold standard” of academic research.

Evaluation research is research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems. We already know the problems, and someone has already come up with solutions. There might be a program, say, for first-generation college students on your campus. Does this program work? Are first-generation students who participate in the program more likely to graduate than those who do not? These are the types of questions addressed by evaluation research. There are two types of research within this broader frame; however, one more action-oriented than the next. In summative evaluation , an overall judgment about the effectiveness of a program or policy is made. Should we continue our first-gen program? Is it a good model for other campuses? Because the purpose of such summative evaluation is to measure success and to determine whether this success is scalable (capable of being generalized beyond the specific case), quantitative data is more often used than qualitative data. In our example, we might have “outcomes” data for thousands of students, and we might run various tests to determine if the better outcomes of those in the program are statistically significant so that we can generalize the findings and recommend similar programs elsewhere. Qualitative data in the form of focus groups or interviews can then be used for illustrative purposes, providing more depth to the quantitative analyses. In contrast, formative evaluation attempts to improve a program or policy (to help “form” or shape its effectiveness). Formative evaluations rely more heavily on qualitative data—case studies, interviews, focus groups. The findings are meant not to generalize beyond the particular but to improve this program. If you are a student seeking to improve your qualitative research skills and you do not care about generating basic research, formative evaluation studies might be an attractive option for you to pursue, as there are always local programs that need evaluation and suggestions for improvement. Again, be very clear about your purpose when talking through your research proposal with your committee.

Action research takes a further step beyond evaluation, even formative evaluation, to being part of the solution itself. This is about as far from basic research as one could get and definitely falls beyond the scope of “science,” as conventionally defined. The distinction between action and research is blurry, the research methods are often in constant flux, and the only “findings” are specific to the problem or case at hand and often are findings about the process of intervention itself. Rather than evaluate a program as a whole, action research often seeks to change and improve some particular aspect that may not be working—maybe there is not enough diversity in an organization or maybe women’s voices are muted during meetings and the organization wonders why and would like to change this. In a further step, participatory action research , those women would become part of the research team, attempting to amplify their voices in the organization through participation in the action research. As action research employs methods that involve people in the process, focus groups are quite common.

If you are working on a thesis or dissertation, chances are your committee will expect you to be contributing to fundamental knowledge and theory ( basic research ). If your interests lie more toward the action end of the continuum, however, it is helpful to talk to your committee about this before you get started. Knowing your purpose in advance will help avoid misunderstandings during the later stages of the research process!

The Research Question

Once you have written your paragraph and clarified your purpose and truly know that this study is the best study for you to be doing right now , you are ready to write and refine your actual research question. Know that research questions are often moving targets in qualitative research, that they can be refined up to the very end of data collection and analysis. But you do have to have a working research question at all stages. This is your “anchor” when you get lost in the data. What are you addressing? What are you looking at and why? Your research question guides you through the thicket. It is common to have a whole host of questions about a phenomenon or case, both at the outset and throughout the study, but you should be able to pare it down to no more than two or three sentences when asked. These sentences should both clarify the intent of the research and explain why this is an important question to answer. More on refining your research question can be found in chapter 4.

Chances are, you will have already done some prior reading before coming up with your interest and your questions, but you may not have conducted a systematic literature review. This is the next crucial stage to be completed before venturing further. You don’t want to start collecting data and then realize that someone has already beaten you to the punch. A review of the literature that is already out there will let you know (1) if others have already done the study you are envisioning; (2) if others have done similar studies, which can help you out; and (3) what ideas or concepts are out there that can help you frame your study and make sense of your findings. More on literature reviews can be found in chapter 9.

In addition to reviewing the literature for similar studies to what you are proposing, it can be extremely helpful to find a study that inspires you. This may have absolutely nothing to do with the topic you are interested in but is written so beautifully or organized so interestingly or otherwise speaks to you in such a way that you want to post it somewhere to remind you of what you want to be doing. You might not understand this in the early stages—why would you find a study that has nothing to do with the one you are doing helpful? But trust me, when you are deep into analysis and writing, having an inspirational model in view can help you push through. If you are motivated to do something that might change the world, you probably have read something somewhere that inspired you. Go back to that original inspiration and read it carefully and see how they managed to convey the passion that you so appreciate.

At this stage, you are still just getting started. There are a lot of things to do before setting forth to collect data! You’ll want to consider and choose a research tradition and a set of data-collection techniques that both help you answer your research question and match all your aims and goals. For example, if you really want to help migrant workers speak for themselves, you might draw on feminist theory and participatory action research models. Chapters 3 and 4 will provide you with more information on epistemologies and approaches.

Next, you have to clarify your “units of analysis.” What is the level at which you are focusing your study? Often, the unit in qualitative research methods is individual people, or “human subjects.” But your units of analysis could just as well be organizations (colleges, hospitals) or programs or even whole nations. Think about what it is you want to be saying at the end of your study—are the insights you are hoping to make about people or about organizations or about something else entirely? A unit of analysis can even be a historical period! Every unit of analysis will call for a different kind of data collection and analysis and will produce different kinds of “findings” at the conclusion of your study. [2]

Regardless of what unit of analysis you select, you will probably have to consider the “human subjects” involved in your research. [3] Who are they? What interactions will you have with them—that is, what kind of data will you be collecting? Before answering these questions, define your population of interest and your research setting. Use your research question to help guide you.

Let’s use an example from a real study. In Geographies of Campus Inequality , Benson and Lee ( 2020 ) list three related research questions: “(1) What are the different ways that first-generation students organize their social, extracurricular, and academic activities at selective and highly selective colleges? (2) how do first-generation students sort themselves and get sorted into these different types of campus lives; and (3) how do these different patterns of campus engagement prepare first-generation students for their post-college lives?” (3).

Note that we are jumping into this a bit late, after Benson and Lee have described previous studies (the literature review) and what is known about first-generation college students and what is not known. They want to know about differences within this group, and they are interested in ones attending certain kinds of colleges because those colleges will be sites where academic and extracurricular pressures compete. That is the context for their three related research questions. What is the population of interest here? First-generation college students . What is the research setting? Selective and highly selective colleges . But a host of questions remain. Which students in the real world, which colleges? What about gender, race, and other identity markers? Will the students be asked questions? Are the students still in college, or will they be asked about what college was like for them? Will they be observed? Will they be shadowed? Will they be surveyed? Will they be asked to keep diaries of their time in college? How many students? How many colleges? For how long will they be observed?

Recommendation

Take a moment and write down suggestions for Benson and Lee before continuing on to what they actually did.

Have you written down your own suggestions? Good. Now let’s compare those with what they actually did. Benson and Lee drew on two sources of data: in-depth interviews with sixty-four first-generation students and survey data from a preexisting national survey of students at twenty-eight selective colleges. Let’s ignore the survey for our purposes here and focus on those interviews. The interviews were conducted between 2014 and 2016 at a single selective college, “Hilltop” (a pseudonym ). They employed a “purposive” sampling strategy to ensure an equal number of male-identifying and female-identifying students as well as equal numbers of White, Black, and Latinx students. Each student was interviewed once. Hilltop is a selective liberal arts college in the northeast that enrolls about three thousand students.

How did your suggestions match up to those actually used by the researchers in this study? It is possible your suggestions were too ambitious? Beginning qualitative researchers can often make that mistake. You want a research design that is both effective (it matches your question and goals) and doable. You will never be able to collect data from your entire population of interest (unless your research question is really so narrow to be relevant to very few people!), so you will need to come up with a good sample. Define the criteria for this sample, as Benson and Lee did when deciding to interview an equal number of students by gender and race categories. Define the criteria for your sample setting too. Hilltop is typical for selective colleges. That was a research choice made by Benson and Lee. For more on sampling and sampling choices, see chapter 5.

Benson and Lee chose to employ interviews. If you also would like to include interviews, you have to think about what will be asked in them. Most interview-based research involves an interview guide, a set of questions or question areas that will be asked of each participant. The research question helps you create a relevant interview guide. You want to ask questions whose answers will provide insight into your research question. Again, your research question is the anchor you will continually come back to as you plan for and conduct your study. It may be that once you begin interviewing, you find that people are telling you something totally unexpected, and this makes you rethink your research question. That is fine. Then you have a new anchor. But you always have an anchor. More on interviewing can be found in chapter 11.

Let’s imagine Benson and Lee also observed college students as they went about doing the things college students do, both in the classroom and in the clubs and social activities in which they participate. They would have needed a plan for this. Would they sit in on classes? Which ones and how many? Would they attend club meetings and sports events? Which ones and how many? Would they participate themselves? How would they record their observations? More on observation techniques can be found in both chapters 13 and 14.

At this point, the design is almost complete. You know why you are doing this study, you have a clear research question to guide you, you have identified your population of interest and research setting, and you have a reasonable sample of each. You also have put together a plan for data collection, which might include drafting an interview guide or making plans for observations. And so you know exactly what you will be doing for the next several months (or years!). To put the project into action, there are a few more things necessary before actually going into the field.

First, you will need to make sure you have any necessary supplies, including recording technology. These days, many researchers use their phones to record interviews. Second, you will need to draft a few documents for your participants. These include informed consent forms and recruiting materials, such as posters or email texts, that explain what this study is in clear language. Third, you will draft a research protocol to submit to your institutional review board (IRB) ; this research protocol will include the interview guide (if you are using one), the consent form template, and all examples of recruiting material. Depending on your institution and the details of your study design, it may take weeks or even, in some unfortunate cases, months before you secure IRB approval. Make sure you plan on this time in your project timeline. While you wait, you can continue to review the literature and possibly begin drafting a section on the literature review for your eventual presentation/publication. More on IRB procedures can be found in chapter 8 and more general ethical considerations in chapter 7.

Once you have approval, you can begin!

Research Design Checklist

Before data collection begins, do the following:

• Write a paragraph explaining your aims and goals (personal/political, practical/strategic, professional/academic).
• Define your research question; write two to three sentences that clarify the intent of the research and why this is an important question to answer.
• Review the literature for similar studies that address your research question or similar research questions; think laterally about some literature that might be helpful or illuminating but is not exactly about the same topic.
• Find a written study that inspires you—it may or may not be on the research question you have chosen.
• Consider and choose a research tradition and set of data-collection techniques that (1) help answer your research question and (2) match your aims and goals.
• Define your population of interest and your research setting.
• Define the criteria for your sample (How many? Why these? How will you find them, gain access, and acquire consent?).
• If you are conducting interviews, draft an interview guide.
•  If you are making observations, create a plan for observations (sites, times, recording, access).
• Acquire any necessary technology (recording devices/software).
• Draft consent forms that clearly identify the research focus and selection process.
• Create recruiting materials (posters, email, texts).
• Apply for IRB approval (proposal plus consent form plus recruiting materials).
• Block out time for collecting data.
• At the end of the chapter, you will find a " Research Design Checklist " that summarizes the main recommendations made here ↵
• For example, if your focus is society and culture , you might collect data through observation or a case study. If your focus is individual lived experience , you are probably going to be interviewing some people. And if your focus is language and communication , you will probably be analyzing text (written or visual). ( Marshall and Rossman 2016:16 ). ↵
• You may not have any "live" human subjects. There are qualitative research methods that do not require interactions with live human beings - see chapter 16 , "Archival and Historical Sources." But for the most part, you are probably reading this textbook because you are interested in doing research with people. The rest of the chapter will assume this is the case. ↵

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

A methodological tradition of inquiry and research design that focuses on an individual case (e.g., setting, institution, or sometimes an individual) in order to explore its complexity, history, and interactive parts.  As an approach, it is particularly useful for obtaining a deep appreciation of an issue, event, or phenomenon of interest in its particular context.

The controlling force in research; can be understood as lying on a continuum from basic research (knowledge production) to action research (effecting change).

In its most basic sense, a theory is a story we tell about how the world works that can be tested with empirical evidence.  In qualitative research, we use the term in a variety of ways, many of which are different from how they are used by quantitative researchers.  Although some qualitative research can be described as “testing theory,” it is more common to “build theory” from the data using inductive reasoning , as done in Grounded Theory .  There are so-called “grand theories” that seek to integrate a whole series of findings and stories into an overarching paradigm about how the world works, and much smaller theories or concepts about particular processes and relationships.  Theory can even be used to explain particular methodological perspectives or approaches, as in Institutional Ethnography , which is both a way of doing research and a theory about how the world works.

Research that is interested in generating and testing hypotheses about how the world works.

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

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

Research that contributes knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment.

Research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems.  There are two kinds: summative and formative .

Research in which an overall judgment about the effectiveness of a program or policy is made, often for the purpose of generalizing to other cases or programs.  Generally uses qualitative research as a supplement to primary quantitative data analyses.  Contrast formative evaluation research .

Research designed to improve a program or policy (to help “form” or shape its effectiveness); relies heavily on qualitative research methods.  Contrast summative evaluation research

Research carried out at a particular organizational or community site with the intention of affecting change; often involves research subjects as participants of the study.  See also participatory action research .

Research in which both researchers and participants work together to understand a problematic situation and change it for the better.

The level of the focus of analysis (e.g., individual people, organizations, programs, neighborhoods).

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A fictional name assigned to give anonymity to a person, group, or place.  Pseudonyms are important ways of protecting the identity of research participants while still providing a “human element” in the presentation of qualitative data.  There are ethical considerations to be made in selecting pseudonyms; some researchers allow research participants to choose their own.

A requirement for research involving human participants; the documentation of informed consent.  In some cases, oral consent or assent may be sufficient, but the default standard is a single-page easy-to-understand form that both the researcher and the participant sign and date.   Under federal guidelines, all researchers "shall seek such consent only under circumstances that provide the prospective subject or the representative sufficient opportunity to consider whether or not to participate and that minimize the possibility of coercion or undue influence. The information that is given to the subject or the representative shall be in language understandable to the subject or the representative.  No informed consent, whether oral or written, may include any exculpatory language through which the subject or the representative is made to waive or appear to waive any of the subject's rights or releases or appears to release the investigator, the sponsor, the institution, or its agents from liability for negligence" (21 CFR 50.20).  Your IRB office will be able to provide a template for use in your study .

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

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

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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

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Article contents

Qualitative design research methods.

• Michael Domínguez Michael Domínguez San Diego State University
• https://doi.org/10.1093/acrefore/9780190264093.013.170
• Published online: 19 December 2017

Emerging in the learning sciences field in the early 1990s, qualitative design-based research (DBR) is a relatively new methodological approach to social science and education research. As its name implies, DBR is focused on the design of educational innovations, and the testing of these innovations in the complex and interconnected venue of naturalistic settings. As such, DBR is an explicitly interventionist approach to conducting research, situating the researcher as a part of the complex ecology in which learning and educational innovation takes place.

With this in mind, DBR is distinct from more traditional methodologies, including laboratory experiments, ethnographic research, and large-scale implementation. Rather, the goal of DBR is not to prove the merits of any particular intervention, or to reflect passively on a context in which learning occurs, but to examine the practical application of theories of learning themselves in specific, situated contexts. By designing purposeful, naturalistic, and sustainable educational ecologies, researchers can test, extend, or modify their theories and innovations based on their pragmatic viability. This process offers the prospect of generating theory-developing, contextualized knowledge claims that can complement the claims produced by other forms of research.

Because of this interventionist, naturalistic stance, DBR has also been the subject of ongoing debate concerning the rigor of its methodology. In many ways, these debates obscure the varied ways DBR has been practiced, the varied types of questions being asked, and the theoretical breadth of researchers who practice DBR. With this in mind, DBR research may involve a diverse range of methods as researchers from a variety of intellectual traditions within the learning sciences and education research design pragmatic innovations based on their theories of learning, and document these complex ecologies using the methodologies and tools most applicable to their questions, focuses, and academic communities.

DBR has gained increasing interest in recent years. While it remains a popular methodology for developmental and cognitive learning scientists seeking to explore theory in naturalistic settings, it has also grown in importance to cultural psychology and cultural studies researchers as a methodological approach that aligns in important ways with the participatory commitments of liberatory research. As such, internal tension within the DBR field has also emerged. Yet, though approaches vary, and have distinct genealogies and commitments, DBR might be seen as the broad methodological genre in which Change Laboratory, design-based implementation research (DBIR), social design-based experiments (SDBE), participatory design research (PDR), and research-practice partnerships might be categorized. These critically oriented iterations of DBR have important implications for educational research and educational innovation in historically marginalized settings and the Global South.

• design-based research
• learning sciences
• social-design experiment
• qualitative research
• research methods

Educational research, perhaps more than many other disciplines, is a situated field of study. Learning happens around us every day, at all times, in both formal and informal settings. Our worlds are replete with complex, dynamic, diverse communities, contexts, and institutions, many of which are actively seeking guidance and support in the endless quest for educational innovation. Educational researchers—as a source of potential expertise—are necessarily implicated in this complexity, linked to the communities and institutions through their very presence in spaces of learning, poised to contribute with possible solutions, yet often positioned as separate from the activities they observe, creating dilemmas of responsibility and engagement.

So what are educational scholars and researchers to do? These tensions invite a unique methodological challenge for the contextually invested researcher, begging them to not just produce knowledge about learning, but to participate in the ecology, collaborating on innovations in the complex contexts in which learning is taking place. In short, for many educational researchers, our backgrounds as educators, our connections to community partners, and our sociopolitical commitments to the process of educational innovation push us to ensure that our work is generative, and that our theories and ideas—our expertise—about learning and education are made pragmatic, actionable, and sustainable. We want to test what we know outside of laboratories, designing, supporting, and guiding educational innovation to see if our theories of learning are accurate, and useful to the challenges faced in schools and communities where learning is messy, collaborative, and contested. Through such a process, we learn, and can modify our theories to better serve the real needs of communities. It is from this impulse that qualitative design-based research (DBR) emerged as a new methodological paradigm for education research.

Qualitative design-based research will be examined, documenting its origins, the major tenets of the genre, implementation considerations, and methodological issues, as well as variance within the paradigm. As a relatively new methodology, much tension remains in what constitutes DBR, and what design should mean, and for whom. These tensions and questions, as well as broad perspectives and emergent iterations of the methodology, will be discussed, and considerations for researchers looking toward the future of this paradigm will be considered.

The Origins of Design-Based Research

Qualitative design-based research (DBR) first emerged in the learning sciences field among a group of scholars in the early 1990s, with the first articulation of DBR as a distinct methodological construct appearing in the work of Ann Brown ( 1992 ) and Allan Collins ( 1992 ). For learning scientists in the 1970s and 1980s, the traditional methodologies of laboratory experiments, ethnographies, and large-scale educational interventions were the only methods available. During these decades, a growing community of learning science and educational researchers (e.g., Bereiter & Scardamalia, 1989 ; Brown, Campione, Webber, & McGilley, 1992 ; Cobb & Steffe, 1983 ; Cole, 1995 ; Scardamalia & Bereiter, 1991 ; Schoenfeld, 1982 , 1985 ; Scribner & Cole, 1978 ) interested in educational innovation and classroom interventions in situated contexts began to find the prevailing methodologies insufficient for the types of learning they wished to document, the roles they wished to play in research, and the kinds of knowledge claims they wished to explore. The laboratory, or laboratory-like settings, where research on learning was at the time happening, was divorced from the complexity of real life, and necessarily limiting. Alternatively, most ethnographic research, while more attuned to capturing these complexities and dynamics, regularly assumed a passive stance 1 and avoided interceding in the learning process, or allowing researchers to see what possibility for innovation existed from enacting nascent learning theories. Finally, large-scale interventions could test innovations in practice but lost sight of the nuance of development and implementation in local contexts (Brown, 1992 ; Collins, Joseph, & Bielaczyc, 2004 ).

Dissatisfied with these options, and recognizing that in order to study and understand learning in the messiness of socially, culturally, and historically situated settings, new methods were required, Brown ( 1992 ) proposed an alternative: Why not involve ourselves in the messiness of the process, taking an active, grounded role in disseminating our theories and expertise by becoming designers and implementers of educational innovations? Rather than observing from afar, DBR researchers could trace their own iterative processes of design, implementation, tinkering, redesign, and evaluation, as it unfolded in shared work with teachers, students, learners, and other partners in lived contexts. This premise, initially articulated as “design experiments” (Brown, 1992 ), would be variously discussed over the next decade as “design research,” (Edelson, 2002 ) “developmental research,” (Gravemeijer, 1994 ), and “design-based research,” (Design-Based Research Collective, 2003 ), all of which reflect the original, interventionist, design-oriented concept. The latter term, “design-based research” (DBR), is used here, recognizing this as the prevailing terminology used to refer to this research approach at present. 2

Regardless of the evolving moniker, the prospects of such a methodology were extremely attractive to researchers. Learning scientists acutely aware of various aspects of situated context, and interested in studying the applied outcomes of learning theories—a task of inquiry into situated learning for which canonical methods were rather insufficient—found DBR a welcome development (Bell, 2004 ). As Barab and Squire ( 2004 ) explain: “learning scientists . . . found that they must develop technological tools, curriculum, and especially theories that help them systematically understand and predict how learning occurs” (p. 2), and DBR methodologies allowed them to do this in proactive, hands-on ways. Thus, rather than emerging as a strict alternative to more traditional methodologies, DBR was proposed to fill a niche that other methodologies were ill-equipped to cover.

Effectively, while its development is indeed linked to an inherent critique of previous research paradigms, neither Brown nor Collins saw DBR in opposition to other forms of research. Rather, by providing a bridge from the laboratory to the real world, where learning theories and proposed innovations could interact and be implemented in the complexity of lived socio-ecological contexts (Hoadley, 2004 ), new possibilities emerged. Learning researchers might “trace the evolution of learning in complex, messy classrooms and schools, test and build theories of teaching and learning, and produce instructional tools that survive the challenges of everyday practice” (Shavelson, Phillips, Towne, & Feuer, 2003 , p. 25). Thus, DBR could complement the findings of laboratory, ethnographic, and large-scale studies, answering important questions about the implementation, sustainability, limitations, and usefulness of theories, interventions, and learning when introduced as innovative designs into situated contexts of learning. Moreover, while studies involving these traditional methodologies often concluded by pointing toward implications—insights subsequent studies would need to take up—DBR allowed researchers to address implications iteratively and directly. No subsequent research was necessary, as emerging implications could be reflexively explored in the context of the initial design, offering considerable insight into how research is translated into theory and practice.

Since its emergence in 1992 , DBR as a methodological approach to educational and learning research has quickly grown and evolved, used by researchers from a variety of intellectual traditions in the learning sciences, including developmental and cognitive psychology (e.g., Brown & Campione, 1996 , 1998 ; diSessa & Minstrell, 1998 ), cultural psychology (e.g., Cole, 1996 , 2007 ; Newman, Griffin, & Cole, 1989 ; Gutiérrez, Bien, Selland, & Pierce, 2011 ), cultural anthropology (e.g., Barab, Kinster, Moore, Cunningham, & the ILF Design Team, 2001 ; Polman, 2000 ; Stevens, 2000 ; Suchman, 1995 ), and cultural-historical activity theory (e.g., Engeström, 2011 ; Espinoza, 2009 ; Espinoza & Vossoughi, 2014 ; Gutiérrez, 2008 ; Sannino, 2011 ). Given this plurality of epistemological and theoretical fields that employ DBR, it might best be understood as a broad methodology of educational research, realized in many different, contested, heterogeneous, and distinct iterations, and engaging a variety of qualitative tools and methods (Bell, 2004 ). Despite tensions among these iterations, and substantial and important variances in the ways they employ design-as-research in community settings, there are several common, methodological threads that unite the broad array of research that might be classified as DBR under a shared, though pluralistic, paradigmatic umbrella.

The Tenets of Design-Based Research

Why design-based research.

As we turn to the core tenets of the design-based research (DBR) paradigm, it is worth considering an obvious question: Why use DBR as a methodology for educational research? To answer this, it is helpful to reflect on the original intentions for DBR, particularly, that it is not simply the study of a particular, isolated intervention. Rather, DBR methodologies were conceived of as the complete, iterative process of designing, modifying, and assessing the impact of an educational innovation in a contextual, situated learning environment (Barab & Kirshner, 2001 ; Brown, 1992 ; Cole & Engeström, 2007 ). The design process itself—inclusive of the theory of learning employed, the relationships among participants, contextual factors and constraints, the pedagogical approach, any particular intervention, as well as any changes made to various aspects of this broad design as it proceeds—is what is under study.

Considering this, DBR offers a compelling framework for the researcher interested in having an active and collaborative hand in designing for educational innovation, and interested in creating knowledge about how particular theories of learning, pedagogical or learning practices, or social arrangements function in a context of learning. It is a methodology that can put the researcher in the position of engineer , actively experimenting with aspects of learning and sociopolitical ecologies to arrive at new knowledge and productive outcomes, as Cobb, Confrey, diSessa, Lehrer, and Schauble ( 2003 ) explain:

Prototypically, design experiments entail both “engineering” particular forms of learning and systematically studying those forms of learning within the context defined by the means of supporting them. This designed context is subject to test and revision, and the successive iterations that result play a role similar to that of systematic variation in experiment. (p. 9)

This being said, how directive the engineering role the researcher takes on varies considerably among iterations of DBR. Indeed, recent approaches have argued strongly for researchers to take on more egalitarian positionalities with respect to the community partners with whom they work (e.g., Zavala, 2016 ), acting as collaborative designers, rather than authoritative engineers.

Method and Methodology in Design-Based Research

Now, having established why we might use DBR, a recurring question that has faced the DBR paradigm is whether DBR is a methodology at all. Given the variety of intellectual and ontological traditions that employ it, and thus the pluralism of methods used in DBR to enact the “engineering” role (whatever shape that may take) that the researcher assumes, it has been argued that DBR is not, in actuality a methodology at all (Kelly, 2004 ). The proliferation and diversity of approaches, methods, and types of analysis purporting to be DBR have been described as a lack of coherence that shows there is no “argumentative grammar” or methodology present in DBR (Kelly, 2004 ).

Now, the conclusions one will eventually draw in this debate will depend on one’s orientations and commitments, but it is useful to note that these demands for “coherence” emerge from previous paradigms in which methodology was largely marked by a shared, coherent toolkit for data collection and data analysis. These previous paradigmatic rules make for an odd fit when considering DBR. Yet, even if we proceed—within the qualitative tradition from which DBR emerges—defining methodology as an approach to research that is shaped by the ontological and epistemological commitments of the particular researcher, and methods as the tools for research, data collection, and analysis that are chosen by the researcher with respect to said commitments (Gutiérrez, Engeström, & Sannino, 2016 ), then a compelling case for DBR as a methodology can be made (Bell, 2004 ).

Effectively, despite the considerable variation in how DBR has been and is employed, and tensions within the DBR field, we might point to considerable, shared epistemic common ground among DBR researchers, all of whom are invested in an approach to research that involves engaging actively and iteratively in the design and exploration of learning theory in situated, natural contexts. This common epistemic ground, even in the face of pluralistic ideologies and choices of methods, invites in a new type of methodological coherence, marked by “intersubjectivity without agreement” (Matusov, 1996 ), that links DBR from traditional developmental and cognitive psychology models of DBR (e.g., Brown, 1992 ; Brown & Campione, 1998 ; Collins, 1992 ), to more recent critical and sociocultural manifestations (e.g., Bang & Vossoughi, 2016 ; Engeström, 2011 ; Gutiérrez, 2016 ), and everything in between.

Put in other terms, even as DBR researchers may choose heterogeneous methods for data collection, data analysis, and reporting results complementary to the ideological and sociopolitical commitments of the particular researcher and the types of research questions that are under examination (Bell, 2004 ), a shared epistemic commitment gives the methodology shape. Indeed, the common commitment toward design innovation emerges clearly across examples of DBR methodological studies ranging in method from ethnographic analyses (Salvador, Bell, & Anderson, 1999 ) to studies of critical discourse within a design (Kärkkäinen, 1999 ), to focused examinations of metacognition of individual learners (White & Frederiksen, 1998 ), and beyond. Rather than indicating a lack of methodology, or methodological weakness, the use of varying qualitative methods for framing data collection and retrospective analyses within DBR, and the tensions within the epistemic common ground itself, simply reflects the scope of its utility. Learning in context is complex, contested, and messy, and the plurality of methods present across DBR allow researchers to dynamically respond to context as needed, employing the tools that fit best to consider the questions that are present, or may arise.

All this being the case, it is useful to look toward the coherent elements—the “argumentative grammar” of DBR, if you will—that can be identified across the varied iterations of DBR. Understanding these shared features, in the context and terms of the methodology itself, help us to appreciate what is involved in developing robust and thorough DBR research, and how DBR seeks to make strong, meaningful claims around the types of research questions it takes up.

Coherent Features of Design-Based Research

Several scholars have provided comprehensive overviews and listings of what they see as the cross-cutting features of DBR, both in the context of more traditional models of DBR (e.g., Cobb et al., 2003 ; Design-Based Research Collective, 2003 ), and in regards to newer iterations (e.g., Gutiérrez & Jurow, 2016 ; Bang & Vossoughi, 2016 ). Rather than try to offer an overview of each of these increasingly pluralistic classifications, the intent here is to attend to three broad elements that are shared across articulations of DBR and reflect the essential elements that constitute the methodological approach DBR offers to educational researchers.

Design research is concerned with the development, testing, and evolution of learning theory in situated contexts

This first element is perhaps most central to what DBR of all types is, anchored in what Brown ( 1992 ) was initially most interested in: testing the pragmatic validity of theories of learning by designing interventions that engaged with, or proposed, entire, naturalistic, ecologies of learning. Put another way, while DBR studies may have various units of analysis, focuses, and variables, and may organize learning in many different ways, it is the theoretically informed design for educational innovation that is most centrally under evaluation. DBR actively and centrally exists as a paradigm that is engaged in the development of theory, not just the evaluation of aspects of its usage (Bell, 2004 ; Design-Based Research Collective, 2003 ; Lesh & Kelly, 2000 ; van den Akker, 1999 ).

Effectively, where DBR is taking place, theory as a lived possibility is under examination. Specifically, in most DBR, this means a focus on “intermediate-level” theories of learning, rather than “grand” ones. In essence, DBR does not contend directly with “grand” learning theories (such as developmental or sociocultural theory writ large) (diSessa, 1991 ). Rather, DBR seeks to offer constructive insights by directly engaging with particular learning processes that flow from these theories on a “grounded,” “intermediate” level. This is not, however, to say DBR is limited in what knowledge it can produce; rather, tinkering in this “intermediate” realm can produce knowledge that informs the “grand” theory (Gravemeijer, 1994 ). For example, while cognitive and motivational psychology provide “grand” theoretical frames, interest-driven learning (IDL) is an “intermediate” theory that flows from these and can be explored in DBR to both inform the development of IDL designs in practice and inform cognitive and motivational psychology more broadly (Joseph, 2004 ).

Crucially, however, DBR entails putting the theory in question under intense scrutiny, or, “into harm’s way” (Cobb et al., 2003 ). This is an especially core element to DBR, and one that distinguishes it from the proliferation of educational-reform or educational-entrepreneurship efforts that similarly take up the discourse of “design” and “innovation.” Not only is the reflexive, often participatory element of DBR absent from such efforts—that is, questioning and modifying the design to suit the learning needs of the context and partners—but the theory driving these efforts is never in question, and in many cases, may be actively obscured. Indeed, it is more common to see educational-entrepreneur design innovations seek to modify a context—such as the way charter schools engage in selective pupil recruitment and intensive disciplinary practices (e.g., Carnoy et al., 2005 ; Ravitch, 2010 ; Saltman, 2007 )—rather than modify their design itself, and thus allow for humility in their theory. Such “innovations” and “design” efforts are distinct from DBR, which must, in the spirit of scientific inquiry, be willing to see the learning theory flail and struggle, be modified, and evolve.

This growth and evolution of theory and knowledge is of course central to DBR as a rigorous research paradigm; moving it beyond simply the design of local educational programs, interventions, or innovations. As Barab and Squire ( 2004 ) explain:

Design-based research requires more than simply showing a particular design works but demands that the researcher (move beyond a particular design exemplar to) generate evidence-based claims about learning that address contemporary theoretical issues and further the theoretical knowledge of the field. (pp. 5–6)

DBR as a research paradigm offers a design process through which theories of learning can be tested; they can be modified, and by allowing them to operate with humility in situated conditions, new insights and knowledge, even new theories, may emerge that might inform the field, as well as the efforts and directions of other types of research inquiry. These productive, theory-developing outcomes, or “ontological innovations” (diSessa & Cobb, 2004 ), represent the culmination of an effective program of DBR—the production of new ways to understand, conceptualize, and enact learning as a lived, contextual process.

Design research works to understand learning processes, and the design that supports them in situated contexts

As a research methodology that operates by tinkering with “grounded” learning theories, DBR is itself grounded, and seeks to develop its knowledge claims and designs in naturalistic, situated contexts (Brown, 1992 ). This is, again, a distinguishing element of DBR—setting it apart from laboratory research efforts involving design and interventions in closed, controlled environments. Rather than attempting to focus on singular variables, and isolate these from others, DBR is concerned with the multitude of variables that naturally occur across entire learning ecologies, and present themselves in distinct ways across multiple planes of possible examination (Rogoff, 1995 ; Collins, Joseph, & Bielaczyc, 2004 ). Certainly, specific variables may be identified as dependent, focal units of analysis, but identifying (while not controlling for) the variables beyond these, and analyzing their impact on the design and learning outcomes, is an equally important process in DBR (Collins et al., 2004 ; Barab & Kirshner, 2001 ). In practice, this of course varies across iterations in its depth and breadth. Traditional models of developmental or cognitive DBR may look to account for the complexity and nuance of a setting’s social, developmental, institutional, and intellectual characteristics (e.g., Brown, 1992 ; Cobb et al., 2003 ), while more recent, critical iterations will give increased attention to how historicity, power, intersubjectivity, and culture, among other things, influence and shape a setting, and the learning that occurs within it (e.g., Gutiérrez, 2016 ; Vakil, de Royston, Nasir, & Kirshner, 2016 ).

Beyond these variations, what counts as “design” in DBR varies widely, and so too will what counts as a naturalistic setting. It has been well documented that learning occurs all the time, every day, and in every space imaginable, both formal and informal (Leander, Phillips, & Taylor, 2010 ), and in ways that span strictly defined setting boundaries (Engeström, Engeström, & Kärkkäinen, 1995 ). DBR may take place in any number of contexts, based on the types of questions asked, and the learning theories and processes that a researcher may be interested in exploring. DBR may involve one-to-one tutoring and learning settings, single classrooms, community spaces, entire institutions, or even holistically designed ecologies (Design-Based Research Collective, 2003 ; Engeström, 2008 ; Virkkunen & Newnham, 2013 ). In all these cases, even the most completely designed experimental ecology, the setting remains naturalistic and situated because DBR actively embraces the uncontrollable variables that participants bring with them to the learning process for and from their situated worlds, lives, and experiences—no effort is made to control for these complicated influences of life, simply to understand how they operate in a given ecology as innovation is attempted. Thus, the extent of the design reflects a broader range of qualitative and theoretical study, rather than an attempt to control or isolate some particular learning process from outside influence.

While there is much variety in what design may entail, where DBR takes place, what types of learning ecologies are under examination, and what methods are used, situated ecologies are always the setting of this work. In this way, conscious of naturalistic variables, and the influences that culture, historicity, participation, and context have on learning, researchers can use DBR to build on prior research, and extend knowledge around the learning that occurs in the complexity of situated contexts and lived practices (Collins et al., 2004 ).

Design based research is iterative; it changes, grows, and evolves to meet the needs and emergent questions of the context, and this tinkering process is part of the research

The final shared element undergirding models of DBR is that it is an iterative, active, and interventionist process, interested in and focused on producing educational innovation by actually and actively putting design innovations into practice (Brown, 1992 , Collins, 1992 ; Gutiérrez, 2008 ). Given this interventionist, active stance, tinkering with the design and the theory of learning informing the design is as much a part of the research process as the outcome of the intervention or innovation itself—we learn what impacts learning as much, if not more, than we learn what was learned. In this sense, DBR involves a focus on analyzing the theory-driven design itself, and its implementation as an object of study (Edelson, 2002 ; Penuel, Fishman, Cheng, & Sabelli, 2011 ), and is ultimately interested in the improvement of the design—of how it unfolds, how it shifts, how it is modified, and made to function productively for participants in their contexts and given their needs (Kirshner & Polman, 2013 ).

While DBR is iterative and contextual as a foundational methodological principle, what this means varies across conceptions of DBR. For instance, in more traditional models, Brown and Campione ( 1996 ) pointed out the dangers of “lethal mutation” in which a design, introduced into a context, may become so warped by the influence, pressures, incomplete implementation, or misunderstanding of participants in the local context, that it no longer reflects or tests the theory under study. In short, a theory-driven intervention may be put in place, and then subsumed to such a degree by participants based on their understanding and needs, that it remains the original innovative design in name alone. The assertion here is that in these cases, the research ceases to be DBR in the sense that the design is no longer central, actively shaping learning. We cannot, they argue, analyze a design—and the theory it was meant to reflect—as an object of study when it has been “mutated,” and it is merely a banner under which participants are enacting their idiosyncratic, pragmatic needs.

While the ways in which settings and individuals might disrupt designs intended to produce robust learning is certainly a tension to be cautious of in DBR, it is also worth noting that in many critical approaches to DBR, such mutations—whether “lethal” to the original design or not—are seen as compelling and important moments. Here, where collaboration and community input is more central to the design process, iterative is understood differently. Thus, a “mutation” becomes a point where reflexivity, tension, and contradiction might open the door for change, for new designs, for reconsiderations of researcher and collaborative partner positionalities, or for ethnographic exploration into how a context takes up, shapes, and ultimately engages innovations in a particular sociocultural setting. In short, accounting for and documenting changes in design is a vital part of the DBR process, allowing researchers to respond to context in a variety of ways, always striving for their theories and designs to act with humility, and in the interest of usefulness .

With this in mind, the iterative nature of DBR means that the relationships researchers have with other design partners (educators and learners) in the ecology are incredibly important, and vital to consider (Bang et al., 2016 ; Engeström, 2007 ; Engeström, Sannino, & Virkkunen, 2014 ). Different iterations of DBR might occur in ways in which the researcher is more or less intimately involved in the design and implementation process, both in terms of actual presence and intellectual ownership of the design. Regarding the former, in some cases, a researcher may hand a design off to others to implement, periodically studying and modifying it, while in other contexts or designs, the researcher may be actively involved, tinkering in every detail of the implementation and enactment of the design. With regard to the latter, DBR might similarly range from a somewhat prescribed model, in which the researcher is responsible for the original design, and any modifications that may occur based on their analyses, without significant input from participants (e.g., Collins et al., 2004 ), to incredibly participatory models, in which all parties (researchers, educators, learners) are part of each step of the design-creation, modification, and research process (e.g., Bang, Faber, Gurneau, Marin, & Soto, 2016 ; Kirshner, 2015 ).

Considering the wide range of ideological approaches and models for DBR, we might acknowledge that DBR can be gainfully conducted through many iterations of “openness” to the design process. However, the strength of the research—focused on analyzing the design itself as a unit of study reflective of learning theory—will be bolstered by thoughtfully accounting for how involved the researcher will be, and how open to participation the modification process is. These answers should match the types of questions, and conceptual or ideological framing, with which researchers approach DBR, allowing them to tinker with the process of learning as they build on prior research to extend knowledge and test theory (Barab & Kirshner, 2001 ), while thoughtfully documenting these changes in the design as they go.

Implementation and Research Design

As with the overarching principles of design-based research (DBR), even amid the pluralism of conceptual frameworks of DBR researchers, it is possible, and useful, to trace the shared contours in how DBR research design is implemented. Though texts provide particular road maps for undertaking various iterations of DBR consistent with the specific goals, types of questions, and ideological orientations of these scholarly communities (e.g., Cole & Engeström, 2007 ; Collins, Joseph, & Bielaczyc, 2004 ; Fishman, Penuel, Allen, Cheng, & Sabelli, 2013 ; Gutiérrez & Jurow, 2016 ; Virkkunen & Newnham, 2013 ), certain elements, realized differently, can be found across all of these models, and may be encapsulated in five broad methodological phases.

Considering the Design Focus

DBR begins by considering what the focus of the design, the situated context, and the units of analysis for research will be. Prospective DBR researchers will need to consider broader research in regard to the “grand” theory of learning with which they work to determine what theoretical questions they have, or identify “intermediate” aspects of the theories that might be studied and strengthened by a design process in situated contexts, and what planes of analysis (Rogoff, 1995 ) will be most suitable for examination. This process allows for the identification of the critical theoretical elements of a design, and articulation of initial research questions.

Given the conceptual framework, theoretical and research questions, and sociopolitical interests at play, researchers may undertake this, and subsequent steps in the process, on their own, or in close collaboration with the communities and individuals in the situated contexts in which the design will unfold. As such, across iterations of DBR, and with respect to the ways DBR researchers choose to engage with communities, the origin of the design will vary, and might begin in some cases with theoretical questions, or arise in others as a problem of practice (Coburn & Penuel, 2016 ), though as has been noted, in either case, theory and practice are necessarily linked in the research.

Creating and Implementing a Designed Innovation

From the consideration and identification of the critical elements, planned units of analysis, and research questions that will drive a design, researchers can then actively create (either on their own or in conjunction with potential design partners) a designed intervention reflecting these critical elements, and the overarching theory.

Here, the DBR researcher should consider what partners exist in the process and what ownership exists around these partnerships, determine exactly what the pragmatic features of the intervention/design will be and who will be responsible for them, and consider when checkpoints for modification and evaluation will be undertaken, and by whom. Additionally, researchers should at this stage consider questions of timeline and of recruiting participants, as well as what research materials will be needed to adequately document the design, its implementation, and its outcomes, and how and where collected data will be stored.

Once a design (the planned, theory-informed innovative intervention) has been produced, the DBR researcher and partners can begin the implementation process, putting the design into place and beginning data collection and documentation.

Assessing the Impact of the Design on the Learning Ecology

Chronologically, the next two methodological steps happen recursively in the iterative process of DBR. The researcher must assess the impact of the design, and then, make modifications as necessary, before continuing to assess the impact of these modifications. In short, these next two steps are a cycle that continues across the life and length of the research design.

Once a design has been created and implemented, the researcher begins to observe and document the learning, the ecology, and the design itself. Guided by and in conversation with the theory and critical elements, the researcher should periodically engage in ongoing data analysis, assessing the success of the design, and of learning, paying equal attention to the design itself, and how its implementation is working in the situated ecology.

Within the realm of qualitative research, measuring or assessing variables of learning and assessing the design may look vastly different, require vastly different data-collection and data-analysis tools, and involve vastly different research methods among different researchers.

Modifying the Design

Modification, based on ongoing assessment of the design, is what makes DBR iterative, helping the researcher extend the field’s knowledge about the theory, design, learning, and the context under examination.

Modification of the design can take many forms, from complete changes in approach or curriculum, to introducing an additional tool or mediating artifact into a learning ecology. Moreover, how modification unfolds involves careful reflection from the researcher and any co-designing participants, deciding whether modification will be an ongoing, reflexive, tinkering process, or if it will occur only at predefined checkpoints, after formal evaluation and assessment. Questions of ownership, issues of resource availability, technical support, feasibility, and communication are all central to the work of design modification, and answers will vary given the research questions, design parameters, and researchers’ epistemic commitments.

Each moment of modification indicates a new phase in a DBR project, and a new round of assessing—through data analysis—the impact of the design on the learning ecology, either to guide continued or further modification, report the results of the design, or in some cases, both.

Reporting the Results of the Design

The final step in DBR methodology is to report on the results of the designed intervention, how it contributed to understandings of theory, and how it impacted the local learning ecology or context. The format, genre, and final data analysis methods used in reporting data and research results will vary across iterations of DBR. However, it is largely understood that to avoid methodological confusion, DBR researchers should clearly situate themselves in the DBR paradigm by clearly describing and detailing the design itself; articulating the theory, central elements, and units of analysis under scrutiny, what modifications occurred and what precipitated these changes, and what local effects were observed; and exploring any potential contributions to learning theory, while accounting for the context and their interventionist role and positionality in the design. As such, careful documentation of pragmatic and design decisions for retrospective data analysis, as well as research findings, should be done at each stage of this implementation process.

Methodological Issues in the Design-Based Research Paradigm

Because of its pluralistic nature, its interventionist, nontraditional stance, and the fact that it remains in its conceptual infancy, design-based research (DBR) is replete with ongoing methodological questions and challenges, both from external and internal sources. While there are many more that may exist, addressed will be several of the most pressing the prospective DBR researcher may encounter, or want to consider in understanding the paradigm and beginning a research design.

Challenges to Rigor and Validity

Perhaps the place to begin this reflection on tensions in the DBR paradigm is the recurrent and ongoing challenge to the rigor and validity of DBR, which has asked: Is DBR research at all? Given the interventionist and activist way in which DBR invites the researcher to participate, and the shift in orientation from long-accepted research paradigms, such critiques are hardly surprising, and fall in line with broader challenges to the rigor and objectivity of qualitative social science research in general. Historically, such complaints about DBR are linked to decades of critique of any research that does not adhere to the post-positivist approach set out as the U.S. Department of Education began to prioritize laboratory and large-scale randomized control-trial experimentation as the “gold standard” of research design (e.g., Mosteller & Boruch, 2002 ).

From the outset, DBR, as an interventionist, local, situated, non-laboratory methodology, was bound to run afoul of such conservative trends. While some researchers involved in (particularly traditional developmental and cognitive) DBR have found broader acceptance within these constraints, the rigor of DBR remains contested. It has been suggested that DBR is under-theorized and over-methologized, a haphazard way for researchers to do activist work without engaging in the development of robust knowledge claims about learning (Dede, 2004 ), and an approach lacking in coherence that sheltered interventionist projects of little impact to developing learning theory and allowed researchers to make subjective, pet claims through selective analysis of large bodies of collected data (Kelly, 2003 , 2004 ).

These critiques, however, impose an external set of criteria on DBR, desiring it to fit into the molds of rigor and coherence as defined by canonical methodologies. Bell ( 2004 ) and Bang and Vossoughi ( 2016 ) have made compelling cases for the wide variety of methods and approaches present in DBR not as a fracturing, but as a generative proliferation of different iterations that can offer powerful insights around the different types of questions that exist about learning in the infinitely diverse settings in which it occurs. Essentially, researchers have argued that within the DBR paradigm, and indeed within educational research more generally, the practical impact of research on learning, context, and practices should be a necessary component of rigor (Gutiérrez & Penuel, 2014 ), and the pluralism of methods and approaches available in DBR ensures that the practical impacts and needs of the varied contexts in which the research takes place will always drive the design and research tools.

These moves are emblematic of the way in which DBR is innovating and pushing on paradigms of rigor in educational research altogether, reflecting how DBR fills a complementary niche with respect to other methodologies and attends to elements and challenges of learning in lived, real environments that other types of research have consistently and historically missed. Beyond this, Brown ( 1992 ) was conscious of the concerns around data collection, validity, rigor, and objectivity from the outset, identifying this dilemma—the likelihood of having an incredible amount of data collected in a design only a small fraction of which can be reported and shared, thus leading potentially to selective data analysis and use—as the Bartlett Effect (Brown, 1992 ). Since that time, DBR researchers have been aware of this challenge, actively seeking ways to mitigate this threat to validity by making data sets broadly available, documenting their design, tinkering, and modification processes, clearly situating and describing disconfirming evidence and their own position in the research, and otherwise presenting the broad scope of human and learning activity that occurs within designs in large learning ecologies as comprehensively as possible.

Ultimately, however, these responses are likely to always be insufficient as evidence of rigor to some, for the root dilemma is around what “counts” as education science. While researchers interested and engaged in DBR ought rightly to continue to push themselves to ensure the methodological rigor of their work and chosen methods, it is also worth noting that DBR should seek to hold itself to its own criteria of assessment. This reflects broader trends in qualitative educational research that push back on narrow constructions of what “counts” as science, recognizing the ways in which new methodologies and approaches to research can help us examine aspects of learning, culture, and equity that have continued to be blind spots for traditional education research; invite new voices and perspectives into the process of achieving rigor and validity (Erickson & Gutiérrez, 2002 ); bolster objectivity by bringing it into conversation with the positionality of the researcher (Harding, 1993 ); and perhaps most important, engage in axiological innovation (Bang, Faber, Gurneau, Marin, & Soto, 2016 ), or the exploration of and design for what is, “good right, true, and beautiful . . . in cultural ecologies” (p. 2).

Questions of Generalizability and Usefulness

The generalizability of research results in DBR has been an ongoing and contentious issue in the development of the paradigm. Indeed, by the standards of canonical methods (e.g., laboratory experimentation, ethnography), these local, situated interventions should lack generalizability. While there is reason to discuss and question the merit of generalizability as a goal of qualitative research at all, researchers in the DBR paradigm have long been conscious of this issue. Understanding the question of generalizability around DBR, and how the paradigm has responded to it, can be done in two ways.

First, by distinguishing questions specific to a particular design from the generalizability of the theory. Cole’s (Cole & Underwood, 2013 ) 5th Dimension work, and the nationwide network of linked, theoretically similar sites, operating nationwide with vastly different designs, is a powerful example of this approach to generalizability. Rather than focus on a single, unitary, potentially generalizable design, the project is more interested in variability and sustainability of designs across local contexts (e.g., Cole, 1995 ; Gutiérrez, Bien, Selland, & Pierce, 2011 ; Jurow, Tracy, Hotchkiss, & Kirshner, 2012 ). Through attention to sustainable, locally effective innovations, conscious of the wide variation in culture and context that accompanies any and all learning processes, 5th Dimension sites each derive their idiosyncratic structures from sociocultural theory, sharing some elements, but varying others, while seeking their own “ontological innovations” based on the affordances of their contexts. This pattern reflects a key element of much of the DBR paradigm: that questions of generalizability in DBR may be about the generalizability of the theory of learning, and the variability of learning and design in distinct contexts, rather than the particular design itself.

A second means of addressing generalizability in DBR has been to embrace the pragmatic impacts of designing innovations. This response stems from Messick ( 1992 ) and Schoenfeld’s ( 1992 ) arguments early on in the development of DBR that the consequentialness and validity of DBR efforts as potentially generalizable research depend on the “ usefulness ” of the theories and designs that emerge. Effectively, because DBR is the examination of situated theory, a design must be able to show pragmatic impact—it must succeed at showing the theory to be useful . If there is evidence of usefulness to both the context in which it takes place, and the field of educational research more broadly, then the DBR researcher can stake some broader knowledge claims that might be generalizable. As a result, the DBR paradigm tends to “treat changes in [local] contexts as necessary evidence for the viability of a theory” (Barab & Squire, 2004 , p. 6). This of course does not mean that DBR is only interested in successful efforts. A design that fails or struggles can provide important information and knowledge to the field. Ultimately, though, DBR tends to privilege work that proves the usefulness of designs, whose pragmatic or theoretical findings can then be generalized within the learning science and education research fields.

With this said, the question of usefulness is not always straightforward, and is hardly unitary. While many DBR efforts—particularly those situated in developmental and cognitive learning science traditions—are interested in the generalizability of their useful educational designs (Barab & Squire, 2004 ; Cobb, Confrey, diSessa, Lehrer, & Schauble, 2003 ; Joseph, 2004 ; Steffe & Thompson, 2000 ), not all are. Critical DBR researchers have noted that if usefulness remains situated in the extant sociopolitical and sociocultural power-structures—dominant conceptual and popular definitions of what useful educational outcomes are—the result will be a bar for research merit that inexorably bends toward the positivist spectrum (Booker & Goldman, 2016 ; Dominguez, 2015 ; Zavala, 2016 ). This could potentially, and likely, result in excluding the non-normative interventions and innovations that are vital for historically marginalized communities, but which might have vastly different-looking outcomes, that are nonetheless useful in the sociopolitical context they occur in. Alternative framings to this idea of usefulness push on and extend the intention, and seek to involve the perspectives and agency of situated community partners and their practices in what “counts” as generative and rigorous research outcomes (Gutiérrez & Penuel, 2014 ). An example in this regard is the idea of consequential knowledge (Hall & Jurow, 2015 ; Jurow & Shea, 2015 ), which suggests outcomes that are consequential will be taken up by participants in and across their networks, and over-time—thus a goal of consequential knowledge certainly meets the standard of being useful , but it also implicates the needs and agency of communities in determining the success and merit of a design or research endeavor in important ways that strict usefulness may miss.

Thus, the bar of usefulness that characterizes the DBR paradigm should not be approached without critical reflection. Certainly designs that accomplish little for local contexts should be subject to intense questioning and critique, but considering the sociopolitical and systemic factors that might influence what “counts” as useful in local contexts and education science more generally, should be kept firmly in mind when designing, choosing methods, and evaluating impacts (Zavala, 2016 ). Researchers should think deeply about their goals, whether they are reaching for generalizability at all, and in what ways they are constructing contextual definitions of success, and be clear about these ideologically influenced answers in their work, such that generalizability and the usefulness of designs can be adjudicated based on and in conversation with the intentions and conceptual framework of the research and researcher.

Ethical Concerns of Sustainability, Participation, and Telos

While there are many external challenges to rigor and validity of DBR, another set of tensions comes from within the DBR paradigm itself. Rather than concerns about rigor or validity, these internal critiques are not unrelated to the earlier question of the contested definition of usefulness , and more accurately reflect questions of research ethics and grow from ideological concerns with how an intentional, interventionist stance is taken up in research as it interacts with situated communities.

Given that the nature of DBR is to design and implement some form of educational innovation, the DBR researcher will in some way be engaging with an individual or community, becoming part of a situated learning ecology, complete with a sociopolitical and cultural history. As with any research that involves providing an intervention or support, the question of what happens when the research ends is as much an ethical as a methodological one. Concerns then arise given how traditional models of DBR seem intensely focused on creating and implementing a “complete” cycle of design, but giving little attention to what happens to the community and context afterward (Engeström, 2011 ). In contrast to this privileging of “completeness,” sociocultural and critical approaches to DBR have suggested that if research is actually happening in naturalistic, situated contexts that authentically recognize and allow social and cultural dimensions to function (i.e., avoid laboratory-type controls to mitigate independent variables), there can never be such a thing as “complete,” for the design will, and should, live on as part of the ecology of the space (Cole, 2007 ; Engeström, 2000 ). Essentially, these internal critiques push DBR to consider sustainability, and sustainable scale, as equally important concerns to the completeness of an innovation. Not only are ethical questions involved, but accounting for the unbounded and ongoing nature of learning as a social and cultural activity can help strengthen the viability of knowledge claims made, and what degree of generalizability is reasonably justified.

Related to this question of sustainability are internal concerns regarding the nature and ethics of participation in DBR, whether partners in a design are being adequately invited to engage in the design and modification processes that will unfold in their situated contexts and lived communities (Bang et al., 2016 ; Engeström, 2011 ). DBR has actively sought to examine multiple planes of analysis in learning that might be occurring in a learning ecology but has rarely attended to the subject-subject dynamics (Bang et al., 2016 ), or “relational equity” (DiGiacomo & Gutiérrez, 2015 ) that exists between researchers and participants as a point of focus. Participatory design research (PDR) (Bang & Vossoughi, 2016 ) models have recently emerged as a way to better attend to these important dimensions of collective participation (Engeström, 2007 ), power (Vakil et al., 2016 ), positionality (Kirshner, 2015 ), and relational agency (Edwards, 2007 , 2009 ; Sannino & Engeström, 2016 ) as they unfold in DBR.

Both of these ethical questions—around sustainability and participation—reflect challenges to what we might call the telos —or direction—that DBR takes to innovation and research. These are questions related to whose voices are privileged, in what ways, for what purposes, and toward what ends. While DBR, like many other forms of educational research, has involved work with historically marginalized communities, it has, like many other forms of educational research, not always done so in humanizing ways. Put another way, there are ethical and political questions surrounding whether the designs, goals, and standards of usefulness we apply to DBR efforts should be purposefully activist, and have explicitly liberatory ends. To this point, critical and decolonial perspectives have pushed on the DBR paradigm, suggesting that DBR should situate itself as being a space of liberatory innovation and potential, in which communities and participants can become designers and innovators of their own futures (Gutiérrez, 2005 ). This perspective is reflected in the social design experiment (SDE) approach to DBR (Gutiérrez, 2005 , 2008 ; Gutierréz & Vossoughi, 2010 ; Gutiérrez, 2016 ; Gutiérrez & Jurow, 2016 ), which begins in participatory fashion, engaging a community in identifying its own challenges and desires, and reflecting on the historicity of learning practices, before proleptic design efforts are undertaken that ensure that research is done with , not on , communities of color (Arzubiaga, Artiles, King, & Harris-Murri, 2008 ), and intentionally focused on liberatory goals.

Global Perspectives and Unique Iterations

While design-based research (DBR) has been a methodology principally associated with educational research in the United States, its development is hardly limited to the U.S. context. Rather, while DBR emerged in U.S. settings, similar methods of situated, interventionist research focused on design and innovation were emerging in parallel in European contexts (e.g., Gravemeijer, 1994 ), most significantly in the work of Vygotskian scholars both in Europe and the United States (Cole, 1995 ; Cole & Engeström, 1993 , 2007 ; Engeström, 1987 ).

Particularly, where DBR began in the epistemic and ontological terrain of developmental and cognitive psychology, this vein of design-based research work began deeply grounded in cultural-historical activity theory (CHAT). This ontological and epistemic grounding meant that the approach to design that was taken was more intensively conscious of context, historicity, hybridity, and relational factors, and framed around understanding learning as a complex, collective activity system that, through design, could be modified and transformed (Cole & Engeström, 2007 ). The models of DBR that emerged in this context abroad were the formative intervention (Engeström, 2011 ; Engeström, Sannino, & Virkkunen, 2014 ), which relies heavily on Vygotskian double-stimulation to approach learning in nonlinear, unbounded ways, accounting for the role of learner, educator, and researcher in a collective process, shifting and evolving and tinkering with the design as the context needs and demands; and the Change Laboratory (Engeström, 2008 ; Virkkunen & Newnham, 2013 ), which similarly relies on the principle of double stimulation, while presenting holistic way to approach transforming—or changing—entire learning activity systems in fundamental ways through designs that encourage collective “expansive learning” (Engeström, 2001 ), through which participants can produce wholly new activity systems as the object of learning itself.

Elsewhere in the United States, still parallel to the developmental- or cognitive-oriented DBR work that was occurring, American researchers employing CHAT began to leverage the tools and aims of expansive learning in conversation with the tensions and complexity of the U.S. context (Cole, 1995 ; Gutiérrez, 2005 ; Gutiérrez & Rogoff, 2003 ). Like the CHAT design research of the European context, there was a focus on activity systems, historicity, nonlinear and unbounded learning, and collective learning processes and outcomes. Rather than a simple replication, however, these researchers put further attention on questions of equity, diversity, and justice in this work, as Gutiérrez, Engeström, and Sannino ( 2016 ) note:

The American contribution to a cultural historical activity theoretic perspective has been its attention to diversity, including how we theorize, examine, and represent individuals and their communities. (p. 276)

Effectively, CHAT scholars in parts of the United States brought critical and decolonial perspectives to bear on their design-focused research, focusing explicitly on the complex cultural, racial, and ethnic terrain in which they worked, and ensuring that diversity, equity, justice, and non-dominant perspectives would become central principles to the types of design research conducted. The result was the emergence of the aforementioned social design experiments (e.g., Gutiérrez, 2005 , 2016 ), and participatory design research (Bang & Vossoughi, 2016 ) models, which attend intentionally to historicity and relational equity, tailor their methods to the liberation of historically marginalized communities, aim intentionally for liberatory outcomes as key elements of their design processes, and seek to produce outcomes in which communities of learners become designers of new community futures (Gutiérrez, 2016 ). While these approaches emerged in the United States, their origins reflect ontological and ideological perspectives quite distinct from more traditional learning science models of DBR, and dominant U.S. ontologies in general. Indeed, these iterations of DBR are linked genealogically to the ontologies, ideologies, and concerns of peoples in the Global South, offering some promise for the method in those regions, though DBR has yet to broadly take hold among researchers beyond the United States and Europe.

There is, of course, much more nuance to these models, and each of these models (formative interventions, Change Laboratories, social design experiments, and participatory design research) might itself merit independent exploration and review well beyond the scope here. Indeed, there is some question as to whether all adherents of these CHAT design-based methodologies, with their unique genealogies and histories, would even consider themselves under the umbrella of DBR. Yet, despite significant ontological divergences, these iterations share many of the same foundational tenets of the traditional models (though realized differently), and it is reasonable to argue that they do indeed share the same, broad methodological paradigm (DBR), or at the very least, are so intimately related that any discussion of DBR, particularly one with a global view, should consider the contributions CHAT iterations have made to the DBR methodology in the course of their somewhat distinct, but parallel, development.

Possibilities and Potentials for Design-Based Research

Since its emergence in 1992 , the DBR methodology for educational research has continued to grow in popularity, ubiquity, and significance. Its use has begun to expand beyond the confines of the learning sciences, taken up by researchers in a variety of disciplines, and across a breadth of theoretical and intellectual traditions. While still not as widely recognized as more traditional and well-established research methodologies, DBR as a methodology for rigorous research is unquestionably here to stay.

With this in mind, the field ought to still be cautious of the ways in which the discourse of design is used. Not all design is DBR, and preserving the integrity, rigor, and research ethics of the paradigm (on its own terms) will continue to require thoughtful reflection as its pluralistic parameters come into clearer focus. Yet the proliferation of methods in the DBR paradigm should be seen as a positive. There are far too many theories of learning and ideological perspectives that have meaningful contributions to make to our knowledge of the world, communities, and learning to limit ourselves to a unitary approach to DBR, or set of methods. The paradigm has shown itself to have some core methodological principles, but there is no reason not to expect these to grow, expand, and evolve over time.

In an increasingly globalized, culturally diverse, and dynamic world, there is tremendous potential for innovation couched in this proliferation of DBR. Particularly in historically marginalized communities and across the Global South, we will need to know how learning theories can be lived out in productive ways in communities that have been understudied, and under-engaged. The DBR paradigm generally, and critical and CHAT iterations particularly, can fill an important need for participatory, theory-developing research in these contexts that simultaneously creates lived impacts. Participatory design research (PDR), social design experiments (SDE), and Change Laboratory models of DBR should be of particular interest and attention moving forward, as current trends toward culturally sustaining pedagogies and learning will need to be explored in depth and in close collaboration with communities, as participatory design partners, in the press toward liberatory educational innovations.

Bibliography

The following special issues of journals are encouraged starting points for engaging more deeply with current and past trends in design-based research.

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1. The reader should note the emergence of critical ethnography (e.g., Carspecken, 1996 ; Fine, 1994 ), and other more participatory models of ethnography that deviated from this traditional paradigm during this same time period. These new forms of ethnography comprised part of the genealogy of the more critical approaches to DBR, described later in this article.

2. The reader will also note that the adjective “qualitative” largely drops away from the acronym “DBR.” This is largely because, as described, DBR, as an exploration of naturalistic ecologies with multitudes of variables, and social and learning dynamics, necessarily demands a move beyond what can be captured by quantitative measurement alone. The qualitative nature of the research is thus implied and embedded as part of what makes DBR a unique and distinct methodology.

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Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

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

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

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

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

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

Qualitative research can be used to:

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

Examples of qualitative research questions include: 

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

Characteristics 

Naturalistic setting.

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

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

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

Reality is socially constructed

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

The primacy of subject matter 

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

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

Variables are complex, interwoven, and difficult to measure

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

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

Emic (insider’s point of view)

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

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

Interpretive analysis

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

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

Collecting Qualitative Data

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

Observations

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

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

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

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

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

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

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

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

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

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

Focus groups 

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

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

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

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

Ethnography

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

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

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

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

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

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

Qualitative Data Analysis Methods

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

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

Grounded theory

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

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

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

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

Content analysis

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

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

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

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

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

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

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

Discourse analysis

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

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

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

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

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

Thematic analysis

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

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

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

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

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

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

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

Phases of Thematic Analysis

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

Template analysis

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

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

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

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

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

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

Frame analysis

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

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

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

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

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

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

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

Preventing Bias in Qualitative Research

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

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

Co-coding 

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

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

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

Member checking

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

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

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

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

Reflexivity in qualitative research

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

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

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

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

Reflexivity Example

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

Establishing Trustworthiness in Qualitative Research

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

Credibility in Qualitative Research

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

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

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

Transferability in Qualitative Research

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

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

Dependability in Qualitative Research

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

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

Confirmability in Qualitative Research

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

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

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

Audit Trails in Qualitative Research

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

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

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

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

Discovery of unexpected data

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

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

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

Flexibility

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

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

Naturalistic settings

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

Limitations

Time-consuming .

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

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

Subjectivity 

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

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

Limited generalizability

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

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

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

Extraneous variables

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

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

What sample size should qualitative research be?

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

Are surveys qualitative or quantitative?

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

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

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

What are the ethical considerations of qualitative research?

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

What is triangulation in qualitative research?

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

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

Why is qualitative research important?

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

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

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

What is coding data in qualitative research?

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

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

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

What is the difference between qualitative and quantitative research?

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

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

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

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

What is trustworthiness in qualitative research?

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

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

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

What is data saturation in qualitative research?

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

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

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

Why is sampling in qualitative research important?

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

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

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

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Research Article

"You don’t get side effects from social prescribing”—A qualitative study exploring community pharmacists’ attitudes to social prescribing

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

Roles Writing – review & editing

Affiliation Independent Research Pharmacist, United Kingdom

Roles Writing – original draft, Writing – review & editing

Affiliation Nuffield Department of Primary Care Science, University of Oxford, Oxford, United Kingdom

Roles Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

Roles Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

• Adam Pattison Rathbone, 
• Harry Pearson, 
• Oluwafunmi Akinyemi, 
• Nia Cartwright, 
• Stephanie Tierney, 
• Gill Rowlands, 
• Laura Lindsey

• Published: May 16, 2024
• https://doi.org/10.1371/journal.pone.0301076
• Reader Comments

Social prescribing is an approach that enables the referral of patients to non-clinical support and places a focus on holistic care. This study explored views of community pharmacists regarding social prescribing in pharmacies.

Study design

A qualitative phenomenological approach was used.

A convenience sample of eleven community pharmacists from Northern England were recruited via social media (Twitter, Facebook) and took part in a semi-structured, one-to-one qualitative interviews that asked about their knowledge of social prescribing, the advantages of community pharmacist involvement and any barriers they predicted to its implementation. Interviews were transcribed verbatim and thematically analysed.

The sample included largely male pharmacists (63.3%) with less than five years’ experience (45.5%) and included pharmacists working as employees (63.6%), locums (27.3%) and owners (9%) in both chain (36%) and independent stores (54.5%). The main findings indicate an enthusiasm for but limited understanding of social prescribing. Factors which appeared to influence involvement were training requirements and time available to complete an additional service in busy pharmacies. Opportunities centred on the broader pharmacy team’s role to optimise health outcomes.

Conclusions

The findings indicate pharmacists may be an underused resource due to a poor understanding of the full scale and scope of social prescribing beyond health promotion, lifestyle interventions. Further work is needed to explore the transferability of the findings to the broader pharmacy workforce to understand how social prescribing can be positioned within pharmacy practice.

Citation: Rathbone AP, Pearson H, Akinyemi O, Cartwright N, Tierney S, Rowlands G, et al. (2024) "You don’t get side effects from social prescribing”—A qualitative study exploring community pharmacists’ attitudes to social prescribing. PLoS ONE 19(5): e0301076. https://doi.org/10.1371/journal.pone.0301076

Editor: Simon White, Keele University, UNITED KINGDOM

Received: September 18, 2023; Accepted: March 9, 2024; Published: May 16, 2024

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

Data Availability: There are ethical and legal restrictions on sharing the de-identified data set. Participants did not give explicit consent for the de-identified data set to be shared as participants were told the data would be kept confidential. Anonymized data is held at an online repository under embargo. This restriction is imposed by the University Ethics Committee. Please contact [email protected] for further information.

Funding: The author(s0 received no specific funding for this work.

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

Introduction

Social prescribing has been described as: “ a means for health-care workers to connect patients to a range of non-clinical services in the community to improve health and well-being . Social prescribing can help to address the underlying causes of patients’ health and well-being issues , as opposed to simply treating symptoms .” [ 1 ]. It has become a cornerstone of healthcare policy in the UK [ 2 ] and overseas [ 3 ]. Patients are often referred for social prescribing by a General Practitioner (GP), who contacts a link worker to meet with the patient to identify appropriate support for their non-medical needs [ 3 ]. Exercise classes, arts and crafts, volunteering, gardening, and cookery classes, as well as accommodation and debt management services, are among the types of support patients might be connected to by a link worker [ 4 – 6 ].

It is argued that social prescribing can be useful for people living with long-term health conditions, mental health problems, socioeconomic struggles and social isolation–the latter of which has become more prevalent following the COVID-19 pandemic [ 7 ]. There is some evidence suggesting engagement in social prescribing may reduce demand for non-elective healthcare [ 8 ] and GP attendances [ 9 ]. Hence, it may be a means of addressing demand on overstretched healthcare services, supporting the broader well-being of vulnerable, socioeconomically disadvantaged communities. However, it should be noted, claims social prescribing reduces health inequalities for socioeconomically deprived communities are still considered contentious and their impact on reducing demand of some healthcare services, such as pharmacies, is not known [ 10 – 13 ].

Approaches to expanding access to social prescribing are being explored in the UK through ‘proactive social prescribing’ [ 14 ], where patient populations are screened by primary care professionals to identify target groups with unmet needs. Other examples of improving access to social prescribing are schemes such as digital self-referrals, where an app matches patients with appropriate support in the community [ 15 ]. In addition, an appeal for further healthcare professionals, including pharmacists, to take a role in social prescribing has been made [ 16 ].

Pharmacies offer open access to healthcare for a wide range of people, in both rural and urban settings. Pharmacies are known to be more accessible than GPs in areas of high socioeconomic deprivation [ 17 ]. Evidence suggests during the pandemic in the UK in 2020, over a third of patients visited their community pharmacy instead of their GP practice [ 18 ] although it was unclear if this prevented follow-up visits to GP practices. Although pharmacies are reporting high workloads [ 19 – 21 ], their accessibility makes them suitable for healthcare initiatives to reduce demand on existing health services [ 15 , 17 ]. Despite this, funding for pharmacies in the UK is focused primarily on dispensing medications rather than patient-facing services [ 22 ]. Recent changes to policy, such as the NHS Long Term Plan, Healthy Living Pharmacy Framework, Pharmacy First and the Community Pharmacy Consultation Scheme, indicate pharmacies will be an increasingly important place for the delivery of healthcare services, both urgent and preventative, in the future [ 2 , 23 – 26 ].

The evidence evaluating social prescribing interventions in pharmacy is limited. A systematic review in 2017 found little evidence of the efficacy of social prescribing in community settings, due to the short-term nature of the evaluations [ 27 ]. Other work focusing specifically on pharmacies found similarly limited literature [ 28 ]. To improve the existing evidence for social prescribing in pharmacy, evaluations must start from the foundations and work up; identifying capabilities, opportunities and motivations of pharmacy teams as well as the impact on patients, community groups and other health and social care professionals in the system. Little is known about pharmacists’ awareness and understanding of social prescribing and what factors influence their involvement in this non-clinical activity [ 28 , 29 ]. What evidence does exist suggests workload and funding may limit the involvement of pharmacists, and that these professionals may have limited awareness of what social prescribing is [ 29 ]. This existing evidence is based on quantitative methods and, thus, did not provide a detailed description of pharmacists’ experiences of social prescribing in practice. Hence, the aim of our study was to explore community pharmacists’ experiences, perspectives and attitudes to social prescribing in practice.

A qualitative phenomenological approach was adopted, which drew on the Capability Opportunity Motivation–Behaviour (COM-B) model [ 30 ]. A phenomenological approach allowed the study to focus on community pharmacists’ experiences of what happens in practice and how it happens [ 31 ]. Specifically, the COM-B model was used to create a topic guide to use during interviews and added structure to the findings following the identification of themes. As evidence in relation to pharmacists’ roles in social prescribing is limited, an exploratory design was appropriate [ 30 ].

Participants and recruitment

A convenience sampling method was adopted. A form was posted on social media (Twitter and Facebook) to allow participants to self-screen against inclusion criteria. The criteria included having experience working as a community pharmacist in Northern England, conversant in English, and capacity to consent to research. Snowball sampling was also used to recruit participants to the study.

The form was created and posted on social media via the research team (APR, HP, LL) outlining the study. Users completed screening questions for eligibility and were prompted to give an email address and telephone number to be contacted. The decision to limit to Northern England was pragmatic as researchers were based there. It also allowed the study to recruit pharmacists practicing in areas of high deprivation, where pharmacists are known to be more readily accessed by patients than in areas of low deprivation [ 17 ]. A participant information sheet was provided to interviewees in advance, and verbal consent was taken prior to participation, which was recorded by the interviewer (HP) and witnessed by the supervisor (APR).

Methods of data collection

Semi-structured interviews were conducted via the online platforms Zoom and Microsoft Teams, and over the telephone between Monday 5th October 2020 and Friday 29 th January 2021. The semi-structured nature of the interviews allowed for an in-depth exploration of pharmacists’ views, which would be unobtainable via a survey [ 32 ]. Interviews were conducted at a time convenient to the participant. Interviews lasted between 30 and 45 minutes (average = 39 minutes). A topic guide was used (see Tables 1 and 2 ) that included questions such as: i) What are your experiences of social prescribing? ii) What do you understand as the advantages of social prescribing in community pharmacy? iii) What do you think are the barriers to implementing social prescribing in community pharmacy?

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One-to-one interviews were conducted by a final year pharmacy student (HP). He was trained by experienced qualitative health researchers (LL, GR, APR). Interviews were audio-recorded and transcribed manually by one author (HP) with a 10% sample quality checked by listening back to the audio and reading the transcript (APR) [ 33 , 34 ]. Transcripts were anonymised by removing the names of participants, people, and places [ 33 , 34 ]. Data collection ceased at the point of theoretical data sufficiency [ 35 ], which occurred after ten interviews; one additional interview was conducted to confirm this was the case. Theoretical data sufficiency relates to the point in the study at which no new findings are being identified in the data. This was operationalised in the study through regular weekly supervision meetings during data collection and analysis to interrogate, explore and identify when no new findings were being found. This point indicates the research team had access to sufficient data to draw conclusions, though due to the nature of qualitative inquiry, further findings may be found by new researchers looking at the same data.

Data processing and analysis

Transcripts were imported into NVivo and inductive thematic analysis was completed by three researchers (HP, APR, LL) using the method outlined by Braun and Clarke (33). The first step of this analysis began with familiarisation with the data, next there was generation of initial codes, then clusters were created, and finally themes. A constant comparative approach was adopted, which meant codes, clusters and themes were compared with one another and findings interrogated in data presentation meetings involving the authors. This process was underpinned by a phenomenological understanding of experiences, which focuses on what the essence of an experience is and how this happens–i.e., what was happening and how was it happening. The COM-B Model was then used to contextualise the themes to link what happened to behavioural theory.

As part of the analysis, participants were categorised based on their role within community pharmacy and the length of time since registration. Participants with experience of less than five years were classified as ‘pharmacists with less experience’. This classification is in line with The Royal Pharmaceutical Society (RPS) Foundation Pharmacist Framework [ 36 ]. Participants with experience greater than this were categorised as ‘pharmacists with more experience’. This follows the RPS Advanced Pharmacy Framework [ 37 ]. Credibility was defined as the ability of the findings of the study to be reasonably believed and dependability was defined as the ability to trust the research process was carried out accurately. Credibility and dependability were established by involving more than one person in the analysis (sometimes referred to as analytical triangulation), through presentation and discussion of data at regular coding meetings with the research team. Weekly meetings were also used to ensure senior authors (LL, GR, APR) provided suitable training, support, supervision and accountability to the team (HP, NC). The study processes and findings were also reviewed by external collaborators (OA, ST) which further enhanced the credibility and dependability of the findings.

Reflexivity

Reflexivity allows research authors to become aware of, respond to and acknowledge how their own personal characteristics, identity and perspectives influences research [ 38 ]. In this study, the authors came from working class, middle class and upper middle-class backgrounds, where mostly White and came from Britain. Two authors were not from Britain and two authors were not White. Three authors were pharmacists, one was a general practitioner and one a psychologist. Four authors had completed, and one author was completing, a PhD. The research was led by a team based in a School of Pharmacy and this meant members of the research team may have been well known to participants as former educators (LL, APR) or colleagues (HP, OA). This meant there was a shared understanding of language and terminology between participants and researchers which enriched the subjectivity of the study during data collection. However, other members of the research team (NC, ST, GR) were less professionally connected to the pharmacy sector and so provided an objective perspective during data analysis and interpretation.

Research ethics

Ethical approval for this study was obtained through Newcastle University (reference number 6162/2020).

Participant demographics

Eleven participants were recruited and demographics are summarised in Table 1 .

Thematic findings

Findings are described below, with codes, clusters and themes shown in Table 2 . Data extracts describe findings in participants’ own words. Quotes denote if participants were employee, locum or owner pharmacists and which ‘type’ of pharmacy they worked in–either an ‘independent’ pharmacy which refers to a small chain, local, or single pharmacy business or a ‘multiple’ pharmacy chain which refers to a large, multi-national pharmacy corporation with many pharmacy businesses operating under a single banner.

Theme 1) Varied knowledge and understanding of social prescribing.

Most participants seemed to have an awareness of and enthusiasm for social prescribing, although they reported little knowledge of it. The setting where participants worked, their status or level of experience did not appear to influence knowledge or reported beliefs about social prescribing. Participants who were aware of social prescribing appeared to know about it either from involvement in a social prescribing event, through prescribing community-based, non-clinical support themselves, or having heard about the process in previous employment.

“ I was running group clinics…where we don’t just talk about their medicines, we talk about interventions like exercise, or lifestyle advice or diet. It was much more informal, but we would make recommendations to patients like a Tai Chi class for example, that you would benefit from, or you might be better off doing some core strengthening exercises given your type of arthritis.” Participant 6 (Pharmacist, Independent)

Despite a limited understanding, pharmacists appeared to believe they had capability to support social prescribing. However, they appeared to view it as a clinician-led approach, focusing on the physical symptoms of disease, rather than a person-centred approach to address socioeconomic factors of health and well-being, directed by the patient to address their specific needs. This indicated social prescribing was being conflated with public health promotion, lifestyle campaigns.

“ I can really see where [social prescribing] would fit in that remit, so the kind of physical and the recommendation for physical activity, how it can help with a number of different medical conditions… we’ve got a really good knowledge base of different health conditions and generally kind of how the body works. So why not use that and I don’t think we’re using it a lot at the minute.” Participant 1 (Locum, Independent)

These findings demonstrate the nuance of pharmacists’ approach to social prescribing, in that, enthusiasm toward social prescribing was reported, but that this appeared to be based on an understanding of social prescribing as an aspect of health promotion and lifestyle interventions based on physical disease states, rather than socio-economic circumstances of the patient.

Theme 2) Factors influencing involvement in social prescribing.

Concern about the economics of a pharmacy businesses, the balance of workload and funding, was a recurring factor which appeared to shape participants’ thoughts about involvement in social prescribing. Participants reported the busy nature of community pharmacy and highlighted how much additional time would be needed to engage with social prescribing.

“ Well with the increase of both dispensing items, the more and more consultations that we are having to do, as well as the fact some stores due to cuts [to funding] have had to get rid of managers, that then falls on the pharmacist’s desk, there’s a lot less time for patient-pharmacist discussions. So timing is going to be a major issue I think.” Participant 11 (Employee, Multiple)

Additional demands and additional time pressure, following on from the relentless experience encountered during the pandemic for many pharmacists through involvement in vaccination and increased workload, were concerns participants shared.

“ I certainly have worked over the COVID-19 pandemic in community myself, I know how ridiculously busy we’ve been. You know to try and fit in another additional service on top of all of the ones that are already being offered… but I think currently I’ve never known pharmacy this busy in my entire career…” Participant 6 (Employee, Independent)

Hesitancy also related to patients’ responses to being offered social prescribing in a pharmacy setting.

“ They might feel embarrassed to accept that help. And they might find it quite intrusive, they might not expect a pharmacist to be involved and…nobody wants to be categorised as a vulnerable or isolated patient particularly.” Participant 4 (Employee, Multiple)

Participants reported feeling that larger ‘multiples’ companies had greater resources and financial capital and would therefore find it easier to implement social prescribing services than independent organisations.

“ And the small pharmacies I worry that because they’re so, their [funding is] so tight they are trying to make the best they can being an independent that they won’t sort of have the capacity necessary to widen to some of these sorts of wider societal things that they can have input in.” Participant 5 (Employee, Multiple)

Conversely, others suggested larger organisations with more capital may focus on profits rather than supporting patient communities, unlike independent organisations.

“ I’ve worked for [supermarket pharmacy 1] and [supermarket pharmacy 2] before which are bigger companies, and I know they’re much more focused around [funding]…rather than the kind of community support and health advice [in social prescribing]. I think an independent might do it because of the benefit to the community and to be seen to be giving extra services which might attract and keep their customer base.” Participant 6 (Employee, Independent)

The only participant who was a pharmacy owner (and therefore responsible for organisational structure and financial targets of a community pharmacy business) reported social prescribing was an individual, professional decision of the pharmacist in charge, rather than the priorities of the business or owner. This appeared to diminish the role of organizational policy, working conditions (such as opening times and staffing levels), and the funding landscape, suggesting engagement with social prescribing will come down to personal preference of the individual pharmacist.

“ There’s no [funding] difference between the individual pharmacists, whoever they work for. So, under those circumstances it doesn’t matter if it’s an independent or a multiple pharmacy, they will organise themselves differently, but it’ll come down to the individuals, not the policy of the owner.” Participant 10 (Owner, Independent)

Collectively, these findings indicate pharmacists’ motivations to deliver social prescribing services are influenced by access to appropriate levels of economic capital and resources to manage workload and patient expectations.

Theme 3) Outcomes of social prescribing in community pharmacy.

This theme describes social prescribing as an opportunity for pharmacies to improve patient outcomes by involving all members of the pharmacy team, not just pharmacists. The inclusion of all staff into social prescribing was raised by participants. The knowledge and trust shared with patients was considered to make them a good resource to facilitate social prescribing. Participants felt dispensing staff, delivery drivers, and pharmacy technicians, as well as pharmacists, represented valuable assets to facilitate social prescribing, if given appropriate training and links to social prescribing networks.

Participants appeared to clearly understand the accessibility of pharmacy and highlighted the patient-centeredness of pharmacies, in comparison to other healthcare settings for patients, was aligned to social prescribing principles.

“ We are the most accessible healthcare professional in every community, and patients know they can just pop in for that source of advice. We have a lot more time [than other healthcare professionals] to tailor to individual patients” Participant 11 (Employee, Multiple)

Participants reported valuing the role that social prescribing could play in improving health outcomes for patients, lessening the need for medication and expensive treatments.

“ …you’ve got the obvious benefits to the patients around outcomes…it might be that they are prescribed metformin for type 2 diabetes, which alongside social prescribing around diet and exercise…as a result of the diet and exercise intervention that the whole…type 2 diabetes will be better off.” Participant 5 (Employee, Multiple)

Additionally, participants appeared to recognise opportunities to improve patient care by providing an alternative to medications.

“ You know it’s got loads of benefits for patients because you know you don’t get side effects from social prescribing.” Participant 6 (Employee, Independent)

Pharmacistsreported the need to work with others who are already social prescribing to learn, share best practice and develop a common understanding.

“ Ultimately though this isn’t something pharmacies could just do on their own, we need to be linked up with other people doing this, like is there a national body of social prescribers or like standardised training about how to do it? If we knew more about social prescribers we would be linked in with that network more.” Participant 10 (Owner, Independent)

Collectively this theme demonstrates complexity in pharmacists’ views of the outcomes of social prescribing, primarily being reliant on the social capital pharmacists have with patients and other staff in their premises but also on building social capital by engaging with other social prescribing networks and experts.

Discussion and conclusion

Summary of findings.

The key finding of this study is participants appeared to recognise, understand and value social prescribing as a means of supporting patients’ health and well-being, but misunderstood social prescribing as a form of disease-focused, public health promotion. Limited training, experience and resources to facilitate social prescribing in practice were identified as learning needs in this study. Participants reported willing to be involved in social prescribing, reporting interests to better understand the process of social prescribing and expressing beliefs that this could expand the current role of community pharmacists and their team members. Many participants reported limited exposure to or involvement with social prescribing in current practice and education. This indicates a need for further collaboration and involvement in social prescribing networks. Professional bodies may also need to support education, learning and training of pharmacists and their teams to implement social prescribing services. The unique accessibility of community pharmacy teams and the rapport they have with their patients were seen as opportunities to contribute to social prescribing to improve patient outcomes.

A strength of the study is it provides a conceptualization of pharmacists’ understanding of social prescribing. The study met theoretical data sufficiency and used qualitative methods to identify insights. Additionally, the sample included pharmacists from a range of practice settings across North East England, which means the findings may be transferable to different contexts of practice. However, using convenience sampling meant these findings may not include the range of views across the pharmacy profession–particularly from those outside of North East England. Additionally, recruitment via social media introduces self-selection bias (whereby pharmacists with little interest in social prescribing would have been recruited) which may positively skew the findings in terms of participants’ reported willingness and enthusiasm for social prescribing rather than the reported limited exposure and understanding of it.

Comparison to existing literature

The findings presented here add to the literature, demonstrating pharmacists are enthusiastic, but do not fully appreciate the scope and impact of social prescribing. The findings are congruent with a survey completed by 120 pharmacists, showing poor understanding of social prescribing, and the need for increased staff training and funding [ 29 ]. Existing literature has suggested pharmacists could adopt multiple roles to implement social prescribing–as screeners, identifiers, link workers or providers of social interventions [ 28 , 39 ] to reduce the demand on existing health services [ 8 ]. However, with such a limited understanding shown in this research, the role pharmacists could adopt to implement social prescribing at present may be limited.

Some existing literature has stated that the impact of social prescribing may be overestimated [ 13 ]. A key reason for this, put forward by Gibson, Pollard (13), using a Bourdieuan lens, is focused on patients’ structural contexts; access to economic, social and cultural capital influences engagement with social prescribing interventions. Our study extends the argument from patients to pharmacists, highlighting that structural context also influences professional engagement with social prescribing interventions. Our study demonstrated that pharmacists have little cultural, economic and social capital to invest in social prescribing—their conceptualisation of it is limited (cultural capital), funding is poor and workload is high (economic capital) and their connections to professional social prescribing networks and bodies is poor (social capital) [ 40 , 41 ]. This may hinder the capability, opportunity and motivation for pharmacists to engage in social prescribing. Further research such as feasibility and pilot studies, as well as trials, are needed to understand and consider the effectiveness of pharmacists and their teams bridging the gap between health and social care to help communities most in need.

Implications for policy and practice

The NHS has made a commitment to increase social prescribing activity and expand the number of link workers [ 2 , 42 ]. Pharmacists, with adequate economic, social and cultural capital, could support this—either by identifying patients for referral to link workers or providing link worker services ‘in house’ [ 28 ]. However, this study has shown that although pharmacists are interested in social prescribing, it appears to be positioned within current pharmacy practice as ‘healthy lifestyle changes’, ‘health promotion’ and ‘public health’ initiative, rather than supporting patients to deal with broader socioeconomic determinants of health, such as poor housing, economic hardship, and abusive relationships–which many link workers currently deal with through social prescribing [ 9 , 10 , 43 ]. If pharmacists are going to refer patients to social prescribing, then additional training, access and engagement with link workers will be needed to upskill the current workforce. Furthermore, establishing ways to build social connections of pharmacists with those involved in delivering social prescribing are required. If pharmacists themselves are going to act as link workers ‘in house’, then the findings suggest a much greater effort will be needed to enable them to have the skills, expertise, supervision and support structures to build their cultural capital to deal with non-clinical social issues to optimise health outcomes. Our findings show pharmacists believe they know what social prescribing is but their beliefs are not aligned to what social prescribing link workers actually do in reality. It shows there is a gap between pharmacists’ beliefs and social prescribing practice. This provides a very specific target for educators and policy makers to create an intervention to change pharmacists’ perceptions of social prescribing from a ‘healthy lifestyle intervention’ to a new praxis of ‘social pharmaceutical care’. This raises questions for policy makers and practitioners, and the profession as a whole–is social prescribing something community pharmacy teams want to do, given current high workloads in the sector?

This study aimed to explore community pharmacists’ experiences of social prescribing. It has shown how they recognise and value social prescribing, but currently have limited understanding, training, experience and resources to incorporate it into their practice. These findings provide an insight into pharmacists understanding but may not be generalisable or transferable. Further work is therefore needed to explore if, when and how pharmacists and their teams could engage with social prescribing.

Acknowledgments

The authors would like to thank the participants for taking part in this study.

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• Open access
• Published: 18 May 2024

Determinants of appropriate antibiotic and NSAID prescribing in unscheduled outpatient settings in the veterans health administration

• Michael J. Ward 1 , 2 , 3 , 4 ,
• Michael E. Matheny 1 , 4 , 5 , 6 ,
• Melissa D. Rubenstein 3 ,
• Kemberlee Bonnet 7 ,
• Chloe Dagostino 7 ,
• David G. Schlundt 7 ,
• Shilo Anders 4 , 8 ,
• Thomas Reese 4 &
• Amanda S. Mixon 1 , 9  

BMC Health Services Research volume  24 , Article number:  640 ( 2024 ) Cite this article

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Despite efforts to enhance the quality of medication prescribing in outpatient settings, potentially inappropriate prescribing remains common, particularly in unscheduled settings where patients can present with infectious and pain-related complaints. Two of the most commonly prescribed medication classes in outpatient settings with frequent rates of potentially inappropriate prescribing include antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs). In the setting of persistent inappropriate prescribing, we sought to understand a diverse set of perspectives on the determinants of inappropriate prescribing of antibiotics and NSAIDs in the Veterans Health Administration.

We conducted a qualitative study guided by the Consolidated Framework for Implementation Research and Theory of Planned Behavior. Semi-structured interviews were conducted with clinicians, stakeholders, and Veterans from March 1, 2021 through December 31, 2021 within the Veteran Affairs Health System in unscheduled outpatient settings at the Tennessee Valley Healthcare System. Stakeholders included clinical operations leadership and methodological experts. Audio-recorded interviews were transcribed and de-identified. Data coding and analysis were conducted by experienced qualitative methodologists adhering to the Consolidated Criteria for Reporting Qualitative Studies guidelines. Analysis was conducted using an iterative inductive/deductive process.

We conducted semi-structured interviews with 66 participants: clinicians ( N  = 25), stakeholders ( N  = 24), and Veterans ( N  = 17). We identified six themes contributing to potentially inappropriate prescribing of antibiotics and NSAIDs: 1) Perceived versus actual Veterans expectations about prescribing; 2) the influence of a time-pressured clinical environment on prescribing stewardship; 3) Limited clinician knowledge, awareness, and willingness to use evidence-based care; 4) Prescriber uncertainties about the Veteran condition at the time of the clinical encounter; 5) Limited communication; and 6) Technology barriers of the electronic health record and patient portal.

Conclusions

The diverse perspectives on prescribing underscore the need for interventions that recognize the detrimental impact of high workload on prescribing stewardship and the need to design interventions with the end-user in mind. This study revealed actionable themes that could be addressed to improve guideline concordant prescribing to enhance the quality of prescribing and to reduce patient harm.

Peer Review reports

Adverse drug events (ADEs) are the most common iatrogenic injury. [ 1 ] Efforts to reduce these events have primarily focused on the inpatient setting. However, the emergency department (ED), urgent care, and urgent primary care clinics are desirable targets for interventions to reduce ADEs because approximately 70% of all outpatient encounters occur in one of these settings. [ 2 ] Two of the most commonly prescribed drug classes during acute outpatient care visits that have frequent rates of potentially inappropriate prescribing include antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs). [ 3 , 4 ]

An estimated 30% of all outpatient oral antibiotic prescriptions may be unnecessary. [ 5 , 6 ] The World Health Organization identified overuse of antibiotics and its resulting antimicrobial resistance as a global threat. [ 7 ] The Centers for Disease Control and Prevention (CDC) conservatively estimates that in the US there are nearly 3 million antibiotic-resistant infections that cause 48,000 deaths annually. [ 8 ] Antibiotics were the second most common source of adverse events with nearly one ADE resulting in an ED visit for every 100 prescriptions. [ 9 ] Inappropriate antibiotic prescriptions (e.g., antibiotic prescription for a viral infection) also contribute to resistance and iatrogenic infections such as C. difficile (antibiotic associated diarrhea) and Methicillin-resistant Staphylococcus aureus (MRSA) . [ 8 ] NSAID prescriptions, on the other hand, result in an ADE at more than twice the rate of antibiotics (2.2%), [ 10 ] are prescribed to patients at an already increased risk of potential ADEs, [ 4 , 11 ] and frequently interact with other medications. [ 12 ] Inappropriate NSAID prescriptions contribute to serious gastrointestinal, [ 13 ] renal, [ 14 ] and cardiovascular [ 15 , 16 ] ADEs such as gastrointestinal bleeding, acute kidney injury, and myocardial infarction or heart failure, respectively. Yet, the use of NSAIDs is ubiquitous; according to the CDC, between 2011 and 2014, 5% of the US population were prescribed an NSAID whereas an additional 2% take NSAIDs over the counter. [ 11 ]

Interventions to reduce inappropriate antibiotic prescribing commonly take the form of antimicrobial stewardship programs. However, no such national programs exist for NSAIDs, particularly in acute outpatient care settings. There is a substantial body of evidence supporting the evidence of such stewardship programs. [ 17 ] The CDC recognizes that such outpatient programs should consist of four core elements of antimicrobial stewardship, [ 18 ] including commitment, action for policy and practice, tracking and reporting, and education and expertise. However, the opportunities to extend antimicrobial stewardship in EDs are vast. Despite the effectiveness, there is a recognized need to understand which implementation strategies and how to implement multifaceted interventions. [ 19 ] Given the unique time-pressured environment of acute outpatient care settings, not all antimicrobial stewardship strategies work in these settings necessitating the development of approaches tailored to these environments. [ 19 , 20 ]

One particularly vulnerable population is within the Veterans Health Administration. With more than 9 million enrollees in the Veterans Health Administration, Veterans who receive care in Veteran Affairs (VA) hospitals and outpatient clinics may be particularly vulnerable to ADEs. Older Veterans have greater medical needs than younger patients, given their concomitant medical and mental health conditions as well as cognitive and social issues. Among Veterans seen in VA EDs and Urgent Care Clinics (UCCs), 50% are age 65 and older, [ 21 ] nearly three times the rate of non-VA emergency care settings (18%). [ 22 ] Inappropriate prescribing in ED and UCC settings is problematic with inappropriate antibiotic prescribing estimated to be higher than 40%. [ 23 ] In a sample of older Veterans discharged from VA ED and UCC settings, NSAIDs were found to be implicated in 77% of drug interactions. [ 24 ]

Learning from antimicrobial stewardship programs and applying to a broader base of prescribing in acute outpatient care settings, it is necessary to understand not only why potentially inappropriate prescribing remains a problem for antibiotics, but for medications (e.g., NSAIDs) which have received little stewardship focus previously. This understanding is essential to develop and implement interventions to reduce iatrogenic harm for vulnerable patients seen in unscheduled settings. In the setting of the Veterans Health Administration, we sought to use these two drug classes (antibiotics and NSAIDs) that have frequent rates of inappropriate prescribing in unscheduled outpatient care settings, to understand a diverse set of perspectives on why potentially inappropriate prescribing continues to occur.

Selection of participants

Participants were recruited from three groups in outpatient settings representing emergency care, urgent care, and urgent primary care in the VA: 1) Clinicians-VA clinicians such as physicians, advanced practice providers, and pharmacists 2) Stakeholders-VA and non-VA clinical operational and clinical content experts such as local and regional medical directors, national clinical, research, and administrative leadership in emergency care, primary care, and pharmacy including geriatrics; and 3) Veterans seeking unscheduled care for infectious or pain symptoms.

Clinicians and stakeholders were recruited using email, informational flyers, faculty/staff meetings, national conferences, and snowball sampling, when existing participants identify additional potential research subjects for recruitment. [ 25 ] Snowball sampling is useful for identifying and recruiting participants who may not be readily apparent to investigators and/or hard to reach. Clinician inclusion criteria consisted of: 1) at least 1 year of VA experience; and 2) ≥ 1 clinical shift in the last 30 days at any VA ED, urgent care, or primary care setting in which unscheduled visits occur. Veterans were recruited in-person at the VA by key study personnel. Inclusion criteria consisted of: 1) clinically stable as determined by the treating clinician; 2) 18 years or older; and 3) seeking care for infectious or pain symptoms in the local VA Tennessee Valley Healthcare System (TVHS). TVHS includes an ED at the Nashville campus with over 30,000 annual visits, urgent care clinic in Murfreesboro, TN with approximately 15,000 annual visits, and multiple primary care locations throughout the middle Tennessee region. This study was approved by the VA TVHS Institutional Review Board as minimal risk.

Data collection

Semi-structured interview guides (Supplemental Table 1) were developed using the Consolidated Framework for Implementation Research (CFIR) [ 26 ] and the Theory of Planned Behavior [ 27 , 28 ] to understand attitudes and beliefs as they relate to behaviors, and potential determinants of a future intervention. Interview guides were modified and finalized by conducting pilot interviews with three members of each participant group. Interview guides were tailored to each group of respondents and consisted of questions relating to: 1) determinants of potentially inappropriate prescribing; and 2) integration into practice (Table. 1 ). Clinicians were also asked about knowledge and awareness of evidence-based prescribing practices for antibiotics and NSAIDs. The interviewer asked follow-up questions to elicit clarity of responses and detail.

Each interview was conducted by a trained interviewer (MDR). Veteran interviews were conducted in-person while Veterans waited for clinical care so as not to disrupt clinical operations. Interviews with clinicians and stakeholders were scheduled virtually. All interviews (including in-person) were recorded and transcribed in a manner compliant with VA information security policies using Microsoft Teams (Redmond, WA). The audio-recorded interviews were transcribed and de-identified by a transcriptionist and stored securely behind the VA firewall using Microsoft Teams. Study personnel maintained a recording log on a password-protected server and each participant was assigned a unique participant ID number. Once 15 interviews were conducted per group, we planned to review interviews with the study team to discuss content, findings, and to decide collectively when thematic saturation was achieved, the point at which no new information was obtained. [ 29 ] If not achieved, we planned to conduct at least 2 additional interviews prior to group review for saturation. We estimated that approximately 20–25 interviews per group were needed to achieve thematic saturation.

Qualitative data coding and analysis was managed by the Vanderbilt University Qualitative Research Core. A hierarchical coding system (Supplemental Table 2) was developed and refined using an iterative inductive/deductive approach [ 30 , 31 , 32 ] guided by a combination of: 1) Consolidated Framework for Implementation Research (CFIR) [ 26 ]; 2) the Theory of Planned Behavior [ 27 , 28 ]; 3) interview guide questions; and 4) a preliminary review of the transcripts. Eighteen major categories (Supplemental Table 3) were identified and were further divided into subcategories, with some subcategories having additional levels of hierarchical division. Definitions and rules were written for the use of each of the coding categories. The process was iterative in that the coding system was both theoretically informed and derived from the qualitative data. The coding system was finalized after it was piloted by the coders. Data coding and analysis met the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. [ 33 ]

Four experienced qualitative coders were trained by independently coding two transcripts from each of the three participant categories. Coding was then compared, and any discrepancies resolved by reconciliation. After establishing reliability in using the coding system, the coders divided and independently coded the remaining transcripts in sequential order. Each statement was treated as a separate quote and could be assigned up to 21 different codes. Coded transcripts were combined and sorted by code.

Following thematic saturation, the frequency of each code was calculated to understand the distribution of quotes. Quotes were then cross-referenced with coding as a barrier to understand potential determinants of inappropriate prescribing. A thematic analysis of the barriers was conducted and presented in an iterative process with the research team of qualitative methodologists and clinicians to understand the nuances and refine the themes and subthemes from the coded transcripts. Transcripts, quotations, and codes were managed using Microsoft Excel and SPSS version 28.0.

We approached 132 individuals and 66 (50%) agreed to be interviewed. Participants included 25 clinicians, 24 stakeholders, and 17 Veterans whose demographic characteristics are presented in Table 2 . The clinicians were from 14 VA facilities throughout the US and 20 physicians, and five advanced practice providers. Of the clinicians, 21 (84%) worked in either an ED or urgent care while the remainder practiced in primary care. The 24 stakeholders included 13 (54%) clinical service chiefs or deputy chief (including medical directors), five (21%) national directors, and six (25%) experts in clinical content and methodology. The 17 Veterans interviewed included 15 (88%) who were seen for pain complaints.

Results are organized by the six thematic categories with several subthemes in each category. Themes and subthemes are presented in Table 3  and are visually represented in Fig.  1 . The six themes were: 1) perceived versus actual Veterans expectations about prescribing, 2) the influence of a time-pressured clinical environment on prescribing stewardship, 3) limited clinician knowledge, awareness, and willingness to use evidence-based care, 4) uncertainties about the Veteran condition at the time of the clinical encounter, 5) limited communication, and 6) technology barriers.

Visual representation of themes and subthemes from 66 clinician, stakeholder, and Veteran interviews

Theme 1: Perception that Veterans routinely expect a medication from their visit, despite clinical inappropriateness

According to clinicians, Veterans frequently expect to receive a prescription even when this decision conflicts with good clinical practice.

Certainly lots of people would say you know if you feel like you’re up against some strong expectations from the patients or caregivers or families around the utility of an antibiotic when it’s probably not indicated…In the emergency department the bias is to act and assume the worst and assume like the worst for the clinical trajectory for the patient rather than the reverse. [Clinician 49, Physician, ED]

In addition, stakeholders further stated that patient prescription expectations are quite influential and are likely shaped by Veterans’ prior experiences.

I think the patients, particularly for antibiotics, have strong feelings about whether they should or shouldn’t get something prescribed. [Stakeholder 34] You know I think the biggest challenge, I think, is adjusting patients’ expectations because you know they got better the last time they were doing an antibiotic. [Stakeholder 64]

Patient satisfaction and clinician workload may also influence the clinician’s prescription decision.

We have a lot of patients that come in with back pain or knee pain or something. We’ll get an x-ray and see there’s nothing actually wrong physically that can be identified on x-ray at least and you have to do something. Otherwise, patient satisfaction will dip, and patients leave angry. [Clinician 28, Physician, urgent care clinic] For some clinicians it’s just easier to prescribe an antibiotic when they know that’s the patient’s expectation and it shortens their in-room discussion and evaluation. [Clinician 55, Physician, ED]

Despite clinician perception, Veterans communicated that they did not necessarily expect a prescription and were instead focused on the clinical interaction and the clinician’s decision.

I’m not sure if they’ll give me [unintelligible] a prescription or what they’ll do. I don’t care as long as they stop the pain. [Patient 40, urgent care clinic] I don’t expect to [receive a prescription], but I mean whatever the doctor finds is wrong with me I will follow what he says. [Patient 31, ED]

Theme 2: Hectic clinical environments and unique practice conditions in unscheduled settings provide little time to focus on prescribing practices

Clinicians and stakeholders reported that the time-constrained clinical environment and need to move onto the next patient were major challenges to prescribing stewardship.

The number one reason is to get a patient out of your office or exam bay and move on to the next one. [Stakeholder 28] It takes a lot of time and you have to be very patient and understanding. So, you end up having to put a fair bit of emotional investment and intelligence into an encounter to not prescribe. [Stakeholder 1]

Stakeholders also noted that unique shift conditions and clinician perceptions that their patients were “different” might influence prescribing practices.

A common pushback was ‘well my patients are different.’ [Stakeholder 4] Providers who worked different types of shifts, so if you happened to work on a Monday when the clinics were open and had more adults from the clinics you were more likely to prescribe antibiotics than if you worked over night and had fewer patients. Providers who worked primarily holidays or your Friday prescribing pattern may be very different if you could get them into a primary care provider the next day. [Stakeholder 22]

Clinicians also reported that historical practices in the clinical environment practices may also contribute to inappropriate prescribing.

I came from working in the [outpatient] Clinic as a new grad and they’re very strict about prescribing only according to evidence-based practice. And then when I came here things are with other colleagues are a little more loose with that type of thing. It can be difficult because you start to adopt that practice to. [Clinician 61, Nurse Practitioner, ED]

Theme 3: Clinician knowledge, awareness, and willingness to use evidence-based care

Stakeholders felt that clinicians had a lack of knowledge about prescribing of NSAIDs and antibiotics.

Sometimes errors are a lack of knowledge or awareness of the need to maybe specifically dose for let’s say impaired kidney function or awareness of current up to date current antibiotic resistance patterns in the location that might inform a more tailored antibiotic choice for a given condition. [Stakeholder 37] NSAIDs are very commonly used in the emergency department for patients of all ages…the ED clinician is simply not being aware that for specific populations this is not recommended and again just doing routine practice for patients of all ages and not realizing that for older patients you actually probably should not be using NSAIDs. [Stakeholder 40]

Some clinicians may be unwilling to change their prescribing practices due to outright resistance, entrenched habits, or lack of interest in doing so.

It sounds silly but there’s always some opposition to people being mandated to do something. But there are some people who would look and go ‘okay we already have a handle on that so why do we need something else? I know who prescribes inappropriately and who doesn’t. Is this a requirement, am I evaluated on it? That would come from supervisors. Is this one more thing on my annual review?’ [Stakeholder 28] If people have entrenched habits that are difficult to change and are physicians are very individualistic people who think that they are right more often than the non-physician because of their expensive training and perception of professionalism. [Stakeholder 4]

Theme 4: Uncertainty about whether an adverse event will occur

Clinicians cited the challenge of understanding the entirety of a Veteran’s condition, potential drug-drug interactions, and existing comorbidities in knowing whether an NSAID prescription may result in an adverse event.

It’s oftentimes a judgement call if someone has renal function that’s right at the precipice of being too poor to merit getting NSAIDs that may potentially cause issues. [Clinician 43, Physician, inpatient and urgent care] It depends on what the harm is. So, for instance, you can’t always predict allergic reactions. Harm from the non-steroidals would be more if you didn’t pre-identify risk factors for harm. So, they have ulcer disease, they have kidney problems where a non-steroidal would not be appropriate for that patient. Or potential for a drug-drug interaction between that non-steroid and another medication in particular. [Clinician 16, Physician, ED]

Rather than be concerned about the adverse events resulting from the medication itself, stakeholders identified the uncertainty that clinicians experience about whether a Veteran may experience an adverse event from an infection if nothing is done. This uncertainty contributes to the prescription of an antibiotic.

My experience in working with providers at the VA over the years is that they worry more about the consequences of not treating an infection than about the consequences of the antibiotic itself. [Stakeholder 19] Sometimes folks like to practice conservatively and they’ll say even though I didn’t really see any hard evidence of a bacterial infection, the patient’s older and sicker and they didn’t want to risk it. [Stakeholder 16]

Theme 5: Limited communication during and after the clinical encounter

The role and type of communication about prescribing depended upon the respondent. Clinicians identified inadequate communication and coordination with the Veteran’s primary care physician during the clinical encounter.

I would like to have a little more communication with the primary doctors. They don’t seem to be super interested in talking to anyone in the emergency room about their patients… A lot of times you don’t get an answer from the primary doctor or you get I’m busy in clinic. You can just pick something or just do what you think is right. [Clinician 25, Physician, ED]

Alternatively, stakeholders identified post-encounter patient outcome and clinical performance feedback as potential barriers.

Physicians tend to think that they are doing their best for every individual patient and without getting patient by patient feedback there is a strong cognitive bias to think well there must have been some exception and reason that I did it in this setting. [Stakeholder 34] It’s really more their own awareness of like their clinical performance and how they’re doing. [Stakeholder 40]

Veterans, however, prioritized communication during the clinical encounter. They expressed the need for clear and informative communication with the clinician, and the need for the clinician to provide a rationale for the choice and medication-specific details along with a need to ask any questions.

I expect him to tell me why I’m taking it, what it should do, and probably the side effects. [Patient 25, ED] I’d like to have a better description of how to take it because I won’t remember all the time and sometimes what they put on the bottle is not quite as clear. [Patient 22, ED]

Veterans reported their desire for a simple way to learn about medication information. They provided feedback on the current approaches to educational materials about prescriptions.

Probably most pamphlets that people get they’re not going to pay attention to them. Websites can be overwhelming. [Patient 3, ED] Posters can be offsetting. If you’re sick, you’re not going to read them…if you’re sick you may glance at that poster and disregard it. So, you’re not really going to see it but if you give them something in the hand people will tend to look at it because it’s in their hand. [Patient 19, ED] It would be nice if labels or something just told me what I needed to know. You know take this exactly when and reminds me here’s why you’re taking it for and just real clear and not small letters. [Patient 7, ED]

Theme 6: Technology barriers limited the usefulness of clinical decision support for order checking and patient communication tools

Following the decision to prescribe a medication, clinicians complained that electronic health record pop-ups with clinical decision support warnings for potential safety concerns (e.g., drug-drug interactions) were both excessive and not useful in a busy clinical environment.

The more the pop ups, the more they get ignored. So, it’s finding that sweet spot right where you’re not constantly having to click out of something because you’re so busy. Particularly in our clinical setting where we have very limited amount of time to read the little monograph. Most of the time you click ‘no’ and off you go. (Clinician 16, Physician, ED) Some of these mechanisms like the EMR [electronic medical record] or pop-up decision-making windows really limit your time. If you know the guidelines appropriately and doing the right thing, even if you’re doing the right thing it takes you a long time to get through something. (Clinician 19, Physician, Primary care clinic)

For post-encounter communication that builds on Theme 5 about patient communication, patients reported finding using the VA patient portal (MyHealtheVet) challenging for post-event communication with their primary care physician and to review the medications they were prescribed.

I’ve got to get help to get onto MyHealtheVet but I would probably like to try and use that, but I haven’t been on it in quite some time. [Patient 22, ED] I tried it [MyHealtheVet] once and it’s just too complicated so I’m not going to deal with it. [Patient 37, Urgent care]

This work examined attitudes and perceptions of barriers to appropriate prescribing of antibiotics and NSAIDs in unscheduled outpatient care settings in the Veterans Health Administration. Expanding on prior qualitative work on antimicrobial stewardship programs, we also included an examination of NSAID prescribing, a medication class which has received little attention focused on prescribing stewardship. This work seeks to advance the understanding of fundamental problems underlying prescribing stewardship to facilitate interventions designed to improve not only the decision to prescribe antibiotics and NSAIDs, but enhances the safety checks once a decision to prescribe is made. Specifically, we identified six themes during these interviews: perceived versus actual Veteran expectations about prescribing, the influence of a time-pressured clinical environment on prescribing stewardship, limited clinician knowledge, awareness, and willingness to use evidence-based care, uncertainties about the Veteran condition at the time of the clinical encounter, limited communication, and technology barriers.

Sensitive to patient expectations, clinicians believed that Veterans would be dissatisfied if they did not receive an antibiotic prescription, [ 34 ] even though most patients presenting to the ED for upper respiratory tract infections do not expect antibiotics. [ 35 ] However, recent work by Staub et al. found that among patients with respiratory tract infections, receipt of an antibiotic was not independently associated with improved satisfaction. [ 36 ] Instead, they found that receipt of antibiotics had to match the patient’s expectations to affect patient satisfaction and recommended that clinicians communicate with their patients about prescribing expectations. This finding complements our results in the present study and the importance of communication about expectations is similarly important for NSAID prescribing as well.

A commitment to stewardship and modification of clinician behavior may be compromised by the time-pressured clinical environment, numerous potential drug interactions, comorbidities of a vulnerable Veteran population, and normative practices. The decision to prescribe medications such as antibiotics is a complex clinical decision and may be influenced by both clinical and non-clinical factors. [ 34 , 37 , 38 ] ED crowding, which occurs when the demand for services exceeds a system’s ability to provide care, [ 39 ] is a well-recognized manifestation of a chaotic clinical environment and is associated with detrimental effects on the hospital system and patient outcomes. [ 40 , 41 ] The likelihood that congestion and wait times will improve is unlikely as the COVID-19 pandemic has exacerbated the already existing crowding and boarding crisis in EDs. [ 42 , 43 ]

Another theme was the uncertainty in the anticipation of adverse events that was exacerbated by the lack of a feedback loop. Feedback on clinical care processes and patient outcomes is uncommonly provided in emergency care settings, [ 44 ] yet may provide an opportunity to change clinician behavior, particularly for antimicrobial stewardship. [ 45 ] However, the frequent use of ineffective feedback strategies [ 46 ] compromises the ability to implement effective feedback interventions; feedback must be specific [ 47 ] and address the Intention-to-Action gap [ 48 ] by including co-interventions to address recipient characteristics (i.e., beliefs and capabilities) and context to maximize impact. Without these, feedback may be ineffective.

An additional barrier identified from this work is the limited communication with primary care following discharge. A 2017 National Quality Forum report on ED care transitions [ 49 ] recommended that EDs and their supporting hospital systems should expand infrastructure and enhance health information technology to support care transitions as Veterans may not understand discharge instructions, may not receive post-ED or urgent care, [ 50 , 51 , 52 ] or may not receive a newly prescribed medication. [ 24 ] While there are existing mechanisms to communicate between the ED and primary care teams such as notifications when a Veteran presents to the ED and when an emergency clinician copies a primary care physician on a note, these mechanisms are insufficient to address care transition gaps and are variable in best practice use. To address this variability, the VA ED PACT Tool was developed using best practices (standardized processes, "closed-loop" communication, embedding into workflow) to facilitate and standardize communication between VA EDs and follow-up care clinicians. [ 53 ] While the ED PACT Tool is implemented at the Greater Los Angeles VA and can create a care coordination order upon ED discharge, its use is not yet widely adopted throughout the VA.

In the final theme about technology barriers, once the decision has been made to prescribe a medication, existing electronic tools that are key components of existing stewardship interventions designed to curtail potentially inappropriate prescriptions may be compromised by their lack of usability. For example, clinician and stakeholder interview respondents described how usability concerns were exacerbated in a time-pressured clinical environment (e.g., electronic health record clinical decision support tools). Clinical decision support is an effective tool to improve healthcare process measures in a diverse group of clinical environments; [ 54 ] however, usability remains a barrier when alerts must be frequently overridden. [ 55 , 56 ] Alert fatigue, as expressed in our interviews for order checking and recognized within the VA’s EHR, [ 57 , 58 ] may contribute to excessive overrides reducing the benefit of clinical decision support, [ 56 , 59 ] there was a notable lack of discussion about the decision to initiate appropriate prescriptions, which is a key action of the CDC’s outpatient antibiotic stewardship campaign. [ 18 ] Thus, a potentially more effective, albeit challenging approach, is to “nudge” clinicians towards appropriate prescribing and away from the initial decision to prescribe (e.g., inappropriate antibiotic prescribing for viral upper respiratory tract infections) with either default order sets for symptom management or to enhance prescription decisions through reminders about potential contraindications to specific indications (e.g., high risk comorbidities). Beyond EHR-based solutions that might change clinician behavior, the CDC’s outpatient antibiotic stewardship program provides a framework to change the normative practices around inappropriate prescribing and includes a commitment to appropriate prescribing, action for policy and change, tracking and reporting, and education and expertise. [ 18 ]

Another technical barrier faces patients through patient-facing electronic tools such as the VA’s MyHealtheVet portal, which was developed to enhance patient communication following care transitions and to allow Veterans to review their medications and to communicate with their primary care clinical team. Patient portals can be an effective tool for medication adherence [ 60 ] and offer promise to provide patient education [ 61 ] following a clinical encounter. However, they are similarly limited by usability concerns, representing an adoption barrier to broader Veteran use after unscheduled outpatient care visits [ 62 ], particularly in an older patient population.

These interviews further underscored that lack of usability of clinical decision support for order checking that arises from ineffective design and is a key barrier preventing health information technology from reaching its promise of improving patient safety. [ 63 ] A common and recognized reason for these design challenges include the failure to place the user (i.e., acute care clinician) at the center of the design process resulting in underutilization, workarounds, [ 64 ] and unintended consequences, [ 65 ] all of which diminish patient safety practices and fail to change clinician behavior (i.e., prescribing). Complex adaptive systems work best when the relative strengths of humans (e.g., context sensitivity, situation specificity) are properly integrated with the information processing power of computerized systems. [ 66 ] One potential approach to address usability concerns is through the integration of user-centered design into technology design represents an opportunity to design more clinician- and patient-centric systems of care to advance prescribing stewardship interventions that may have lacked broader adoption previously. As antimicrobial stewardship and additional prescribing stewardship efforts focus on time-pressured environments where usability is essential to adoption, taking a user-centered design approach to not only the development of electronic tools but also in addressing the identified barriers in prescribing represents a promising approach to enhance the quality of prescribing.

Limitations

The study findings should be considered in light of its limitations. First, the setting for this work was the Veterans Health Administration, the largest integrated health system in the US. Also, while we focused on the stewardship of two drug classes, there are numerous additional drug classes that are prescribed in these settings. Studies in other settings or on other drug classes may not generalize to other settings and drug classes. Second, while clinicians and stakeholder perspectives included diverse, national representation, the Veterans interviewed were local to the Tennessee Valley Healthcare System. Given the concurrent COVID-19 pandemic at the time of enrollment, most of the Veterans were seen for pain-related complaints, and only two infectious-related complaints were included. However, we also asked them about antibiotic prescribing. Clinician and stakeholder narratives may not completely reflect their practice patterns as their responses could be influenced by social desirability bias. Third, responses may be subject to recall bias and may influence the data collected. Finally, the themes and subthemes identified may overlap and have potential interactions. While we used an iterative process to identify discrete themes and subthemes, prescription decisions represent a complex decision process that are influenced by numerous patient and contextual factors and may not be completely independent.

Despite numerous interventions to improve the quality of prescribing, the appropriate prescription of antibiotics and NSAIDs in unscheduled outpatient care settings remains a challenge. Using the Veterans Health Administration, this study found that challenges to high quality prescribing include perceived Veteran expectations about receipt of medications, a hectic clinical environment deprioritizing stewardship, limited clinician knowledge, awareness, and willingness to use evidence-based care, uncertainty about the potential for adverse events, limited communication, and technology barriers. Findings from these interviews suggest that interventions should consider the detrimental impact of high workload on prescribing stewardship, clinician workflow, the initial decision to prescribe medications, and incorporate end-users into the intervention design process. Doing so is a promising approach to enhance adoption of high quality prescribing practices in order to improve the quality and patient outcomes from NSAID and antibiotic prescribing.

Availability of data and materials

De-identified datasets used and/or analysed during the current study will be made available from the corresponding author on reasonable request.

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Acknowledgements

This material is based upon work supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development (I01HX003057). The content is solely the responsibility of the authors and does not necessarily represent the official views of the VA.

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Conceptualization: MJW, ASM, MEM, DS, SA. Methodology: MJW, ASM, MEM, DS, KB, SA, TR. Formal analysis: KB, DS, CD, MJW. Investigation: MJW, MDR, DS. Resources: MJW, MEM. Writing—Original Draft. Preparation: MJW, ASM, KB, MDR. Writing—Review & Editing: All investigators. Supervision: MJW, ASM, MEM. Funding acquisition: MJW, MEM.

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Ward, M.J., Matheny, M.E., Rubenstein, M.D. et al. Determinants of appropriate antibiotic and NSAID prescribing in unscheduled outpatient settings in the veterans health administration. BMC Health Serv Res 24 , 640 (2024). https://doi.org/10.1186/s12913-024-11082-0

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• Introduction

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

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

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

Qualitative Research Approaches

Ethnography

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

Grounded theory

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

Phenomenology

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

Narrative research

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

Research Paradigm

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

Positivist versus postpositivist

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

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

Constructivist

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

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

Data Sampling 

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

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

Data Collection and Analysis

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

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

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

Dissemination

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

Applications

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

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

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

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

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

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

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

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

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

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

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

• Issues of Concern

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

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

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

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

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

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

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

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

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

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

• Enhancing Healthcare Team Outcomes

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

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

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

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

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

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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