qualitative research research design example

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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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• 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 objectives 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, other interesting articles, frequently asked questions about research design.

• 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
• 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 analyzing 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, organizations, 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 generalize 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 generalize 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, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors 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 kinds of 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.

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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 high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization 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 research bias and ensure a representative sample?

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

Using descriptive statistics , you can summarize 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 analyzing 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.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Qualitative Research Design: Start

Qualitative Research Design

What is Qualitative research design?

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much . It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

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

While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Research Paradigms 

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

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

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

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

Qualitative Coding

Using Community Data to improve Outcome (Grant Writing)

Survey Design  

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

Community Engagement and Collaboration Event 

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

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

Event Moderators:

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

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

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

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

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

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

Scott Wolovich , Director of New Sun Rising, Pittsburgh 

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

Workshop Organizer

Qualitative Research Methods

What are Qualitative Research methods?

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

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

According to Doody and Bailey (2014);

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

Helps to have a directed research aim and objective.

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

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

Qualitative Research Questions

What constitutes a good Qualitative research question?

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

Examples of good Qualitative Research Questions:

What are people's thoughts on the new library? 

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

Difference example (between Qualitative and Quantitative research questions):

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

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

• Qualitative Research Design Workshop Powerpoint

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

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

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

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

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

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

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

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

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

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

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

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

• Last Updated: Feb 14, 2024 4:25 PM
• URL: https://guides.library.cmu.edu/c.php?g=1346006

18 Qualitative Research Examples

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Qualitative research is an approach to scientific research that involves using observation to gather and analyze non-numerical, in-depth, and well-contextualized datasets.

It serves as an integral part of academic, professional, and even daily decision-making processes (Baxter & Jack, 2008).

Methods of qualitative research encompass a wide range of techniques, from in-depth personal encounters, like ethnographies (studying cultures in-depth) and autoethnographies (examining one’s own cultural experiences), to collection of diverse perspectives on topics through methods like interviewing focus groups (gatherings of individuals to discuss specific topics).

Qualitative Research Examples

1. ethnography.

Definition: Ethnography is a qualitative research design aimed at exploring cultural phenomena. Rooted in the discipline of anthropology , this research approach investigates the social interactions, behaviors, and perceptions within groups, communities, or organizations.

Ethnographic research is characterized by extended observation of the group, often through direct participation, in the participants’ environment. An ethnographer typically lives with the study group for extended periods, intricately observing their everyday lives (Khan, 2014).

It aims to present a complete, detailed and accurate picture of the observed social life, rituals, symbols, and values from the perspective of the study group.

Example of Ethnographic Research

Title: “ The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity “

Citation: Evans, J. (2010). The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity. Peter Lang.

Overview: This study by Evans (2010) provides a rich narrative of young adult male identity as experienced in everyday life. The author immersed himself among a group of young men, participating in their activities and cultivating a deep understanding of their lifestyle, values, and motivations. This research exemplified the ethnographic approach, revealing complexities of the subjects’ identities and societal roles, which could hardly be accessed through other qualitative research designs.

Read my Full Guide on Ethnography Here

2. Autoethnography

Definition: Autoethnography is an approach to qualitative research where the researcher uses their own personal experiences to extend the understanding of a certain group, culture, or setting. Essentially, it allows for the exploration of self within the context of social phenomena.

Unlike traditional ethnography, which focuses on the study of others, autoethnography turns the ethnographic gaze inward, allowing the researcher to use their personal experiences within a culture as rich qualitative data (Durham, 2019).

The objective is to critically appraise one’s personal experiences as they navigate and negotiate cultural, political, and social meanings. The researcher becomes both the observer and the participant, intertwining personal and cultural experiences in the research.

Example of Autoethnographic Research

Title: “ A Day In The Life Of An NHS Nurse “

Citation: Osben, J. (2019). A day in the life of a NHS nurse in 21st Century Britain: An auto-ethnography. The Journal of Autoethnography for Health & Social Care. 1(1).

Overview: This study presents an autoethnography of a day in the life of an NHS nurse (who, of course, is also the researcher). The author uses the research to achieve reflexivity, with the researcher concluding: “Scrutinising my practice and situating it within a wider contextual backdrop has compelled me to significantly increase my level of scrutiny into the driving forces that influence my practice.”

Read my Full Guide on Autoethnography Here

3. Semi-Structured Interviews

Definition: Semi-structured interviews stand as one of the most frequently used methods in qualitative research. These interviews are planned and utilize a set of pre-established questions, but also allow for the interviewer to steer the conversation in other directions based on the responses given by the interviewee.

In semi-structured interviews, the interviewer prepares a guide that outlines the focal points of the discussion. However, the interview is flexible, allowing for more in-depth probing if the interviewer deems it necessary (Qu, & Dumay, 2011). This style of interviewing strikes a balance between structured ones which might limit the discussion, and unstructured ones, which could lack focus.

Example of Semi-Structured Interview Research

Title: “ Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review “

Citation: Puts, M., et al. (2014). Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review. Annals of oncology, 25 (3), 564-577.

Overview: Puts et al. (2014) executed an extensive systematic review in which they conducted semi-structured interviews with older adults suffering from cancer to examine the factors influencing their adherence to cancer treatment. The findings suggested that various factors, including side effects, faith in healthcare professionals, and social support have substantial impacts on treatment adherence. This research demonstrates how semi-structured interviews can provide rich and profound insights into the subjective experiences of patients.

4. Focus Groups

Definition: Focus groups are a qualitative research method that involves organized discussion with a selected group of individuals to gain their perspectives on a specific concept, product, or phenomenon. Typically, these discussions are guided by a moderator.

During a focus group session, the moderator has a list of questions or topics to discuss, and participants are encouraged to interact with each other (Morgan, 2010). This interactivity can stimulate more information and provide a broader understanding of the issue under scrutiny. The open format allows participants to ask questions and respond freely, offering invaluable insights into attitudes, experiences, and group norms.

Example of Focus Group Research

Title: “ Perspectives of Older Adults on Aging Well: A Focus Group Study “

Citation: Halaweh, H., Dahlin-Ivanoff, S., Svantesson, U., & Willén, C. (2018). Perspectives of older adults on aging well: a focus group study. Journal of aging research .

Overview: This study aimed to explore what older adults (aged 60 years and older) perceived to be ‘aging well’. The researchers identified three major themes from their focus group interviews: a sense of well-being, having good physical health, and preserving good mental health. The findings highlight the importance of factors such as positive emotions, social engagement, physical activity, healthy eating habits, and maintaining independence in promoting aging well among older adults.

5. Phenomenology

Definition: Phenomenology, a qualitative research method, involves the examination of lived experiences to gain an in-depth understanding of the essence or underlying meanings of a phenomenon.

The focus of phenomenology lies in meticulously describing participants’ conscious experiences related to the chosen phenomenon (Padilla-Díaz, 2015).

In a phenomenological study, the researcher collects detailed, first-hand perspectives of the participants, typically via in-depth interviews, and then uses various strategies to interpret and structure these experiences, ultimately revealing essential themes (Creswell, 2013). This approach focuses on the perspective of individuals experiencing the phenomenon, seeking to explore, clarify, and understand the meanings they attach to those experiences.

Example of Phenomenology Research

Title: “ A phenomenological approach to experiences with technology: current state, promise, and future directions for research ”

Citation: Cilesiz, S. (2011). A phenomenological approach to experiences with technology: Current state, promise, and future directions for research. Educational Technology Research and Development, 59 , 487-510.

Overview: A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

6. Grounded Theory

Definition: Grounded theory is a systematic methodology in qualitative research that typically applies inductive reasoning . The primary aim is to develop a theoretical explanation or framework for a process, action, or interaction grounded in, and arising from, empirical data (Birks & Mills, 2015).

In grounded theory, data collection and analysis work together in a recursive process. The researcher collects data, analyses it, and then collects more data based on the evolving understanding of the research context. This ongoing process continues until a comprehensive theory that represents the data and the associated phenomenon emerges – a point known as theoretical saturation (Charmaz, 2014).

Example of Grounded Theory Research

Title: “ Student Engagement in High School Classrooms from the Perspective of Flow Theory “

Citation: Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18 (2), 158–176.

Overview: Shernoff and colleagues (2003) used grounded theory to explore student engagement in high school classrooms. The researchers collected data through student self-reports, interviews, and observations. Key findings revealed that academic challenge, student autonomy, and teacher support emerged as the most significant factors influencing students’ engagement, demonstrating how grounded theory can illuminate complex dynamics within real-world contexts.

7. Narrative Research

Definition: Narrative research is a qualitative research method dedicated to storytelling and understanding how individuals experience the world. It focuses on studying an individual’s life and experiences as narrated by that individual (Polkinghorne, 2013).

In narrative research, the researcher collects data through methods such as interviews, observations , and document analysis. The emphasis is on the stories told by participants – narratives that reflect their experiences, thoughts, and feelings.

These stories are then interpreted by the researcher, who attempts to understand the meaning the participant attributes to these experiences (Josselson, 2011).

Example of Narrative Research

Title: “Narrative Structures and the Language of the Self”

Citation: McAdams, D. P., Josselson, R., & Lieblich, A. (2006). Identity and story: Creating self in narrative . American Psychological Association.

Overview: In this innovative study, McAdams et al. (2006) employed narrative research to explore how individuals construct their identities through the stories they tell about themselves. By examining personal narratives, the researchers discerned patterns associated with characters, motivations, conflicts, and resolutions, contributing valuable insights about the relationship between narrative and individual identity.

8. Case Study Research

Definition: Case study research is a qualitative research method that involves an in-depth investigation of a single instance or event: a case. These ‘cases’ can range from individuals, groups, or entities to specific projects, programs, or strategies (Creswell, 2013).

The case study method typically uses multiple sources of information for comprehensive contextual analysis. It aims to explore and understand the complexity and uniqueness of a particular case in a real-world context (Merriam & Tisdell, 2015). This investigation could result in a detailed description of the case, a process for its development, or an exploration of a related issue or problem.

Example of Case Study Research

Title: “ Teacher’s Role in Fostering Preschoolers’ Computational Thinking: An Exploratory Case Study “

Citation: Wang, X. C., Choi, Y., Benson, K., Eggleston, C., & Weber, D. (2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education and Development , 32 (1), 26-48.

Overview: This study investigates the role of teachers in promoting computational thinking skills in preschoolers. The study utilized a qualitative case study methodology to examine the computational thinking scaffolding strategies employed by a teacher interacting with three preschoolers in a small group setting. The findings highlight the importance of teachers’ guidance in fostering computational thinking practices such as problem reformulation/decomposition, systematic testing, and debugging.

Read about some Famous Case Studies in Psychology Here

9. Participant Observation

Definition: Participant observation has the researcher immerse themselves in a group or community setting to observe the behavior of its members. It is similar to ethnography, but generally, the researcher isn’t embedded for a long period of time.

The researcher, being a participant, engages in daily activities, interactions, and events as a way of conducting a detailed study of a particular social phenomenon (Kawulich, 2005).

The method involves long-term engagement in the field, maintaining detailed records of observed events, informal interviews, direct participation, and reflexivity. This approach allows for a holistic view of the participants’ lived experiences, behaviours, and interactions within their everyday environment (Dewalt, 2011).

Example of Participant Observation Research

Title: Conflict in the boardroom: a participant observation study of supervisory board dynamics

Citation: Heemskerk, E. M., Heemskerk, K., & Wats, M. M. (2017). Conflict in the boardroom: a participant observation study of supervisory board dynamics. Journal of Management & Governance , 21 , 233-263.

Overview: This study examined how conflicts within corporate boards affect their performance. The researchers used a participant observation method, where they actively engaged with 11 supervisory boards and observed their dynamics. They found that having a shared understanding of the board’s role called a common framework, improved performance by reducing relationship conflicts, encouraging task conflicts, and minimizing conflicts between the board and CEO.

10. Non-Participant Observation

Definition: Non-participant observation is a qualitative research method in which the researcher observes the phenomena of interest without actively participating in the situation, setting, or community being studied.

This method allows the researcher to maintain a position of distance, as they are solely an observer and not a participant in the activities being observed (Kawulich, 2005).

During non-participant observation, the researcher typically records field notes on the actions, interactions, and behaviors observed , focusing on specific aspects of the situation deemed relevant to the research question.

This could include verbal and nonverbal communication , activities, interactions, and environmental contexts (Angrosino, 2007). They could also use video or audio recordings or other methods to collect data.

Example of Non-Participant Observation Research

Title: Mental Health Nurses’ attitudes towards mental illness and recovery-oriented practice in acute inpatient psychiatric units: A non-participant observation study

Citation: Sreeram, A., Cross, W. M., & Townsin, L. (2023). Mental Health Nurses’ attitudes towards mental illness and recovery‐oriented practice in acute inpatient psychiatric units: A non‐participant observation study. International Journal of Mental Health Nursing .

Overview: This study investigated the attitudes of mental health nurses towards mental illness and recovery-oriented practice in acute inpatient psychiatric units. The researchers used a non-participant observation method, meaning they observed the nurses without directly participating in their activities. The findings shed light on the nurses’ perspectives and behaviors, providing valuable insights into their attitudes toward mental health and recovery-focused care in these settings.

11. Content Analysis

Definition: Content Analysis involves scrutinizing textual, visual, or spoken content to categorize and quantify information. The goal is to identify patterns, themes, biases, or other characteristics (Hsieh & Shannon, 2005).

Content Analysis is widely used in various disciplines for a multitude of purposes. Researchers typically use this method to distill large amounts of unstructured data, like interview transcripts, newspaper articles, or social media posts, into manageable and meaningful chunks.

When wielded appropriately, Content Analysis can illuminate the density and frequency of certain themes within a dataset, provide insights into how specific terms or concepts are applied contextually, and offer inferences about the meanings of their content and use (Duriau, Reger, & Pfarrer, 2007).

Example of Content Analysis

Title: Framing European politics: A content analysis of press and television news .

Citation: Semetko, H. A., & Valkenburg, P. M. (2000). Framing European politics: A content analysis of press and television news. Journal of Communication, 50 (2), 93-109.

Overview: This study analyzed press and television news articles about European politics using a method called content analysis. The researchers examined the prevalence of different “frames” in the news, which are ways of presenting information to shape audience perceptions. They found that the most common frames were attribution of responsibility, conflict, economic consequences, human interest, and morality.

Read my Full Guide on Content Analysis Here

12. Discourse Analysis

Definition: Discourse Analysis, a qualitative research method, interprets the meanings, functions, and coherence of certain languages in context.

Discourse analysis is typically understood through social constructionism, critical theory , and poststructuralism and used for understanding how language constructs social concepts (Cheek, 2004).

Discourse Analysis offers great breadth, providing tools to examine spoken or written language, often beyond the level of the sentence. It enables researchers to scrutinize how text and talk articulate social and political interactions and hierarchies.

Insight can be garnered from different conversations, institutional text, and media coverage to understand how topics are addressed or framed within a specific social context (Jorgensen & Phillips, 2002).

Example of Discourse Analysis

Title: The construction of teacher identities in educational policy documents: A critical discourse analysis

Citation: Thomas, S. (2005). The construction of teacher identities in educational policy documents: A critical discourse analysis. Critical Studies in Education, 46 (2), 25-44.

Overview: The author examines how an education policy in one state of Australia positions teacher professionalism and teacher identities. While there are competing discourses about professional identity, the policy framework privileges a  narrative that frames the ‘good’ teacher as one that accepts ever-tightening control and regulation over their professional practice.

Read my Full Guide on Discourse Analysis Here

13. Action Research

Definition: Action Research is a qualitative research technique that is employed to bring about change while simultaneously studying the process and results of that change.

This method involves a cyclical process of fact-finding, action, evaluation, and reflection (Greenwood & Levin, 2016).

Typically, Action Research is used in the fields of education, social sciences , and community development. The process isn’t just about resolving an issue but also developing knowledge that can be used in the future to address similar or related problems.

The researcher plays an active role in the research process, which is normally broken down into four steps: 

• developing a plan to improve what is currently being done
• implementing the plan
• observing the effects of the plan, and
• reflecting upon these effects (Smith, 2010).

Example of Action Research

Title: Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing

Citation: Ellison, M., & Drew, C. (2020). Using digital sandbox gaming to improve creativity within boys’ writing. Journal of Research in Childhood Education , 34 (2), 277-287.

Overview: This was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

Read my Full Guide on Action Research Here

14. Semiotic Analysis

Definition: Semiotic Analysis is a qualitative method of research that interprets signs and symbols in communication to understand sociocultural phenomena. It stems from semiotics, the study of signs and symbols and their use or interpretation (Chandler, 2017).

In a Semiotic Analysis, signs (anything that represents something else) are interpreted based on their significance and the role they play in representing ideas.

This type of research often involves the examination of images, sounds, and word choice to uncover the embedded sociocultural meanings. For example, an advertisement for a car might be studied to learn more about societal views on masculinity or success (Berger, 2010).

Example of Semiotic Research

Title: Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia

Citation: Symes, C. (2023). Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia. Semiotica , 2023 (250), 167-190.

Overview: This study examines school badges in New South Wales, Australia, and explores their significance through a semiotic analysis. The badges, which are part of the school’s visual identity, are seen as symbolic representations that convey meanings. The analysis reveals that these badges often draw on heraldic models, incorporating elements like colors, names, motifs, and mottoes that reflect local culture and history, thus connecting students to their national identity. Additionally, the study highlights how some schools have shifted from traditional badges to modern logos and slogans, reflecting a more business-oriented approach.

15. Qualitative Longitudinal Studies

Definition: Qualitative Longitudinal Studies are a research method that involves repeated observation of the same items over an extended period of time.

Unlike a snapshot perspective, this method aims to piece together individual histories and examine the influences and impacts of change (Neale, 2019).

Qualitative Longitudinal Studies provide an in-depth understanding of change as it happens, including changes in people’s lives, their perceptions, and their behaviors.

For instance, this method could be used to follow a group of students through their schooling years to understand the evolution of their learning behaviors and attitudes towards education (Saldaña, 2003).

Example of Qualitative Longitudinal Research

Title: Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study

Citation: Hackett, J., Godfrey, M., & Bennett, M. I. (2016). Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study.  Palliative medicine ,  30 (8), 711-719.

Overview: This article examines how patients and their caregivers manage pain in advanced cancer through a qualitative longitudinal study. The researchers interviewed patients and caregivers at two different time points and collected audio diaries to gain insights into their experiences, making this study longitudinal.

Read my Full Guide on Longitudinal Research Here

16. Open-Ended Surveys

Definition: Open-Ended Surveys are a type of qualitative research method where respondents provide answers in their own words. Unlike closed-ended surveys, which limit responses to predefined options, open-ended surveys allow for expansive and unsolicited explanations (Fink, 2013).

Open-ended surveys are commonly used in a range of fields, from market research to social studies. As they don’t force respondents into predefined response categories, these surveys help to draw out rich, detailed data that might uncover new variables or ideas.

For example, an open-ended survey might be used to understand customer opinions about a new product or service (Lavrakas, 2008).

Contrast this to a quantitative closed-ended survey, like a Likert scale, which could theoretically help us to come up with generalizable data but is restricted by the questions on the questionnaire, meaning new and surprising data and insights can’t emerge from the survey results in the same way.

Example of Open-Ended Survey Research

Title: Advantages and disadvantages of technology in relationships: Findings from an open-ended survey

Citation: Hertlein, K. M., & Ancheta, K. (2014). Advantages and disadvantages of technology in relationships: Findings from an open-ended survey.  The Qualitative Report ,  19 (11), 1-11.

Overview: This article examines the advantages and disadvantages of technology in couple relationships through an open-ended survey method. Researchers analyzed responses from 410 undergraduate students to understand how technology affects relationships. They found that technology can contribute to relationship development, management, and enhancement, but it can also create challenges such as distancing, lack of clarity, and impaired trust.

17. Naturalistic Observation

Definition: Naturalistic Observation is a type of qualitative research method that involves observing individuals in their natural environments without interference or manipulation by the researcher.

Naturalistic observation is often used when conducting research on behaviors that cannot be controlled or manipulated in a laboratory setting (Kawulich, 2005).

It is frequently used in the fields of psychology, sociology, and anthropology. For instance, to understand the social dynamics in a schoolyard, a researcher could spend time observing the children interact during their recess, noting their behaviors, interactions, and conflicts without imposing their presence on the children’s activities (Forsyth, 2010).

Example of Naturalistic Observation Research

Title: Dispositional mindfulness in daily life: A naturalistic observation study

Citation: Kaplan, D. M., Raison, C. L., Milek, A., Tackman, A. M., Pace, T. W., & Mehl, M. R. (2018). Dispositional mindfulness in daily life: A naturalistic observation study. PloS one , 13 (11), e0206029.

Overview: In this study, researchers conducted two studies: one exploring assumptions about mindfulness and behavior, and the other using naturalistic observation to examine actual behavioral manifestations of mindfulness. They found that trait mindfulness is associated with a heightened perceptual focus in conversations, suggesting that being mindful is expressed primarily through sharpened attention rather than observable behavioral or social differences.

Read my Full Guide on Naturalistic Observation Here

18. Photo-Elicitation

Definition: Photo-elicitation utilizes photographs as a means to trigger discussions and evoke responses during interviews. This strategy aids in bringing out topics of discussion that may not emerge through verbal prompting alone (Harper, 2002).

Traditionally, Photo-Elicitation has been useful in various fields such as education, psychology, and sociology. The method involves the researcher or participants taking photographs, which are then used as prompts for discussion.

For instance, a researcher studying urban environmental issues might invite participants to photograph areas in their neighborhood that they perceive as environmentally detrimental, and then discuss each photo in depth (Clark-Ibáñez, 2004).

Example of Photo-Elicitation Research

Title: Early adolescent food routines: A photo-elicitation study

Citation: Green, E. M., Spivak, C., & Dollahite, J. S. (2021). Early adolescent food routines: A photo-elicitation study. Appetite, 158 .

Overview: This study focused on early adolescents (ages 10-14) and their food routines. Researchers conducted in-depth interviews using a photo-elicitation approach, where participants took photos related to their food choices and experiences. Through analysis, the study identified various routines and three main themes: family, settings, and meals/foods consumed, revealing how early adolescents view and are influenced by their eating routines.

Features of Qualitative Research

Qualitative research is a research method focused on understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

Some key features of this method include:

• Naturalistic Inquiry: Qualitative research happens in the natural setting of the phenomena, aiming to understand “real world” situations (Patton, 2015). This immersion in the field or subject allows the researcher to gather a deep understanding of the subject matter.
• Emphasis on Process: It aims to understand how events unfold over time rather than focusing solely on outcomes (Merriam & Tisdell, 2015). The process-oriented nature of qualitative research allows researchers to investigate sequences, timing, and changes.
• Interpretive: It involves interpreting and making sense of phenomena in terms of the meanings people assign to them (Denzin & Lincoln, 2011). This interpretive element allows for rich, nuanced insights into human behavior and experiences.
• Holistic Perspective: Qualitative research seeks to understand the whole phenomenon rather than focusing on individual components (Creswell, 2013). It emphasizes the complex interplay of factors, providing a richer, more nuanced view of the research subject.
• Prioritizes Depth over Breadth: Qualitative research favors depth of understanding over breadth, typically involving a smaller but more focused sample size (Hennink, Hutter, & Bailey, 2020). This enables detailed exploration of the phenomena of interest, often leading to rich and complex data.

Qualitative vs Quantitative Research

Qualitative research centers on exploring and understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

It involves an in-depth approach to the subject matter, aiming to capture the richness and complexity of human experience.

Examples include conducting interviews, observing behaviors, or analyzing text and images.

There are strengths inherent in this approach. In its focus on understanding subjective experiences and interpretations, qualitative research can yield rich and detailed data that quantitative research may overlook (Denzin & Lincoln, 2011).

Additionally, qualitative research is adaptive, allowing the researcher to respond to new directions and insights as they emerge during the research process.

However, there are also limitations. Because of the interpretive nature of this research, findings may not be generalizable to a broader population (Marshall & Rossman, 2014). Well-designed quantitative research, on the other hand, can be generalizable.

Moreover, the reliability and validity of qualitative data can be challenging to establish due to its subjective nature, unlike quantitative research, which is ideally more objective.

Compare Qualitative and Quantitative Research Methodologies in This Guide Here

In conclusion, qualitative research methods provide distinctive ways to explore social phenomena and understand nuances that quantitative approaches might overlook. Each method, from Ethnography to Photo-Elicitation, presents its strengths and weaknesses but they all offer valuable means of investigating complex, real-world situations. The goal for the researcher is not to find a definitive tool, but to employ the method best suited for their research questions and the context at hand (Almalki, 2016). Above all, these methods underscore the richness of human experience and deepen our understanding of the world around us.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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

• Introduction and overview
• What is qualitative research?

What is qualitative data?

• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Introduction

When is qualitative research useful?

What are the different approaches to qualitative research, what are the most common qualitative research methods, focus groups.

• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Qualitative research methods, types & examples

In the next few sections, we will examine different methods common to qualitative research . Researchers may find the discussion on qualitative research methodologies important to laying the theoretical foundation for common practices in the qualitative research process.

Qualitative research methods often contrast with quantitative research methods , which are usually found in the hard sciences such as biology and chemistry. Both orientations are used in the social sciences and behavioral sciences methodology as they contribute to the empirical understanding of scientific knowledge. Quantitative methods tend to measure trends concerning a phenomenon, while qualitative research methods are well-suited to describing a given phenomenon in depth.

Think of how people might choose a smartphone and how many choices there are. The decision could be based on quantitative data, such as the phone's weight, storage size (e.g., how many photos or songs it can hold), and battery life. Customers can decide by comparing the numbers of different models.

On the other hand, the decision could be made based on qualitative data, such as customers' experiences regarding how easy it is to operate a smartphone or how visually appealing a smartphone is. These determinations are challenging to characterize numerically, requiring more extended descriptions to allow people to make reasonable comparisons.

Researchers conduct qualitative research to gather data on and answer questions about intricate social processes that are difficult to quantify. Qualitative methods can be used to conceptualize these processes and develop new theories that shed light on the complex social phenomena in our world.

Qualitative data refers to rich, in-depth, and nuanced information that captures the complexity and diversity of human experiences and social realities. Rather than focusing on quantities or measurements, qualitative data aims to understand the intricate nature of phenomena, uncovering the 'why' and 'how' rather than 'how much.'

It often includes words, descriptions, visual images, symbols, or personal narratives. This data is typically unstructured or semi-structured, featuring open-ended responses that allow for expressive, detailed, and context-specific responses. It explores subjective experiences, individual perceptions, emotions, beliefs, and behaviors in social and cultural contexts.

Qualitative data can reveal patterns, themes, and categories that reflect the depth of participants' experiences and the structures of their world. It can potentially capture unforeseen phenomena, leading to new insights or theories.

Whether collected directly from participants or indirectly from various artifacts or environmental observations, qualitative data provides an understanding of the complex interplay between individuals and their contexts. It aims to provide rich, holistic insights into people's experiences and societal phenomena.

Ultimately, qualitative data offers a rich canvas for researchers to paint a comprehensive and detailed picture of the subject of their inquiry beyond numerical metrics and predefined categories.

The qualitative data collection methods that researchers choose depend on their qualitative inquiry. Qualitative studies take on many forms, with the most common approaches listed below.

Case study research

The case study research approach provides a rich, detailed analysis of a specific 'case,' which can be a single individual, group, event, or organization. Researchers employing this method gather data using multiple sources, such as interviews , observations , and documents, which offer a more complete picture of the case under study. The inherent flexibility of case study research enables the exploration of complex issues in their real-world settings.

Case study research is particularly beneficial when researchers aim to answer 'how' and 'why' questions. It's about digging deep into the aspects often overlooked by other research methods. However, it's crucial to remember that findings from a single case study may not be generalizable to other cases. Some case study designs purposefully include multiple cases in their study design to allow for cross-case comparisons and the development of insights that are more transferable to different cases. Broadly, though, the method's strength lies not in replication but in its depth of understanding and insight.

Ethnographic research

Ethnographic research is a method deeply rooted in cultural anthropology, where the researcher immerses themselves in the everyday life of the group or community they are studying. It involves long-term engagement and close observation of the group, often through participation in their activities. The primary aim is to gain an insider's perspective of the group's social dynamics, beliefs, rituals, and behaviors.

Ethnographic research can be used in various fields, not limited to anthropology. For example, in user experience research, ethnographic methods can be employed to understand user behavior and needs in the context of their natural environment. Nevertheless, conducting ethnographic research requires extensive data collection time and a deep understanding and respect for the culture being studied.

Grounded theory research

Grounded theory research is a qualitative method that seeks to develop a theory rooted in the data. Rather than beginning with a hypothesis , researchers using grounded theory start with an area of study and collect data related to this area. The key feature of this method is its systematic procedure of data collection and analysis, which is designed to facilitate the development of theory that emerges from the data.

The process of grounded theory involves several stages, including open coding , axial coding , and selective coding, which assist in organizing data into categories, establishing relationships among categories, and forming a theoretical framework, respectively. This method is particularly useful when existing theories fail to explain a phenomenon adequately. Nonetheless, conducting grounded theory research requires significant time and analytical effort to ensure that the emerging theory is robust and grounded in the data.

Narrative research

Researchers who focus on narratives are centered on the stories that individuals tell about their experiences and life events. These narratives offer a window into individuals' perspectives, providing insights into their feelings, motives, and actions. Researchers utilizing this method collect narratives through interviews, autobiographies, oral histories, or diaries and analyze them to understand individuals' experiences and how they make sense of their world.

The second part of the narrative research process is the interpretation of these stories. Researchers analyze these narratives not only for their content but also for how they're structured and told, looking for patterns and themes that reveal more profound meanings. However, it's essential to remember that narratives are subjective and can change over time as people reinterpret their experiences and memories. Thus, narrative research provides a rich, nuanced understanding of individual experiences that are closely tied to the context in which the narrative was produced.

Phenomenological research

Phenomenological research focuses on understanding individuals' lived experiences concerning a particular phenomenon. The aim here is to grasp the essence of the experience or the underlying meanings and interpretations that individuals assign to their experiences. This method involves deep, often philosophically-rooted thinking, requiring the researcher to bracket their preconceptions to truly understand the participants' perspectives.

A phenomenological study involves detailed interviews , observations , or diary entries, allowing the researcher to delve into the intricate details of people's experiences and feelings. Analysis of the data seeks to identify themes or essences that capture the nature of the phenomenon under investigation. While phenomenological research can provide profound insights into human experiences, it is a complex and time-consuming process, requiring rigorous analysis and a high degree of reflexivity from the researcher.

Action research

Action research is a collaborative, participatory approach to research that aims to solve real-world problems. In this approach, researchers work closely with community members or stakeholders, who are actively involved in all stages of the research process, from identifying the problem to implementing and evaluating the solution. This makes action research a highly dynamic and iterative process.

This method is usually employed in educational, organizational, or community settings, where researchers and participants learn from each other and effect change together. Action research not only aims to generate knowledge but also to produce practical outcomes and empower participants. Despite its benefits, it requires a significant commitment of time and resources, and its success is dependent on the effective collaboration and active participation of all members involved.

Which approach to qualitative methods is best?

Choosing the best approach to qualitative research depends on various factors, including the nature of the research question , the context of the study, the researcher's familiarity with the approach, and ethical considerations . Here are some guiding questions:

• What is the main purpose of the study?
• What kind of data is needed to effectively answer the research question?
• What is the context in which the research is being conducted?
• What are the ethical considerations associated with each approach?
• Which approach aligns best with the researcher's skills and interests?

Understanding these aspects will allow the researcher to choose the most suitable approach for their particular study.

For instance, a grounded theory approach can be an appropriate choice for your research design when there is little theory to guide the analysis of a phenomenon and the data collection itself. For research in areas that have more guiding theory to help you, you can consider an approach like ethnography or case study research, depending on the scope of data you wish to collect. Finally, if you are conducting research because you are interested in enacting social change, then action research will most likely be the most appropriate approach for your study.

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Any research method that produces unstructured data can be considered a qualitative research method. However, three types of qualitative methods are commonly used today to conduct data collection.

Observations

The simplest way to study a phenomenon is to look at it. Research conducted through direct observation involves collecting data in field notes , recordings of audio and video, and images for data analysis .

This means that researchers can turn most forms of information into data that can be analyzed with qualitative methods. The illustrative examples qualitative research methods generate can help research audiences understand observed phenomena more clearly. ATLAS.ti can help with this process by allowing qualitative researchers to code major forms of data , including images and audio .

Interviews are a fundamental method in qualitative research, allowing researchers to gather in-depth information about individuals' thoughts, feelings, experiences, and interpretations. Interviews can take various forms, from highly structured with predetermined questions, to semi-structured with some guidance , to unstructured or 'open-ended' where the conversation evolves based on the interviewee's responses.

Conducting interviews offers a direct interaction with participants, enabling the researcher to probe deeper into the topics under discussion, clarify responses, and ask for elaborations. Interviews can yield rich, detailed data that provide a deep understanding of a person's perspective. However, they also require a significant investment of time and resources. Skilled interviewing and good rapport building are essential for collecting meaningful and accurate data.

A focus group consists of a group of participants collectively discussing a topic, speaking among themselves even more than they might speak to the researcher or focus group moderator. The aim is to inquire about people's perceptions, opinions, beliefs, and attitudes towards the topic of study, which could be a feature of social life, such as body art or a specific product, such as market research for a new campaign. Since the researcher can observe and speak with a group of people, focus groups are ideal for understanding the social construction of a phenomenon or how meaning is collectively co-constructed.

Focus groups are especially popular in market research. Still, qualitative researchers who want to observe how people interact with each other could consider conducting a focus group. For example, how people discuss their opinions and perspectives in groups is an essential inquiry in sociology and linguistics that focus groups can help explore.

Surveys in qualitative research often differ from those in quantitative research, because an important part of these surveys is the collection of open-ended responses that allow participants to provide detailed responses in their own words. Surveys can be a cost-effective and efficient method to collect data from a larger number of participants compared to other qualitative methods.

However, designing a good survey requires careful thought to ensure questions are clear, unbiased, and able to elicit rich, meaningful responses. Unlike interviews and focus groups, surveys do not provide an opportunity for the researcher to ask for clarifications or probe for more elaborate responses. Additionally, low response rates and self-selection bias can be potential challenges in survey research. Regardless, when designed and implemented effectively, surveys can provide valuable insights into participants' perspectives and experiences.

Document collection

Document collection is a versatile method in qualitative research that involves the analysis of existing texts. These texts can come in a variety of forms, such as official documents, newspapers, letters, diaries, transcripts, literary works, photographs, or even digital content like social media posts , blogs, and websites. The content of these documents can provide valuable insights into the phenomenon under investigation, contextual factors, and historical trends.

The strength of document analysis lies in its ability to provide a behind-the-scenes look at events, settings, or groups, often complementing the data obtained through other methods. For example, it can be useful for triangulating data in a mixed-methods study or providing a historical context in a case study. However, the researcher needs to be cautious about the authenticity, bias, and representativeness of the documents. Despite these challenges, when used effectively, document collection can enrich a study by providing a diverse range of data and a deeper understanding of the research subject.

Other research methods

Ultimately, the potential for qualitative data collection is broad as it encompasses any research method that collects unstructured data that can be systematically organized and analyzed. With that in mind, let's briefly look at other methods that are useful in qualitative research.

Participant observation - This is a method used frequently in ethnographic research. Researchers immerse themselves in the environment or culture they are studying, often participating in the activities of the group. This allows them to observe behaviors, interactions, and events as they naturally occur, leading to a deep understanding of the group's dynamics.

Visual methods - These involve the use of visual materials, such as photographs, drawings, videos, or maps. Participants may be asked to create or interact with these materials as part of the data collection process. Visual methods can offer unique insights and are particularly useful when exploring topics that are difficult to express in words.

Diaries and journals - In this method, participants are asked to keep a record of their experiences, thoughts, and feelings over a certain period. These records can provide rich, detailed, and longitudinal data. For example, diaries and journals are often used in health and social care research to study people's daily lives, experiences of illness, or caring roles.

Life history - Collecting life histories is a type of narrative research where participants are asked to tell their life story or focus on a particular aspect or period of their life. Life history can reveal how people interpret and give meaning to their experiences over time.

How do I choose the best qualitative research method?

Think about what you want to study concerning a particular topic or concept. If your topic is education, for example, are you interested in what happens in education, what people think about it, or how people talk about it? Observations can tap into the experiences within a particular context, while interviews and focus groups can shed light on people's opinions.

Also, keep in mind that the use of multiple qualitative research methods can provide a deeper exploration of a concept than the use of one method alone. A good research design for an in-depth qualitative study can even apply quantitative research methods in what is called mixed methods research to examine a phenomenon from different angles.

Any study first begins with the research question and topic. From there, you can reflect on which qualitative research methods are best suited to answering your research question.

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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.

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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.

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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On This Page:

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

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

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

Qualitative research can be used to:

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

Examples of qualitative research questions include: 

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

Characteristics 

Naturalistic setting.

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

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

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

Reality is socially constructed

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

The primacy of subject matter 

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

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

Variables are complex, interwoven, and difficult to measure

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

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

Emic (insider’s point of view)

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

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

Interpretive analysis

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

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

Collecting Qualitative Data

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

Observations

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

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

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

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

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

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

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

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

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

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

Focus groups 

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

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

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

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

Ethnography

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

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

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

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

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

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

Qualitative Data Analysis Methods

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

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

Grounded theory

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

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

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

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

Content analysis

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

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

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

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

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

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

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

Discourse analysis

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

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

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

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

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

Thematic analysis

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

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

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

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

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

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

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

Phases of Thematic Analysis

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

Template analysis

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

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

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

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

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

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

Frame analysis

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

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

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

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

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

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

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

Preventing Bias in Qualitative Research

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

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

Co-coding 

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

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

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

Member checking

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

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

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

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

Reflexivity in qualitative research

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

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

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

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

Reflexivity Example

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

Establishing Trustworthiness in Qualitative Research

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

1. Credibility in Qualitative Research

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

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

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

2. Transferability in Qualitative Research

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

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

3. Dependability in Qualitative Research

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

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

4. Confirmability in Qualitative Research

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

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

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

Audit Trails in Qualitative Research

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

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

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

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

Discovery of unexpected data

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

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

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

Flexibility

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

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

Naturalistic settings

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

Limitations

Time-consuming .

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

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

Subjectivity 

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

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

Limited generalizability

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

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

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

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

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

Extraneous variables

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

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

What sample size should qualitative research be?

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

Are surveys qualitative or quantitative?

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

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

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

What are the ethical considerations of qualitative research?

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

What is triangulation in qualitative research?

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

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

Why is qualitative research important?

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

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

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

What is coding data in qualitative research?

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

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

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

What is the difference between qualitative and quantitative research?

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

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

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

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

What is trustworthiness in qualitative research?

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

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

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

What is data saturation in qualitative research?

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

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

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

Why is sampling in qualitative research important?

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

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

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

What is narrative analysis?

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

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

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

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

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9.4 Types of qualitative research designs

Learning objectives.

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

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

Focus Groups

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

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

Who should be in your focus group?

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

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

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

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

The role of the moderator

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

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

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

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

Strengths and weaknesses of focus groups

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

Grounded Theory

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

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

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

Case study research

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

Ethnography

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

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

Phenomenology

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

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

Key Takeaways

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

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

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

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

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

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Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalisability

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

• Labour-intensive

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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• Types of qualitative research designs

Last updated

20 February 2023

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

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

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• What are qualitative research designs?

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

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

• Characteristics of qualitative research design

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

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

Natural environment

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

Empathy is key

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

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

Participant selection

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

Different research methods or multiple data sources

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

Emergent design

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

Inductive reasoning

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

Interpretive data

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

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

Holistic account

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

• When to use qualitative research design

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

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

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

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

You can use qualitative research in these situations:

Developing a hypothesis for testing in a quantitative study

Identifying customer needs

Developing a new feature

Adding context to the results of a quantitative study

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

Difference between qualitative and quantitative research design

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

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

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

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

Historical study

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

Phenomenology

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

Grounded theory

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

Ethnography

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

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

• Advantages and disadvantages of qualitative research

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

Natural settings

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

The ability to paint a picture with data

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

Flexibility

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

Generation of new ideas

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

Small sample sizes

You can generate meaningful results with small groups.

Disadvantages

Potentially unreliable.

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

Subjectivity

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

Labor-intensive

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

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

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Explore top qualitative research types and examples.

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Qualitative research types offer rich insights into human behaviors, motivations, and experiences. In exploring these methodologies, researchers can uncover nuanced perspectives that quantitative approaches may overlook. This section delves into various qualitative research types, illustrating their distinctive characteristics and applications.

By understanding these research types, stakeholders can select the most suitable methods for their objectives. From in-depth interviews to focus groups, each type serves a unique purpose in gathering data. This nuanced understanding not only enhances the research quality but also informs decision-making processes across various sectors.

Top Qualitative Research Types

Qualitative research types provide valuable insights through a variety of methods focused on understanding human behavior, emotions, and experiences. Among the most popular types are interviews, focus groups, ethnography, and case studies. Each method offers unique advantages in collecting data that reflects the subjective perspectives of participants.

In-depth interviews allow researchers to explore personal experiences and motivations, yielding rich qualitative data. Focus groups facilitate dynamic discussions and highlight collective opinions or trends among participants. Ethnography immerses researchers in the participants' environments, often revealing how context influences behavior. Finally, case studies focus on a specific instance or population, helping to gather detailed information that can inform broader conclusions. Understanding these qualitative research types is crucial for anyone looking to gain deeper insights into human behavior and societal trends.

Ethnography: Exploring Cultural Phenomena

Ethnography serves as a vital qualitative research type, delving deep into cultural phenomena. By immersing researchers in specific communities, it unveils the subtleties of social interactions and behaviors. This form of research not only captures the lived experiences of individuals but also highlights the broader cultural narratives at play. Ethnographers may conduct interviews, observe daily activities, and participate in community events, providing striking insights into cultural values and practices.

This method enriches our understanding of human experiences by revealing trends that survey data may overlook. It empowers researchers to explore topics such as identity, tradition, and social structures in authentic contexts. Ethnography, therefore, plays an essential role in qualitative research types, making it a powerful tool for uncovering the complexities of cultural dynamics. By appreciating these nuances, we can foster a deeper understanding of society and its diverse practices.

Phenomenology: Understanding Human Experiences

Phenomenology focuses on understanding human experiences by exploring how individuals perceive and make sense of their worlds. In qualitative research types, it emphasizes lived experiences, allowing researchers to delve deeply into individuals' subjective realities. This approach seeks to gather rich, detailed descriptions of experiences and identify common themes that resonate within these narratives.

When employing phenomenology, researchers often conduct in-depth interviews, facilitating open-ended conversations that encourage participants to share their feelings and thoughts. By analyzing these narratives, researchers can uncover underlying meanings and insights about specific phenomena. This qualitative research type not only enhances comprehension but also fosters empathy towards diverse human experiences, making it invaluable in fields like psychology, healthcare, and education. Through this approach, phenomenology enriches qualitative research, clarifying the complexities of human existence and behavior.

Examples of Qualitative Research Types in Practice

Qualitative research types are widely used to better understand people's experiences, behaviors, and emotions. One prominent method is in-depth interviews, where researchers engage participants in one-on-one discussions to gather rich, detailed insights. This approach allows the researcher to explore participants' feelings and motivations behind their actions, providing a deeper context than quantitative data alone.

Another common practice is focus groups, which facilitate dynamic discussions among a small group of individuals. This setting encourages participants to share ideas and opinions, often leading to revelations about group dynamics and collective preferences. Observational studies also play a crucial role; researchers directly observe subjects in natural settings to capture authentic behavior. These qualitative research types collectively enhance understanding of complex social phenomena, making them invaluable tools for exploring nuanced human experiences.

Ethnographic Study: Observing Social Interactions

Ethnographic study involves immersive research methods aimed at understanding social interactions within specific cultures or communities. Researchers typically observe participants in their natural environments, noting behaviors, rituals, and social dynamics over time. This approach helps uncover nuanced insights that quantitative methods might overlook.

One key element involves participant observation, where researchers engage with subjects to gain deeper insights into their experiences and perspectives. Another crucial aspect is in-depth interviews, allowing researchers to capture personal narratives and contextual stories that enrich the data. Moreover, annotating environmental factors, like the setting and community dynamics, enables a comprehensive understanding of social interactions. Ultimately, through these qualitative research types, ethnographic studies reveal complex layers of meaning within social contexts, offering invaluable insights for various fields, including anthropology, sociology, and market research.

Phenomenological Study: Investigating Lived Experiences

Phenomenological studies focus on exploring the lived experiences of individuals, aiming to understand how they interpret their reality. This qualitative research type is grounded in the belief that personal experiences shape human perception and understanding. By engaging with participants through in-depth interviews, researchers can delve into the subjective meanings assigned to different experiences, such as health challenges, educational journeys, or traumatic events.

The process involves multiple steps, including selecting participants, designing open-ended questions, and analyzing responses for thematic patterns. Each participant's unique perspective contributes to the richness of the data collected. This method enables researchers to gain profound insights into how individuals navigate and make sense of their world, ultimately informing broader implications for practices, policies, and community understanding. Through such interpretations, phenomenological studies illuminate the essence of human experiences, contributing significantly to qualitative research types.

Conclusion: The Importance of Qualitative Research Types

Qualitative research types play a pivotal role in understanding human behavior, emotions, and reactions. By embracing methods such as interviews, focus groups, and observations, researchers can gather deep insights that are often overlooked in quantitative studies. These qualitative insights aid in uncovering motivations and pain points, leading to more targeted solutions for real-world problems.

Moreover, the diversity of qualitative research approaches allows for a comprehensive view of complex issues. This breadth enables researchers to adapt their methods to suit specific contexts, ensuring that the data collected is both relevant and actionable. In conclusion, qualitative research types are indispensable for obtaining meaningful insights that foster innovation and improve decision-making across various fields.

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12 min read Qualitative research gives you the ‘why’ behind what’s happening in your business, and brings a human dimension to quantitative data. Here’s what goes into creating a great qualitative research program.

Recap: qualitative research vs. quantitative research

If quantitative research is a drone capturing aerial footage, qualitative research is a portrait photographer studying a local neighborhood.

• Qualitative research involves non-numerical data, most often related to social or personal topics. It may investigate people’s opinions, thoughts, feelings and preferences. Qualitative research often relies on a researcher’s first-hand interactions with research participants through techniques like focus groups and interviews. It tends to be deep and detailed, rather than broad and far-reaching.
• Quantitative research , on the other hand, deals with quantities, proportions and other countable data. Examples of quantitative research might be finding out the number of people in a city or the amount of income earned by a business in a year. In contrast, quantitative research methods are more likely to be large-scale and generalized, even abstract. It doesn’t tend to require direct contact between researchers and the populations they are studying.

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What makes a great qualitative research study?

Qualitative research focuses on seeing, listening and understanding – as a researcher on behalf of a business, your goal is to capture not only the particulars of what is happening, but the deeper ‘why’ of consumer behaviors and opinions.

Accurate observation and recording are critical to the success of qualitative research, but without the right foundations laid in terms of your methods, you may end up with qualitative data that are of limited use.

Designing a great qualitative research program means paying as much attention to the design of your study and the choice of methods as actually performing data collection. Presenting your results well matters too. People remember stories, not figures, so qualitative data is a great way to bring your insights to life.

Although qualitative and quantitative studies differ in important ways, it’s worth remembering that your business question may benefit from both qualitative and quantitative research . For example, you might use qualitative research to uncover the attitudes and behaviors of your target audience, then use quantitative analysis to find out how widespread these phenomena are in your research population . Conversely, you might use qualitative analysis to provide the ‘why’ behind your quantitative data.

Online or offline?

Many traditional qualitative research methods involve direct contact between researcher and participant, but thanks to advances in technology, online methods are both effective and widespread. The emergence of COVID-19 in 2020 prompted a shift in the direction of online qualitative methods.

Establishing your research goals in qualitative research

As with any program of research, the first step is to define your aims. You and everyone working with you must have a clear, shared understanding of what your qualitative research program is trying to achieve.

Defining the research question

A research question sums up the overall goal of your qualitative research project. It identifies the information you’re trying to find through your research.

Examples of possible questions:

• ‘What is the effect of inflation on the purchasing behavior of young people in America?’
• ‘How does access to social media affect the emotional development of children?’
• ‘How does climate change affect rural communities in South Sudan?’

Larger studies may have more than one research question. However, as the purpose is to narrow down the scope of your research into one explicitly defined area, don’t add extra research questions unless they’re required for a very complex research process.

Sampling and recruitment

To do qualitative research well, you need to know who you’re studying, and you need a reliable way to identify them. Going back to your research questions, think about who the population of interest is for your study. Who will your participants be as you carry out data collection? Will you use a conventional probability sampling method to find a random representative sample of the research population? Or is it more appropriate to use alternative methods, such as snowball sampling or quota sampling?

Qualitative approaches typically use smaller numbers of participants than quantitative research methods, studying smaller numbers of people in-depth rather than seeking out a broad consensus. That means your selection of participants becomes very important.

Note that while outliers in a research population can create anomalies in quantitative research, in qualitative research it is important to include them, as you’re looking to get a broad range of perspectives and opinions into your study.

Approaches to qualitative research

There are five core approaches to qualitative research:

1. Grounded theory

This is an exploratory approach where the researcher’s hypothesis emerges from their qualitative data as they collect it. Using inductive reasoning – where a theory is developed by generalizing from a range of observations – researchers adapt their understanding of their research topic as they go along. Although grounded theory requires open-ended thinking, it’s quite structured. Researchers use a system of codes to link observations to overarching theories, eventually extrapolating these theories to even higher levels that result in deeper insights.

2. Ethnographic research

This approach involves studying people in the context of their normal environment. It may involve face-to-face interaction or indirect observations.

3. Action research

In action research, also called participatory action research (PAR), participants and researchers with a shared purpose are involved in a change process in a specific context and situation. The researcher learns about what kind of change is required through their own experiences and the reflections of the participants. This kind of qualitative research is more common in fields like healthcare and education.

4. Phenomenological research

Phenomenological research is the study of phenomena – events or states, often noteworthy or unusual ones, through the people who live through them. Phenomenological research focuses on people’s lived experiences and how their lives have influenced their beliefs and behaviors – in particular their response to the phenomena being studied. It deals with groups of people in shared contexts, such as women in the military, minorities in the workplace, asylum seekers in the education system and so on.

5. Narrative research

Also known as narrative enquiry, narrative research is a qualitative method that examines the way people tell their own stories. In particular, it looks at how people ascribe meaning to their experiences and how this influences their views and life choices. As well as interpersonal research using techniques like interviews and focus groups , narrative research can gather data from journals, autobiographies or letters.

Qualitative research methods

There are a broad range of qualitative research approaches, with new qualitative methods that take advantage of advances in communication technology. The COVID-19 pandemic has expedited the shift to online research, not only for asynchronous data collection such as document-based study, but in live interactive formats like video interviews and moderated Zoom groups.

However, some of the best-known types of qualitative study have stood the test of time regardless of world events. Here are some of the most popular ways to carry out qualitative research.

Maybe the classic among data collection methods, in depth interviews allow the researcher to connect personally with the participants and gather data in a one-on-one session. As well as listening to the participant, the researcher can gain insights by observing body language, tone of voice and facial expression to gather information in greater detail. Open ended questions are used to encourage participants to express their perspectives and feelings in their own words.

Focus groups

Like qualitative interviews, focus groups provide in depth insights through personal communication and direct observations. They often take place in natural settings with the researcher acting as moderator for a small group discussion. Focus groups are a type of qualitative study best known for their market research application, but they can be used by qualitative researchers in a wide range of fields, such as health sciences, social research and social sciences.

Observational studies

This kind of qualitative study involves data collection without direct interaction between participant and researcher. Instead, qualitative data is drawn from direct observation of people, often in their natural settings. This gives the qualitative researchers less specific information on their thoughts and feelings, but may offer a better understanding of behaviors and relationships in specific contexts, such as a busy office, a clinic or a classroom.

Discussion boards

Online discussion boards allow researchers to study conversations on the topics they are interested in, as well as asking questions themselves. One benefit is that the wealth of discussion boards online offers data on almost every topic imaginable, since this format is one of the internet’s oldest, significantly pre-dating social media. Discussion board data can even be analyzed at scale with the use of text mining.

Qualitative data analysis

Once the data collection phase is over, you’re ready to move on to the next stage of the research process – data analysis. Qualitative data analysis involves finding the patterns and trends in the data collected during your study. However, unlike a quantitative study with numerical data, it doesn’t come in a consistent format. That means your research team will need to spend a significant chunk of your project time on a careful analysis of the results, particularly if you’re working without specialist data analysis software.

1. Standardize your qualitative data

Unlike the data collection itself, this can be a systematic process where your field notes, literature review, participant observations and video recordings are transcribed so that everything is standardized in a written format that you can easily navigate and analyze. Software can be extremely valuable in turning handwritten notes, and even voice and video files, into text automatically.

2. Code and categorize

You can assign codes in qualitative data analysis to highlight where a particular topic is mentioned in your data so that it’s easier to find. Categorization can also be useful, especially if you’re working with a number of other people who need to work on the findings. Using data analysis tools with semantic analysis and natural language processing can speed things up enormously, as this work is done for you automatically.

3. Find patterns and trends

With all of your data collected and categorized, it’s time to look at it as a whole and examine any patterns or trends that seem to emerge. Again, software that has the capacity to identify trends and relationships, even in natural language data, can be a valuable addition to your analysis process, especially as it will offer an impartial perspective to help you avoid any blind spots or bias arising from personal thoughts and feelings about the research topic.

4. Revisit your hypothesis

Finally, you can return to your research question with new knowledge. Are you closer to an answer? Was the question the right one, and would you ask the same one now given what you’ve learned? This may be a jumping-off point for further research into your findings, or the start of a whole new qualitative inquiry.

5. Report your findings

The quality of your reporting can make all the difference in terms of how effective your research data will be. When presenting your findings to stakeholders, use engaging formats and clear language to summarize your discoveries and to link them to business goals. Video highlight reels are a powerful way to bring insights to life, and can be used to bring the customer’s experience direct to stakeholders through personal stories and testimonials.

Related resources

Primary vs secondary research 14 min read, business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, request demo.

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

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

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

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

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

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

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

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

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

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

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

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

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

Towards a Definition of Qualitative Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analysis – What is Qualitative Research?

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

Qualitative and Quantitative

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

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

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

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

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

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

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

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

Qualitative Research

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

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

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

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

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

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

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

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

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

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

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

Grounded Theory

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

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

Defining Qualitative Research

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

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

Distinctions

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

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

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

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

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

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

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

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

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

Improved Understanding

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

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

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

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

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

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

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

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

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

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

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

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

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

Acknowledgements

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

Biographies

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

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

Publisher’s Note

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Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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• Published: 31 August 2024

Development and validation of a higher-order thinking skills (HOTS) scale for major students in the interior design discipline for blended learning

• Dandan Li 1 ,
• Xiaolei Fan 2 &
• Lingchao Meng 3  

Scientific Reports volume  14 , Article number:  20287 ( 2024 ) Cite this article

Metrics details

• Environmental social sciences

Assessing and cultivating students’ HOTS are crucial for interior design education in a blended learning environment. However, current research has focused primarily on the impact of blended learning instructional strategies, learning tasks, and activities on the development of HOTS, whereas few studies have specifically addressed the assessment of these skills through dedicated scales in the context of blended learning. This study aimed to develop a comprehensive scale for assessing HOTS in interior design major students within the context of blended learning. Employing a mixed methods design, the research involved in-depth interviews with 10 education stakeholders to gather qualitative data, which informed the development of a 66-item soft skills assessment scale. The scale was administered to a purposive sample of 359 undergraduate students enrolled in an interior design program at a university in China. Exploratory and confirmatory factor analyses were also conducted to evaluate the underlying factor structure of the scale. The findings revealed a robust four-factor model encompassing critical thinking skills, problem-solving skills, teamwork skills, and practical innovation skills. The scale demonstrated high internal consistency (Cronbach's alpha = 0.948–0.966) and satisfactory convergent and discriminant validity. This scale provides a valuable instrument for assessing and cultivating HOTS among interior design major students in blended learning environments. Future research can utilize a scale to examine the factors influencing the development of these skills and inform instructional practices in the field.

Introduction

In the contemporary landscape of the twenty-first century, students face numerous challenges that necessitate the development of competitive skills, with a particular emphasis on the cultivation of HOTS 1 , 2 , 3 , this has become a crucial objective in educational reform. Notably, it is worth noting that the National Education Association (NEA, 2012) has clearly identified critical thinking and problem-solving, communication, collaboration, creativity, and innovation as key competencies that students must possess in the current era, which are considered important components of twenty-first century skills 4 , 5 , 6 , 7 . As learners in the fields of creativity and design, students in the interior design profession also need to possess HOTS to address complex design problems and the evolving demands of the industry 8 , 9 .

Currently, blended learning has become an important instructional model in interior design education 10 , 11 . It serves as a teaching approach that combines traditional face-to-face instruction with online learning, providing students with a more flexible and personalized learning experience 12 , 13 . Indeed, several scholars have recognized the benefits of blended learning in providing students with diverse learning resources, activities, and opportunities for interaction, thereby fostering HOTS 14 , 15 , 16 , 17 . For example, blended learning, as evidenced by studies conducted by Anthony et al. 10 and Castro 11 , has demonstrated its efficacy in enhancing students' HOTS. The integration of online resources, virtual practices, and online discussions in blended learning fosters active student engagement and improves critical thinking, problem solving, and creative thinking skills. Therefore, teachers need to determine appropriate assessment methods and construct corresponding assessment tasks to assess students' expected learning outcomes. This decision requires teachers to have a clear understanding of students' learning progress and the development of various skills, whereas students have knowledge of only their scores and lack awareness of their individual skill development 18 , 19 .

Nevertheless, the precise assessment of students' HOTS in the blended learning milieu poses a formidable challenge. The dearth of empirically validated assessment tools impedes researchers from effectively discerning students' levels of cognitive aptitude and developmental growth within the blended learning realm 20 , 21 , 22 . In addition, from the perspective of actual research topics, current studies on blended learning focus mainly on the "concept, characteristics, mechanisms, models, and supporting technologies of blended learning 23 . " Research on "measuring students' HOTS in blended learning" is relatively limited, with most of the focus being on elementary, middle, and high school students 24 , 25 . Few studies have specifically examined HOTS measurement in the context of university students 26 , 27 , particularly in practical disciplines such as interior design. For example, Bervell et al. 28 suggested that the lack of high-quality assessment scales inevitably impacts the quality of research. Additionally, Schmitt 29 proposed the “Three Cs” principle for measurement, which includes clarity, coherence, and consistency. He highlighted that high-quality assessment scales should possess clear and specific measurement objectives, logically coherent items, and consistent measurement results to ensure the reliability and validity of the data. This reflects the importance of ensuring the alignment of the measurement goals of assessment scales with the research questions and the content of the discipline in the design of assessments.

The development of an assessment scale within the blended learning environment is expected to address the existing gap in measuring and assessing HOTS scores in interior design education. This scale not only facilitates the assessment of students' HOTS but also serves as a guide for curriculum design, instructional interventions, and student support initiatives. Ultimately, the integration of this assessment scale within the blended learning environment has the potential to optimize the development of HOTS among interior design students, empowering them to become adept critical thinkers, creative problem solvers, and competent professionals in the field.

Therefore, this study follows a scientific scale development procedure to develop an assessment scale specifically designed to measure the HOTS of interior design students in blended learning environments. This endeavor aims to provide educators with a reliable instrument for assessing students' progress in cultivating and applying HOTS, thus enabling the implementation of more effective teaching strategies and enhancing the overall quality of interior design education. The research questions are as follows:

What key dimensions should be considered when developing a HOTS assessment scale to accurately capture students' HOTS in an interior design major blended learning environment?

How can an advanced thinking skills assessment scale for blended learning in interior design be developed?

How can the reliability and validity of the HOTS assessment scale be verified and ensured, and is it reliable and effective in the interior design of major blended learning environments?

Key dimensions of HOTS assessment scale in an interior design major blended learning environment

The research results indicate that in the blended learning environment of interior design, this study identified 16 initial codes representing key dimensions for enhancing students' HOTS. These codes were further categorized into 8 main categories and 4 overarching themes: critical thinking, problem-solving, teamwork skills and practical innovation skills. They provide valuable insights for data comprehension and analysis, serving as a comprehensive framework for the HOTS scale. Analyzing category frequency and assessing its significance and universality in a qualitative dataset hold significant analytical value 30 , 31 . High-frequency terms indicate the central position of specific categories in participants' narratives, texts, and other data forms 32 . Through interviews with interior design experts and teachers, all core categories were mentioned more than 20 times, providing compelling evidence of their universality and importance within the field of interior design's HOTS dimensions. As shown in Table 1 .

Themes 1: critical thinking skills

Critical thinking skills constitute a key core category in blended learning environments for interior design and are crucial for cultivating students' HOTS. This discovery emphasizes the importance of critical thinking in interior design learning. This mainly includes the categories of logical reasoning and judgment, doubt and reflection, with a frequency of more than 8, highlighting the importance of critical thinking skills. Therefore, a detailed discussion of each feature is warranted. As shown in Table 2 .

Category 1: logical reasoning and judgment

The research results indicate that in a blended learning environment for interior design, logical reasoning and judgment play a key role in cultivating critical thinking skills. Logical reasoning refers to inferring reasonable conclusions from information through analysis and evaluation 33 . Judgment is based on logic and evidence for decision-making and evaluation. The importance of these concepts lies in their impact on the development and enhancement of students' HOTS. According to the research results, interior design experts and teachers unanimously believe that logical reasoning and judgment are very important. For example, as noted by Interviewee 1, “For students, logical reasoning skills are still very important. Especially in indoor space planning, students use logical reasoning to determine whether the layout of different functional areas is reasonable”. Similarly, Interviewee 2 also stated that “logical reasoning can help students conduct rational analysis of various design element combinations during the conceptual design stage, such as color matching, material selection, and lighting application”.

As emphasized by interviewees 1 and 2, logical reasoning and judgment are among the core competencies of interior designers in practical applications. These abilities enable designers to analyze and evaluate design problems and derive reasonable solutions from them. In the interior design industry, being able to conduct accurate logical reasoning and judgment is one of the key factors for success. Therefore, through targeted training and practice, students can enhance their logical thinking and judgment, thereby better addressing design challenges and providing innovative solutions.

Category 2: skepticism and reflection

Skepticism and reflection play crucial roles in cultivating students' critical thinking skills in a blended learning environment for interior design. Doubt can prompt students to question and explore information and viewpoints, whereas reflection helps students think deeply and evaluate their own thinking process 34 . These abilities are crucial for cultivating students' higher-order thinking skills. According to the research findings, most interior design experts and teachers agree that skepticism and reflection are crucial. For example, as noted by interviewees 3, “Sometimes, when facing learning tasks, students will think about how to better meet the needs of users”. Meanwhile, Interviewee 4 also agreed with this viewpoint. As emphasized by interviewees 3 and 4, skepticism and reflection are among the core competencies of interior designers in practical applications. These abilities enable designers to question existing perspectives and practices and propose innovative design solutions through in-depth thinking and evaluation. Therefore, in the interior design industry, designers with the ability to doubt and reflect are better able to respond to complex design needs and provide clients with unique and valuable design solutions.

Themes 2: problem-solving skills

The research findings indicate that problem-solving skills constitute a key core category in blended learning environments for interior design and are crucial for cultivating students' HOTS. This discovery emphasizes the importance of problem-solving skills in interior design learning. Specifically, categories such as identifying and defining problems, as well as developing and implementing plans, have been studied more than 8 times, highlighting the importance of problem-solving skills. Therefore, it is necessary to discuss each function in detail to better understand and cultivate students' problem-solving skills. As shown in Table 3 .

Category 1: identifying and defining issues

The research findings indicate that in a blended learning environment for interior design, identifying and defining problems play a crucial role in fostering students' problem-solving skills. Identifying and defining problems require students to possess the ability to analyze and evaluate problems, enabling them to accurately determine the essence of the problems and develop effective strategies and approaches to solve them 35 . Interior design experts and teachers widely recognize the importance of identifying and defining problems as core competencies in interior design practice. For example, Interviewee 5 emphasized the importance of identifying and defining problems, stating, "In interior design, identifying and defining problems is the first step in addressing design challenges. Students need to be able to clearly identify the scope, constraints, and objectives of the problems to engage in targeted thinking and decision-making in the subsequent design process." Interviewee 6 also supported this viewpoint. As stressed by Interviewees 5 and 6, identifying and defining problems not only require students to possess critical thinking abilities but also necessitate broad professional knowledge and understanding. Students need to comprehend principles of interior design, spatial planning, human behavior, and other relevant aspects to accurately identify and define problems associated with design tasks.

Category 2: developing and implementing a plan

The research results indicate that in a blended learning environment for interior design, developing and implementing plans plays a crucial role in cultivating students' problem-solving abilities. The development and implementation of a plan refers to students identifying and defining problems, devising specific solutions, and translating them into concrete implementation plans. Specifically, after determining the design strategy, students refine it into specific implementation steps and timelines, including drawing design drawings, organizing PPT reports, and presenting design proposals. For example, Interviewee 6 noted, “Students usually break down design strategies into specific tasks and steps by refining them.” Other interviewees also unanimously support this viewpoint. As emphasized by respondent 6, developing and implementing plans can help students maintain organizational, systematic, and goal-oriented problem-solving skills, thereby enhancing their problem-solving skills.

Themes 3: teamwork skills

The research results indicate that teamwork skills constitute a key core category in blended learning environments for interior design and are crucial for cultivating students' HOTS. This discovery emphasizes the importance of teamwork skills in interior design learning. This mainly includes communication and coordination and division of labor and collaboration, which are mentioned frequently in the interview documents. Therefore, it is necessary to discuss each function in detail to better understand and cultivate students' teamwork skills. As shown in Table 4 .

Category 1: communication and coordination

The research results indicate that communication and collaboration play crucial roles in cultivating students' teamwork abilities in a blended learning environment for interior design. Communication and collaboration refer to the ability of students to effectively share information, understand each other's perspectives, and work together to solve problems 36 . Specifically, team members need to understand each other's resource advantages integrate and share these resources to improve work efficiency and project quality. For example, Interviewee 7 noted, “In interior design, one member may be skilled in spatial planning, while another member may be skilled in color matching. Through communication and collaboration, team members can collectively utilize this expertise to improve work efficiency and project quality.” Other interviewees also unanimously believe that this viewpoint can promote students' teamwork skills, thereby promoting the development of their HOTS. As emphasized by the viewpoints of these interviewees, communication and collaboration enable team members to collectively solve problems and overcome challenges. Through effective communication, team members can exchange opinions and suggestions with each other, provide different solutions, and make joint decisions. Collaboration and cooperation among team members contribute to brainstorming and finding the best solution.

Category 2: division of labor and collaboration

The research results indicate that in the blended learning environment of interior design, the division of labor and collaboration play crucial roles in cultivating students' teamwork ability. The division of labor and collaboration refer to the ability of team members to assign different tasks and roles in a project based on their respective expertise and responsibilities and work together to complete the project 37 . For example, Interviewee 8 noted, “In an internal design project, some students are responsible for space planning, some students are responsible for color matching, and some students are responsible for rendering production.” Other interviewees also support this viewpoint. As emphasized by interviewee 8, the division of labor and collaboration help team members fully utilize their respective expertise and abilities, promote resource integration and complementarity, cultivate a spirit of teamwork, and enable team members to collaborate, support, and trust each other to achieve project goals together.

Themes 4: practical innovation skills

The research results indicate that practical innovation skills constitute a key core category in blended learning environments for interior design and are crucial for cultivating students' HOTS. This discovery emphasizes the importance of practical innovation skills in interior design learning. This mainly includes creative conception and design expression, as well as innovative application of materials and technology, which are often mentioned in interview documents. Therefore, it is necessary to discuss each function in detail to better understand and cultivate students' practical innovation skills. As shown in Table 5 .

Category 1: creative conception and design expression

The research results indicate that in the blended learning environment of interior design, creative ideation and design expression play crucial roles in cultivating students' practical and innovative skills. Creative ideation and design expression refer to the ability of students to break free from traditional thinking frameworks and try different design ideas and methods through creative ideation, which helps stimulate their creativity and cultivate their ability to think independently and solve problems. For example, interviewee 10 noted that "blended learning environments combine online and offline teaching modes, allowing students to acquire knowledge and skills more flexibly. Through learning and practice, students can master various expression tools and techniques, such as hand-drawn sketches, computer-aided design software, model making, etc., thereby more accurately conveying their design concepts." Other interviewees also expressed the importance of this viewpoint, emphasizing the importance of creative ideas and design expression in blended learning environments that cannot be ignored. As emphasized by interviewee 10, creative ideation and design expression in the blended learning environment of interior design can not only enhance students' creative thinking skills and problem-solving abilities but also strengthen their application skills in practical projects through diverse expression tools and techniques. The cultivation of these skills is crucial for students' success in their future careers.

Category 2: innovative application of materials and technology

Research findings indicate that the innovative application of materials and technology plays a crucial role in developing students' practical and creative skills within a blended learning environment for interior design. The innovative application of materials and technology refers to students' exploration and utilization of new materials and advanced technologies, enabling them to overcome the limitations of traditional design thinking and experiments with diverse design methods and approaches. This process not only stimulates their creativity but also significantly enhances their problem-solving skills. Specifically, the innovative application of materials and technology involves students gaining a deep understanding of the properties of new materials and their application methods in design, as well as becoming proficient in various advanced technological tools and equipment, such as 3D printing, virtual reality (VR), and augmented reality (AR). These skills enable students to more accurately realize their design concepts and effectively apply them in real-world projects.

For example, Interviewee 1 stated, "The blended learning environment combines online and offline teaching modes, allowing students to flexibly acquire the latest knowledge on materials and technology and apply these innovations in real projects." Other interviewees also emphasized the importance of this view. Therefore, the importance of the innovative application of materials and technology in a blended learning environment cannot be underestimated. As emphasized by interviewee 1, the innovative application of materials and technologies is crucial in the blended learning environment of interior design. This process not only enables students to flexibly acquire the latest materials and technical knowledge but also enables them to apply these innovations to practice in practical projects, thereby improving their practical abilities and professional ethics.

In summary, through research question 1 research, the dimensions of the HOTS assessment scale in blended learning for interior design include four main aspects: critical thinking skills, problem-solving skills, teamwork skills, and practical innovation skills. Based on the assessment scales developed by previous scholars in various dimensions, the researcher developed a HOTS assessment scale suitable for blended learning environments in interior design and collected feedback from interior design experts through interviews.

Development of the HOTS assessment scale

The above research results indicate that the dimensions of the HOTS scale mainly include critical thinking, problem-solving, teamwork skills and practical innovation skills. The dimensions of a scale represent the abstract characteristics and structure of the concept being measured. Since these dimensions are often abstract and difficult to measure directly, they need to be converted into several concrete indicators that can be directly observed or self-reported 38 . These concrete indicators, known as dimension items, operationalize the abstract dimensions, allowing for the measurement and evaluation of various aspects of the concept. This process transforms the abstract dimensions into specific, measurable components. The following content is based on the results of research question 1 to develop an advanced thinking skills assessment scale for mixed learning in interior design.

Dimension 1: critical thinking skills

The research results indicate that critical thinking skills constitute a key core category in blended learning environments for interior design and are crucial for cultivating students' HOTS. Critical thinking skills refer to the ability to analyze information objectively and make a reasoned judgment 39 . Scholars tend to emphasize this concept as a method of general skepticism, rational thinking, and self-reflection 7 , 40 . For example, Goodsett 26 suggested that it should be based on rational skepticism and careful thought about external matters as well as open self-reflection about internal thoughts and actions. Moreover, the California Critical Thinking Disposition Inventory (CCTDI) is widely used to measure critical thinking skills, including dimensions such as seeking truth, confidence, questioning and courage to seek truth, curiosity and openness, as well as analytical and systematic methods 41 . In addition, maturity means continuous adjustment and improvement of a person's cognitive system and learning activities through continuous awareness, reflection, and self-awareness 42 . Moreover, Nguyen 43 confirmed that critical thinking and cognitive maturity can be achieved through these activities, emphasizing that critical thinking includes cognitive skills such as analysis, synthesis, and evaluation, as well as emotional tendencies such as curiosity and openness.

In addition, in a blended learning environment for interior design, critical thinking skills help students better understand, evaluate, and apply design knowledge and skills, cultivating independent thinking and innovation abilities 44 . If students lack these skills, they may accept superficial information and solutions without sufficient thinking and evaluation, resulting in the overlooking of important details or the selection of inappropriate solutions in the design process. Therefore, for the measurement of critical thinking skills, the focus should be on cognitive skills such as analysis, synthesis, and evaluation, as well as curiosity and open mindedness. The specific items for critical thinking skills are shown in Table 6 .

Dimension 2: problem-solving skills

Problem-solving skills constitute a key core category in blended learning environments for interior design and are crucial for cultivating students' HOTS. Problem-solving skills involve the ability to analyze and solve problems by understanding them, identifying their root causes, and developing appropriate solutions 45 . According to the 5E-based STEM education approach, problem-solving skills encompass the following abilities: problem identification and definition, formulation of problem-solving strategies, problem representation, resource allocation, and monitoring and evaluation of solution effectiveness 7 , 46 . Moreover, D'zurilla and Nezu 47 and Tan 48 indicated that attitudes, beliefs, and knowledge skills during problem solving, as well as the quality of proposed solutions and observable outcomes, are demonstrated. In addition, D'Zurilla and Nezu devised the Social Problem-Solving Inventory (SPSI), which comprises seven subscales: cognitive response, emotional response, behavioral response, problem identification, generation of alternative solutions, decision-making, and solution implementation. Based on these research results, the problem-solving skills dimension questions designed in this study are shown in Table 7 .

Dimension 3: teamwork skills

The research results indicate that teamwork skills constitute a key core category in blended learning environments for interior design and are crucial for cultivating students' HOTS. Teamwork skills refer to the ability to effectively collaborate, coordinate, and communicate with others in a team environment 49 . For example, the Teamwork Skills Assessment Tool (TWKSAT) developed by Stevens and Campion 50 identifies five core dimensions of teamwork: conflict management; collaborative problem-solving; communication; goal setting; performance management; decision-making; and task coordination. The design of this tool highlights the essential skills in teamwork and provides a structured approach for evaluating these skills. In addition, he indicated that successful teams need to have a range of skills for problem solving, including situational control, conflict management, decision-making and coordination, monitoring and feedback, and an open mindset. These skills help team members effectively address complex challenges and demonstrate the team’s collaboration and flexibility. Therefore, the assessment of learners' teamwork skills needs to cover the above aspects. As shown in Table 8 .

Dimension 4: practice innovative skills

The research results indicate that practical innovation skills constitute a key core category in blended learning environments for interior design, which is crucial for cultivating students' HOTS. The practice of innovative skills encompasses the utilization of creative cognitive processes and problem-solving strategies to facilitate the generation of original ideas, solutions, and approaches 51 . This practice places significant emphasis on two critical aspects: creative conception and design expression, as well as the innovative application of materials and technology. Tang et al. 52 indicated that creative conception and design expression involve the generation and articulation of imaginative and inventive ideas within a given context. With the introduction of concepts such as 21st-century learning skills, the "5C" competency framework, and core student competencies, blended learning has emerged as the goal and direction of educational reform. It aims to promote the development of students' HOTS, equipping them with the essential qualities and key abilities needed for lifelong development and societal advancement. Blended learning not only emphasizes the mastery of core learning content but also requires students to develop critical thinking, complex problem-solving, creative thinking, and practical innovation skills. To adapt to the changes and developments in the blended learning environment, this study designed 13 preliminary test items based on 21st-century learning skills, the "5C" competency framework, core student competencies, and the TTCT assessment scale developed by Torrance 53 . These items aim to assess students' practice of innovative skills within a blended learning environment, as shown in Table 9 .

The researchers' results indicate that the consensus among the interviewed expert participants is that the structural integrity of the scale is satisfactory and does not require modification. However, certain measurement items have been identified as problematic and require revision. The primary recommendations are as follows: Within the domain of problem-solving skills, the item "I usually conduct classroom and online learning with questions and clear goals" was deemed biased because of its emphasis on the "online" environment. Consequently, the evaluation panel advised splitting this item into two separate components: (1) "I am adept at frequently adjusting and reversing a negative team atmosphere" and (2) "I consistently engage in praising and encouraging others, fostering harmonious relationships. “The assessment process requires revisions and adjustments to specific projects, forming a pilot test scale consisting of 66 observable results from the original 65 items. In addition, there were other suggestions about linguistic formulation and phraseology, which are not expounded upon herein.

Verify the effectiveness of the HOTS assessment scale

The research results indicate that there are significant differences in the average scores of the four dimensions of the HOTS, including critical thinking skills (A1–A24 items), problem-solving skills (B1–B13 items), teamwork skills (C1–C16 items), and practical innovation skills (D1–D13 items). Moreover, this also suggests that each item has discriminative power. Specifically, this will be explained through the following aspects.

Project analysis based on the CR value

The critical ratio (CR) method, which uses the CR value (decision value) to remove measurement items with poor discrimination, is the most used method in project analysis. The specific process involves the use of the CR value (critical value) to identify and remove such items. First, the modified pilot test scale data are aggregated and sorted. Individuals representing the top and bottom 27% of the distribution were subsequently selected, constituting 66 respondents in each group. The high-score group comprises individuals with a total score of 127 or above (including 127), whereas the low-score group comprises individuals with a total score of 99 or below (including 99). Finally, an independent sample t test was conducted to determine the significant differences in the mean scores for each item between the high-score and low-score groups. The statistical results are presented in Table 10 .

The above table shows that independent sample t tests were conducted for all the items; their t values were greater than 3, and their p values were less than 0.001, indicating that the difference between the highest and lowest 27% of the samples was significant and that each item had discriminative power.

In summary, based on previous research and relevant theories, the HOTS scale for interior design was revised. This revision process involved interviews with interior design experts, teachers, and students, followed by item examination and homogeneity testing via the critical ratio (CR) method. The results revealed significant correlations ( p  < 0.01) between all the items and the total score, with correlation coefficients (R) above 0.4. Therefore, the scale exhibits good accuracy and internal consistency in capturing measured HOTS. These findings provide a reliable foundation for further research and practical applications.

Pilot study exploratory factor analysis

This study used SPSS (version 28) to conduct the KMO and Bartlett tests on the scale. The total HOTS test scale as well as the KMO and Bartlett sphericities were first calculated for the four subscales to ensure that the sample data were suitable for factor analysis 7 . The overall KMO value is 0.946, indicating that the data are highly suitable for factor analysis. Additionally, Bartlett's test of sphericity was significant, further supporting the appropriateness of conducting factor analysis ( p  < 0.05). All the values are above 0.7, indicating that the data for these subscales are also suitable for factor analysis. According to Javadi et al. 54 , these results suggest the presence of shared factors among the items within the subscales, as shown in Table 11 .

For each subscale, exploratory factor analysis was conducted to extract factors with eigenvalues greater than 1 while eliminating items with communalities less than 0.30, loadings less than 0.50, and items that cross multiple (more than one) common factors 55 , 56 . Additionally, items that were inconsistent with the assumed structure of the measure were identified and eliminated to ensure the best structural validity. These principles were applied to the factor analysis of each subscale, ensuring that the extracted factor structure and observed items are consistent with the hypothesized measurement structure and analysis results, as shown in the table 55 , 58 . In the exploratory factor analysis (EFA), the latent variables were effectively interpreted and demonstrated a significant response, with cumulative explained variances of the common factors exceeding 60%. This finding confirms the alignment between the scale structure, comprising the remaining items, and the initial theoretical framework proposed in this study. Additionally, the items were systematically reorganized to construct the final questionnaire. Consequently, items A1 to A24 were associated with the critical thinking skills dimension, items B25 to B37 were linked to problem-solving skills, items C38 to C53 were indicative of teamwork skills, and items D54 to D66 were reflective of practical innovation skills. As shown in Table 12 below.

In addition, the criterion for extracting principal components in factor analysis is typically based on eigenvalues, with values greater than 1 indicating greater explanatory power than individual variables. The variance contribution ratio reflects the proportion of variance explained by each principal component relative to the total variance and signifies the ability of the principal component to capture comprehensive information. The cumulative variance contribution ratio measures the accumulated proportion of variance explained by the selected principal components, aiding in determining the optimal number of components to retain while minimizing information loss. The above table shows that four principal components can be extracted from the data, and their cumulative variance contribution rate reaches 59.748%.

However, from the scree plot (as shown in Fig.  1 ), the slope flattens starting from the fifth factor, indicating that no distinct factors can be extracted beyond that point. Therefore, retaining four factors seems more appropriate. The factor loading matrix is the core of factor analysis, and the values in the matrix represent the factor loading of each item on the common factors. Larger values indicate a stronger correlation between the item variable and the common factor. For ease of analysis, this study used the maximum variance method to rotate the initial factor loading matrix, redistributing the relationships between the factors and original variables and making the correlation coefficients range from 0 to 1, which facilitates interpretation. In this study, factor loadings with absolute values less than 0.4 were filtered out. According to the analysis results, the items of the HOTS assessment scale can be divided into four dimensions, which is consistent with theoretical expectations.

Gravel plot of factors.

Through the pretest of the scale and selection of measurement items, 66 measurement items were ultimately determined. On this basis, a formal scale for assessing HOTS in a blended learning environment was developed, and the reliability and validity of the scale were tested to ultimately confirm its usability.

Confirmatory factor analysis of final testing

Final test employed that AMOS (version 26.0), a confirmatory factor analysis (CFA) was conducted on the retested sample data to validate the stability of the HOTS structural model obtained through exploratory factor analysis. This analysis aimed to assess the fit between the measurement results and the actual data, confirming the robustness of the derived HOTS structure and its alignment with the empirical data. The relevant model was constructed based on the factor structure of each component obtained through EFA and the observed variables, as shown in the diagram. The model fit indices are presented in Fig.  2 (among them, A represents critical thinking skills, B represents problem-solving skills, C represents teamwork skills, and D represents practical innovation skills). The models strongly support the "4-dimensional" structure of the HOTS, which includes four first-order factors: critical thinking skills, problem-solving skills, teamwork skills, and practical innovation skills. Critical thinking skills play a pivotal role in the blended learning environment of interior design, connecting problem-solving skills, teamwork skills, and innovative practices. These four dimensions form the assessment structure of HOTS, with critical thinking skills serving as the core element, inspiring individuals to assess problems and propose innovative solutions. By providing appropriate learning resources, diverse learning activities, and learning tasks, as well as designing items for assessment scales, it is possible to delve into the measurement and development of HOTS in the field of interior design, providing guidance for educational and organizational practices. This comprehensive approach to learning and assessment helps cultivate students' HOTS and lays a solid foundation for their comprehensive abilities in the field of interior design. Thus, the CFA structural models provide strong support for the initial hypothesis of the proposed HOTS assessment structure in this study. As shown in Fig.  2 .

Confirmatory factor analysis based on 4 dimensions. *A represents the dimension of critical thinking. B represents the dimension of problem-solving skills. C represents the dimension of teamwork skills. D represents the dimension of practical innovation skills.

Additionally, χ2. The fitting values of RMSEA and SRMR are both below the threshold, whereas the fitting values of the other indicators are all above the threshold, indicating that the model fits well. As shown in Table 13 .

Reliability and validity analysis

The reliability and validity of the scale need to be assessed after the model fit has been determined through validation factor analysis 57 . Based on the findings of Marsh et al. 57 , the following conclusions can be drawn. In terms of hierarchical and correlational model fit, the standardized factor loadings of each item range from 0.700 to 0.802, all of which are greater than or equal to 0.7. This indicates a strong correspondence between the observed items and each latent variable. Furthermore, the Cronbach's α coefficients, which are used to assess the internal consistency or reliability of the scale, ranged from 0.948 to 0.966 for each dimension, indicating a high level of data reliability and internal consistency. The composite reliabilities ranged from 0.948 to 0.967, exceeding the threshold of 0.6 and demonstrating a substantial level of consistency (as shown in Table 14 ).

Additionally, the diagonal bold font represents the square root of the AVE for each dimension. All the dimensions have average variance extracted (AVE) values ranging from 0.551 to 0.589, all of which are greater than 0.5, indicating that the latent variables have strong explanatory power for their corresponding items. These results suggest that the scale structure constructed in this study is reliable and effective. Furthermore, according to the results presented in Table 15 , the square roots of the AVE values for each dimension are greater than the absolute values of the correlations with other dimensions, indicating discriminant validity of the data. Therefore, these four subscales demonstrate good convergent and discriminant validity, indicating that they are both interrelated and independent. This implies that they can effectively capture the content required to complete the HOTS test scale.

Discussion and conclusion

The assessment scale for HOTS in interior design blended learning encompasses four dimensions: critical thinking skills, problem-solving skills, teamwork skills, and practical innovation skills. The selection of these dimensions is based on the characteristics and requirements of the interior design discipline, which aims to comprehensively evaluate students' HOTS demonstrated in blended learning environments to better cultivate their ability to successfully address complex design projects in practice. Notably, multiple studies have shown that HOTSs include critical thinking, problem-solving skills, creative thinking, and decision-making skills, which are considered crucial in various fields, such as education, business, and engineering 20 , 59 , 60 , 61 . Compared with prior studies, these dimensions largely mirror previous research outcomes, with notable distinctions in the emphasis on teamwork skills and practical innovation skills 62 , 63 . Teamwork skills underscore the critical importance of collaboration in contemporary design endeavors, particularly within the realm of interior design 64 . Effective communication and coordination among team members are imperative for achieving collective design objectives.

Moreover, practical innovation skills aim to increase students' capacity for creatively applying theoretical knowledge in practical design settings. Innovation serves as a key driver of advancement in interior design, necessitating students to possess innovative acumen and adaptability to evolving design trends for industry success. Evaluating practical innovation skills aims to motivate students toward innovative thinking, exploration of novel concepts, and development of unique design solutions, which is consistent with the dynamic and evolving nature of the interior design sector. Prior research suggests a close interplay between critical thinking, problem-solving abilities, teamwork competencies, and creative thinking, with teamwork skills acting as a regulatory factor for critical and creative thought processes 7 , 65 . This interconnected nature of HOTS provides theoretical support for the construction and validation of a holistic assessment framework for HOTS.

After the examination by interior design expert members, one item needed to be split into two items. The results of the CR (construct validity) analysis of the scale items indicate that independent sample t tests were subsequently conducted on all the items. The t values were greater than 3, with p values less than 0.001, indicating significant differences between the top and bottom 27% of the samples and demonstrating the discriminant validity of each item. This discovery highlights the diversity and effectiveness of the scale's internal items, revealing the discriminatory power of the scale in assessing the study subjects. The high t values and significant p values reflect the substantiality of the internal items in distinguishing between different sample groups, further confirming the efficacy of these items in evaluating the target characteristics. These results provide a robust basis for further refinement and optimization of the scale and offer guidance for future research, emphasizing the importance of scale design in research and providing strong support for data interpretation and analysis.

This process involves evaluating measurement scales through EFA, and it was found that the explanatory variance of each subscale reached 59.748%, and the CR, AVE, Cronbach's alpha, and Pearson correlation coefficient values of the total scale and subscales were in a better state, which strongly demonstrates the structure, discrimination, and convergence effectiveness of the scale 57 .

The scale structure and items of this study are reliable and effective, which means that students in the field of interior design can use them to test their HOTS level and assess their qualities and abilities. In addition, scholars can use this scale to explore the relationships between students' HOTS and external factors, personal personalities, etc., to determine different methods and strategies for developing and improving HOTS.

Limitations and future research

The developed mixed learning HOTS assessment scale for interior design also has certain limitations that need to be addressed in future research. The first issue is that, owing to the requirement of practical innovation skills, students need to have certain practical experience and innovative abilities. First-grade students usually have not yet had sufficient opportunities for learning and practical experience, so it may not be possible to evaluate their abilities effectively in this dimension. Therefore, when this scale is used for assessment, it is necessary to consider students' grade level and learning experience to ensure the applicability and accuracy of the assessment tool. For first-grade students, it may be necessary to use other assessment tools that are suitable for their developmental stage and learning experience to evaluate other aspects of their HOTS 7 . Future research should focus on expanding the scope of this dimension to ensure greater applicability.

The second issue is that the sample comes from ordinary private undergraduate universities in central China and does not come from national public universities or key universities. Therefore, there may be regional characteristics in the obtained data. These findings suggest that the improved model should be validated with a wider range of regional origins, a more comprehensive school hierarchy, and a larger sample size. The thirdly issue is the findings of this study are derived from self-reported data collected from participants through surveys. However, it is important to note that the literature suggests caution in heavily relying on such self-reported data, as perception does not always equate to actions 66 . In addition, future research can draw on this scale to evaluate the HOTS of interior design students, explore the factors that affect their development, determine their training and improvement paths, and cultivate skilled talent for the twenty-first century.

This study adopts a mixed method research approach, combining qualitative and quantitative methods to achieve a comprehensive understanding of the phenomenon 67 . By integrating qualitative and quantitative research methods, mixed methods research provides a comprehensive and detailed exploration of research questions, using multiple data sources and analytical methods to obtain accurate and meaningful answers 68 . To increase the quality of the research, the entire study followed the guidelines for scale development procedures outlined by Professor Li after the data were obtained. As shown in Fig.  3

Scale development program.

Basis of theory

This study is guided by educational objectives such as 21st-century learning skills, the "5C" competency framework, and students' core abilities 4 . The construction process of the scale is based on theoretical foundations, including Bloom's taxonomy. Drawing from existing research, such as the CCTDI 41 , SPSI 69 , and TWKSAT scales, the dimensions and preliminary items of the scale were developed. Additionally, to enhance the validity and reliability of the scale, dimensions related to HOTS in interior design were obtained through semi-structured interviews, and the preliminary project adapted or directly cited existing research results. The preliminary items were primarily adapted or directly referenced from existing research findings. Based on existing research, such as the CCTDI, SPSI, TWKSAT, and twenty-first century skills frameworks, this study takes "critical thinking skills, problem-solving skills, teamwork skills, and practical innovative skills" as the four basic dimensions of the scale.

Participants and procedures

This study is based on previous research and develops a HOTS assessment scale to measure the thinking levels of interior design students in blended learning. By investigating the challenges and opportunities students encounter in blended learning environments and exploring the complexity and diversity of their HOTS, this study aims to obtain comprehensive insights. For research question 1, via the purposive sampling method, 10 interior design experts are selected to investigate the dimensions and evaluation indicators of HOTS in blended learning of interior design. The researcher employed a semi structured interview method, and a random sampling technique was used to select 10 senior experts and teachers in the field of interior design, holding the rank of associate professor or above. This included 5 males and 5 females. As shown in Table 16 .

For research question 2 and 3, the research was conducted at an undergraduate university in China, in the field of interior design and within a blended learning environment. In addition, a statement confirms that all experimental plans have been approved by the authorized committee of Zhengzhou University of Finance and Economics. In the process of practice, the methods used were all in accordance with relevant guidelines and regulations, and informed consent was obtained from all participants. The Interior Design Blended Learning HOTS assessment scale was developed based on sample data from 350 students who underwent one pre-test and retest. The participants in the study consisted of second-, third-, and fourth-grade students who had participated in at least one blended learning course. The sample sizes were 115, 118, and 117 for the respective grade levels, totaling 350 individuals. Among the participants, there were 218 male students and 132 female students, all of whom were within the age range of 19–22 years. Through purposeful sampling, this study ensured the involvement of relevant participants and focused on a specific university environment with diverse demographic characteristics and rich educational resources.

This approach enhances the reliability and generalizability of the research and contributes to a deeper understanding of the research question (as shown in Table 17 ).

Instruments

The tools used in this study include semi structured interview guidelines and the HOTS assessment scale developed by the researchers. For research question 1, the semi structured interview guidelines were reviewed by interior design experts to ensure the accuracy and appropriateness of their content and questions. In addition, for research question 2 and 3, the HOTS assessment scale developed by the researchers will be checked via the consistency ratio (CR) method to assess the consistency and reliability of the scale items and validate their effectiveness.

Data analysis

For research question 1, the researcher will utilize the NVivo version 14 software tool to conduct thematic analysis on the data obtained through semi structured interviews. Thematic analysis is a commonly used qualitative research method that aims to identify and categorize themes, concepts, and perspectives that emerge within a dataset 70 . By employing NVivo software, researchers can effectively organize and manage large amounts of textual data and extract themes and patterns from them.

For research question 2, the critical ratio (CR) method was employed to conduct item analysis and homogeneity testing on the items of the pilot test questionnaire. The CR method allows for the assessment of each item's contribution to the total score and the evaluation of the interrelationships among the items within the questionnaire. These analytical techniques served to facilitate the evaluation and validation of the scale's reliability and validity.

For research question 3, this study used SPSS (version 26), in which confirmatory factor analysis (CFA) was conducted on the confirmatory sample data via maximum likelihood estimation. The purpose of this analysis was to verify whether the hypothesized factor structure model of the questionnaire aligned with the actual survey data. Finally, several indices, including composite reliability (CR), average variance extracted (CR), average variance extracted (AVE), Cronbach's alpha coefficient, and the Pearson correlation coefficient, were computed to assess the reliability and validity of the developed scale and assess its reliability and validity.

In addition, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are commonly utilized techniques in questionnaire development and adaptation research 31 , 70 . The statistical software packages SPSS and AMOS are frequently employed for implementing these analytical techniques 71 , 72 , 73 . CFA is a data-driven approach to factor generation that does not require a predetermined number of factors or specific relationships with observed variables. Its focus lies in the numerical characteristics of the data. Therefore, prior to conducting CFA, survey questionnaires are typically constructed through EFA to reveal the underlying structure and relationships between observed variables and the latent structure.

In contrast, CFA tests the hypothesized model structure under specific theoretical assumptions or structural hypotheses, including the interrelationships among factors and the known number of factors. Its purpose is to validate the hypothesized model structure. Thus, the initial validity of the questionnaire structure, established through EFA, necessitates further confirmation through CFA 57 , 70 . Additionally, a sample size of at least 200 is recommended for conducting the validation factor analysis. In this study, confirmatory factor analysis was performed on a sample size of 317.

Data availability

All data generated or analyzed during this study are included in this published article. All the experimental protocols were approved by the Zhengzhou College of Finance and Economics licensing committee.

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D.L. Conceptualized a text experiment, and wrote the main manuscript text. D.L. and X.F. conducted experiments, D.L., X.F. and L.M. analyzed the results. L.M. contributed to the conceptualization, methodology and editing, and critically reviewed the manuscript. All authors have reviewed the manuscript.

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Li, D., Fan, X. & Meng, L. Development and validation of a higher-order thinking skills (HOTS) scale for major students in the interior design discipline for blended learning. Sci Rep 14 , 20287 (2024). https://doi.org/10.1038/s41598-024-70908-3

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DOI : https://doi.org/10.1038/s41598-024-70908-3

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