do qualitative studies have a research question

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• Product Demos
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence

Market Research

• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Qualitative Research Questions

Try Qualtrics for free

How to write qualitative research questions.

11 min read Here’s how to write effective qualitative research questions for your projects, and why getting it right matters so much.

What is qualitative research?

Qualitative research is a blanket term covering a wide range of research methods and theoretical framing approaches. The unifying factor in all these types of qualitative study is that they deal with data that cannot be counted. Typically this means things like people’s stories, feelings, opinions and emotions , and the meanings they ascribe to their experiences.

Qualitative study is one of two main categories of research, the other being quantitative research. Quantitative research deals with numerical data – that which can be counted and quantified, and which is mostly concerned with trends and patterns in large-scale datasets.

What are research questions?

Research questions are questions you are trying to answer with your research. To put it another way, your research question is the reason for your study, and the beginning point for your research design. There is normally only one research question per study, although if your project is very complex, you may have multiple research questions that are closely linked to one central question.

A good qualitative research question sums up your research objective. It’s a way of expressing the central question of your research, identifying your particular topic and the central issue you are examining.

Research questions are quite different from survey questions, questions used in focus groups or interview questions. A long list of questions is used in these types of study, as opposed to one central question. Additionally, interview or survey questions are asked of participants, whereas research questions are only for the researcher to maintain a clear understanding of the research design.

Research questions are used in both qualitative and quantitative research , although what makes a good research question might vary between the two.

In fact, the type of research questions you are asking can help you decide whether you need to take a quantitative or qualitative approach to your research project.

Discover the fundamentals of qualitative research

Quantitative vs. qualitative research questions

Writing research questions is very important in both qualitative and quantitative research, but the research questions that perform best in the two types of studies are quite different.

Quantitative research questions

Quantitative research questions usually relate to quantities, similarities and differences.

It might reflect the researchers’ interest in determining whether relationships between variables exist, and if so whether they are statistically significant. Or it may focus on establishing differences between things through comparison, and using statistical analysis to determine whether those differences are meaningful or due to chance.

• How much? This kind of research question is one of the simplest. It focuses on quantifying something. For example:

How many Yoruba speakers are there in the state of Maine?

• What is the connection?

This type of quantitative research question examines how one variable affects another.

For example:

How does a low level of sunlight affect the mood scores (1-10) of Antarctic explorers during winter?

• What is the difference? Quantitative research questions in this category identify two categories and measure the difference between them using numerical data.

Do white cats stay cooler than tabby cats in hot weather?

If your research question fits into one of the above categories, you’re probably going to be doing a quantitative study.

Qualitative research questions

Qualitative research questions focus on exploring phenomena, meanings and experiences.

Unlike quantitative research, qualitative research isn’t about finding causal relationships between variables. So although qualitative research questions might touch on topics that involve one variable influencing another, or looking at the difference between things, finding and quantifying those relationships isn’t the primary objective.

In fact, you as a qualitative researcher might end up studying a very similar topic to your colleague who is doing a quantitative study, but your areas of focus will be quite different. Your research methods will also be different – they might include focus groups, ethnography studies, and other kinds of qualitative study.

A few example qualitative research questions:

• What is it like being an Antarctic explorer during winter?
• What are the experiences of Yoruba speakers in the USA?
• How do white cat owners describe their pets?

Qualitative research question types

Marshall and Rossman (1989) identified 4 qualitative research question types, each with its own typical research strategy and methods.

• Exploratory questions

Exploratory questions are used when relatively little is known about the research topic. The process researchers follow when pursuing exploratory questions might involve interviewing participants, holding focus groups, or diving deep with a case study.

• Explanatory questions

With explanatory questions, the research topic is approached with a view to understanding the causes that lie behind phenomena. However, unlike a quantitative project, the focus of explanatory questions is on qualitative analysis of multiple interconnected factors that have influenced a particular group or area, rather than a provable causal link between dependent and independent variables.

• Descriptive questions

As the name suggests, descriptive questions aim to document and record what is happening. In answering descriptive questions , researchers might interact directly with participants with surveys or interviews, as well as using observational studies and ethnography studies that collect data on how participants interact with their wider environment.

• Predictive questions

Predictive questions start from the phenomena of interest and investigate what ramifications it might have in the future. Answering predictive questions may involve looking back as well as forward, with content analysis, questionnaires and studies of non-verbal communication (kinesics).

Why are good qualitative research questions important?

We know research questions are very important. But what makes them so essential? (And is that question a qualitative or quantitative one?)

Getting your qualitative research questions right has a number of benefits.

• It defines your qualitative research project Qualitative research questions definitively nail down the research population, the thing you’re examining, and what the nature of your answer will be.This means you can explain your research project to other people both inside and outside your business or organization. That could be critical when it comes to securing funding for your project, recruiting participants and members of your research team, and ultimately for publishing your results. It can also help you assess right the ethical considerations for your population of study.
• It maintains focus Good qualitative research questions help researchers to stick to the area of focus as they carry out their research. Keeping the research question in mind will help them steer away from tangents during their research or while they are carrying out qualitative research interviews. This holds true whatever the qualitative methods are, whether it’s a focus group, survey, thematic analysis or other type of inquiry.That doesn’t mean the research project can’t morph and change during its execution – sometimes this is acceptable and even welcome – but having a research question helps demarcate the starting point for the research. It can be referred back to if the scope and focus of the project does change.
• It helps make sure your outcomes are achievable

Because qualitative research questions help determine the kind of results you’re going to get, it helps make sure those results are achievable. By formulating good qualitative research questions in advance, you can make sure the things you want to know and the way you’re going to investigate them are grounded in practical reality. Otherwise, you may be at risk of taking on a research project that can’t be satisfactorily completed.

Developing good qualitative research questions

All researchers use research questions to define their parameters, keep their study on track and maintain focus on the research topic. This is especially important with qualitative questions, where there may be exploratory or inductive methods in use that introduce researchers to new and interesting areas of inquiry. Here are some tips for writing good qualitative research questions.

1. Keep it specific

Broader research questions are difficult to act on. They may also be open to interpretation, or leave some parameters undefined.

Strong example: How do Baby Boomers in the USA feel about their gender identity?

Weak example: Do people feel different about gender now?

2. Be original

Look for research questions that haven’t been widely addressed by others already.

Strong example: What are the effects of video calling on women’s experiences of work?

Weak example: Are women given less respect than men at work?

3. Make it research-worthy

Don’t ask a question that can be answered with a ‘yes’ or ‘no’, or with a quick Google search.

Strong example: What do people like and dislike about living in a highly multi-lingual country?

Weak example: What languages are spoken in India?

4. Focus your question

Don’t roll multiple topics or questions into one. Qualitative data may involve multiple topics, but your qualitative questions should be focused.

Strong example: What is the experience of disabled children and their families when using social services?

Weak example: How can we improve social services for children affected by poverty and disability?

4. Focus on your own discipline, not someone else’s

Avoid asking questions that are for the politicians, police or others to address.

Strong example: What does it feel like to be the victim of a hate crime?

Weak example: How can hate crimes be prevented?

5. Ask something researchable

Big questions, questions about hypothetical events or questions that would require vastly more resources than you have access to are not useful starting points for qualitative studies. Qualitative words or subjective ideas that lack definition are also not helpful.

Strong example: How do perceptions of physical beauty vary between today’s youth and their parents’ generation?

Weak example: Which country has the most beautiful people in it?

Related resources

Qualitative research design 12 min read, 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, request demo.

Ready to learn more about Qualtrics?

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Seminal Authors
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Locating Theoretical and Conceptual Frameworks This link opens in a new window
• Quantitative Research Questions

Qualitative Research Questions

• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

What’s in a Qualitative Research Question?

Qualitative research questions are driven by the need for the study. Ideally, research questions are formulated as a result of the problem and purpose, which leads to the identification of the methodology. When a qualitative methodology is chosen, research questions should be exploratory and focused on the actual phenomenon under study.

From the Dissertation Center, Chapter 1: Research Question Overview , there are several considerations when forming a qualitative research question. Qualitative research questions should

Below is an example of a qualitative phenomenological design. Note the use of the term “lived experience” in the central research question. This aligns with phenomenological design.

RQ1: “ What are the lived experiences of followers of mid-level managers in the financial services sector regarding their well-being on the job?”

If the researcher wants to focus on aspects of the theory used to support the study or dive deeper into aspects of the central RQ, sub-questions might be used. The following sub-questions could be formulated to seek further insight:

RQ1a.   “How do followers perceive the quality and adequacy of the leader-follower exchanges between themselves and their novice leaders?”

RQ1b.  “Under what conditions do leader-member exchanges affect a follower’s own level of well-being?”

Qualitative research questions also display the desire to explore or describe phenomena. Qualitative research seeks the lived experience, the personal experiences, the understandings, the meanings, and the stories associated with the concepts present in our studies.

We want to ensure our research questions are answerable and that we are not making assumptions about our sample. View the questions below:

How do healthcare providers perceive income inequality when providing care to poor patients?

In Example A, we see that there is no specificity of location or geographic areas. This could lead to findings that are varied, and the researcher may not find a clear pattern. Additionally, the question implies the focus is on “income inequality” when the actual focus is on the provision of care. The term “poor patients” can also be offensive, and most providers will not want to seem insensitive and may perceive income inequality as a challenge (of course!).

How do primary care nurses in outreach clinics describe providing quality care to residents of low-income urban neighborhoods?

In Example B, we see that there is greater specificity in the type of care provider. There is also a shift in language so that the focus is on how the individuals describe what they think about, experience, and navigate providing quality care.

Other Qualitative Research Question Examples

Vague : What are the strategies used by healthcare personnel to assist injured patients?

Try this : What is the experience of emergency room personnel in treating patients with a self-inflicted household injury?

The first question is general and vague. While in the same topic area, the second question is more precise and gives the reader a specific target population and a focus on the phenomenon they would have experienced. This question could be in line with a phenomenological study as we are seeking their experience or a case study as the ER personnel are a bounded entity.

Unclear : How do students experience progressing to college?

Try this : How do first-generation community members describe the aspects of their culture that promote aspiration to postsecondary education?

The first question does not have a focus on what progress is or what students are the focus. The second question provides a specific target population and provides the description to be provided by the participants. This question could be in line with a descriptive study.

• << Previous: Quantitative Research Questions
• Next: Trustworthiness of Qualitative Data >>
• Last Updated: Sep 7, 2024 9:42 AM
• URL: https://resources.nu.edu/researchtools

Chapter 4. Finding a Research Question and Approaches to Qualitative Research

We’ve discussed the research design process in general and ways of knowing favored by qualitative researchers.  In chapter 2, I asked you to think about what interests you in terms of a focus of study, including your motivations and research purpose.  It might be helpful to start this chapter with those short paragraphs you wrote about motivations and purpose in front of you.  We are now going to try to develop those interests into actual research questions (first part of this chapter) and then choose among various “traditions of inquiry” that will be best suited to answering those questions.  You’ve already been introduced to some of this (in chapter 1), but we will go further here.

Developing a Research Question

Research questions are different from general questions people have about the social world.  They are narrowly tailored to fit a very specific issue, complete with context and time boundaries.  Because we are engaged in empirical science and thus use “data” to answer our questions, the questions we ask must be answerable by data.  A question is not the same as stating a problem.  The point of the entire research project is to answer a particular question or set of questions.  The question(s) should be interesting, relevant, practical, and ethical.  Let’s say I am generally interested in the problem of student loan debt.  That’s a good place to start, but we can’t simply ask,

General question: Is student loan debt really a problem today?

How could we possibly answer that question? What data could we use? Isn’t this really an axiological (values-based) question? There are no clues in the question as to what data would be appropriate here to help us get started. Students often begin with these large unanswerable questions. They are not research questions. Instead, we could ask,

Poor research question: How many people have debt?

This is still not a very good research question. Why not? It is answerable, although we would probably want to clarify the context. We could add some context to improve it so that the question now reads,

Mediocre research question: How many people in the US have debt today? And does this amount vary by age and location?

Now we have added some context, so we have a better idea of where to look and who to look at. But this is still a pretty poor or mediocre research question. Why is that? Let’s say we did answer it. What would we really know? Maybe we would find out that student loan debt has increased over time and that young people today have more of it. We probably already know this. We don’t really want to go through a lot of trouble answering a question whose answer we already have. In fact, part of the reason we are even asking this question is that we know (or think) it is a problem. Instead of asking what you already know, ask a question to which you really do not know the answer. I can’t stress this enough, so I will say it again: Ask a question to which you do not already know the answer . The point of research is not to prove or make a point but to find out something unknown. What about student loan debt is still a mystery to you? Reviewing the literature could help (see chapter 9). By reviewing the literature, you can get a good sense of what is still mysterious or unknown about student loan debt, and you won’t be reinventing the wheel when you conduct your research. Let’s say you review the literature, and you are struck by the fact that we still don’t understand the true impact of debt on how people are living their lives. A possible research question might be,

Fair research question: What impact does student debt have on the lives of debtors?

Good start, but we still need some context to help guide the project. It is not nearly specific enough.

Better research question: What impact does student debt have on young adults (ages twenty-five to thirty-five) living in the US today?

Now we’ve added context, but we can still do a little bit better in narrowing our research question so that it is both clear and doable; in other words, we want to frame it in a way that provides a very clear research program:

Optimal research question: How do young adults (ages twenty-five to thirty-five) living in the US today who have taken on $30,000 or more in student debt describe the impact of their debt on their lives in terms of finding/choosing a job, buying a house, getting married, and other major life events?

Now you have a research question that can be answered and a clear plan of how to answer it. You will talk to young adults living in the US today who have high debt loads and ask them to describe the impacts of debt on their lives. That is all now in the research question. Note how different this very specific question is from where we started with the “problem” of student debt.

Take some time practicing turning the following general questions into research questions:

• What can be done about the excessive use of force by police officers?
• Why haven’t societies taken firmer steps to address climate change?
• How do communities react to / deal with the opioid epidemic?
• Who has been the most adversely affected by COVID?
• When did political polarization get so bad?

Hint: Step back from each of the questions and try to articulate a possible underlying motivation, then formulate a research question that is specific and answerable.

It is important to take the time to come up with a research question, even if this research question changes a bit as you conduct your research (yes, research questions can change!). If you don’t have a clear question to start your research, you are likely to get very confused when designing your study because you will not be able to make coherent decisions about things like samples, sites, methods of data collection, and so on. Your research question is your anchor: “If we don’t have a question, we risk the possibility of going out into the field thinking we know what we’ll find and looking only for proof of what we expect to be there. That’s not empirical research (it’s not systematic)” ( Rubin 2021:37 ).

Researcher Note

How do you come up with ideas for what to study?

I study what surprises me. Usually, I come across a statistic that suggests something is common that I thought was rare. I tend to think it’s rare because the theories I read suggest it should be, and there’s not a lot of work in that area that helps me understand how the statistic came to be. So, for example, I learned that it’s common for Americans to marry partners who grew up in a different class than them and that about half of White kids born into the upper-middle class are downwardly mobile. I was so shocked by these facts that they naturally led to research questions. How do people come to marry someone who grew up in a different class? How do White kids born near the top of the class structure fall?

—Jessi Streib, author of The Power of the Past and Privilege Lost

What if you have literally no idea what the research question should be? How do you find a research question? Even if you have an interest in a topic before you get started, you see the problem now: topics and issues are not research questions! A research question doesn’t easily emerge; it takes a lot of time to hone one, as the practice above should demonstrate. In some research designs, the research question doesn’t even get clearly articulated until the end of data collection . More on that later. But you must start somewhere, of course. Start with your chosen discipline. This might seem obvious, but it is often overlooked. There is a reason it is called a discipline. We tend to think of “sociology,” “public health,” and “physics” as so many clusters of courses that are linked together by subject matter, but they are also disciplines in the sense that the study of each focuses the mind in a particular way and for particular ends. For example, in my own field, sociology, there is a loosely shared commitment to social justice and a general “sociological imagination” that enables its practitioners to connect personal experiences to society at large and to historical forces. It is helpful to think of issues and questions that are germane to your discipline. Within that overall field, there may be a particular course or unit of study you found most interesting. Within that course or unit of study, there may be an issue that intrigued you. And finally, within that issue, there may be an aspect or topic that you want to know more about.

When I was pursuing my dissertation research, I was asked often, “Why did you choose to study intimate partner violence among Native American women?” This question is necessary, and each time I answered, it helped shape me into a better researcher. I was interested in intimate partner violence because I am a survivor. I didn’t have intentions to work with a particular population or demographic—that came from my own deep introspection on my role as a researcher. I always questioned my positionality: What privileges do I hold as an academic? How has public health extracted information from institutionally marginalized populations? How can I build bridges between communities using my position, knowledge, and power? Public health as a field would not exist without the contributions of Indigenous people. So I started hanging out with them at community events, making friends, and engaging in self-education. Through these organic relationships built with Native women in the community, I saw that intimate partner violence was a huge issue. This led me to partner with Indigenous organizations to pursue a better understanding of how Native survivors of intimate partner violence seek support.

—Susanna Y. Park, PhD, mixed-methods researcher in public health and author of “How Native Women Seek Support as Survivors of Intimate Partner Violence: A Mixed-Methods Study”

One of the most exciting and satisfying things about doing academic research is that whatever you end up researching can become part of the body of knowledge that we have collectively created. Don’t make the mistake of thinking that you are doing this all on your own from scratch. Without even being aware of it, no matter if you are a first-year undergraduate student or a fourth-year graduate student, you have been trained to think certain questions are interesting. The very fact that you are majoring in a particular field or have signed up for years of graduate study in a program testifies to some level of commitment to a discipline. What we are looking for, ideally, is that your research builds on in some way (as extension, as critique, as lateral move) previous research and so adds to what we, collectively, understand about the social world. It is helpful to keep this in mind, as it may inspire you and also help guide you through the process. The point is, you are not meant to be doing something no one has ever thought of before, even if you are trying to find something that does not exactly duplicate previous research: “You may be trying to be too clever—aiming to come up with a topic unique in the history of the universe, something that will have people swooning with admiration at your originality and intellectual precociousness. Don’t do it. It’s safer…to settle on an ordinary, middle-of-the-road topic that will lend itself to a nicely organized process of project management. That’s the clever way of proceeding.… You can always let your cleverness shine through during the stages of design, analysis, and write-up. Don’t make things more difficult for yourself than you need to do” ( Davies 2007:20 ).

Rubin ( 2021 ) suggests four possible ways to develop a research question (there are many more, of course, but this can get you started). One way is to start with a theory that interests you and then select a topic where you can apply that theory. For example, you took a class on gender and society and learned about the “glass ceiling.” You could develop a study that tests that theory in a setting that has not yet been explored—maybe leadership at the Oregon Country Fair. The second way is to start with a topic that interests you and then go back to the books to find a theory that might explain it. This is arguably more difficult but often much more satisfying. Ask your professors for help—they might have ideas of theories or concepts that could be relevant or at least give you an idea of what books to read. The third way is to be very clever and select a question that already combines the topic and the theory. Rubin gives as one example sentencing disparities in criminology—this is both a topic and a theory or set of theories. You then just have to figure out particulars like setting and sample. I don’t know if I find this third way terribly helpful, but it might help you think through the possibilities. The fourth way involves identifying a puzzle or a problem, which can be either theoretical (something in the literature just doesn’t seem to make sense and you want to tackle addressing it) or empirical (something happened or is happening, and no one really understands why—think, for example, of mass school shootings).

Once you think you have an issue or topic that is worth exploring, you will need to (eventually) turn that into a good research question. A good research question is specific, clear, and feasible .

Specific . How specific a research question needs to be is somewhat related to the disciplinary conventions and whether the study is conceived inductively or deductively. In deductive research, one begins with a specific research question developed from the literature. You then collect data to test the theory or hypotheses accompanying your research question. In inductive research, however, one begins with data collection and analysis and builds theory from there. So naturally, the research question is a bit vaguer. In general, the more closely aligned to the natural sciences (and thus the deductive approach), the more a very tight and specific research question (along with specific, focused hypotheses) is required. This includes disciplines like psychology, geography, public health, environmental science, and marine resources management. The more one moves toward the humanities pole (and the inductive approach), the more looseness is permitted, as there is a general belief that we go into the field to find what is there, not necessarily what we imagine we are looking for (see figure 4.2). Disciplines such as sociology, anthropology, and gender and sexuality studies and some subdisciplines of public policy/public administration are closer to the humanities pole in this sense.

Regardless of discipline and approach, however, it is a good idea for beginning researchers to create a research question as specific as possible, as this will serve as your guide throughout the process. You can tweak it later if needed, but start with something specific enough that you know what it is you are doing and why. It is more difficult to deal with ambiguity when you are starting out than later in your career, when you have a better handle on what you are doing. Being under a time constraint means the more specific the question, the better. Questions should always specify contexts, geographical locations, and time frames. Go back to your practice research questions and make sure that these are included.

Clear . A clear research question doesn’t only need to be intelligible to any reader (which, of course, it should); it needs to clarify any meanings of particular words or concepts (e.g., What is excessive force?). Check all your concepts to see if there are ways you can clarify them further—for example, note that we shifted from impact of debt to impact of high debt load and specified this as beginning at $30,000. Ideally, we would use the literature to help us clarify what a high debt load is or how to define “excessive” force.

Feasible . In order to know if your question is feasible, you are going to have to think a little bit about your entire research design. For example, a question that asks about the real-time impact of COVID restrictions on learning outcomes would require a time machine. You could tweak the question to ask instead about the long-term impacts of COVID restrictions, as measured two years after their end. Or let’s say you are interested in assessing the damage of opioid abuse on small-town communities across the United States. Is it feasible to cover the entire US? You might need a team of researchers to do this if you are planning on on-the-ground observations. Perhaps a case study of one particular community might be best. Then your research question needs to be changed accordingly.

Here are some things to consider in terms of feasibility:

• Is the question too general for what you actually intend to do or examine? (Are you specifying the world when you only have time to explore a sliver of that world?)
• Is the question suitable for the time you have available? (You will need different research questions for a study that can be completed in a term than one where you have one to two years, as in a master’s program, or even three to eight years, as in a doctoral program.)
• Is the focus specific enough that you know where and how to begin?
• What are the costs involved in doing this study, including time? Will you need to travel somewhere, and if so, how will you pay for it?
• Will there be problems with “access”? (More on this in later chapters, but for now, consider how you might actually find people to interview or places to observe and whether gatekeepers exist who might keep you out.)
• Will you need to submit an application proposal for your university’s IRB (institutional review board)? If you are doing any research with live human subjects, you probably need to factor in the time and potential hassle of an IRB review (see chapter 8). If you are under severe time constraints, you might need to consider developing a research question that can be addressed with secondary sources, online content, or historical archives (see chapters 16 and 17).

In addition to these practicalities, you will also want to consider the research question in terms of what is best for you now. Are you engaged in research because you are required to be—jumping a hurdle for a course or for your degree? If so, you really do want to think about your project as training and develop a question that will allow you to practice whatever data collection and analysis techniques you want to develop. For example, if you are a grad student in a public health program who is interested in eventually doing work that requires conducting interviews with patients, develop a research question and research design that is interview based. Focus on the practicality (and practice) of the study more than the theoretical impact or academic contribution, in other words. On the other hand, if you are a PhD candidate who is seeking an academic position in the future, your research question should be pitched in a way to build theoretical knowledge as well (the phrasing is typically “original contribution to scholarship”).

The more time you have to devote to the study and the larger the project, the more important it is to reflect on your own motivations and goals when crafting a research question (remember chapter 2?). By “your own motivations and goals,” I mean what interests you about the social world and what impact you want your research to have, both academically and practically speaking. Many students have secret (or not-so-secret) plans to make the world a better place by helping address climate change, pointing out pressure points to fight inequities, or bringing awareness to an overlooked area of concern. My own work in graduate school was motivated by the last of these three—the not-so-secret goal of my research was to raise awareness about obstacles to success for first-generation and working-class college students. This underlying goal motivated me to complete my dissertation in a timely manner and then to further continue work in this area and see my research get published. I cared enough about the topic that I was not ready to put it away. I am still not ready to put it away. I encourage you to find topics that you can’t put away, ever. That will keep you going whenever things get difficult in the research process, as they inevitably will.

On the other hand, if you are an undergraduate and you really have very little time, some of the best advice I have heard is to find a study you really like and adapt it to a new context. Perhaps you read a study about how students select majors and how this differs by class ( Hurst 2019 ). You can try to replicate the study on a small scale among your classmates. Use the same research question, but revise for your context. You can probably even find the exact questions I  used and ask them in the new sample. Then when you get to the analysis and write-up, you have a comparison study to guide you, and you can say interesting things about the new context and whether the original findings were confirmed (similar) or not. You can even propose reasons why you might have found differences between one and the other.

Another way of thinking about research questions is to explicitly tie them to the type of purpose of your study. Of course, this means being very clear about what your ultimate purpose is! Marshall and Rossman ( 2016 ) break down the purpose of a study into four categories: exploratory, explanatory, descriptive, and emancipatory ( 78 ). Exploratory purpose types include wanting to investigate little-understood phenomena, or identifying or discovering important new categories of meaning, or generating hypotheses for further research. For these, research questions might be fairly loose: What is going on here? How are people interacting on this site? What do people talk about when you ask them about the state of the world? You are almost (but never entirely) starting from scratch. Be careful though—just because a topic is new to you does not mean it is really new. Someone else (or many other someones) may already have done this exploratory research. Part of your job is to find this out (more on this in “What Is a ‘Literature Review’?” in chapter 9). Descriptive purposes (documenting and describing a phenomenon) are similar to exploratory purposes but with a much clearer goal (description). A good research question for a descriptive study would specify the actions, events, beliefs, attitudes, structures, and/or processes that will be described.

Most researchers find that their topic has already been explored and described, so they move to trying to explain a relationship or phenomenon. For these, you will want research questions that capture the relationships of interest. For example, how does gender influence one’s understanding of police brutality (because we already know from the literature that it does, so now we are interested in understanding how and why)? Or what is the relationship between education and climate change denialism? If you find that prior research has already provided a lot of evidence about those relationships as well as explanations for how they work, and you want to move the needle past explanation into action, you might find yourself trying to conduct an emancipatory study. You want to be even more clear in acknowledging past research if you find yourself here. Then create a research question that will allow you to “create opportunities and the will to engage in social action” ( Marshall and Rossman 2016:78 ). Research questions might ask, “How do participants problematize their circumstances and take positive social action?” If we know that some students have come together to fight against student debt, how are they doing this, and with what success? Your purpose would be to help evaluate possibilities for social change and to use your research to make recommendations for more successful emancipatory actions.

Recap: Be specific. Be clear. Be practical. And do what you love.

Choosing an Approach or Tradition

Qualitative researchers may be defined as those who are working with data that is not in numerical form, but there are actually multiple traditions or approaches that fall under this broad category. I find it useful to know a little bit about the history and development of qualitative research to better understand the differences in these approaches. The following chart provides an overview of the six phases of development identified by Denzin and Lincoln ( 2005 ):

Table 4.1. Six Phases of Development

There are other ways one could present the history as well. Feminist theory and methodologies came to the fore in the 1970s and 1980s and had a lot to do with the internal critique of more positivist approaches. Feminists were quite aware that standpoint matters—that the identity of the researcher plays a role in the research, and they were ardent supporters of dismantling unjust power systems and using qualitative methods to help advance this mission. You might note, too, that many of the internal disputes were basically epistemological disputes about how we know what we know and whether one’s social location/position delimits that knowledge. Today, we are in a bountiful world of qualitative research, one that embraces multiple forms of knowing and knowledge. This is good, but it means that you, the student, have more choice when it comes to situating your study and framing your research question, and some will expect you to signal the choices you have made in any research protocols you write or publications and presentations.

Creswell’s ( 1998 ) definition of qualitative research includes the notion of distinct traditions of inquiry: “Qualitative research is an inquiry process of understanding based on distinct methodological traditions of inquiry that explore a social or human problem. The research builds complex,   holistic pictures, analyzes words, reports detailed views of informants , and conducted the study in a natural setting” (15; emphases added). I usually caution my students against taking shelter under one of these approaches, as, practically speaking, there is a lot of mixing of traditions among researchers. And yet it is useful to know something about the various histories and approaches, particularly as you are first starting out. Each tradition tends to favor a particular epistemological perspective (see chapter 3), a way of reasoning (see “ Advanced: Inductive versus Deductive Reasoning ”), and a data-collection technique.

There are anywhere from ten to twenty “traditions of inquiry,” depending on how one draws the boundaries. In my accounting, there are twelve, but three approaches tend to dominate the field.

Ethnography

Ethnography was developed from the discipline of anthropology, as the study of (other) culture(s). From a relatively positivist/objective approach to writing down the “truth” of what is observed during the colonial era (where this “truth” was then often used to help colonial administrators maintain order and exploit people and extract resources more effectively), ethnography was adopted by all kinds of social science researchers to get a better understanding of how groups of people (various subcultures and cultures) live their lives. Today, ethnographers are more likely to be seeking to dismantle power relations than to support them. They often study groups of people that are overlooked and marginalized, and sometimes they do the obverse by demonstrating how truly strange the familiar practices of the dominant group are. Ethnography is also central to organizational studies (e.g., How does this institution actually work?) and studies of education (e.g., What is it like to be a student during the COVID era?).

Ethnographers use methods of participant observation and intensive fieldwork in their studies, often living or working among the group under study for months at a time (and, in some cases, years). I’ve called this “deep ethnography,” and it is the subject of chapter 14. The data ethnographers analyze are copious “field notes” written while in the field, often supplemented by in-depth interviews and many more casual conversations. The final product of ethnographers is a “thick” description of the culture. This makes reading ethnographies enjoyable, as the goal is to write in such a way that the reader feels immersed in the culture.

There are variations on the ethnography, such as the autoethnography , where the researcher uses a systematic and rigorous study of themselves to better understand the culture in which they find themselves. Autoethnography is a relatively new approach, even though it is derived from one of the oldest approaches. One can say that it takes to heart the feminist directive to “make the personal political,” to underscore the connections between personal experiences and larger social and political structures. Introspection becomes the primary data source.

Grounded Theory

Grounded Theory holds a special place in qualitative research for a few reasons, not least of which is that nonqualitative researchers often mistakenly believe that Grounded Theory is the only qualitative research methodology . Sometimes, it is easier for students to explain what they are doing as “Grounded Theory” because it sounds “more scientific” than the alternative descriptions of qualitative research. This is definitely part of its appeal. Grounded Theory is the name given to the systematic inductive approach first developed by Glaser and Strauss in 1967, The Discovery of Grounded Theory: Strategies for Qualitative Research . Too few people actually read Glaser and Strauss’s book. It is both groundbreaking and fairly unremarkable at the same time. As a historical intervention into research methods generally, it is both a sharp critique of positivist methods in the social sciences (theory testing) and a rejection of purely descriptive accounts-building qualitative research. Glaser and Strauss argued for an approach whose goal was to construct (middle-level) theories from recursive data analysis of nonnumerical data (interviews and observations). They advocated a “constant comparative method” in which coding and analysis take place simultaneously and recursively. The demands are fairly strenuous. If done correctly, the result is the development of a new theory about the social world.

So why do I call this “fairly unremarkable”? To some extent, all qualitative research already does what Glaser and Strauss ( 1967 ) recommend, albeit without denoting the processes quite so specifically. As will be seen throughout the rest of this textbook, all qualitative research employs some “constant comparisons” through recursive data analyses. Where Grounded Theory sets itself apart from a significant number of qualitative research projects, however, is in its dedication to inductively building theory. Personally, I think it is important to understand that Glaser and Strauss were rejecting deductive theory testing in sociology when they first wrote their book. They were part of a rising cohort who rejected the positivist mathematical approaches that were taking over sociology journals in the 1950s and 1960s. Here are some of the comments and points they make against this kind of work:

Accurate description and verification are not so crucial when one’s purpose is to generate theory. ( 28 ; further arguing that sampling strategies are different when one is not trying to test a theory or generalize results)

Illuminating perspectives are too often suppressed when the main emphasis is verifying theory. ( 40 )

Testing for statistical significance can obscure from theoretical relevance. ( 201 )

Instead, they argued, sociologists should be building theories about the social world. They are not physicists who spend time testing and refining theories. And they are not journalists who report descriptions. What makes sociologists better than journalists and other professionals is that they develop theory from their work “In their driving efforts to get the facts [research sociologists] tend to forget that the distinctive offering of sociology to our society is sociological theory, not research description” ( 30–31 ).

Grounded Theory’s inductive approach can be off-putting to students who have a general research question in mind and a working hypothesis. The true Grounded Theory approach is often used in exploratory studies where there are no extant theories. After all, the promise of this approach is theory generation, not theory testing. Flying totally free at the start can be terrifying. It can also be a little disingenuous, as there are very few things under the sun that have not been considered before. Barbour ( 2008:197 ) laments that this approach is sometimes used because the researcher is too lazy to read the relevant literature.

To summarize, Glaser and Strauss justified the qualitative research project in a way that gave it standing among the social sciences, especially vis-à-vis quantitative researchers. By distinguishing the constant comparative method from journalism, Glaser and Strauss enabled qualitative research to gain legitimacy.

So what is it exactly, and how does one do it? The following stages provide a succinct and basic overview, differentiating the portions that are similar to/in accordance with qualitative research methods generally and those that are distinct from the Grounded Theory approach:

Step 1. Select a case, sample, and setting (similar—unless you begin with a theory to test!).

Step 2. Begin data collection (similar).

Step 3. Engage data analysis (similar in general but specificity of details somewhat unique to Grounded Theory): (1) emergent coding (initial followed by focused), (2) axial (a priori) coding , (3) theoretical coding , (4) creation of theoretical categories; analysis ends when “theoretical saturation ” has been achieved.

Grounded Theory’s prescriptive (i.e., it has a set of rules) framework can appeal to beginning students, but it is unnecessary to adopt the entire approach in order to make use of some of its suggestions. And if one does not exactly follow the Grounded Theory rulebook, it can mislead others if you tend to call what you are doing Grounded Theory when you are not:

Grounded theory continues to be a misunderstood method, although many researchers purport to use it. Qualitative researchers often claim to conduct grounded theory studies without fully understanding or adopting its distinctive guidelines. They may employ one or two of the strategies or mistake qualitative analysis for grounded theory. Conversely, other researchers employ grounded theory methods in reductionist, mechanistic ways. Neither approach embodies the flexible yet systematic mode of inquiry, directed but open-ended analysis, and imaginative theorizing from empirical data that grounded theory methods can foster. Subsequently, the potential of grounded theory methods for generating middle-range theory has not been fully realized ( Charmaz 2014 ).

Phenomenology

Where Grounded Theory sets itself apart for its inductive systematic approach to data analysis, phenomenologies are distinct for their focus on what is studied—in this case, the meanings of “lived experiences” of a group of persons sharing a particular event or circumstance. There are phenomenologies of being working class ( Charlesworth 2000 ), of the tourist experience ( Cohen 1979 ), of Whiteness ( Ahmed 2007 ). The phenomenon of interest may also be an emotion or circumstance. One can study the phenomenon of “White rage,” for example, or the phenomenon of arranged marriage.

The roots of phenomenology lie in philosophy (Husserl, Heidegger, Merleau-Ponty, Sartre) but have been adapted by sociologists in particular. Phenomenologists explore “how human beings make sense of experience and transform experience into consciousness, both individually and as shared meaning” ( Patton 2002:104 ).

One of the most important aspects of conducting a good phenomenological study is getting the sample exactly right so that each person can speak to the phenomenon in question. Because the researcher is interested in the meanings of an experience, in-depth interviews are the preferred method of data collection. Observations are not nearly as helpful here because people may do a great number of things without meaning to or without being conscious of their implications. This is important to note because phenomenologists are studying not “the reality” of what happens at all but an articulated understanding of a lived experience. When reading a phenomenological study, it is important to keep this straight—too often I have heard students critique a study because the interviewer didn’t actually see how people’s behavior might conflict with what they say (which is, at heart, an epistemological issue!).

In addition to the “big three,” there are many other approaches; some are variations, and some are distinct approaches in their own right. Case studies focus explicitly on context and dynamic interactions over time and can be accomplished with quantitative or qualitative methods or a mixture of both (for this reason, I am not considering it as one of the big three qualitative methods, even though it is a very common approach). Whatever methods are used, a contextualized deep understanding of the case (or cases) is central.

Critical inquiry is a loose collection of techniques held together by a core argument that understanding issues of power should be the focus of much social science research or, to put this another way, that it is impossible to understand society (its people and institutions) without paying attention to the ways that power relations and power dynamics inform and deform those people and institutions. This attention to power dynamics includes how research is conducted too. All research fundamentally involves issues of power. For this reason, many critical inquiry traditions include a place for collaboration between researcher and researched. Examples include (1) critical narrative analysis, which seeks to describe the meaning of experience for marginalized or oppressed persons or groups through storytelling; (2) participatory action research, which requires collaboration between the researcher and the research subjects or community of interest; and (3) critical race analysis, a methodological application of Critical Race Theory (CRT), which posits that racial oppression is endemic (if not always throughout time and place, at least now and here).

Do you follow a particular tradition of inquiry? Why?

Shawn Wilson’s book, Research Is Ceremony: Indigenous Research Methods , is my holy grail. It really flipped my understanding of research and relationships. Rather than thinking linearly and approaching research in a more canonical sense, Wilson shook my world view by drawing me into a pattern of inquiry that emphasized transparency and relational accountability. The Indigenous research paradigm is applicable in all research settings, and I follow it because it pushes me to constantly evaluate my position as a knowledge seeker and knowledge sharer.

Autoethnography takes the researcher as the subject. This is one approach that is difficult to explain to more quantitatively minded researchers, as it seems to violate many of the norms of “scientific research” as understood by them. First, the sample size is quite small—the n is 1, the researcher. Two, the researcher is not a neutral observer—indeed, the subjectivity of the researcher is the main strength of this approach. Autoethnographies can be extremely powerful for their depth of understanding and reflexivity, but they need to be conducted in their own version of rigor to stand up to scrutiny by skeptics. If you are skeptical, read one of the excellent published examples out there—I bet you will be impressed with what you take away. As they say, the proof is in the pudding on this approach.

Advanced: Inductive versus Deductive Reasoning

There has been a great deal of ink shed in the discussion of inductive versus deductive approaches, not all of it very instructive. Although there is a huge conceptual difference between them, in practical terms, most researchers cycle between the two, even within the same research project. The simplest way to explain the difference between the two is that we are using deductive reasoning when we test an existing theory (move from general to particular), and we are using inductive reasoning when we are generating theory (move from particular to general). Figure 4.2 provides a schematic of the deductive approach. From the literature, we select a theory about the impact of student loan debt: student loan debt will delay homeownership among young adults. We then formulate a hypothesis based on this theory: adults in their thirties with high debt loads will be less likely to own homes than their peers who do not have high debt loads. We then collect data to test the hypothesis and analyze the results. We find that homeownership is substantially lower among persons of color and those who were the first in their families to graduate from college. Notably, high debt loads did not affect homeownership among White adults whose parents held college degrees. We thus refine the theory to match the new findings: student debt loads delay homeownership among some young adults, thereby increasing inequalities in this generation. We have now contributed new knowledge to our collective corpus.

The inductive approach is contrasted in figure 4.3. Here, we did not begin with a preexisting theory or previous literature but instead began with an observation. Perhaps we were conducting interviews with young adults who held high amounts of debt and stumbled across this observation, struck by how many were renting apartments or small houses. We then noted a pattern—not all the young adults we were talking to were renting; race and class seemed to play a role here. We would then probably expand our study in a way to be able to further test this developing theory, ensuring that we were not seeing anomalous patterns. Once we were confident about our observations and analyses, we would then develop a theory, coming to the same place as our deductive approach, but in reverse.

A third form of reasoning, abductive (sometimes referred to as probabilistic reasoning) was developed in the late nineteenth century by American philosopher Charles Sanders Peirce. I have included some articles for further reading for those interested.

Among social scientists, the deductive approach is often relaxed so that a research question is set based on the existing literature rather than creating a hypothesis or set of hypotheses to test. Some journals still require researchers to articulate hypotheses, however. If you have in mind a publication, it is probably a good idea to take a look at how most articles are organized and whether specific hypotheses statements are included.

Table 4.2. Twelve Approaches. Adapted from Patton 2002:132-133.

Further Readings

The following readings have been examples of various approaches or traditions of inquiry:

Ahmed, Sara. 2007. “A Phenomenology of Whiteness.” Feminist Theory 8(2):149–168.

Charlesworth, Simon. 2000. A Phenomenology of Working-Class Experience . Cambridge: Cambridge University Press.*

Clandinin, D. Jean, and F. Michael Connelly. 2000. Narrative Inquiry: Experience and Story in Qualitative Research . San Francisco: Jossey-Bass.

Cohen, E. 1979. “A Phenomenology of Tourist Experiences.” Sociology 13(2):179–201.

Cooke, Bill, and Uma Kothari, eds. 2001. Participation: The New Tyranny? London: Zed Books. A critique of participatory action.

Corbin, Juliet, and Anselm Strauss. 2008. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 3rd ed. Thousand Oaks, CA: SAGE.

Crabtree, B. F., and W. L. Miller, eds. 1999. Doing Qualitative Research: Multiple Strategies . Thousand Oaks, CA: SAGE.

Creswell, John W. 1997. Qualitative Inquiry and Research Design: Choosing among Five Approaches. Thousand Oaks, CA: SAGE.

Glaser, Barney G., and Anselm Strauss. 1967. The Discovery of Grounded Theory: Strategies for Qualitative Research . New York: Aldine.

Gobo, Giampetro, and Andrea Molle. 2008. Doing Ethnography . Thousand Oaks, CA: SAGE.

Hancock, Dawson B., and Bob Algozzine. 2016. Doing Case Study Research: A Practical Guide for Beginning Research . 3rd ed. New York: Teachers College Press.

Harding, Sandra. 1987. Feminism and Methodology . Bloomington: Indiana University Press.

Husserl, Edmund. (1913) 2017. Ideas: Introduction to Pure Phenomenology . Eastford, CT: Martino Fine Books.

Rose, Gillian. 2012. Visual Methodologies . 3rd ed. London: SAGE.

Van der Riet, M. 2009. “Participatory Research and the Philosophy of Social Science: Beyond the Moral Imperative.” Qualitative Inquiry 14(4):546–565.

Van Manen, Max. 1990. Researching Lived Experience: Human Science for an Action Sensitive Pedagogy . Albany: State University of New York.

Wortham, Stanton. 2001. Narratives in Action: A Strategy for Research and Analysis . New York: Teachers College Press.

Inductive, Deductive, and Abductive Reasoning and Nomothetic Science in General

Aliseda, Atocha. 2003. “Mathematical Reasoning vs. Abductive Reasoning: A Structural Approach.” Synthese 134(1/2):25–44.

Bonk, Thomas. 1997. “Newtonian Gravity, Quantum Discontinuity and the Determination of Theory by Evidence.” Synthese 112(1):53–73. A (natural) scientific discussion of inductive reasoning.

Bonnell, Victoria E. 1980. “The Uses of Theory, Concepts and Comparison in Historical Sociology.” C omparative Studies in Society and History 22(2):156–173.

Crane, Mark, and Michael C. Newman. 1996. “Scientific Method in Environmental Toxicology.” Environmental Reviews 4(2):112–122.

Huang, Philip C. C., and Yuan Gao. 2015. “Should Social Science and Jurisprudence Imitate Natural Science?” Modern China 41(2):131–167.

Mingers, J. 2012. “Abduction: The Missing Link between Deduction and Induction. A Comment on Ormerod’s ‘Rational Inference: Deductive, Inductive and Probabilistic Thinking.’” Journal of the Operational Research Society 63(6):860–861.

Ormerod, Richard J. 2010. “Rational Inference: Deductive, Inductive and Probabilistic Thinking.” Journal of the Operational Research Society 61(8):1207–1223.

Perry, Charner P. 1927. “Inductive vs. Deductive Method in Social Science Research.” Southwestern Political and Social Science Quarterly 8(1):66–74.

Plutynski, Anya. 2011. “Four Problems of Abduction: A Brief History.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 1(2):227–248.

Thompson, Bruce, and Gloria M. Borrello. 1992. “Different Views of Love: Deductive and Inductive Lines of Inquiry.” Current Directions in Psychological Science 1(5):154–156.

Tracy, Sarah J. 2012. “The Toxic and Mythical Combination of a Deductive Writing Logic for Inductive Qualitative Research.” Qualitative Communication Research 1(1):109–141.

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

A person who introduces the researcher to a field site’s culture and population.  Also referred to as guides.  Used in ethnography .

A form of research and a methodological tradition of inquiry in which the researcher uses self-reflection and writing to explore personal experiences and connect this autobiographical story to wider cultural, political, and social meanings and understandings.  “Autoethnography is a research method that uses a researcher's personal experience to describe and critique cultural beliefs, practices, and experiences” ( Adams, Jones, and Ellis 2015 ).

The philosophical framework in which research is conducted; the approach to “research” (what practices this entails, etc.).  Inevitably, one’s epistemological perspective will also guide one’s methodological choices, as in the case of a constructivist who employs a Grounded Theory approach to observations and interviews, or an objectivist who surveys key figures in an organization to find out how that organization is run.  One of the key methodological distinctions in social science research is that between quantitative and qualitative research.

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

A later stage coding process used in Grounded Theory in which data is reassembled around a category, or axis.

A later stage-coding process used in Grounded Theory in which key words or key phrases capture the emergent theory.

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

A methodological tradition of inquiry that focuses on the meanings held by individuals and/or groups about a particular phenomenon (e.g., a “phenomenology of whiteness” or a “phenomenology of first-generation college students”).  Sometimes this is referred to as understanding “the lived experience” of a particular group or culture.  Interviews form the primary tool of data collection for phenomenological studies.  Derived from the German philosophy of phenomenology (Husserl 1913; 2017).

The number of individuals (or units) included in your sample

A form of reasoning which employs a “top-down” approach to drawing conclusions: it begins with a premise or hypothesis and seeks to verify it (or disconfirm it) with newly collected data.  Inferences are made based on widely accepted facts or premises.  Deduction is idea-first, followed by observations and a conclusion.  This form of reasoning is often used in quantitative research and less often in qualitative research.  Compare to inductive reasoning .  See also abductive reasoning .

A form of reasoning that employs a “bottom-up” approach to drawing conclusions: it begins with the collection of data relevant to a particular question and then seeks to build an argument or theory based on an analysis of that data.  Induction is observation first, followed by an idea that could explain what has been observed.  This form of reasoning is often used in qualitative research and seldom used in qualitative research.  Compare to deductive reasoning .  See also abductive reasoning .

An “interpretivist” form of reasoning in which “most likely” conclusions are drawn, based on inference.  This approach is often used by qualitative researchers who stress the recursive nature of qualitative data analysis.  Compare with deductive reasoning and inductive reasoning .

A form of social science research that generally follows the scientific method as established in the natural sciences.  In contrast to idiographic research , the nomothetic researcher looks for general patterns and “laws” of human behavior and social relationships.  Once discovered, these patterns and laws will be expected to be widely applicable.  Quantitative social science research is nomothetic because it seeks to generalize findings from samples to larger populations.  Most qualitative social science research is also nomothetic, although generalizability is here understood to be theoretical in nature rather than statistical .  Some qualitative researchers, however, espouse the idiographic research paradigm instead.

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.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved September 6, 2024, from https://www.scribbr.com/methodology/qualitative-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, what is your plagiarism score.

Qualitative Research Questions: Gain Powerful Insights + 25 Examples

We review the basics of qualitative research questions, including their key components, how to craft them effectively, & 25 example questions.

Einstein was many things—a physicist, a philosopher, and, undoubtedly, a mastermind. He also had an incredible way with words. His quote, "Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted," is particularly poignant when it comes to research. 

Some inquiries call for a quantitative approach, for counting and measuring data in order to arrive at general conclusions. Other investigations, like qualitative research, rely on deep exploration and understanding of individual cases in order to develop a greater understanding of the whole. That’s what we’re going to focus on today.

Qualitative research questions focus on the "how" and "why" of things, rather than the "what". They ask about people's experiences and perceptions , and can be used to explore a wide range of topics.

The following article will discuss the basics of qualitative research questions, including their key components, and how to craft them effectively. You'll also find 25 examples of effective qualitative research questions you can use as inspiration for your own studies.

Let’s get started!

What are qualitative research questions, and when are they used?

When researchers set out to conduct a study on a certain topic, their research is chiefly directed by an overarching question . This question provides focus for the study and helps determine what kind of data will be collected.

By starting with a question, we gain parameters and objectives for our line of research. What are we studying? For what purpose? How will we know when we’ve achieved our goals?

Of course, some of these questions can be described as quantitative in nature. When a research question is quantitative, it usually seeks to measure or calculate something in a systematic way.

For example:

• How many people in our town use the library?
• What is the average income of families in our city?
• How much does the average person weigh?

Other research questions, however—and the ones we will be focusing on in this article—are qualitative in nature. Qualitative research questions are open-ended and seek to explore a given topic in-depth.

According to the Australian & New Zealand Journal of Psychiatry , “Qualitative research aims to address questions concerned with developing an understanding of the meaning and experience dimensions of humans’ lives and social worlds.”

This type of research can be used to gain a better understanding of people’s thoughts, feelings and experiences by “addressing questions beyond ‘what works’, towards ‘what works for whom when, how and why, and focusing on intervention improvement rather than accreditation,” states one paper in Neurological Research and Practice .

Qualitative questions often produce rich data that can help researchers develop hypotheses for further quantitative study.

• What are people’s thoughts on the new library?
• How does it feel to be a first-generation student at our school?
• How do people feel about the changes taking place in our town?

As stated by a paper in Human Reproduction , “...‘qualitative’ methods are used to answer questions about experience, meaning, and perspective, most often from the standpoint of the participant. These data are usually not amenable to counting or measuring.”

Both quantitative and qualitative questions have their uses; in fact, they often complement each other. A well-designed research study will include a mix of both types of questions in order to gain a fuller understanding of the topic at hand.

If you would like to recruit unlimited participants for qualitative research for free and only pay for the interview you conduct, try using Respondent  today. 

Crafting qualitative research questions for powerful insights

Now that we have a basic understanding of what qualitative research questions are and when they are used, let’s take a look at how you can begin crafting your own.

According to a study in the International Journal of Qualitative Studies in Education, there is a certain process researchers should follow when crafting their questions, which we’ll explore in more depth.

1. Beginning the process 

Start with a point of interest or curiosity, and pose a draft question or ‘self-question’. What do you want to know about the topic at hand? What is your specific curiosity? You may find it helpful to begin by writing several questions.

For example, if you’re interested in understanding how your customer base feels about a recent change to your product, you might ask: 

• What made you decide to try the new product?
• How do you feel about the change?
• What do you think of the new design/functionality?
• What benefits do you see in the change?

2. Create one overarching, guiding question 

At this point, narrow down the draft questions into one specific question. “Sometimes, these broader research questions are not stated as questions, but rather as goals for the study.”

As an example of this, you might narrow down these three questions: 

into the following question: 

• What are our customers’ thoughts on the recent change to our product?

3. Theoretical framing 

As you read the relevant literature and apply theory to your research, the question should be altered to achieve better outcomes. Experts agree that pursuing a qualitative line of inquiry should open up the possibility for questioning your original theories and altering the conceptual framework with which the research began.

If we continue with the current example, it’s possible you may uncover new data that informs your research and changes your question. For instance, you may discover that customers’ feelings about the change are not just a reaction to the change itself, but also to how it was implemented. In this case, your question would need to reflect this new information: 

• How did customers react to the process of the change, as well as the change itself?

4. Ethical considerations 

A study in the International Journal of Qualitative Studies in Education stresses that ethics are “a central issue when a researcher proposes to study the lives of others, especially marginalized populations.” Consider how your question or inquiry will affect the people it relates to—their lives and their safety. Shape your question to avoid physical, emotional, or mental upset for the focus group.

In analyzing your question from this perspective, if you feel that it may cause harm, you should consider changing the question or ending your research project. Perhaps you’ve discovered that your question encourages harmful or invasive questioning, in which case you should reformulate it.

5. Writing the question 

The actual process of writing the question comes only after considering the above points. The purpose of crafting your research questions is to delve into what your study is specifically about” Remember that qualitative research questions are not trying to find the cause of an effect, but rather to explore the effect itself.

Your questions should be clear, concise, and understandable to those outside of your field. In addition, they should generate rich data. The questions you choose will also depend on the type of research you are conducting: 

• If you’re doing a phenomenological study, your questions might be open-ended, in order to allow participants to share their experiences in their own words.
• If you’re doing a grounded-theory study, your questions might be focused on generating a list of categories or themes.
• If you’re doing ethnography, your questions might be about understanding the culture you’re studying.

Whenyou have well-written questions, it is much easier to develop your research design and collect data that accurately reflects your inquiry.

In writing your questions, it may help you to refer to this simple flowchart process for constructing questions:

Download Free E-Book 

25 examples of expertly crafted qualitative research questions

It's easy enough to cover the theory of writing a qualitative research question, but sometimes it's best if you can see the process in practice. In this section, we'll list 25 examples of B2B and B2C-related qualitative questions.

Let's begin with five questions. We'll show you the question, explain why it's considered qualitative, and then give you an example of how it can be used in research.

1. What is the customer's perception of our company's brand?

Qualitative research questions are often open-ended and invite respondents to share their thoughts and feelings on a subject. This question is qualitative because it seeks customer feedback on the company's brand. 

This question can be used in research to understand how customers feel about the company's branding, what they like and don't like about it, and whether they would recommend it to others.

2. Why do customers buy our product?

This question is also qualitative because it seeks to understand the customer's motivations for purchasing a product. It can be used in research to identify the reasons  customers buy a certain product, what needs or desires the product fulfills for them, and how they feel about the purchase after using the product.

3. How do our customers interact with our products?

Again, this question is qualitative because it seeks to understand customer behavior. In this case, it can be used in research to see how customers use the product, how they interact with it, and what emotions or thoughts the product evokes in them.

4. What are our customers' biggest frustrations with our products?

By seeking to understand customer frustrations, this question is qualitative and can provide valuable insights. It can be used in research to help identify areas in which the company needs to make improvements with its products.

5. How do our customers feel about our customer service?

Rather than asking why customers like or dislike something, this question asks how they feel. This qualitative question can provide insights into customer satisfaction or dissatisfaction with a company. 

This type of question can be used in research to understand what customers think of the company's customer service and whether they feel it meets their needs.

20 more examples to refer to when writing your question

Now that you’re aware of what makes certain questions qualitative, let's move into 20 more examples of qualitative research questions:

• How do your customers react when updates are made to your app interface?
• How do customers feel when they complete their purchase through your ecommerce site?
• What are your customers' main frustrations with your service?
• How do people feel about the quality of your products compared to those of your competitors?
• What motivates customers to refer their friends and family members to your product or service?
• What are the main benefits your customers receive from using your product or service?
• How do people feel when they finish a purchase on your website?
• What are the main motivations behind customer loyalty to your brand?
• How does your app make people feel emotionally?
• For younger generations using your app, how does it make them feel about themselves?
• What reputation do people associate with your brand?
• How inclusive do people find your app?
• In what ways are your customers' experiences unique to them?
• What are the main areas of improvement your customers would like to see in your product or service?
• How do people feel about their interactions with your tech team?
• What are the top five reasons people use your online marketplace?
• How does using your app make people feel in terms of connectedness?
• What emotions do people experience when they're using your product or service?
• Aside from the features of your product, what else about it attracts customers?
• How does your company culture make people feel?

As you can see, these kinds of questions are completely open-ended. In a way, they allow the research and discoveries made along the way to direct the research. The questions are merely a starting point from which to explore.

This video offers tips on how to write good qualitative research questions, produced by Qualitative Research Expert, Kimberly Baker.

Wrap-up: crafting your own qualitative research questions.

Over the course of this article, we've explored what qualitative research questions are, why they matter, and how they should be written. Hopefully you now have a clear understanding of how to craft your own.

Remember, qualitative research questions should always be designed to explore a certain experience or phenomena in-depth, in order to generate powerful insights. As you write your questions, be sure to keep the following in mind:

• Are you being inclusive of all relevant perspectives?
• Are your questions specific enough to generate clear answers?
• Will your questions allow for an in-depth exploration of the topic at hand?
• Do the questions reflect your research goals and objectives?

If you can answer "yes" to all of the questions above, and you've followed the tips for writing qualitative research questions we shared in this article, then you're well on your way to crafting powerful queries that will yield valuable insights.

Download Free E-Book

Asking the right questions in the right way is the key to research success. That’s true for not just the discussion guide but for every step of a research project. Following are 100+ questions that will take you from defining your research objective through  screening and participant discussions.

Fill out the form below to access free e-book! 

Recommend Resources:

• How to Recruit Participants for Qualitative Research
• The Best UX Research Tools of 2022
• 10 Smart Tips for Conducting Better User Interviews
• 50 Powerful Questions You Should Ask In Your Next User Interview
• How To Find Participants For User Research: 13 Ways To Make It Happen
• UX Diary Study: 5 Essential Tips For Conducing Better Studies
• User Testing Recruitment: 10 Smart Tips To Find Participants Fast
• Qualitative Research Questions: Gain Powerful Insights + 25
• How To Successfully Recruit Participants for A Study (2022 Edition)
• How To Properly Recruit Focus Group Participants (2022 Edition)
• The Best Unmoderated Usability Testing Tools of 2022

50 Powerful User Interview Questions You Should Consider Asking

We researched the best user interview questions you can use for your qualitative research studies. Use these 50 sample questions for your next...

A Guide to Usability Testing Questions (Including 100 Examples)

Asking the right questions in the right way is the key to the success of your UX research project. With tips and 100+ question examples, Respondent...

How To ​​Unleash Your Extra Income Potential With Respondent

The number one question we get from new participants is “how can I get invited to participate in more projects.” In this article, we’ll discuss a few...

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Qualitative Research Questions: What it is and how to write it

Qualitative research questions are like a compass that points researchers in the right direction to find rich stories, untangle complicated social relationships, and get a clear picture of how people act in subtle ways. Unlike their quantitative counterparts, these questions go beyond numbers and figures to explore the subjective, contextual, and complex parts of the human experience.

It’s well-established that all forms of research come with their own theories and implementation methods. Qualitative research is much the same. Qualitative research is conducted to understand the thought process of both the respondents as well as researchers. It usually is conducted in a natural setup where respondents will be their true selves and would respond transparently. 

Results achieved from this research will not be generalized to the entire population but asked research questions , and their vocabulary gives away the researcher’s motive making it easier for respondents to participate in qualitative market research .

LEARN ABOUT: Research Process Steps

Qualitative research survey questions are created to understand a particular topic better or to inspect a new subject to understand the nerve of respondent experiences.

Content Index

What are qualitative research questions?

How to write qualitative research questions, types of qualitative research questions, how to choose qualitative research questions, what should be the process of forming qualitative research questions and questionnaires.

Qualitative research questions are the inquiries that lead to qualitative research studies and investigations. They are meant to help people explore and understand phenomena, experiences, meanings, and views from the participant’s point of view. 

Different from quantitative research questions, which often try to measure and quantify variables, qualitative research questions try to understand the richness and complexity of human experiences and social events.

Most qualitative research questions are open-ended and allow for in-depth study. They want more than simple yes/no answers but instead want people to talk about their thoughts, feelings, views, and experiences. These questions try to find deeper meanings, patterns, and connections in a given situation.

Here are some examples of qualitative study questions in different fields:

• In psychology: How do individuals experience and cope with traumatic events?
• In sociology: What factors influence a student’s decision to pursue higher education?
• In anthropology: How do cultural norms and values shape gender roles in a specific community?
• In education: What are the challenges faced by teachers in implementing project-based learning in the classroom?
• In healthcare: What are the experiences and perspectives of patients undergoing long-term treatment for a chronic illness?

Qualitative research questions should be straightforward, specific, and tailored to the research’s goals. They guide the process of gathering data through interviews, observations, or document analysis and give a method for analyzing and interpreting data.

Writing the right qualitative research questions requires careful thought about the research goals, the event being studied, and the wanted level of understanding. Here are some tips to help you write good qualitative research questions:

Begin with a broad research question

Start by posing an all-encompassing question that probes the subject or phenomenon of interest. Exploring and learning from the answer to this open-ended question should be possible.

Specify the research objectives

Clearly state the objectives and purposes of your research. What do you want your qualitative study to accomplish? What facets or dimensions of the subject do you wish to investigate?

Focus on the phenomenon

Decide on whatever specific subject or phenomenon you want to research. Any pertinent topic, including social behavior, cultural customs, personal experiences, and more, may be used.

Use open-ended and exploratory language

In qualitative research, open-ended questions should be used to enable participants to offer thorough and in-depth responses. Avoid yes/no questions and queries with a one-word answer. Use words like “how,” “what,” “why,” or “describe” instead to compel people to express their thoughts and experiences.

LEARN ABOUT: Qualitative Interview

Consider the context and participants

Consider your research’s background as well as the qualities of your subjects. Make sure your qualitative methods are specific to the people you will be studying so that they are pertinent and meaningful to them.

Incorporate theory and literature

Your research questions should be based on pertinent theories and available literature. This gives your investigation a theoretical foundation and places your study within the body of knowledge.

Balance breadth and depth

When formulating your research topics, try to strike a balance between depth and breadth. To fully understand the subject, you should investigate it broadly to get a variety of viewpoints and intensively delve into certain areas.

Avoid leading or biased questions

Ensure your questions are neutral and unbiased. Avoid leading participants towards a particular response. Instead, create questions that allow participants to express their thoughts and experiences freely.

Pilot test your questions

Pilot-test your research questions with a small group of people before finalizing them. This will make it easier to spot any possible problems, ambiguities, or places where clarity may be increased.

Revise and refine

Revise and clarify your research questions based on the comments and understandings received from the pilot testing. Aim for consistency, coherence, and congruence with your research goals.

Remember, qualitative market research questions should be flexible and adaptable throughout the research process. They serve as a guide but may evolve as you delve deeper into the data and discover new insights.

LEARN ABOUT: Steps in Qualitative Research

There are several types of qualitative research questions focus that can be used to guide qualitative studies. Here are some common types:

1. Descriptive questions

These questions aim to describe and understand a phenomenon or topic in detail. They focus on providing a comprehensive account of the subject matter. For example:

• What are the experiences of individuals living with chronic pain?
• How do employees perceive the organizational culture in a specific company?

2. Exploratory questions

These questions are used to explore new or under-researched areas. They seek to gain a deeper understanding of a topic or phenomenon. For example:

• What are the factors influencing consumers’ decision-making process when purchasing organic food?
• How do teachers perceive the implementation of project-based learning in the classroom?

3. Experiential questions

These questions focus on understanding individuals’ experiences, perspectives, and subjective meanings related to a particular phenomenon. They aim to capture personal experiences and emotions. For example:

• What are the challenges first-generation college students face during their transition to higher education?
• How do individuals with social anxiety disorder experience social interactions?

4. Comparative questions

These questions involve comparing and contrasting different groups, contexts, or perspectives to identify similarities, differences, or patterns. They explore variations in experiences or phenomena. For example:

• How do parenting practices differ between cultures A and B in terms of child discipline?
• What are the similarities and differences in the coping strategies used by individuals with individuals and depression questionnaire with anxiety disorders?

5. Process-oriented questions

These questions focus on understanding a phenomenon’s processes, mechanisms, or dynamics. They aim to uncover how and why certain outcomes or behaviors occur. For example:

• What are the processes by which teams in a workplace reach a consensus on decision-making?
• How does the negotiation process unfold during conflict resolution in interpersonal relationships?

6. Theoretical questions

These questions seek to generate or refine theory. They explore concepts, relationships, or theoretical frameworks to contribute to the existing body of knowledge. For example:

• How does the concept of “self-efficacy” manifest in the context of entrepreneurship?
• What underlying mechanisms explain the relationship between social support and mental health outcomes?

These are just a few examples of the types of qualitative research questions that can be used. The specific type of question you choose will depend on your research objectives, the phenomenon under investigation, and the depth of understanding you aim to achieve.

Explore Insightfully Contextual Inquiry in Qualitative Research

Choosing a good qualitative research question involves a thoughtful and systematic approach to ensure they align with the objectives of your study and allows for an in-depth exploration of the topic. Here are some steps to help you choose effective qualitative research questions:

Identify your research objectives

Clearly define the purpose of your study. What do you want to explore or understand? What specific insights or knowledge are you seeking to gain through your market research?

Review existing literature

Conduct a thorough review of relevant literature to identify existing research gaps or areas requiring further exploration. This will help you understand the current state of knowledge and inform the development of your research questions.

Brainstorm potential qualitative research question

Generate a list of potential research questions that address your research objectives. Consider different angles, perspectives, and dimensions of your topic. Creating open-ended questions that allow for in-depth exploration rather than simple yes/no answers is important.

Prioritize and refine the questions

Evaluate the generated questions based on their relevance to your research objectives, feasibility, and potential to yield meaningful insights. Prioritize the questions that are most likely to provide rich and valuable data. Refine and rephrase the questions as needed to ensure clarity and focus.

Consider the research design and methodology

Take into account the specific qualitative research design and methodology you plan to use. Different research approaches, such as ethnography, interviews, focus groups, or case studies, may require different types of research questions. Ensure that your questions align with your chosen methodology and will help you gather the desired data.

Pilot test the questions

Before finalizing your research questions, consider conducting a pilot test with a small group of participants. This will allow you to assess your questions’ clarity, appropriateness, and effectiveness. Make necessary revisions based on the feedback received.

Seek feedback

Share your research questions with colleagues, mentors, or experts in your field for feedback and suggestions. They can provide valuable insights and help you refine your questions further.

Finalize your research questions

Based on the steps above, select a set of research questions that are well-aligned with your research objectives, provide scope for exploration, and are feasible within the resources and time available for your study.

1. Mention the purpose of conducting qualitative research. It can be in the form of either of these sentences:

• This study will be on the topic of ….
• The reason for conducting this research is ….

2. Create qualitative statements with a defined objective that can be easily communicated to the target audience .

Keep these pointers in mind while designing this statement:

• Try and form single-sentence statements. Single statements can be much more effective than elaborate ones as they help in communicating important messages in an impactful manner in a short and succinct sentence.
• Clarify the purpose of conducting qualitative research in clear words so that respondents understand their contribution to this research.
• Mention the main topic of research that would prompt respondents to have a clearer idea about what they’re getting into.
• It’s the words that make all the difference. Use qualitative words that demonstrate the quality or feeling behind your purpose, such as understanding, describing, explore.
• Specify details that you would want to communicate to your respondents.
• Mention the name of the research website.

3. Other than the primary qualitative questions, you must create sub-questions so that the purpose is executed in a better manner.

• The main question might be – “What is the state of illiteracy in your state?”
• You can create sub-questions such as: “How does illiteracy hamper progress in your state?” or “How would you best describe your feelings about illiteracy?”

4. Highlight these questions using ‘qualitative’ words:

• Start the questions with “What” or “How” to make sure the respondents provide details about their feelings.
• Communicate what you’re trying to “understand,” “explore,” or “identify” using this Qualitative research online survey questionnaire.
• Questions such as “What happened” can be asked to develop a description of the topic.
• Questions about “how did respondents interpret the what happened question” can be asked to examine the outcome.
• Understand the entire qualitative research process by asking questions about “What happened to you with time?”

5. Develop a skeleton to design the primary questions and also the sub-questions. For example:

• Primary Qualitative research survey question: “How do you think _______ (the main topic of research) means?” or “Describe _____(the main topic of research) as you’ve experienced.”
• Sub-question for qualitative research: “What _________ (characteristic) does __________ (respondents) interest in as a _________ (main topic of research)?”

LEARN ABOUT: Structured Questionnaire

Qualitative research questions are key to giving research studies depth and breadth. These questions go into the details and complexities of human experiences, perceptions, and behaviors. This helps researchers get a full picture of a certain occurrence. 

Qualitative research questions are meant to explore, describe, and make sense of subjective truths. Most of the time, they are open-ended, so people can say what they think and feel in their own words. 

QuestionPro is an online poll and research platform with several tools and features that can make it easier to make and use qualitative research questions. Its easy-to-use design and variety of question types help researchers collect qualitative data quickly and easily, improving the whole research process.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Experimental vs Observational Studies: Differences & Examples

Sep 5, 2024

Interactive Forms: Key Features, Benefits, Uses + Design Tips

Sep 4, 2024

Closed-Loop Management: The Key to Customer Centricity

Sep 3, 2024

Net Trust Score: Tool for Measuring Trust in Organization

Sep 2, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• (855) 776-7763

Training Maker

All Products

Qualaroo Insights

ProProfs.com

• Get Started Free

FREE. All Features. FOREVER!

Try our Forever FREE account with all premium features!

How to Write Qualitative Research Questions: Types & Examples

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Qualitative research questions focus on depth and quality, exploring the “why and how” behind decisions, without relying on statistical tools.

Unlike quantitative research, which aims to collect tangible, measurable data from a broader demographic, qualitative analysis involves smaller, focused datasets, identifying patterns for insights.

The information collected by qualitative surveys can vary from text to images, demanding a deep understanding of the subject, and therefore, crafting precise qualitative research questions is crucial for success.

In this guide, we’ll discuss how to write effective qualitative research questions, explore various types, and highlight characteristics of good qualitative research questions.

Let’s dive in!

What Are Qualitative Research Questions?

Qualitative questions aim to understand the depth and nuances of a phenomenon, focusing on “why” and “how” rather than quantifiable measures.

They explore subjective experiences, perspectives, and behaviors, often using open-ended inquiries to gather rich, descriptive data.

Unlike quantitative questions, which seek numerical data, qualitative questions try to find out meanings, patterns, and underlying processes within a specific context.

These questions are essential for exploring complex issues, generating hypotheses, and gaining deeper insights into human behavior and phenomena.

Here’s an example of a qualitative research question:

“How do you perceive and navigate organizational culture within a tech startup environment?”

This question asks about the respondent’s subjective interpretations and experiences of organizational culture within a specific context, such as a tech startup.

It seeks to uncover insights into the values, norms, and practices that shape workplace dynamics and employee behaviors, providing qualitative data for analysis and understanding.

When Should We Use Qualitative Research Questions?

Qualitative research questions typically aim to open up conversations, encourage detailed narratives, and foster a deep understanding of the subject matter. Here are some scenarios they are best suited for:

• Exploring Complex Phenomena : When the research topic involves understanding complex processes, behaviors, or interactions that cannot be quantified easily, qualitative questions help delve into these intricate details.
• Understanding Contexts and Cultures : To grasp the nuances of different social contexts, cultures, or subcultures, qualitative research questions allow for an in-depth exploration of these environments and how they influence individuals and groups.
• Exploring Perceptions and Experiences : When the aim is to understand people’s perceptions, experiences, or feelings about a particular subject, qualitative questions facilitate capturing the depth and variety of these perspectives.
• Developing Concepts or Theories : In the early stages of research, where concepts or theories are not yet well-developed, qualitative questions can help generate hypotheses, identify variables, and develop theoretical frameworks based on observations and interpretations.
• Investigating Processes : To understand how processes unfold over time and the factors that influence these processes, qualitative questions are useful for capturing the dynamics and complexities involved.
• Seeking to Understand Change : When researching how individuals or groups experience change, adapt to new circumstances, or make decisions, qualitative research questions can provide insights into the motivations, challenges, and strategies involved.
• Studying Phenomena Not Easily Quantified : For phenomena that are not easily captured through quantitative measures, such as emotions, beliefs, or motivations, qualitative questions can probe these abstract concepts more effectively.
• Addressing Sensitive or Taboo Topics : In studies where topics may be sensitive, controversial, or taboo, qualitative research questions allow for a respectful and empathetic exploration of these subjects, providing space for participants to share their experiences in their own words.

How to Write Qualitative Research Questions?

Read this guide to learn how you can craft well-thought-out qualitative research questions:

1. Begin with Your Research Goals

The first step in formulating qualitative research questions is to have a clear understanding of what you aim to discover or understand through your research. There are two types of qualitative questionnaires or research – Ontological and Epistemological.

Finding out the nature of your research influences all aspects of your research design, including the formulation of research questions.

Subsequently:

• Identify your main objective : Consider the broader context of your study. Are you trying to explore a phenomenon, understand a process, or interpret the meanings behind behaviors? Your main objective should guide the formulation of your questions, ensuring they are aligned with what you seek to achieve.
• Focus on the ‘how’ and ‘why’ : Qualitative research is inherently exploratory and aims to understand the nuances of human behavior and experience. Starting your questions with “how” or “why” encourages a deeper investigation into the motivations, processes, and contexts underlying the subject matter. This approach facilitates an open-ended exploration, allowing participants to provide rich, detailed responses that illuminate their perspectives and experiences.

Take a quick look at the following visual for a better understanding:

So, if you are doing Ontological research, ensure that the questions focus on the “what” aspects of reality (the premise of your research) and opt for the nature of the knowledge for Epistemological research.

2. Choose the Right Structure

The structure of your research questions significantly impacts the depth and quality of data you collect. Opting for an open-ended format allows respondents the flexibility to express themselves freely, providing insights that pre-defined answers might miss.

• Open-ended format : These questions do not constrain respondents to a set of predetermined answers, unlike closed-ended questions. By allowing participants to articulate their thoughts in their own words, you can uncover nuances and complexities in their responses that might otherwise be overlooked.
• Avoid yes/no questions : Yes/no questions tend to limit the depth of responses. While they might be useful for gathering straightforward factual information, they are not conducive to exploring the depths and nuances that qualitative research seeks to uncover. Encouraging participants to elaborate on their experiences and perspectives leads to richer, more informative data.

For example, take a look at some qualitative questions examples shown in the following image:

3. Be Clear and Specific

Clarity and specificity in your questions are crucial to ensure that participants understand what is being asked and that their responses are relevant to your research objectives.

• Use clear language : Use straightforward, understandable language in your questions. Avoid jargon, acronyms, or overly technical terms that might confuse participants or lead to misinterpretation. The goal is to make your questions accessible to everyone involved in your study.
• Be specific : While maintaining the open-ended nature of qualitative questions, it’s important to narrow down your focus to specific aspects of the phenomenon you’re studying. This specificity helps guide participants’ responses and ensures that the data you collect directly relates to your research objectives.

4. Ensure Relevance and Feasibility

Each question should be carefully considered for its relevance to your research goals and its feasibility, given the constraints of your study.

• Relevance : Questions should be crafted to address the core objectives of your research directly. They should probe areas that are essential to understanding the phenomenon under investigation and should align with your theoretical framework or literature review findings.
• Feasibility : Consider the practical aspects of your research, including the time available for data collection and analysis, resources, and access to participants. Questions should be designed to elicit meaningful responses within the constraints of your study, ensuring that you can gather and analyze data effectively.

5. Focus on a Single Concept or Theme per Question

To ensure clarity and depth, each question should concentrate on a single idea or theme. However, if your main qualitative research question is tough to understand or has a complex structure, you can create sub-questions in limited numbers and with a “ladder structure”.

This will help your respondents understand the overall research objective in mind, and your research can be executed in a better manner.

For example, suppose your main question is – “What is the current state of illiteracy in your state?”

Then, you can create the following subquestions: 

“How does illiteracy block progress in your state?”

“How would you best describe the feelings you have about illiteracy in your state?”

For an even better understanding, you can see the various qualitative research question examples in the following image:

Types of Qualitative Research Questions With Examples

Qualitative survey questions primarily focus on a specific group of respondents that are participating in case studies, surveys, ethnography studies, etc., rather than numbers or statistics.

As a result, the questions are mostly open-ended and can be subdivided into the following types as discussed below:

1. Descriptive Questions

Descriptive research questions aim to detail the “what” of a phenomenon, providing a comprehensive overview of the context, individuals, or situations under study. These questions are foundational, helping to establish a baseline understanding of the research topic.

• What are the daily experiences of teachers in urban elementary schools?
• What strategies do small businesses employ to adapt to rapid technological changes?
• How do young adults describe their transition from college to the workforce?
• What are the coping mechanisms of families with members suffering from chronic illnesses?
• How do community leaders perceive the impact of gentrification in their neighborhoods?

2. Interpretive Questions

Interpretive questions seek to understand the “how” and “why” behind a phenomenon, focusing on the meanings people attach to their experiences. These questions delve into the subjective interpretations and perceptions of participants.

• How do survivors of natural disasters interpret their experiences of recovery and rebuilding?
• Why do individuals engage in voluntary work within their communities?
• How do parents interpret and navigate the challenges of remote schooling for their children?
• Why do consumers prefer local products over global brands in certain markets?
• How do artists interpret the influence of digital media on traditional art forms?

3. Comparative Questions

Comparative research questions are designed to explore differences and similarities between groups, settings, or time periods. These questions can help to highlight the impact of specific variables on the phenomenon under study.

• How do the strategies for managing work-life balance compare between remote and office workers?
• What are the differences in consumer behavior towards sustainable products in urban versus rural areas?
• How do parenting styles in single-parent households compare to those in dual-parent households?
• What are the similarities and differences in leadership styles across different cultures?
• How has the perception of online privacy changed among teenagers over the past decade?

4. Process-oriented Questions

These questions focus on understanding the processes or sequences of events over time. They aim to uncover the “how” of a phenomenon, tracing the development, changes, or evolution of specific situations or behaviors.

• How do non-profit organizations develop and implement community outreach programs?
• What is the process of decision-making in high-stakes business environments?
• How do individuals navigate the process of career transition after significant industry changes?
• What are the stages of adaptation for immigrants in a new country?
• How do social movements evolve from inception to national recognition?

5. Evaluative Questions

Evaluative questions aim to assess the effectiveness, value, or impact of a program, policy, or phenomenon. These questions are critical for understanding the outcomes and implications of various initiatives or situations.

• How effective are online therapy sessions compared to in-person sessions in treating anxiety?
• What is the impact of community gardening programs on neighborhood cohesion?
• How do participants evaluate the outcomes of leadership training programs in their professional development?
• What are the perceived benefits and drawbacks of telecommuting for employees and employers?
• How do residents evaluate the effectiveness of local government policies on waste management?

6. One-on-One Questions

The one-on-one questions are asked to a single person and can be thought of as individual interviews that you can conduct online via phone and video chat as well.

The main aim of such questions is to ask your customers or people in the focus group a series of questions about their purchase motivations. These questions might also come with follow-ups, and if your customers respond with some interesting fact or detail, dig deeper and explore the findings as much as you want.

• What makes you happy in regard to [your research topic]?
• If I could make a wish of yours come true, what do you desire the most?
• What do you still find hard to come to terms with?
• Have you bought [your product] before?
• If so, what was your initial motivation behind the purchase?

7. Exploratory Questions

These questions are designed to enhance your understanding of a particular topic. However, while asking exploratory questions, you must ensure that there are no preconceived notions or biases to it. The more transparent and bias-free your questions are, the better and fair results you will get.

• What is the effect of personal smart devices on today’s youth?
• Do you feel that smart devices have positively or negatively impacted you?
• How do your kids spend their weekends?
• What do you do on a typical weekend morning?

8. Predictive Questions

The predictive questions are used for qualitative research that is focused on the future outcomes of an action or a series of actions. So, you will be using past information to predict the reactions of respondents to hypothetical events that might or might not happen in the future.

These questions come in extremely handy for identifying your customers’ current brand expectations, pain points, and purchase motivation.

• Are you more likely to buy a product when a celebrity promotes it?
• Would you ever try a new product because one of your favorite celebs claims that it actually worked for them?
• Would people in your neighborhood enjoy a park with rides and exercise options?
• How often would you go to a park with your kids if it had free rides?

9. Focus Groups

These questions are mostly asked in person to the customer or respondent groups. The in-person nature of these surveys or studies ensures that the group members get a safe and comfortable environment to express their thoughts and feelings about your brand or services.

• How would you describe your ease of using our product?
• How well do you think you were able to do this task before you started using our product?
• What do you like about our promotional campaigns?
• How well do you think our ads convey the meaning?

10. In-Home Videos

Collecting video feedback from customers in their comfortable, natural settings offers a unique perspective. At home, customers are more relaxed and less concerned about their mannerisms, posture, and choice of words when responding.

This approach is partly why Vogue’s 73 Questions Series is highly popular among celebrities and viewers alike. In-home videos provide insights into customers in a relaxed environment, encouraging them to be honest and share genuine experiences.

• What was your first reaction when you used our product for the first time?
• How well do you think our product performed compared to your expectations?
• What was your worst experience with our product?
• What made you switch to our brand?

11. Online Focus Groups

Online focus groups mirror the traditional, in-person format but are conducted virtually, offering a more cost-effective and efficient approach to gathering data. This digital format extends your reach and allows a rapid collection of responses from a broader audience through online platforms.

You can utilize social media and other digital forums to create communities of respondents and initiate meaningful discussions. Once you have them started, you can simply observe the exchange of thoughts and gather massive amounts of interesting insights!

• What do you like best about our product?
• How familiar are you with this particular service or product we offer?
• What are your concerns with our product?
• What changes can we make to make our product better?

Ask the Right Qualitative Research Questions for Meaningful Insights From Your Respondents

Watch: How to Create a Survey Using ProProfs Survey Maker

By now, you might have realized that manually creating a list of qualitative research questions is a daunting task. Keeping numerous considerations in mind, it’s easy to run out of ideas while crafting qualitative survey questions .

However, investing in smart survey tools, like ProProfs Survey Maker, can significantly streamline this process, allowing you to create various types of surveys in minutes.

With this survey tool , you can generate forms, NPS surveys , tests, quizzes, and assessments.

It’s also useful for conducting polls, sidebar surveys, and in-app surveys. Offering over 100 templates and more than 1,000,000 ready-to-use examples of phenomenological research questions, this software simplifies the task immensely.

Equipped with the right tools and the professional tips shared here, you’re well-prepared to conduct thorough research studies and obtain valuable insights that drive impactful results.

Frequently Asked Questions on Q ualitative Research Questions

1. how do you choose qualitative research questions.

To choose qualitative research questions, identify your main research goal, focus on exploring ‘how’ and ‘why’ aspects, ensure questions are open-ended, and align them with your theoretical framework and methodology.

2. Why are good qualitative research questions important?

Good qualitative research questions are important because they guide the research focus, enable the exploration of depth and complexity, and facilitate the gathering of rich, detailed insights into human experiences and behaviors.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

Related Posts

75+ Human Resources Survey Questions To Ask Your Employees

75+ Student Survey Questions to Collect Valuable Students Feedback

Focus Group in Market Research: Types, Examples and Best Practices

360 Feedback Questions: The Roadmap to Enhanced Employee Performance

150+ Poll Questions to Engage Your Target Audience

Proven Tips to Avoid Leading and Loaded Questions in Your Survey

Qualitative Studies

Qualitative Research Studies: Introduction

Introduction

Research design decides how research materials will be collected. One or more research methods, for example -- experiment, survey, interview, etc. -- are chosen depending on the research objectives. In some research contexts, a survey may be suitable. In other instances, interviews or case studies or observation might be more appropriate. Research design actually provides insights into “how” to conduct research using a particular research methodology. Basically, every researcher has a list of research questions that need to be assessed that can be done with research design.

So research design can be defined as a framework of research methods and techniques applied by a researcher to incorporate different elements & components of research in a systematic manner. Most significantly, research design provides insights into how to Conduct Research using a particular research methodology. 

Qualitative Methods try to gather detailed, rich data allowing for an in-depth understanding of research phenomena.  Seeks the “why” rather than the “how.”

Qualitative Data Collection

Data obtained using qualitative data collection methods can be used to find new ideas, opportunities, and problems, test their value and accuracy, formulate predictions, explore a certain field in more detail, and explain the numbers obtained using quantitative data collection techniques.

Since qualitative data collection methods usually do not involve numbers and mathematical calculations, qualitative data is often seen as more subjective, but at the same time, it allows a greater depth of understanding.

Aspers, P., Corte, U. What is Qualitative in Qualitative Research .  Qual Sociol   42 , 139–160 (2019). 

Types of Qualitative Studies

Qualitative study methods are semi-structured or unstructured, usually involve small sample sizes and lack strong scientific controls.

Qualitative Study Methods

Qualitative study methods employ many of the same methods as quantitative data collection, except that instead of structured or closed, they are semi- or unstructured and open-ended.  Some of the most common qualitative  study techniques include open-ended surveys and questionnaires, interviews, focus groups, observation, case studies, and so on.

There is generally five types of qualitative data collection:

• Ethnography research: Involves semi-structure or unstructured interviews with open-ended questions; participant and non-participant observation; collected materials including documents, books, papers, audio, images, videos etc.
• Phenomenological research : I n-depth interviewing which involves conducting intensive individual interviews with a small number of respondents to explore their perspectives on a particular idea, program, or situation.  The participant interviews may be structured, semi-structured or unstructured; it also includes reflective journals; written oral self-reports; and participant’s aesthetic expressions.
• Grounded theory research: Data collection methods often include in-depth interviews using open-ended questions. Questions can be adjusted as theory emerges. Participant observation and focus groups may also be used as well as collecting and studying …  including documents, books, papers, audio, images, artifacts; videos etc. used by participants in their daily lives.
• Narrative: Participant or non-participant interview, aesthetic expressions; one’s own and other’s observation; storytelling; letter writing; autobiographic writing; collected materials …..; personal information such as values. Narrative analysis focuses on different elements to make diverse but equally substantial and meaningful interpretations and conclusions. It is a genre of analytical frames used by researchers to interpret information with the context of research shared by all in daily life. 
• Case study : Focus groups; semi-structured or unstructured interviews with open-ended questions; participant and non-participant observation; collected materials

Nayar, S., & Stanley, D. M. (Eds.). (2015).  Qualitative research methodologies for occupational science and therapy . London: Routledge.

Frank, G., & Polkinghorne, D. (2010). Qualitative Research in Occupational Therapy: From the First to the Second Generation . OTJR (Thorofare, N.J.), 30(2), 51-57.

How To Search for Qualitative Studies

Databases categorize their records using subject terms or controlled vocabularies. These Subject Headings vary for each database.

Medline/PubMed : MeSH Subject Headings

• Qualitative Research : Any type of research that employs nonnumeric information to explore individual or group characteristics, producing findings not arrived at by statistical procedures or other quantitative means.  Includes Document Analysis & Hermaneutics.
• Interviews as Topic:  Works about conversations with an individual or individuals held in order to obtain information about their background and other personal biographical data, their attitudes and opinions, etc. It includes works about school admission or job interviews.
• Focus Groups : A method of data collection and a QUALITATIVE RESEARCH tool in which a small group of individuals are brought together and allowed to interact in a discussion of their opinions about topics, issues, or questions.
• Grounded Theory : The generation of theories from analysis of empirical data.
• Nursing Methodology Research :  Research carried out by nurses concerning techniques and methods to implement projects and to document information, including methods of interviewing patients, collecting data, and forming inferences. The concept includes exploration of methodological issues such as human subjectivity and human experience.
• Anecdotes As Topic : Works about brief accounts or narratives of an incident or event.
• Narration : The act, process, or an instance of narrating, i.e., telling a story. In the context of MEDICINE or ETHICS, narration includes relating the particular and the personal in the life story of an individual.
• Personal Narratives As Topic:  Works about accounts of individual experience in relation to a particular field or of participation in related activities.
• Observational Studies As Topic : Works about clinical studies in which participants may receive diagnostic, therapeutic, or other types of interventions, but the investigator does not assign participants to specific interventions (as in an interventional study).

CINAHL (Cumulative Index to Nursing & Allied Health) : CINAHL Subject Headings 

• Action Research: Research in which problem definition, data collection, factor formulation, planned change, data analysis, and problem redefinition continue in an ongoing cycle.
• Ethnographic Research: Research which seeks to uncover the symbols and categories that members of a given culture use to interpret their world.
• Ethnological Research: Comparison and contrasting of cultures and societies as a whole.
• Ethnonursing Research: The study and analysis of a designated culture's viewpoints, beliefs, and practices about nursing care behavior.
• Grounded Theory: A qualitative method developed by Glaser and Strauss to unite theory construction and data analysis.
• Naturalist Inquiry: The use of the natural setting in research to enable understanding the whole rather than only part of the reality being studied.
• Phenomenological Research: Research designed to discover and understand the meaning of human life experiences.
• Focus Groups : Small groups of individuals brought together to discuss their opinions regarding specific issues, topics, and questions.
• Interviews:  Face-to-face or telephone meetings with subjects for the purpose of gathering information.
• Narratives : Descriptions or interpretations of events, usually in an informal manner. Often used as a data collection method for research. Do not confuse with STORYTELLING, a form of literature or telling a real or imagined story to an audience or listener.
• Descriptive Research : Research studies that have as their main objective the accurate portrayal of the characteristics of persons, situations, or groups, and the frequency with which certain phenomena occur.
• Observational Methods:  Methods of data collection in which the investigator witnesses and records behaviors of interest.
• Projective Techniques : A variety of methods for measuring by providing respondents with unstructured stimuli to which to respond.

In CINHAL, on the Advanced Search page, there are Search Options.  Scroll down to the Clinical Queries drop down box and choose to limit the search to  Qualitative-High Sensitivity; Qualitative-High Specificity ; Qualitative-Best Balance . High Sensitivity is the broadest search, to include ALL relevant material, but may also include less relevant materials. High Specificity is the most targeted search to include only the most relevant result set, but may miss some relevant materials. Best Balance retrieves the best balance between Sensitivity and Specificity.

PsycINFO: Subject Headings

• Grounded Theory
• Narrative Analysis
• Thematic Analysis : A qualitative research strategy for identifying, analyzing, and reporting identifiable patterns or clusters within data.
• Focus Grou p
• Focus Group Interview
• Semi-Structured Interview
• Interpretive Phenomenological Analysis : A systematic qualitative approach in which a researcher explores how individual's make sense of particular experiences, events, and states, primarily through the analysis of data from structured and semi-structured interviews.
• Qualitative Measures : Measures or tests employing qualitative methods and/or data, such as narratives, interviews, and focus groups.

As with CINAHL, you can limit to Methodology.  Click on Additional Limits, scroll down to "Methodology" and choose "Qualitative Study", "Focus Groups" or "Interview".

NOTE!: Be aware of  Inconsistent indexing. The above subject headings as not always indexed (i.e. added to articles) for qualitative research nor is the publication type/methodology.  So, to successfully find qualitative articles you also need to add keywords to your search strategy or if you are getting too few results, leave off the Clinical Queries or Methodology filters.

Free text keywords

Use selective free text keywords to search in Titles, Abstracts or Keywords of records held in the databases to identify Qualitative Research.  Examples:

When searching, do a combination of subject terms and keywords depending on the type of qualitative study you are looking for:

Qualitative Research [MeSH] OR (qualitative AND (research OR study OR method))

(Grounded Theory[MeSH] OR "grounded theory")

then combine it with your topic of interest

post-traumatic stress disorder OR PTSD

brain injury, OR BTI OR "traumatic, brain injury"

How to Critically Analyze Qualitative Studies

 A critical analysis of a qualitative study considers the “fit” of the research question with the qualitative method used in the study. There are many checklists available for the assessment of qualitative research studies.  Here are a few:

• The Johanna Briggs Institute: The Joanna Briggs Institute Critical Appraisal tools  for use in JBI Systematic Reviews Checklist for  Qualitative Research  
• CASP:  CASP Checklist: 10 questions to help you make sense of a Qualitative research
• McMaster University:  Guidelines for Critical Review Form:  Qualitative Studies (Version 2.0) © Letts, L., Wilkins, S., Law, M., Stewart, D., Bosch, J., & Westmorland, M., 2007  

NOTE:  When using these checklists, be sure to use them critically and with careful consideration of the research context.  In other words, use the checklists as the beginning point in assessing the article and then re-assess the article based on whether the findings can be applied in your setting/population/disease/condition.

Additional Resources

Moorley, C., & Cathala, X. (2019). How to appraise qualitative research .  Evidence-Based Nursing ,  22 (1), 10-13.    ( open access)

Stenfors, T., Kajamaa, A. and Bennett, D. (2020), How to … assess the quality of qualitative research . Clin Teach, 17: 596-599.

Greenhalgh, T., & Taylor, R. (1997). How to read a paper: Papers that go beyond numbers (qualitative research).   BMj ,  315 (7110), 740-743. 

Jeanfreau, S. G., & Jack, L., Jr (2010). Appraising qualitative research in health education: guidelines for public health educators.   Health promotion practice ,  11 (5), 612–617. 

Research Series - Critical appraisal of qualitative research when reading papers Jul 22, 2022 Virtual Tutor; Research Series (Elsevier Health Education) YouTube Video 10:04 min [ This episode Professor Dall'Ora will be looking at qualitative research in more detail. In particular how to critically appraise qualitative studies.]

Hanes K. Chapter 4: Critical appraisal of qualitative research. In: Noyes J, Booth A, Hannes K, Harden A, Harris J, Lewin S, Lockwood C (editors), Supplementary Guidance for Inclusion of Qualitative Research in Cochrane Systematic Reviews of Interventions. Version 1 (updated August 2011). Cochrane Collaboration Qualitative Methods Group, 2011. 

David Tod, Andrew Booth & Brett Smith (2022)  Critical appraisal ,  International Review of Sport and Exercise Psychology, 15:1, 52-72  (open access)

Validity & Reliability in Qualitative Studies

Validity & Reliability

Validity in qualitative research means the “appropriateness” of the tools, processes, and data -- are the tools, processes and data measuring what it is intended to measure to answer the research question?  Assessing for validity is looking to see if the research question is "valid" for the desired outcome -- whether the choice of of the methodology used was appropriate for answering the research question, was the study design valid for the methodology, were the appropriate sampling and data analysis used and finally, were the results and conclusions valid for the sample and within the context of the research question. 

In contrast, reliability concerns the degree of consistency in the results if the study, using the same methodology, can be repeated over and over.

The Basics of Validity and Reliability in Research by Joe O'Brian & Anders Orn, Research Collective.com

Brewer, M., & Crano, W. (2014). Research Design and Issues of Validity. In H. Reis & C. Judd (Eds.),  Handbook of Research Methods in Social and Personality Psychology  (pp. 11-26). Cambridge: Cambridge University Press. 

Golafshani, N. (2003). Understanding Reliability and Validity in Qualitative Research.   The Qualitative Report ,  8 (4), 597-606. 

Cypress, Brigitte S. EdD, RN, CCRN. Rigor or Reliability and Validity in Qualitative Research: Perspectives, Strategies, Reconceptualization, and Recommendations . Dimensions of Critical Care Nursing 36(4):p 253-263, 7/8 2017. 

Leung L. (2015). Validity, reliability, and generalizability in qualitative research .  Journal of family medicine and primary care ,  4 (3), 324–327. 

Understanding Reliability and Validity . Writing@CSU

Rosumeck, S., Wagner, M., Wallraf, S., & Euler, U. (2020). A validation study revealed differences in design and performance of search filters for qualitative research in PsycINFO and CINAHL.   Journal of clinical epidemiology ,  128 , 101–108. 

Wagner, M., Rosumeck, S., Küffmeier, C., Döring, K., & Euler, U. (2020). A validation study revealed differences in design and performance of MEDLINE search filters for qualitative research .  Journal of clinical epidemiology ,  120 , 17–24.

Franzel, B., Schwiegershausen, M., Heusser, P.  et al.   How to locate and appraise qualitative research in complementary and alternative medicine.   BMC Complement Altern Med   13 , 125 (2013). 

Finfgeld-Connett, D. and Johnson, E.D. (2013), Literature search strategies for conducting knowledge-building and theory-generating qualitative systematic reviews. Journal of Advanced Nursing, 69: 194-204. 

Rogers, M, Bethel, A, Abbott, R.  Locating qualitative studies in dementia on MEDLINE, EMBASE, CINAHL, and PsycINFO: A comparison of search strategies.   Res Syn Meth . 2018; 9: 579– 586. 

Booth, A. Searching for qualitative research for inclusion in systematic reviews: a structured methodological review .  Syst Rev   5 , 74 (2016). 

Noyes, J., Hannes, K., Booth, A., Harris, J., Harden, A., Popay, J., ... & Pantoja, T. (2015). Qualitative research and Cochrane reviews .

Citing Sources

Citations are brief notations in the body of a research paper that point to a source in the bibliography or references cited section.

If your paper quotes, paraphrases, summarizes the work of someone else, you need to use citations.

Citation style guides such as APA, Chicago and MLA provide detailed instructions on how citations and bibliographies should be formatted.

Health Sciences Research Toolkit

Resources, tips, and guidelines to help you through the research process., finding information.

Library Research Checklist Helpful hints for starting a library research project.

Search Strategy Checklist and Tips Helpful tips on how to develop a literature search strategy.

Boolean Operators: A Cheat Sheet Boolean logic (named after mathematician George Boole) is a system of logic to designed to yield optimal search results. The Boolean operators, AND, OR, and NOT, help you construct a logical search. Boolean operators act on sets -- groups of records containing a particular word or concept.

Literature Searching Overview and tips on how to conduct a literature search.

Health Statistics and Data Sources Health related statistics and data sources are increasingly available on the Internet. They can be found already neatly packaged, or as raw data sets. The most reliable data comes from governmental sources or health-care professional organizations.

Evaluating Information

Primary, Secondary and Tertiary Sources in the Health Sciences Understand what are considered primary, secondary and tertiary sources.

Scholarly vs Popular Journals/Magazines How to determine what are scholarly journals vs trade or popular magazines.

Identifying Peer-Reviewed Journals A “peer-reviewed” or “refereed” journal is one in which the articles it contains have been examined by people with credentials in the article’s field of study before it is published.

Evaluating Web  Resources When searching for information on the Internet, it is important to be aware of the quality of the information being presented to you. Keep in mind that anyone can host a web site. To be sure that the information you are looking at is credible and of value.

Conducting Research Through An Anti-Racism Lens This guide is for students, staff, and faculty who are incorporating an anti-racist lens at all stages of the research life cycle.

Understanding Research Study Designs Covers case studies, randomized control trials, systematic reviews and meta-analysis.

Qualitative Studies Overview of what is a qualitative study and how to recognize, find and critically appraise.

Writing and Publishing

Citing Sources Citations are brief notations in the body of a research paper that point to a source in the bibliography or references cited section.

Structure of a Research Paper Reports of research studies usually follow the IMRAD format. IMRAD (Introduction, Methods, Results, [and] Discussion) is a mnemonic for the major components of a scientific paper. These elements are included in the overall structure of a research paper.

Top Reasons for Non-Acceptance of Scientific Articles Avoid these mistakes when preparing an article for publication.

Annotated Bibliographies Guide on how to create an annotated bibliography.

Writing guides, Style Manuals and the Publication Process in the Biological and Health Sciences Style manuals, citation guides as well as information on public access policies, copyright and plagiarism.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

8.4 Qualitative research questions

Learning objectives.

• List the key terms associated with qualitative research questions
• Distinguish between qualitative and quantitative research questions

Qualitative research questions differ from quantitative research questions. Qualitative research questions seek to explore or describe phenomena, not provide a neat nomothetic explanation, so they are often more general and vaguely worded. They may include only one concept, though many include more than one. Instead of asking how one variable causes changes in another, we are instead trying to understand the experiences , understandings , and meanings that people have about the concepts in our research question.

Let’s work through an example from our last section. In Table 8.1, a student asked, “What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care?” In this question, it is pretty clear that the student believes that adolescents in foster care who identify as LGBTQ may be at greater risk for homelessness. This is a nomothetic causal relationship—LGBTQ status causes homelessness.

However, what if the student were less interested in predicting homelessness based on LGBTQ status and more interested in understanding the stories of LGBTQ foster care youth that may be at risk for homelessness? In that case, the researcher would be building an idiographic causal explanation. The youths whom the researcher interviews may share stories of how their foster families, caseworkers, and others treated them. They may share stories about how they thought of their own sexuality or gender identity and how it changed over time. They may have different ideas about what it means to transition out of foster care.

Qualitative questions usually look different than quantitative questions because they search for idiographic causal relationships. Table 8.3 below takes the final research questions from Table 8.1 and adapts them for qualitative research. The guidelines for research questions previously described in this chapter still apply, but there are some new elements to qualitative research questions that are not present in quantitative questions. First, qualitative research questions often ask about lived experience, personal experience, understanding, meaning, and stories. These keywords indicate that you will be using qualitative methods. Second, qualitative research questions may be more general and less specific. Instead of asking how one concept causes another, we are asking about how people understand or feel about a concept. They may also contain only one variable, rather than asking about relationships between multiple variables.

Qualitative research questions have one final feature that distinguishes them from quantitative research questions. They can change over the course of a study. Qualitative research is a reflexive process, one in which the researcher adapts their approach based on what participants say and do. The researcher must constantly evaluate whether their question is important and relevant to the participants. As the researcher gains information from participants, it is normal for the focus of the inquiry to shift.

For example, a qualitative researcher may want to study how a new truancy rule impacts youth at risk of expulsion. However, after interviewing some of the youth in their community, a researcher might find that the rule is actually irrelevant to their behavior and thoughts. Instead, their participants will direct the discussion to their frustration with the school administrators or their family’s economic insecurity. This is a natural part of qualitative research, and it is normal for research questions and hypothesis to evolve based on the information gleaned from participants.

Key Takeaways

• Qualitative research questions often contain words like lived experience, personal experience, understanding, meaning, and stories.
• Qualitative research questions can change and evolve as the researcher conducts the study.

Image attributions

Empathy by  Sean MacEntee   CC-BY-2.0

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• make a call
• schedule an appointment
• Chat Loading...
• Request Info

855-725-7614

• Online Graduate Degree Programs
• School of Public Affairs
• Master of Public Administration and Policy
• MS in Counter-Terrorism and Homeland Security
• College of Arts and Sciences
• MA in Economics, Applied Economics Specialization
• MS in Nutrition Education
• School of Communication
• School of Professional and Extended Studies
• MS in Health Promotion Management
• MS in Human Resource Analytics and Management
• MS in Measurement & Evaluation
• MS in Sports Analytics and Management
• Online Graduate Certificates
• School of Professional and Extended Studies Graduate Certificate Programs
• Graduate Certificate in Human Resource Analytics and Management
• Graduate Certificate in Nutrition Education
• Graduate Certificate in Project Monitoring & Evaluation
• Graduate Certificate in Sports Analytics and Management
• Financial Aid and Tuition
• Scholarships
• International Students
• Military Students
• Schedule an Appointment
• University Registrar
• Campus Programs
• Why American University
• Accreditation and Rankings
• President's Message
• Online Student Life
• Virtual Open Houses & Webinars
• Frequently Asked Questions

Qualitative Methods in Monitoring and Evaluation: Qualitative Research Questions

• Online Degrees
• Program Resources

You are here

Selecting your research topic and crafting a qualitative research question from it is the first, and possibly the hardest, step of qualitative research. You will likely start with a topic, and as you start reading and do exploratory research, hone that topic into a research question that can be answered using qualitative methods.

I suggest that students start big and then narrow their topics. As you review the literature and current events around your larger topic, you will likely discover what questions academics and policymakers are asking about that topic. You should identify your topic’s puzzles, those questions that have yet to be answered. Then you should choose one of these puzzles to meld into your research question.

Throughout this process, you should constantly remind yourself of the purpose of qualitative inquiry. As researchers, we use qualitative data collection techniques to gather rich, emic data around a topic. That data highlights experiences and perceptions that help to provide explanation. As you explore your larger topic, focus on those puzzles that need qualitative explanation. As you hone your topic into possible research questions, ask yourself why qualitative data collection techniques would be the best way to provide insight into your topic and answer your research question. This is actually harder than you might think, as many of us tend towards the quantitative. Usually, crafting a qualitative research question means asking a why or a what explains question, NOT a how or a descriptive question.

The best qualitative research questions are:

• Interesting to you. Depending on the purpose of your research and your research output, you will likely spend a lot of time on your topic. Pick a topic that you find interesting, so that you will be engaged throughout the research process.
• Original. When we conduct primary research, we are not summarizing the research of others. We are coming up with our own research question and qualitative design to answer it. Your qualitative research could identify a brand new topic, or it could take a new spin on an old topic, or look at a new topic in a different light.
• Answerable. Your research question should be answerable using qualitative methods. Not every research question can and should be answered using qualitative data collection techniques. You should craft a question that is best answered using qualitative research.
• Manageable. Your research question should be manageable within your time, space, and budget constraints. Craft a question that fits within the purpose and scope of your research. Some qualitative questions might take an article length paper to answer, and some may take a book! Some questions might require a longer time to answer, travel that you are not able to do, or a larger budget than you have to support your research. Craft your question with these constraints and parameters in mind.

Once you have a research question, you will need to draft your qualitative research design. Your design will need to provide specifics on the qualitative data collection techniques you intend to use to answer your research question. You should think in advance about what kinds of data you will need, and what qualitative data collection techniques would be most useful to gather it. You have a number of tools available in your qualitative data collection toolkit, and you need to figure out which is most appropriate for your data collection need. You might use observation, participant observation , interviews , focus groups , or participatory tools , for example. You also need to think through how you will address missing or incomplete data, and how you will manage and analyze the data that you collect.

Qualitative Questions and Evaluation

When we conduct an evaluation , we usually start by crafting a logic model or Logical Framework (LogFrame) . As evaluators, we usually ask qualitative questions that help us to understand an organization’s logic model or to populate its LogFrame. We might ask a broad question such as: What explains this organization’s theory of change? Such a broad question would also have support questions such as: What does this organization do? Why does it do it that way? What are some examples of projects? How are those projects managed? Who are the beneficiaries? What are this organization’s challenges? What are this organization’s risks and assumptions?

Good qualitative research questions that help us to craft an evaluation might include questions around program need, and program conceptualization and design (Rossi, Lipsey, and Freeman, 2004). Depending on the purpose of the evaluation and your evaluation design, you might ask process-focused questions such as who, what, when, where, why, and how; or you might ask outcome focused questions around changes, effects, and impacts.

Your qualitative research and the answers to all of these questions could help you to develop a LogFrame that you could use to guide a future evaluation that asks questions around program operations and service delivery, program outcomes, or program cost efficiency. Your evaluation design would include evaluation questions that likely have a mixed method element that uses a combination of qualitative and quantitative data and methods to help measure progress or change. Our evaluation questions are not necessarily qualitative in nature; they are often questions that require mixed methods or quantitative tools and analyses to answer. However, we often use qualitative research questions and data collection techniques to help us craft our evaluation questions, LogFrame, and evaluation design.

Rossi, Peter, Mark Lipsey, and Howard Freeman. Evaluation: A Systematic Approach. 7th edition. Thousand Oaks, SAGE, 2004.

About The Author

Dr. Beverly Peters has more than twenty years of experience teaching, conducting qualitative research, and managing community development, microcredit, infrastructure, and democratization projects in several countries in Africa. As a consultant, Dr. Peters worked on EU and USAID funded infrastructure, education, and microcredit projects in South Africa and Mozambique. She also conceptualized and developed the proposal for Darfur Peace and Development Organization’s women’s crisis center, a center that provides physical and economic assistance to women survivors of violence in the IDP camps in Darfur. Dr. Peters has a Ph.D. from the University of Pittsburgh. Learn more about Dr. Peters.

To learn more about American University’s online MS in Measurement & Evaluation or Graduate Certificate in Project Monitoring & Evaluation, request more information or call us toll free at 855-725-7614.

(855) 725-7614

• Terms & Conditions
• About American University

83 Qualitative Research Questions & Examples

Free Website Traffic Checker

Discover your competitors' strengths and leverage them to achieve your own success

Qualitative research questions help you understand consumer sentiment. They’re strategically designed to show organizations how and why people feel the way they do about a brand, product, or service. It looks beyond the numbers and is one of the most telling types of market research a company can do.

The UK Data Service describes this perfectly, saying, “The value of qualitative research is that it gives a voice to the lived experience .”

Read on to see seven use cases and 83 qualitative research questions, with the added bonus of examples that show how to get similar insights faster with Similarweb Research Intelligence.

What is a qualitative research question?

A qualitative research question explores a topic in-depth, aiming to better understand the subject through interviews, observations, and other non-numerical data. Qualitative research questions are open-ended, helping to uncover a target audience’s opinions, beliefs, and motivations.

How to choose qualitative research questions?

Choosing the right qualitative research questions can be incremental to the success of your research and the findings you uncover. Here’s my six-step process for choosing the best qualitative research questions.

• Start by understanding the purpose of your research. What do you want to learn? What outcome are you hoping to achieve?
• Consider who you are researching. What are their experiences, attitudes, and beliefs? How can you best capture these in your research questions ?
• Keep your questions open-ended . Qualitative research questions should not be too narrow or too broad. Aim to ask specific questions to provide meaningful answers but broad enough to allow for exploration.
• Balance your research questions. You don’t want all of your questions to be the same type. Aim to mix up your questions to get a variety of answers.
• Ensure your research questions are ethical and free from bias. Always have a second (and third) person check for unconscious bias.
• Consider the language you use. Your questions should be written in a way that is clear and easy to understand. Avoid using jargon , acronyms, or overly technical language.

Types of qualitative research questions

For a question to be considered qualitative, it usually needs to be open-ended. However, as I’ll explain, there can sometimes be a slight cross-over between quantitative and qualitative research questions.

Open-ended questions

These allow for a wide range of responses and can be formatted with multiple-choice answers or a free-text box to collect additional details. The next two types of qualitative questions are considered open questions, but each has its own style and purpose.

• Probing questions are used to delve deeper into a respondent’s thoughts, such as “Can you tell me more about why you feel that way?”
• Comparative questions ask people to compare two or more items, such as “Which product do you prefer and why?” These qualitative questions are highly useful for understanding brand awareness , competitive analysis , and more.

Closed-ended questions

These ask respondents to choose from a predetermined set of responses, such as “On a scale of 1-5, how satisfied are you with the new product?” While they’re traditionally quantitative, adding a free text box that asks for extra comments into why a specific rating was chosen will provide qualitative insights alongside their respective quantitative research question responses.

• Ranking questions get people to rank items in order of preference, such as “Please rank these products in terms of quality.” They’re advantageous in many scenarios, like product development, competitive analysis, and brand awareness.
• Likert scale questions ask people to rate items on a scale, such as “On a scale of 1-5, how satisfied are you with the new product?” Ideal for placement on websites and emails to gather quick, snappy feedback.

Qualitative research question examples

There are many applications of qualitative research and lots of ways you can put your findings to work for the success of your business. Here’s a summary of the most common use cases for qualitative questions and examples to ask.

Qualitative questions for identifying customer needs and motivations

These types of questions help you find out why customers choose products or services and what they are looking for when making a purchase.

• What factors do you consider when deciding to buy a product?
• What would make you choose one product or service over another?
• What are the most important elements of a product that you would buy?
• What features do you look for when purchasing a product?
• What qualities do you look for in a company’s products?
• Do you prefer localized or global brands when making a purchase?
• How do you determine the value of a product?
• What do you think is the most important factor when choosing a product?
• How do you decide if a product or service is worth the money?
• Do you have any specific expectations when purchasing a product?
• Do you prefer to purchase products or services online or in person?
• What kind of customer service do you expect when buying a product?
• How do you decide when it is time to switch to a different product?
• Where do you research products before you decide to buy?
• What do you think is the most important customer value when making a purchase?

Qualitative research questions to enhance customer experience

Use these questions to reveal insights into how customers interact with a company’s products or services and how those experiences can be improved.

• What aspects of our product or service do customers find most valuable?
• How do customers perceive our customer service?
• What factors are most important to customers when purchasing?
• What do customers think of our brand?
• What do customers think of our current marketing efforts?
• How do customers feel about the features and benefits of our product?
• How do customers feel about the price of our product or service?
• How could we improve the customer experience?
• What do customers think of our website or app?
• What do customers think of our customer support?
• What could we do to make our product or service easier to use?
• What do customers think of our competitors?
• What is your preferred way to access our site?
• How do customers feel about our delivery/shipping times?
• What do customers think of our loyalty programs?

Qualitative research question example for customer experience

• ‍♀️ Question: What is your preferred way to access our site?
• Insight sought: How mobile-dominant are consumers? Should you invest more in mobile optimization or mobile marketing?
• Challenges with traditional qualitative research methods: While using this type of question is ideal if you have a large database to survey when placed on a site or sent to a limited customer list, it only gives you a point-in-time perspective from a limited group of people.
• A new approach: You can get better, broader insights quicker with Similarweb Digital Research Intelligence. To fully inform your research, you need to know preferences at the industry or market level.
• ⏰ Time to insight: 30 seconds
• ✅ How it’s done: Similarweb offers multiple ways to answer this question without going through a lengthy qualitative research process. 

First, I’m going to do a website market analysis of the banking credit and lending market in the finance sector to get a clearer picture of industry benchmarks.

Here, I can view device preferences across any industry or market instantly. It shows me the device distribution for any country across any period. This clearly answers the question of how mobile dominate my target audience is , with 59.79% opting to access site via a desktop vs. 40.21% via mobile

I then use the trends section to show me the exact split between mobile and web traffic for each key player in my space. Let’s say I’m about to embark on a competitive campaign that targets customers of Chase and Bank of America ; I can see both their audiences are highly desktop dominant compared with others in their space .

Qualitative question examples for developing new products or services

Research questions like this can help you understand customer pain points and give you insights to develop products that meet those needs.

• What is the primary reason you would choose to purchase a product from our company?
• How do you currently use products or services that are similar to ours?
• Is there anything that could be improved with products currently on the market?
• What features would you like to see added to our products?
• How do you prefer to contact a customer service team?
• What do you think sets our company apart from our competitors?
• What other product or service offerings would like to see us offer?
• What type of information would help you make decisions about buying a product?
• What type of advertising methods are most effective in getting your attention?
• What is the biggest deterrent to purchasing products from us?

Qualitative research question example for service development

• ‍♀️ Question: What type of advertising methods are most effective in getting your attention?
• Insight sought: The marketing channels and/or content that performs best with a target audience .
• Challenges with traditional qualitative research methods: When using qualitative research surveys to answer questions like this, the sample size is limited, and bias could be at play.
• A better approach: The most authentic insights come from viewing real actions and results that take place in the digital world. No questions or answers are needed to uncover this intel, and the information you seek is readily available in less than a minute.
• ⏰ Time to insight: 5 minutes
• ✅ How it’s done: There are a few ways to approach this. You can either take an industry-wide perspective or hone in on specific competitors to unpack their individual successes. Here, I’ll quickly show a snapshot with a whole market perspective.

Using the market analysis element of Similarweb Digital Intelligence, I select my industry or market, which I’ve kept as banking and credit. A quick click into marketing channels shows me which channels drive the highest traffic in my market. Taking direct traffic out of the equation, for now, I can see that referrals and organic traffic are the two highest-performing channels in this market.

Similarweb allows me to view the specific referral partners and pages across these channels. 

Looking closely at referrals in this market, I’ve chosen chase.com and its five closest rivals . I select referrals in the channel traffic element of marketing channels. I see that Capital One is a clear winner, gaining almost 25 million visits due to referral partnerships.

Next, I get to see exactly who is referring traffic to Capital One and the total traffic share for each referrer. I can see the growth as a percentage and how that has changed, along with an engagement score that rates the average engagement level of that audience segment. This is particularly useful when deciding on which new referral partnerships to pursue.  

Once I’ve identified the channels and campaigns that yield the best results, I can then use Similarweb to dive into the various ad creatives and content that have the greatest impact.

These ads are just a few of those listed in the creatives section from my competitive website analysis of Capital One. You can filter this list by the specific campaign, publishers, and ad networks to view those that matter to you most. You can also discover video ad creatives in the same place too.

In just five minutes ⏰ 

• I’ve captured audience loyalty statistics across my market
• Spotted the most competitive players
• Identified the marketing channels my audience is most responsive to
• I know which content and campaigns are driving the highest traffic volume
• I’ve created a target list for new referral partners and have been able to prioritize this based on results and engagement figures from my rivals
• I can see the types of creatives that my target audience is responding to, giving me ideas for ways to generate effective copy for future campaigns

Qualitative questions to determine pricing strategies

Companies need to make sure pricing stays relevant and competitive. Use these questions to determine customer perceptions on pricing and develop pricing strategies to maximize profits and reduce churn.

• How do you feel about our pricing structure?
• How does our pricing compare to other similar products?
• What value do you feel you get from our pricing?
• How could we make our pricing more attractive?
• What would be an ideal price for our product?
• Which features of our product that you would like to see priced differently?
• What discounts or deals would you like to see us offer?
• How do you feel about the amount you have to pay for our product?

Get Faster Answers to Qualitative Research Questions with Similarweb Today

Qualitative research question example for determining pricing strategies

• ‍♀️ Question: What discounts or deals would you like to see us offer?
• Insight sought: The promotions or campaigns that resonate with your target audience.
• Challenges with traditional qualitative research methods: Consumers don’t always recall the types of ads or campaigns they respond to. Over time, their needs and habits change. Your sample size is limited to those you ask, leaving a huge pool of unknowns at play.
• A better approach: While qualitative insights are good to know, you get the most accurate picture of the highest-performing promotion and campaigns by looking at data collected directly from the web. These analytics are real-world, real-time, and based on the collective actions of many, instead of the limited survey group you approach. By getting a complete picture across an entire market, your decisions are better informed and more aligned with current market trends and behaviors.
• ✅ How it’s done: Similarweb’s Popular Pages feature shows the content, products, campaigns, and pages with the highest growth for any website. So, if you’re trying to unpack the successes of others in your space and find out what content resonates with a target audience, there’s a far quicker way to get answers to these questions with Similarweb.

Here, I’m using Capital One as an example site. I can see trending pages on their site showing the largest increase in page views. Other filters include campaign, best-performing, and new–each of which shows you page URLs, share of traffic, and growth as a percentage. This page is particularly useful for staying on top of trending topics , campaigns, and new content being pushed out in a market by key competitors.

Qualitative research questions for product development teams

It’s vital to stay in touch with changing consumer needs. These questions can also be used for new product or service development, but this time, it’s from the perspective of a product manager or development team. 

• What are customers’ primary needs and wants for this product?
• What do customers think of our current product offerings?
• What is the most important feature or benefit of our product?
• How can we improve our product to meet customers’ needs better?
• What do customers like or dislike about our competitors’ products?
• What do customers look for when deciding between our product and a competitor’s?
• How have customer needs and wants for this product changed over time?
• What motivates customers to purchase this product?
• What is the most important thing customers want from this product?
• What features or benefits are most important when selecting a product?
• What do customers perceive to be our product’s pros and cons?
• What would make customers switch from a competitor’s product to ours?
• How do customers perceive our product in comparison to similar products?
• What do customers think of our pricing and value proposition?
• What do customers think of our product’s design, usability, and aesthetics?

Qualitative questions examples to understand customer segments

Market segmentation seeks to create groups of consumers with shared characteristics. Use these questions to learn more about different customer segments and how to target them with tailored messaging.

• What motivates customers to make a purchase?
• How do customers perceive our brand in comparison to our competitors?
• How do customers feel about our product quality?
• How do customers define quality in our products?
• What factors influence customers’ purchasing decisions ?
• What are the most important aspects of customer service?
• What do customers think of our customer service?
• What do customers think of our pricing?
• How do customers rate our product offerings?
• How do customers prefer to make purchases (online, in-store, etc.)?

Qualitative research question example for understanding customer segments

• ‍♀️ Question: Which social media channels are you most active on?
• Insight sought: Formulate a social media strategy . Specifically, the social media channels most likely to succeed with a target audience.
• Challenges with traditional qualitative research methods: Qualitative research question responses are limited to those you ask, giving you a limited sample size. Questions like this are usually at risk of some bias, and this may not be reflective of real-world actions.
• A better approach: Get a complete picture of social media preferences for an entire market or specific audience belonging to rival firms. Insights are available in real-time, and are based on the actions of many, not a select group of participants. Data is readily available, easy to understand, and expandable at a moment’s notice.
• ✅ How it’s done: Using Similarweb’s website analysis feature, you can get a clear breakdown of social media stats for your audience using the marketing channels element. It shows the percentage of visits from each channel to your site, respective growth, and specific referral pages by each platform. All data is expandable, meaning you can select any platform, period, and region to drill down and get more accurate intel, instantly.

This example shows me Bank of America’s social media distribution, with YouTube , Linkedin , and Facebook taking the top three spots, and accounting for almost 80% of traffic being driven from social media.

When doing any type of market research, it’s important to benchmark performance against industry averages and perform a social media competitive analysis to verify rival performance across the same channels.

Qualitative questions to inform competitive analysis

Organizations must assess market sentiment toward other players to compete and beat rival firms. Whether you want to increase market share , challenge industry leaders , or reduce churn, understanding how people view you vs. the competition is key.

• What is the overall perception of our competitors’ product offerings in the market?
• What attributes do our competitors prioritize in their customer experience?
• What strategies do our competitors use to differentiate their products from ours?
• How do our competitors position their products in relation to ours?
• How do our competitors’ pricing models compare to ours?
• What do consumers think of our competitors’ product quality?
• What do consumers think of our competitors’ customer service?
• What are the key drivers of purchase decisions in our market?
• What is the impact of our competitors’ marketing campaigns on our market share ? 10. How do our competitors leverage social media to promote their products?

Qualitative research question example for competitive analysis

• ‍♀️ Question: What other companies do you shop with for x?
• Insight sought: W ho are your competitors? Which of your rival’s sites do your customers visit? How loyal are consumers in your market?
• Challenges with traditional qualitative research methods:  Sample size is limited, and customers could be unwilling to reveal which competitors they shop with, or how often they around. Where finances are involved, people can act with reluctance or bias, and be unwilling to reveal other suppliers they do business with.
• A better approach: Get a complete picture of your audience’s loyalty, see who else they shop with, and how many other sites they visit in your competitive group. Find out the size of the untapped opportunity and which players are doing a better job at attracting unique visitors – without having to ask people to reveal their preferences.
• ✅ How it’s done: Similarweb website analysis shows you the competitive sites your audience visits, giving you access to data that shows cross-visitation habits, audience loyalty, and untapped potential in a matter of minutes.

Using the audience interests element of Similarweb website analysis, you can view the cross-browsing behaviors of a website’s audience instantly. You can see a matrix that shows the percentage of visitors on a target site and any rival site they may have visited.

With the Similarweb audience overlap feature, view the cross-visitation habits of an audience across specific websites. In this example, I chose chase.com and its four closest competitors to review. For each intersection, you see the number of unique visitors and the overall proportion of each site’s audience it represents. It also shows the volume of unreached potential visitors.

Here, you can see a direct comparison of the audience loyalty represented in a bar graph. It shows a breakdown of each site’s audience based on how many other sites they have visited. Those sites with the highest loyalty show fewer additional sites visited.

From the perspective of chase.com, I can see 47% of their visitors do not visit rival sites. 33% of their audience visited 1 or more sites in this group, 14% visited 2 or more sites, 4% visited 3 or more sites, and just 0.8% viewed all sites in this comparison. 

How to answer qualitative research questions with Similarweb

Similarweb Research Intelligence drastically improves market research efficiency and time to insight. Both of these can impact the bottom line and the pace at which organizations can adapt and flex when markets shift, and rivals change tactics.

Outdated practices, while still useful, take time . And with a quicker, more efficient way to garner similar insights, opting for the fast lane puts you at a competitive advantage.

With a birds-eye view of the actions and behaviors of companies and consumers across a market , you can answer certain research questions without the need to plan, do, and review extensive qualitative market research .

Wrapping up

Qualitative research methods have been around for centuries. From designing the questions to finding the best distribution channels, collecting and analyzing findings takes time to get the insights you need. Similarweb Digital Research Intelligence drastically improves efficiency and time to insight. Both of which impact the bottom line and the pace at which organizations can adapt and flex when markets shift.

Similarweb’s suite of digital intelligence solutions offers unbiased, accurate, honest insights you can trust for analyzing any industry, market, or audience.

• Methodologies used for data collection are robust, transparent, and trustworthy.
• Clear presentation of data via an easy-to-use, intuitive platform.
• It updates dynamically–giving you the freshest data about an industry or market.
• Data is available via an API – so you can plug into platforms like Tableau or PowerBI to streamline your analyses.
• Filter and refine results according to your needs.

Are quantitative or qualitative research questions best?

Both have their place and purpose in market research. Qualitative research questions seek to provide details, whereas quantitative market research gives you numerical statistics that are easier and quicker to analyze. You get more flexibility with qualitative questions, and they’re non-directional.

What are the advantages of qualitative research?

Qualitative research is advantageous because it allows researchers to better understand their subject matter by exploring people’s attitudes, behaviors, and motivations in a particular context. It also allows researchers to uncover new insights that may not have been discovered with quantitative research methods.

What are some of the challenges of qualitative research?

Qualitative research can be time-consuming and costly, typically involving in-depth interviews and focus groups. Additionally, there are challenges associated with the reliability and validity of the collected data, as there is no universal standard for interpreting the results.

by Liz March

Digital Research Specialist

Liz March has 15 years of experience in content creation. She enjoys the outdoors, F1, and reading, and is pursuing a BSc in Environmental Science.

Related Posts

Importance of Market Research: 9 Reasons Why It’s Crucial for Your Business

Audience Segmentation: Definition, Importance & Types

Geographic Segmentation: Definition, Pros & Cons, Examples, and More

Demographic Segmentation: The Key To Transforming Your Marketing Strategy

Unlocking Consumer Behavior: What Makes Your Customers Tick?

Customer Segmentation: Expert Tips on Understanding Your Audience

Wondering what similarweb can do for your business.

Give it a try or talk to our insights team — don’t worry, it’s free!

• Usability testing

Run remote usability tests on any digital product to deep dive into your key user flows

• Product analytics

Learn how users are behaving on your website in real time and uncover points of frustration

• Research repository

A tool for collaborative analysis of qualitative data and for building your research repository and database.

• Trymata Blog

How-to articles, expert tips, and the latest news in user testing & user experience

• Knowledge Hub

Detailed explainers of Trymata’s features & plans, and UX research terms & topics

• Plans & Pricing

Get paid to test

• User Experience (UX) testing
• User Interface (UI) testing
• Ecommerce testing
• Remote usability testing
• Plans & Pricing
• Customer Stories

How do you want to use Trymata?

Conduct user testing, desktop usability video.

You’re on a business trip in Oakland, CA. You've been working late in downtown and now you're looking for a place nearby to grab a late dinner. You decided to check Zomato to try and find somewhere to eat. (Don't begin searching yet).

• Look around on the home page. Does anything seem interesting to you?
• How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
• What do the reviews say about the restaurant you've chosen?
• What was the most important factor for you in choosing this spot?
• You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
• Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
• Now go to any restaurant's page and try to leave a review (don't actually submit it).

What was the worst thing about your experience?

It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer

What other aspects of the experience could be improved?

Feedback from the owners would be nice

What did you like about the website?

The flow was good, lots of bright photos

What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.

• Please begin by downloading the app to your device.
• Choose Italian and get started with the first lesson (stop once you reach the first question).
• Now go all the way through the rest of the first lesson, describing your thoughts as you go.
• Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
• After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
• What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.

All accounts, tests, and data have been migrated to our new & improved system!

Use the same email and password to log in:

Legacy login: Our legacy system is still available in view-only mode, login here >

What’s the new system about? Read more about our transition & what it-->

25 Essential Qualitative Research Questions with Context

Conduct End-to-End User Testing & Research

• Health and Well-being:

Question: How do individuals with chronic illnesses perceive and manage their overall well-being?

Context: This question aims to explore the subjective experiences of individuals living with chronic illnesses, focusing on their perceptions of well-being and the strategies they employ to manage their health.

Question: What are the experiences of teachers implementing project-based learning in high school science classrooms?

Context: This question delves into the qualitative aspects of teaching practices, seeking to understand the lived experiences of teachers as they implement a specific instructional approach (project-based learning) in a particular academic context (high school science classrooms).

Question: How do marginalized communities perceive and navigate social inclusion in urban environments?

Context: This question addresses the sociological dimensions of social inclusion within urban settings, focusing on the perspectives and strategies of marginalized communities as they navigate societal structures.

• Psychology:

Question: What are the coping mechanisms employed by individuals facing post-traumatic stress disorder?

Context: This question explores the psychological experiences of individuals dealing with post-traumatic stress disorder, aiming to uncover the qualitative aspects of coping strategies and mechanisms.

• Anthropology:

Question: How does a specific cultural group express identity through traditional rituals and ceremonies?

Context: This anthropological question focuses on cultural practices and rituals as expressions of identity within a specific cultural group, aiming to uncover the meanings and functions of these traditions.

• Gender Studies:

Question: What are the lived experiences of transgender individuals in the workplace, particularly regarding inclusion and discrimination?

Context: This question within gender studies explores the qualitative dimensions of transgender individuals’ workplace experiences, emphasizing the nuanced aspects of inclusion and discrimination they may encounter.

• Environmental Studies:

Question: How do local communities perceive and respond to environmental conservation efforts in their region?

Context: This question addresses the intersection of environmental studies and sociology, aiming to understand the qualitative perspectives of local communities toward conservation initiatives, exploring their perceptions and responses.

• Business and Management:

Question: How do employees perceive leadership styles and their impact on workplace culture?

Context: Within the realm of business and management, this question explores the qualitative aspects of organizational culture, focusing on employees’ perceptions of leadership styles and their influence on the workplace environment.

• Technology and Society:

Question: What are the social implications and user experiences of emerging technologies in the context of augmented reality applications?

Context: This question within the field of technology and society investigates the qualitative dimensions of user experiences and social implications related to the adoption of augmented reality applications.

• Communication Studies:

Question: How do individuals from diverse cultural backgrounds interpret and respond to media representations of body image?

Context: This question explores the intersection of communication studies and cultural studies, aiming to understand the qualitative variations in how individuals from diverse cultural backgrounds interpret and respond to media depictions of body image.

• Political Science:

Question: What are the public perceptions and attitudes toward government policies on climate change?

Context: Within political science, this question delves into the qualitative aspects of public opinion, seeking to understand how individuals perceive and respond to government policies related to climate change.

• Cultural Studies:

Question: How do international students experience acculturation and adaptation in a foreign academic environment?

Context: This question within cultural studies explores the qualitative dimensions of acculturation and adaptation, focusing on the experiences of international students within the context of a foreign academic environment.

• Family Studies:

Question: How do families navigate and negotiate roles and responsibilities in the context of remote work?

Context: In the domain of family studies, this question addresses the qualitative aspects of family dynamics, examining how families navigate and negotiate roles and responsibilities in the context of remote work.

• Public Health:

Question: How do community members perceive and engage with public health campaigns aimed at promoting vaccination in underserved urban areas?

Context: This public health question investigates the qualitative aspects of community perceptions and engagement with vaccination campaigns, particularly in urban areas with limited access to healthcare resources.

• Urban Planning:

Question: What are the experiences of residents in gentrifying neighborhoods regarding changes in their community dynamics, affordability, and social cohesion?

Context: Within urban planning, this question explores the qualitative dimensions of gentrification, focusing on residents’ lived experiences and perceptions of neighborhood transformations.

• Literature and Cultural Criticism:

Question: How do contemporary authors use literature to critique and challenge societal norms around gender roles and identity?

Context: In the realm of literature and cultural criticism, this question examines the qualitative dimensions of literary works, exploring how authors use their craft to challenge and critique societal norms related to gender.

• Social Work:

Question: What are the perceptions of social workers regarding the challenges and opportunities in providing mental health support to homeless populations?

Context: This social work question addresses the qualitative aspects of mental health support within homeless populations, exploring social workers’ perspectives on challenges and opportunities in their roles.

• Tourism and Hospitality:

Question: How do tourists from different cultural backgrounds experience and interpret authenticity in local culinary traditions?

Context: Within tourism and hospitality, this question explores the qualitative aspects of cultural experiences, focusing on tourists’ perceptions and interpretations of authenticity in local culinary traditions.

• Media and Entertainment:

Question: How do audiences engage with and interpret representations of diverse identities in streaming platforms’ original content?

Context: In the realm of media and entertainment, this question investigates the qualitative dimensions of audience engagement and interpretation of diverse identities in content produced by streaming platforms.

• Historical Studies:

Question: What are the narratives and memories of individuals who lived through a significant historical event, and how have these narratives evolved over time?

Context: Within historical studies, this question explores the qualitative aspects of personal narratives and memory, investigating how individuals recall and frame their experiences of a significant historical event.

• Linguistics:

Question: How do multilingual individuals navigate language use and identity in diverse linguistic environments?

Context: In the field of linguistics, this question delves into the qualitative dimensions of language use and identity, focusing on how multilingual individuals navigate linguistic diversity in their environments.

• Cybersecurity:

Question: What are the perceptions and behaviors of employees in organizations regarding cybersecurity practices, and how do these perceptions influence organizational security?

Context: Within cybersecurity, this question explores the qualitative aspects of employees’ perceptions and behaviors related to cybersecurity practices, examining their impact on organizational security.

• Human-Computer Interaction:

Question: How do users experience and adapt to voice-controlled virtual assistants in their daily lives, considering factors such as privacy concerns and usability?

Context: In human-computer interaction, this question investigates the qualitative aspects of user experiences with voice-controlled virtual assistants, considering factors such as privacy concerns and usability challenges.

• International Development:

Question: How do local communities perceive and negotiate the impacts of international development projects on their cultural and economic landscapes?

Context: This international development question explores the qualitative dimensions of community perceptions and negotiations regarding the impacts of international development projects, considering cultural and economic factors.

• Sport Psychology:

Question: What are the psychological experiences and coping mechanisms of athletes during periods of extended competition hiatus, such as the postponement of major sporting events?

Context: In sport psychology, this question delves into the qualitative aspects of athletes’ psychological experiences and coping mechanisms during extended competition hiatus, such as the postponement of major sporting events.

These additional detailed examples provide a broader perspective on qualitative research questions, covering diverse fields of study and highlighting the nuanced inquiries within each domain.

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

What is a Software Testing Tool and Choosing the Best One

What is software testing types, methods, and more, importance of software testing life cycle in development, what are the types of software testing and techniques.

• Media Center
• Not yet translated

Qualitative Research

What is qualitative research.

Qualitative research is a methodology focused on collecting and analyzing descriptive, non-numerical data to understand complex human behavior, experiences, and social phenomena. This approach utilizes techniques such as interviews, focus groups, and observations to explore the underlying reasons, motivations, and meanings behind actions and decisions. Unlike quantitative research, which focuses on measuring and quantifying data, qualitative research delves into the 'why' and 'how' of human behavior, providing rich, contextual insights that reveal deeper patterns and relationships.

The Basic Idea

Ever heard of the saying “quality over quantity”? Well, some researchers feel the same way!

Imagine you are conducting a study looking at consumer behavior for buying potato chips. You’re interested in seeing which factors influence a customer’s choice between purchasing Doritos and Pringles. While you could conduct quantitative research and measure the number of bags purchased, this data alone wouldn’t explain why consumers choose one chip brand over the other; it would just tell you what they are purchasing. To gather more meaningful data, you may conduct interviews or surveys, asking people about their chip preferences and what draws them to one brand over another. Is it the taste of the chips? The font or color of the bag? This qualitative approach dives deeper to uncover why one potato chip is more popular than the other and can help companies make the adjustments that count.

Qualitative research, as seen in the example above, can provide greater insight into behavior, going beyond numbers to understand people’s experiences, attitudes, and perceptions. It helps us to grasp the meaning behind decisions, rather than just describing them. As human behavior is often difficult to qualify, qualitative research is a useful tool for solving complex problems or as a starting point to generate new ideas for research. Qualitative methods are used across all types of research—from consumer behavior to education, healthcare, behavioral science, and everywhere in between!

At its core, qualitative research is exploratory—rather than coming up with a hypothesis and gathering numerical data to support it, qualitative research begins with open-ended questions. Instead of asking “Which chip brand do consumers buy more frequently?”, qualitative research asks “Why do consumers choose one chip brand over another?”. Common methods to obtain qualitative data include focus groups, unstructured interviews, and surveys. From the data gathered, researchers then can make hypotheses and move on to investigating them. 

It’s important to note that qualitative and quantitative research are not two opposing methods, but rather two halves of a whole. Most of the best studies leverage both kinds of research by collecting objective, quantitative data, and using qualitative research to gain greater insight into what the numbers reveal.

You may have heard the world is made up of atoms and molecules, but it’s really made up of stories. When you sit with an individual that’s been here, you can give quantitative data a qualitative overlay. – William Turner, 16th century British scientist 1

Quantitative Research: A research method that involves collecting and analyzing numerical data to test hypotheses, identify patterns, and predict outcomes.

Exploratory Research: An initial study used to investigate a problem that is not clearly defined, helping to clarify concepts and improve research design.

Positivism: A scientific approach that emphasizes empirical evidence and objectivity, often involving the testing of hypotheses based on observable data. 2 

Phenomenology: A research approach that emphasizes the first-person point of view, placing importance on how people perceive, experience, and interpret the world around them. 3

Social Interaction Theory: A theoretical perspective that people make sense of their social worlds by the exchange of meaning through language and symbols. 4

Critical Theory: A worldview that there is no unitary or objective “truth” about people that can be discovered, as human experience is shaped by social, cultural, and historical contexts that influences reality and society. 5

Empirical research: A method of gaining knowledge through direct observation and experimentation, relying on real-world data to test theories. 

Paradigm shift: A fundamental change in the basic assumptions and methodologies of a scientific discipline, leading to the adoption of a new framework. 2

Interpretive/descriptive approach: A methodology that focuses on understanding the meanings people assign to their experiences, often using qualitative methods.

Unstructured interviews: A free-flowing conversation between researcher and participant without predetermined questions that must be asked to all participants. Instead, the researcher poses questions depending on the flow of the interview. 6

Focus Group: Group interviews where a researcher asks questions to guide a conversation between participants who are encouraged to share their ideas and information, leading to detailed insights and diverse perspectives on a specific topic.

Grounded theory : A qualitative methodology that generates a theory directly from data collected through iterative analysis.

When social sciences started to emerge in the 17th and 18th centuries, researchers wanted to apply the same quantitative approach that was used in the natural sciences. At this time, there was a predominant belief that human behavior could be numerically analyzed to find objective patterns and would be generalizable to similar people and situations. Using scientific means to understand society is known as a positivist approach. However, in the early 20th century, both natural and social scientists started to criticize this traditional view of research as being too reductive. 2  

In his book, The Structure of Scientific Revolutions, American philosopher Thomas Kuhn identified that a major paradigm shift was starting to occur. Earlier methods of science were being questioned and replaced with new ways of approaching research which suggested that true objectivity was not possible when studying human behavior. Rather, the importance of context meant research on one group could not be generalized to all groups. 2 Numbers alone were deemed insufficient for understanding the environment surrounding human behavior which was now seen as a crucial piece of the puzzle. Along with this paradigm shift, Western scholars began to take an interest in ethnography , wanting to understand the customs, practices, and behaviors of other cultures. 

Qualitative research became more prominent throughout the 20th century, expanding beyond anthropology and ethnography to being applied across all forms of research; in science, psychology, marketing—the list goes on. Paul Felix Lazarsfield, Austrian-American sociologist and mathematician often known as the father of qualitative research, popularized new methods such as unstructured interviews and group discussions. 7 During the 1940s, Lazarfield brought attention to the fact that humans are not always rational decision-makers, making them difficult to understand through numerical data alone.

The 1920s saw the invention of symbolic interaction theory, developed by George Herbert Mead. Symbolic interaction theory posits society as the product of shared symbols such as language. People attach meanings to these symbols which impacts the way they understand and communicate with the world around them, helping to create and maintain a society. 4 Critical theory was also developed in the 1920s at the University of Frankfurt Institute for Social Research. Following the challenge of positivism, critical theory is a worldview that there is no unitary or objective “truth” about people that can be discovered, as human experience is shaped by social, cultural, and historical contexts. By shedding light on the human experience, it hopes to highlight the role of power, ideology, and social structures in shaping humans, and using this knowledge to create change. 5

Other formalized theories were proposed during the 20th century, such as grounded theory , where researchers started gathering data to form a hypothesis, rather than the other way around. This represented a stark contrast to positivist approaches that had dominated the 17th and 18th centuries.

The 1950s marked a shift toward a more interpretive and descriptive approach which factored in how people make sense of their subjective reality and attach meaning to it. 2 Researchers began to recognize that the why of human behavior was just as important as the what . Max Weber, a German sociologist, laid the foundation of the interpretive approach through the concept of Verstehen (which in English translates to understanding), emphasizing the importance of interpreting the significance people attach to their behavior. 8 With the shift to an interpretive and descriptive approach came the rise of phenomenology, which emphasizes first-person experiences by studying how individuals perceive, experience, and interpret the world around them. 

Today, in the age of big data, qualitative research has boomed, as advancements in digital tools allow researchers to gather vast amounts of data (both qualitative and quantitative), helping us better understand complex social phenomena. Social media patterns can be analyzed to understand public sentiment, consumer behavior, and cultural trends to grasp how people attach subjective meaning to their reality. There is even an emerging field of digital ethnography which is entirely focused on how humans interact and communicate in virtual environments!

Thomas Kuhn

American philosopher who suggested that science does not evolve through merely an addition of knowledge by compiling new learnings onto existing theories, but instead undergoes paradigm shifts where new theories and methodologies replace old ones. In this way, Kuhn suggested that science is a reflection of a community at a particular point in time. 9

Paul Felix Lazarsfeld

Often referred to as the father of qualitative research, Austrian-American sociologist and mathematician Paul Lazarsfield helped to develop modern empirical methods of conducting research in the social sciences such as surveys, opinion polling, and panel studies. Lazarsfeld was best known for combining qualitative and quantitative research to explore America's voting habits and behaviors related to mass communication, such as newspapers, magazines, and radios. 10  

German sociologist and political economist known for his sociological approach of “Verstehen” which emphasized the need to understand individuals or groups by exploring the meanings that people attach to their decisions. While previously, qualitative researchers in ethnography acted like an outside observer to explain behavior from their point of view, Weber believed that an empathetic understanding of behavior, that explored both intent and context, was crucial to truly understanding behavior. 11  

George Herbert Mead

Widely recognized as the father of symbolic interaction theory, Mead was an American philosopher and sociologist who took an interest in how spoken language and symbols contribute to one’s idea of self, and to society at large. 4

Consequences

Humans are incredibly complex beings, whose behaviors cannot always be reduced to mere numbers and statistics. Qualitative research acknowledges this inherent complexity and can be used to better capture the diversity of human and social realities. 

Qualitative research is also more flexible—it allows researchers to pivot as they uncover new insights. Instead of approaching the study with predetermined hypotheses, oftentimes, researchers let the data speak for itself and are not limited by a set of predefined questions. It can highlight new areas that a researcher hadn’t even thought of exploring. 

By providing a deeper explanation of not only what we do, but why we do it, qualitative research can be used to inform policy-making, educational practices, healthcare approaches, and marketing tactics. For instance, while quantitative research tells us how many people are smokers, qualitative research explores what, exactly, is driving them to smoke in the first place. If the research reveals that it is because they are unaware of the gravity of the consequences, efforts can be made to emphasize the risks, such as by placing warnings on cigarette cartons. 

Finally, qualitative research helps to amplify the voices of marginalized or underrepresented groups. Researchers who embrace a true “Verstehen” mentality resist applying their own worldview to the subjects they study, but instead seek to understand the meaning people attach to their own behaviors. In bringing forward other worldviews, qualitative research can help to shift perceptions and increase awareness of social issues. For example, while quantitative research may show that mental health conditions are more prevalent for a certain group, along with the access they have to mental health resources, qualitative research is able to explain the lived experiences of these individuals and uncover what barriers they are facing to getting help. This qualitative approach can support governments and health organizations to better design mental health services tailored to the communities they exist in.

Controversies

Qualitative research aims to understand an individual’s lived experience, which although provides deeper insights, can make it hard to generalize to a larger population. While someone in a focus group could say they pick Doritos over Pringles because they prefer the packaging, it’s difficult for a researcher to know if this is universally applicable, or just one person’s preference. 12 This challenge makes it difficult to replicate qualitative research because it involves context-specific findings and subjective interpretation. 

Moreover, there can be bias in sample selection when conducting qualitative research. Individuals who put themselves forward to be part of a focus group or interview may hold strong opinions they want to share, making the insights gathered from their answers not necessarily reflective of the general population. 13 People may also give answers that they think researchers are looking for leading to skewed results, which is a common example of the observer expectancy effect . 

However, the bias in this interaction can go both ways. While researchers are encouraged to embrace “Verstehen,” there is a possibility that they project their own views onto their participants. For example, if an American researcher is studying eating habits in China and observes someone burping, they may attribute this behavior to rudeness—when in fact, burping can be a sign that you have enjoyed your meal and it is a compliment to the chef. One way to mitigate this risk is through thick description , noting a great amount of contextual detail in their observations. Another way to minimize the researcher’s bias on their observations is through member checking , returning results to participants to check if they feel they accurately capture their experience.

Another drawback of qualitative research is that it is time-consuming. Focus groups and unstructured interviews take longer and are more difficult to logistically arrange, and the data gathered is harder to analyze as it goes beyond numerical data. While advances in technology alleviate some of these labor-intensive processes, they still require more resources. 

Many of these drawbacks can be mitigated through a mixed-method approach, combining both qualitative and quantitative research. Qualitative research can be a good starting point, giving depth and contextual understanding to a behavior, before turning to quantitative data to see if the results are generalizable. Or, the opposite direction can be used—quantitative research can show us the “what,” identifying patterns and correlations, and researchers can then better understand the “why” behind behavior by leveraging qualitative methods. Triangulation —using multiple datasets, methods, or theories—is another way to help researchers avoid bias. 

Linking Adult Behaviors to Childhood Experiences

In the mid-1980s, an obesity program at the KP San Diego Department of Preventive Medicine had a high dropout rate. What was interesting is that a majority of the dropouts were successfully losing weight, posing the question of why they were leaving the program in the first place. In this instance, greater investigation was required to understand the why behind their behaviors.

Researchers conducted in-depth interviews with almost 200 dropouts, finding that many of them had experienced childhood abuse that had led to obesity. In this unfortunate scenario, obesity was a consequence of another problem, rather than the root problem itself. This led Dr. Vincent J. Felitti, who was working for the department, to launch the Adverse Childhood Experiences (ACE) Study, aimed at exploring how childhood experiences impact adult health status. 

Felitti and the Department of Preventive Medicine studied over 17,000 adults with health plans that revealed a strong relationship between emotional experiences as children and negative health behaviors as adults, such as obesity, smoking, and intravenous drug use. This study demonstrates the importance of qualitative research to uncover correlations that would not be discovered by merely looking at numerical data. 14  

Understanding Voter Turnout

Voting is usually considered an important part of political participation in a democracy. However, voter turnout is an issue in many countries, including the US. While quantitative research can tell us how many people vote, it does not provide insights into why people choose to vote or not.

With this in mind, Dawn Merdelin Johnson, a PhD student in philosophy at Walden University, explored how public corruption has impacted voter turnout in Cook County, Illinois. Johnson conducted semi-structured telephone interviews to understand factors that contribute to low voter turnout and the impact of public corruption on voting behaviors. Johnson found that public corruption leads to voters believing public officials prioritize their own well-being over the good of the people, leading to distrust in candidates and the overall political system, and thus making people less likely to vote. Other themes revealed that to increase voter turnout, voting should be more convenient and supply more information about the candidates to help people make more informed decisions.

From these findings, Johnson suggested that the County could experience greater voter turnout through the development of an anti-corruption agency, improved voter registration and maintenance, and enhanced voting accessibility. These initiatives would boost voting engagement and positively impact democratic participation. 15

Related TDL Content

Applying behavioral science in an organization.

At its core, behavioral science is about uncovering the reasons behind why people do what they do. That means that the role of a behavioral scientist can be quite broad, but has many important applications. In this article, Preeti Kotamarthi explains how behavioral science supports different facets of the organization, providing valuable insights for user design, data science, and product marketing. 

Increasing HPV Vaccination in Rural Kenya

While HPV vaccines are an effective method of preventing cervical cancer, there is low intake in low and middle-income countries worldwide. Qualitative research can uncover the social and behavioral barriers to increasing HPV vaccination, revealing that misinformation, skepticism, and fear prevent people from getting the vaccine. In this article, our writer Annika Steele explores how qualitative insights can inform a two-part intervention strategy to increase HPV vaccination rates.

• Versta Research. (n.d.). Bridging the quantitative-qualitative gap . Versta Research. Retrieved August 17, 2024, from https://verstaresearch.com/newsletters/bridging-the-quantitative-qualitative-gap/
• Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
• Smith, D. W. (2018). Phenomenology. In E. N. Zalta (Ed.), Stanford Encyclopedia of Philosophy . Retrieved from https://plato.stanford.edu/entries/phenomenology/#HistVariPhen
• Nickerson, C. (2023, October 16). Symbolic interaction theory . Simply Psychology. https://www.simplypsychology.org/symbolic-interaction-theory.html
• DePoy, E., & Gitlin, L. N. (2016). Introduction to research (5th ed.). Elsevier.
• ATLAS.ti. (n.d.). Unstructured interviews . ATLAS.ti. Retrieved August 17, 2024, from https://atlasti.com/research-hub/unstructured-interviews
• O'Connor, O. (2020, August 14). The history of qualitative research . Medium. https://oliconner.medium.com/the-history-of-qualitative-research-f6e07c58e439
• Sociology Institute. (n.d.). Max Weber: Interpretive sociology & legacy . Sociology Institute. Retrieved August 18, 2024, from https://sociology.institute/introduction-to-sociology/max-weber-interpretive-sociology-legacy
• Kuhn, T. S. (2012). The structure of scientific revolutions (4th ed.). University of Chicago Press.
• Encyclopaedia Britannica. (n.d.). Paul Felix Lazarsfeld . Encyclopaedia Britannica. Retrieved August 17, 2024, from https://www.britannica.com/biography/Paul-Felix-Lazarsfeld
• Nickerson, C. (2019). Verstehen in Sociology: Empathetic Understanding . Simply Psychology. Retrieved August 18, 2024, from: https://www.simplypsychology.org/verstehen.html
• Omniconvert. (2021, October 4). Qualitative research: Definition, methodology, limitations, and examples . Omniconvert. https://www.omniconvert.com/blog/qualitative-research-definition-methodology-limitation-examples/
• Vaughan, T. (2021, August 5). 10 advantages and disadvantages of qualitative research . Poppulo. https://www.poppulo.com/blog/10-advantages-and-disadvantages-of-qualitative-research
• Felitti, V. J. (2002). The relation between adverse childhood experiences and adult health: Turning gold into lead. The Permanente Journal, 6 (1), 44–47. https://www.thepermanentejournal.org/doi/10.7812/TPP/02.994
• Johnson, D. M. (2024). Voters' perception of public corruption and low voter turnout: A qualitative case study of Cook County (Doctoral dissertation). Walden University.

Case studies

From insight to impact: our success stories, is there a problem we can help with, about the author.

Emilie Rose Jones

Emilie currently works in Marketing & Communications for a non-profit organization based in Toronto, Ontario. She completed her Masters of English Literature at UBC in 2021, where she focused on Indigenous and Canadian Literature. Emilie has a passion for writing and behavioural psychology and is always looking for opportunities to make knowledge more accessible. 

We are the leading applied research & innovation consultancy

Our insights are leveraged by the most ambitious organizations.

I was blown away with their application and translation of behavioral science into practice. They took a very complex ecosystem and created a series of interventions using an innovative mix of the latest research and creative client co-creation. I was so impressed at the final product they created, which was hugely comprehensive despite the large scope of the client being of the world's most far-reaching and best known consumer brands. I'm excited to see what we can create together in the future.

Heather McKee

BEHAVIORAL SCIENTIST

GLOBAL COFFEEHOUSE CHAIN PROJECT

OUR CLIENT SUCCESS

Annual revenue increase.

By launching a behavioral science practice at the core of the organization, we helped one of the largest insurers in North America realize $30M increase in annual revenue .

Increase in Monthly Users

By redesigning North America's first national digital platform for mental health, we achieved a 52% lift in monthly users and an 83% improvement on clinical assessment.

Reduction In Design Time

By designing a new process and getting buy-in from the C-Suite team, we helped one of the largest smartphone manufacturers in the world reduce software design time by 75% .

Reduction in Client Drop-Off

By implementing targeted nudges based on proactive interventions, we reduced drop-off rates for 450,000 clients belonging to USA's oldest debt consolidation organizations by 46%

Mental Models

Prisoner’s Dilemma

Reference Point

Eager to learn about how behavioral science can help your organization?

Get new behavioral science insights in your inbox every month..

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

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

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

• Åkerström M. Curiosity and serendipity in qualitative research. Qualitative Sociology Review. 2013; 9 (2):10–18. [ Google Scholar ]
• Alford, Robert R. 1998. The craft of inquiry. Theories, methods, evidence . Oxford: Oxford University Press.
• Alvesson M, Kärreman D. Qualitative research and theory development . Mystery as method . London: SAGE Publications; 2011. [ Google Scholar ]
• Aspers, Patrik. 2006. Markets in Fashion, A Phenomenological Approach. London Routledge.
• Atkinson P. Qualitative research. Unity and diversity. Forum: Qualitative Social Research. 2005; 6 (3):1–15. [ Google Scholar ]
• Becker HS. Outsiders. Studies in the sociology of deviance . New York: The Free Press; 1963. [ Google Scholar ]
• Becker HS. Whose side are we on? Social Problems. 1966; 14 (3):239–247. [ Google Scholar ]
• Becker HS. Sociological work. Method and substance. New Brunswick: Transaction Books; 1970. [ Google Scholar ]
• Becker HS. The epistemology of qualitative research. In: Richard J, Anne C, Shweder RA, editors. Ethnography and human development. Context and meaning in social inquiry. Chicago: University of Chicago Press; 1996. pp. 53–71. [ Google Scholar ]
• Becker HS. Tricks of the trade. How to think about your research while you're doing it. Chicago: University of Chicago Press; 1998. [ Google Scholar ]
• Becker, Howard S. 2017. Evidence . Chigaco: University of Chicago Press.
• Becker H, Geer B, Hughes E, Strauss A. Boys in White, student culture in medical school. New Brunswick: Transaction Publishers; 1961. [ Google Scholar ]
• Berezin M. How do we know what we mean? Epistemological dilemmas in cultural sociology. Qualitative Sociology. 2014; 37 (2):141–151. [ Google Scholar ]
• Best, Joel. 2004. Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , eds . Charles, Ragin, Joanne, Nagel, and Patricia White, 53-54. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf .
• Biernacki R. Humanist interpretation versus coding text samples. Qualitative Sociology. 2014; 37 (2):173–188. [ Google Scholar ]
• Blumer H. Symbolic interactionism: Perspective and method. Berkeley: University of California Press; 1969. [ Google Scholar ]
• Brady H, Collier D, Seawright J. Refocusing the discussion of methodology. In: Henry B, David C, editors. Rethinking social inquiry. Diverse tools, shared standards. Lanham: Rowman and Littlefield; 2004. pp. 3–22. [ Google Scholar ]
• Brown AP. Qualitative method and compromise in applied social research. Qualitative Research. 2010; 10 (2):229–248. [ Google Scholar ]
• Charmaz K. Constructing grounded theory. London: Sage; 2006. [ Google Scholar ]
• Corte, Ugo, and Katherine Irwin. 2017. “The Form and Flow of Teaching Ethnographic Knowledge: Hands-on Approaches for Learning Epistemology” Teaching Sociology 45(3): 209-219.
• Creswell JW. Research design. Qualitative, quantitative, and mixed method approaches. 3. Thousand Oaks: SAGE Publications; 2009. [ Google Scholar ]
• Davidsson D. The myth of the subjective. In: Davidsson D, editor. Subjective, intersubjective, objective. Oxford: Oxford University Press; 1988. pp. 39–52. [ Google Scholar ]
• Denzin NK. The research act: A theoretical introduction to Ssociological methods. Chicago: Aldine Publishing Company Publishers; 1970. [ Google Scholar ]
• Denzin NK, Lincoln YS. Introduction. The discipline and practice of qualitative research. In: Denzin NK, Lincoln YS, editors. Collecting and interpreting qualitative materials. Thousand Oaks: SAGE Publications; 2003. pp. 1–45. [ Google Scholar ]
• Denzin NK, Lincoln YS. Introduction. The discipline and practice of qualitative research. In: Denzin NK, Lincoln YS, editors. The Sage handbook of qualitative research. Thousand Oaks: SAGE Publications; 2005. pp. 1–32. [ Google Scholar ]
• Emerson RM, editor. Contemporary field research. A collection of readings. Prospect Heights: Waveland Press; 1988. [ Google Scholar ]
• Emerson RM, Fretz RI, Shaw LL. Writing ethnographic fieldnotes. Chicago: University of Chicago Press; 1995. [ Google Scholar ]
• Esterberg KG. Qualitative methods in social research. Boston: McGraw-Hill; 2002. [ Google Scholar ]
• Fine, Gary Alan. 1995. Review of “handbook of qualitative research.” Contemporary Sociology 24 (3): 416–418.
• Fine, Gary Alan. 2003. “ Toward a Peopled Ethnography: Developing Theory from Group Life.” Ethnography . 4(1):41-60.
• Fine GA, Hancock BH. The new ethnographer at work. Qualitative Research. 2017; 17 (2):260–268. [ Google Scholar ]
• Fine GA, Hallett T. Stranger and stranger: Creating theory through ethnographic distance and authority. Journal of Organizational Ethnography. 2014; 3 (2):188–203. [ Google Scholar ]
• Flick U. Qualitative research. State of the art. Social Science Information. 2002; 41 (1):5–24. [ Google Scholar ]
• Flick U. Designing qualitative research. London: SAGE Publications; 2007. [ Google Scholar ]
• Frankfort-Nachmias C, Nachmias D. Research methods in the social sciences. 5. London: Edward Arnold; 1996. [ Google Scholar ]
• Franzosi R. Sociology, narrative, and the quality versus quantity debate (Goethe versus Newton): Can computer-assisted story grammars help us understand the rise of Italian fascism (1919- 1922)? Theory and Society. 2010; 39 (6):593–629. [ Google Scholar ]
• Franzosi R. From method and measurement to narrative and number. International journal of social research methodology. 2016; 19 (1):137–141. [ Google Scholar ]
• Gadamer, Hans-Georg. 1990. Wahrheit und Methode, Grundzüge einer philosophischen Hermeneutik . Band 1, Hermeneutik. Tübingen: J.C.B. Mohr.
• Gans H. Participant Observation in an Age of “Ethnography” Journal of Contemporary Ethnography. 1999; 28 (5):540–548. [ Google Scholar ]
• Geertz C. The interpretation of cultures. New York: Basic Books; 1973. [ Google Scholar ]
• Gilbert N. Researching social life. 3. London: SAGE Publications; 2009. [ Google Scholar ]
• Glaeser A. Hermeneutic institutionalism: Towards a new synthesis. Qualitative Sociology. 2014; 37 :207–241. [ Google Scholar ]
• Glaser, Barney G., and Anselm L. Strauss. [1967] 2010. The discovery of grounded theory. Strategies for qualitative research. Hawthorne: Aldine.
• Goertz G, Mahoney J. A tale of two cultures: Qualitative and quantitative research in the social sciences. Princeton: Princeton University Press; 2012. [ Google Scholar ]
• Goffman E. On fieldwork. Journal of Contemporary Ethnography. 1989; 18 (2):123–132. [ Google Scholar ]
• Goodwin J, Horowitz R. Introduction. The methodological strengths and dilemmas of qualitative sociology. Qualitative Sociology. 2002; 25 (1):33–47. [ Google Scholar ]
• Habermas, Jürgen. [1981] 1987. The theory of communicative action . Oxford: Polity Press.
• Hammersley M. The issue of quality in qualitative research. International Journal of Research & Method in Education. 2007; 30 (3):287–305. [ Google Scholar ]
• Hammersley, Martyn. 2013. What is qualitative research? Bloomsbury Publishing.
• Hammersley M. What is ethnography? Can it survive should it? Ethnography and Education. 2018; 13 (1):1–17. [ Google Scholar ]
• Hammersley M, Atkinson P. Ethnography . Principles in practice . London: Tavistock Publications; 2007. [ Google Scholar ]
• Heidegger M. Sein und Zeit. Tübingen: Max Niemeyer Verlag; 2001. [ Google Scholar ]
• Heidegger, Martin. 1988. 1923. Ontologie. Hermeneutik der Faktizität, Gesamtausgabe II. Abteilung: Vorlesungen 1919-1944, Band 63, Frankfurt am Main: Vittorio Klostermann.
• Hempel CG. Philosophy of the natural sciences. Upper Saddle River: Prentice Hall; 1966. [ Google Scholar ]
• Hood JC. Teaching against the text. The case of qualitative methods. Teaching Sociology. 2006; 34 (3):207–223. [ Google Scholar ]
• James W. Pragmatism. New York: Meredian Books; 1907. [ Google Scholar ]
• Jovanović G. Toward a social history of qualitative research. History of the Human Sciences. 2011; 24 (2):1–27. [ Google Scholar ]
• Kalof L, Dan A, Dietz T. Essentials of social research. London: Open University Press; 2008. [ Google Scholar ]
• Katz J. Situational evidence: Strategies for causal reasoning from observational field notes. Sociological Methods & Research. 2015; 44 (1):108–144. [ Google Scholar ]
• King G, Keohane RO, Sidney S, Verba S. Scientific inference in qualitative research. Princeton: Princeton University Press; 1994. Designing social inquiry. [ Google Scholar ]
• Lamont M. Evaluating qualitative research: Some empirical findings and an agenda. In: Lamont M, White P, editors. Report from workshop on interdisciplinary standards for systematic qualitative research. Washington, DC: National Science Foundation; 2004. pp. 91–95. [ Google Scholar ]
• Lamont M, Swidler A. Methodological pluralism and the possibilities and limits of interviewing. Qualitative Sociology. 2014; 37 (2):153–171. [ Google Scholar ]
• Lazarsfeld P, Barton A. Some functions of qualitative analysis in social research. In: Kendall P, editor. The varied sociology of Paul Lazarsfeld. New York: Columbia University Press; 1982. pp. 239–285. [ Google Scholar ]
• Lichterman, Paul, and Isaac Reed I (2014), Theory and Contrastive Explanation in Ethnography. Sociological methods and research. Prepublished 27 October 2014; 10.1177/0049124114554458.
• Lofland J, Lofland L. Analyzing social settings. A guide to qualitative observation and analysis. 3. Belmont: Wadsworth; 1995. [ Google Scholar ]
• Lofland J, Snow DA, Anderson L, Lofland LH. Analyzing social settings. A guide to qualitative observation and analysis. 4. Belmont: Wadsworth/Thomson Learning; 2006. [ Google Scholar ]
• Long AF, Godfrey M. An evaluation tool to assess the quality of qualitative research studies. International Journal of Social Research Methodology. 2004; 7 (2):181–196. [ Google Scholar ]
• Lundberg G. Social research: A study in methods of gathering data. New York: Longmans, Green and Co.; 1951. [ Google Scholar ]
• Malinowski B. Argonauts of the Western Pacific: An account of native Enterprise and adventure in the archipelagoes of Melanesian New Guinea. London: Routledge; 1922. [ Google Scholar ]
• Manicas P. A realist philosophy of science: Explanation and understanding. Cambridge: Cambridge University Press; 2006. [ Google Scholar ]
• Marchel C, Owens S. Qualitative research in psychology. Could William James get a job? History of Psychology. 2007; 10 (4):301–324. [ PubMed ] [ Google Scholar ]
• McIntyre LJ. Need to know. Social science research methods. Boston: McGraw-Hill; 2005. [ Google Scholar ]
• Merton RK, Barber E. The travels and adventures of serendipity . A Study in Sociological Semantics and the Sociology of Science. Princeton: Princeton University Press; 2004. [ Google Scholar ]
• Mannay D, Morgan M. Doing ethnography or applying a qualitative technique? Reflections from the ‘waiting field‘ Qualitative Research. 2015; 15 (2):166–182. [ Google Scholar ]
• Neuman LW. Basics of social research. Qualitative and quantitative approaches. 2. Boston: Pearson Education; 2007. [ Google Scholar ]
• Ragin CC. Constructing social research. The unity and diversity of method. Thousand Oaks: Pine Forge Press; 1994. [ Google Scholar ]
• Ragin, Charles C. 2004. Introduction to session 1: Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , 22, ed. Charles C. Ragin, Joane Nagel, Patricia White. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf
• Rawls, Anne. 2018. The Wartime narrative in US sociology, 1940–7: Stigmatizing qualitative sociology in the name of ‘science,’ European Journal of Social Theory (Online first).
• Schütz A. Collected papers I: The problem of social reality. The Hague: Nijhoff; 1962. [ Google Scholar ]
• Seiffert H. Einführung in die Hermeneutik. Tübingen: Franke; 1992. [ Google Scholar ]
• Silverman D. Doing qualitative research. A practical handbook. 2. London: SAGE Publications; 2005. [ Google Scholar ]
• Silverman D. A very short, fairly interesting and reasonably cheap book about qualitative research. London: SAGE Publications; 2009. [ Google Scholar ]
• Silverman D. What counts as qualitative research? Some cautionary comments. Qualitative Sociology Review. 2013; 9 (2):48–55. [ Google Scholar ]
• Small ML. “How many cases do I need?” on science and the logic of case selection in field-based research. Ethnography. 2009; 10 (1):5–38. [ Google Scholar ]
• Small, Mario L 2008. Lost in translation: How not to make qualitative research more scientific. In Workshop on interdisciplinary standards for systematic qualitative research, ed in Michelle Lamont, and Patricia White, 165–171. Washington, DC: National Science Foundation.
• Snow DA, Anderson L. Down on their luck: A study of homeless street people. Berkeley: University of California Press; 1993. [ Google Scholar ]
• Snow DA, Morrill C. New ethnographies: Review symposium: A revolutionary handbook or a handbook for revolution? Journal of Contemporary Ethnography. 1995; 24 (3):341–349. [ Google Scholar ]
• Strauss AL. Qualitative analysis for social scientists. 14. Chicago: Cambridge University Press; 2003. [ Google Scholar ]
• Strauss AL, Corbin JM. Basics of qualitative research. Techniques and procedures for developing grounded theory. 2. Thousand Oaks: Sage Publications; 1998. [ Google Scholar ]
• Swedberg, Richard. 2017. Theorizing in sociological research: A new perspective, a new departure? Annual Review of Sociology 43: 189–206.
• Swedberg R. The new 'Battle of Methods'. Challenge January–February. 1990; 3 (1):33–38. [ Google Scholar ]
• Timmermans S, Tavory I. Theory construction in qualitative research: From grounded theory to abductive analysis. Sociological Theory. 2012; 30 (3):167–186. [ Google Scholar ]
• Trier-Bieniek A. Framing the telephone interview as a participant-centred tool for qualitative research. A methodological discussion. Qualitative Research. 2012; 12 (6):630–644. [ Google Scholar ]
• Valsiner J. Data as representations. Contextualizing qualitative and quantitative research strategies. Social Science Information. 2000; 39 (1):99–113. [ Google Scholar ]
• Weber, Max. 1904. 1949. Objectivity’ in social Science and social policy. Ed. Edward A. Shils and Henry A. Finch, 49–112. New York: The Free Press.

When to Use the 4 Qualitative Data Collection Methods

Qualitative data collection methods are the different ways to gather descriptive, non-numerical data for your research. 

Popular examples of qualitative data collection methods include surveys, observations, interviews, and focus groups. 

But it’s not enough to know what these methods are. Even more important is knowing when to use them. 

In an article published in Neurological Research and Practice titled, “How to use and assess qualitative research methods,” authors Busetto, Wick, and Gambinger assert that qualitative research is all about “flexibility, openness and responsivity to context . ” 

Because of this, “the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research,” according to the authors. 

This makes sense to me, too. And it means you have to use intuition and a pinch of guidance to know when—and how often—to use a specific qualitative data collection method. 

In this post, you’ll learn when to use the most common methods: interviews, focus groups, observations, and open-ended surveys.

#1. Interviews

An interview is a qualitative data collection method where a researcher has a one-on-one conversation with a participant. 

The goal of an interview is to explore how the participant feels about a specific topic. You’re mining for their unique experiences, perceptions, and thoughts.

There’s usually an element of structure here, with the researcher asking specific questions. But there’s room for organic discussion, too. The interviewer might take notes or record the session—or both—to capture the qualitative data collected.  

Interviews are slower, in some ways, than other qualitative data collection methods. Since you can only talk to one person at a time, you might not get as much data as you would from a survey sent out to 100 people at once. 

But interviews are a great way to go deep into a subject and collect details you wouldn’t get from a static survey response. 

Interviews are ideal to use when: 

• You need to know the “why”: A one-on-one conversation can help participants open up about the reasons they feel the way they do about a certain topic.
• You’re dealing with a sensitive topic: With an interview, you can create a safe space for a person to share their feelings without fear of judgment from other people.
• You want to know someone’s personal, lived experience: In a group setting, no one likes the person who takes over and tells their life story rather than participate in a larger conversation. But if you want that life story—if it’s relevant to your research—an interview is ideal.

There are times when interviews aren’t such a great choice, though. 

Choose another qualitative data collection method when:  

• You need information from lots of people, and quickly. Interviews are slow. If you need less depth and more breadth, go with a survey or questionnaire. 
• You don’t have a lot of resources to spare. It takes a significant amount of time and money to plan and carry out interviews. Most of the time, people don’t jump at the opportunity to participate in your research unless there’s an incentive—usually cash or a gift card. It ends up adding up to quite a bit.

#2. Focus Groups

A focus group is a qualitative data collection method where a small group of people discuss a topic together. A moderator is there to help guide the conversation. The goal here is to get everyone talking about their unique perspectives—and their shared experiences on a topic.

There’s one giant difference between focus groups and interviews, according to the authors of a 2018 article, “The use of focus groups discussion methodology: Insights from two decades of application in conservation,” published in the journal Methods in Ecology and Evolution . The article argues that in a one-on-one interview, the interviewer takes on the role of “investigator” and plays a central role in how the dynamics of the discussion play out. 

But in a focus group, the researcher “takes a peripheral, rather than a centre-stage role in a focus group discussion.”

AKA, researchers don’t have as much control over focus groups as they do interviews. 

And that can be a good thing. 

Focus groups are ideal to use when:  

• You’re in the early stages of research. If you haven’t been able to articulate the deeper questions you want to explore about a topic, a focus group can help you identify compelling areas to dig into. 
• You want to study a wide range of perspectives. A focus group can bring together a very diverse group of people if you want it to—and the conversation that results from this gathering of viewpoints can be incredibly insightful. 

So when should you steer clear of focus groups? 

Another research method might be better if: 

• You need raw, real honesty—from as many people as possible. Some participants might share valuable, sensitive information (like their honest opinions!) in a focus group. But many won’t feel comfortable doing so. The social dynamics in a group of people can greatly influence who shares what. If you want to build rapport with people and create a trusting environment, an interview might be a better choice. 

#3. Observation

Do you remember those strange, slightly special-feeling days in school when a random person, maybe the principal, would sit in on your class? Watching everyone, but especially your teacher? Jotting down mysterious notes from time to time? 

If you were anything like me, you behaved extra-good for a few minutes…and then promptly forgot about the person’s presence as you went about your normal school day.

That’s observation in a nutshell, and it’s a useful way to gather objective qualitative data. You don’t interfere or intrude when you’re observing. 

You just watch. 

Observation is a useful tool when: 

• You need to study natural behavior. Observation is ideal when you want to understand how people behave in a natural (aka non-conference-room) environment without interference. It allows you to see genuine interactions, routines, and practices as they happen. Think of observing kids on a playground or shoppers in a grocery store. 
• Participants may not be likely to accurately self-report behaviors. Sometimes participants might not be fully aware of their behaviors, or they might alter their responses to seem more “normal” or desirable to others. Observation allows you to capture what people do, rather than what they say they do. 

But observation isn’t always the best choice. 

Consider using another qualitative research method when: 

• The topic and/or behaviors studied are private or sensitive. Publicly observable behavior is one thing. Stuff that happens behind closed doors is another. If your research topic requires more of the latter and less of the former, go with interviews or surveys instead.
• You need to know the reasons behind specific behaviors. Observation gets you the what , but not the why . For detailed, in-depth insights, run an interview or open-ended survey.

#4. Open-Ended Surveys/Questionnaires

A survey is a series of questions sent out to a group of people in your target audience. 

In a qualitative survey, the questions are open-ended. This is different from quantitative questions, which are closed, yes-or-no queries. 

There’s a lot more room for spontaneity, opinion, and subjectivity with an open-ended survey question, which is why it’s considered a pillar of qualitative data collection. 

Of course, you can send out a survey that asks closed and open-ended questions. But our focus here is on the value of open-ended surveys.

Consider using an open-ended survey when:  

• You need detailed information from a diverse audience. The beauty of an open-ended questionnaire is you can send it to a lot of people. If you’re lucky, you’ll get plenty of details from each respondent. Not as much detail as you would in an interview, but still a super valuable amount.
• You’re just exploring a topic. If you’re in the early stages of research, an open-ended survey can help you discover angles you hadn’t considered before. You can move from a survey to a different data collection method, like interviews, to follow the threads you find intriguing.
• You want to give respondents anonymity. Surveys can easily be made anonymous in a way other methods, like focus groups, simply can’t. (And you can still collect important quantitative data from anonymous surveys, too, like age range, income level, and years of education completed.)

Useful though they are, open-ended surveys aren’t foolproof. 

Choose another method when:  

• You want to ask more than a few questions about a topic. It takes time and energy to compose an answer to an open-ended question. If you include more than three or four questions, you can expect the answers to get skimpier with each one. Or even completely absent by Question #4. 
• You want consistently high-quality answers. Researchers at Pew Research Center know a thing or two about surveys. According to authors Amina Dunn and Vianney Gómez in a piece for Decoded , Pew Research Center’s behind-the-scenes blog about research methods, “open-ended survey questions can be prone to high rates of nonresponse and wide variation in the quality of responses that are given.” If you need consistent, high-quality answers, consider hosting interviews instead. 

How to Decide Which Qualitative Data Collection Method to Use

Choosing the right qualitative data collection method can feel overwhelming. That’s why I’m breaking it down into a logical, step-by-step guide to help you choose the best method for your needs.

(Psst: you’ll probably end up using more than one of these methods throughout your qualitative research journey. That’s totally normal.)

Okay. Here goes. 

1. Start with your research goal

• If your goal is to understand deep, personal experiences or the reasons behind specific behaviors, then interviews are probably your best choice. There’s just no substitute for the data you’ll get during a one-on-one conversation with a research participant. And then another, and another. 
• If you’re not sure what your research goals are, begin by sending out a survey with general, open-ended questions asking for your respondents’ opinions about a topic. You can dig deeper from there.

2. Consider how sensitive your topic is

• If you’re dealing with a sensitive or private topic, where participants might not feel comfortable sharing in a group setting, interviews are ideal. They create a safe, confidential environment for open discussion between you and the respondent.
• If the topic is less sensitive and you want to see how social dynamics influence opinions, consider using focus groups instead.

3. Evaluate whether you need broad vs. deep data

• If you need broad data from a large number of people quickly, go with open-ended surveys or questionnaires . You don’t have to ask your respondents to write you an essay for each question. A few insightful lines will do just fine.
• If you need deep data, run interviews or focus groups. These allow for more in-depth responses and discussions you won’t get with a survey or observation.

4. Think about the context of your research

• If you want to study behavior in a natural setting without interference, observation is the way to go. More than any other, this method helps you capture genuine behaviors as they happen in real life. 
• But if you need to understand the reasons behind those behaviors, remember that observation only provides the what, not the why. In these cases, follow up with interviews or open-ended surveys for deeper insights.

5. Assess your resources If time and budget are limited, consider how many resources each qualitative data collection method will require. Open-ended surveys are less expensive—and faster to send out and analyze —than interviews or focus groups. The latter options require more time and effort from participants—and probably incentives, too.

Make your website better. Instantly.

Keep reading about user experience.

The 5 Best UI/UX Design Agencies Compared

UI/UX design agencies help bring brands to life and deliver optimized user experiences with well-researched and thoughtfully designed websites, apps, and products. All of the…

Qualitative data collection methods are the different ways to gather descriptive, non-numerical data for your research.  Popular examples of qualitative data collection methods include surveys,…

dscout Review–The Good and Bad

dscout is a great tool for doing qualitative user research, like live interviews or diary studies. But it isn’t the best choice for everyone.  If…

Out Of All Tips to Improve User Experience, 7 Are Legit

Figuring out the most effective ways to improve the user experience can be hard. There is tons of information out there, and it gets overwhelming…

Is Nominal Data Useful? Yes, In These Situations

Nominal data is descriptive information wherein rank and order don’t matter. Still confused? It helps to contrast nominal data with the other three main types…

What Is User Experience? Answers From 7 Top UX Designers

If you Google user experience the definition you’ll find is “the overall experience of a person using a product like a website or computer application,…

How to Do Each Qualitative Data Coding Type (All Steps)

Qualitative data coding is the process of organizing all the descriptive data you collect during a research project.  It has nothing to do with computer…

7 Qualitative Data Examples and Why They Work

Qualitative data presents information using descriptive language, images, and videos instead of numbers. To help make sense of this type of data—as opposed to quantitative…

The 5 Best Usability Testing Tools Compared

Usability testing helps designers, product managers, and other teams figure out how easily users can use a website, app, or product.  With these tools, user…

5 Qualitative Data Analysis Methods + When To Use Each

Qualitative data analysis is the work of organizing and interpreting descriptive data. Interview recordings, open-ended survey responses, and focus group observations all yield descriptive—qualitative—information. This…

The 5 Best UX Research Tools Compared

UX research tools help designers, product managers, and other teams understand users and how they interact with a company’s products and services. The tools provide…

Qualitative vs. Quantitative Data: 7 Key Differences

Qualitative data is information you can describe with words rather than numbers.  Quantitative data is information represented in a measurable way using numbers.  One type…

6 Real Ways AI Has Improved the User Experience

It seems like every other company is bragging about their AI-enhanced user experiences. Consumers and the UX professionals responsible for designing great user experiences are…

12 Key UX Metrics: What They Mean + How To Calculate Each

UX metrics help identify where users struggle when using an app or website and where they are successful. The data collected helps designers, developers, and…

5 Key Principles Of Good Website Usability

Ease of use is a common expectation for a site to be considered well designed. Over the past few years, we have been used to…

Over 300,000 websites use Crazy Egg to improve what's working, fix what isn't and test new ideas.

Last Updated on September 30, 2021

Journal Article Reporting Standards (JARS)

APA Style Journal Article Reporting Standards offer guidance on what information should be included in all manuscript sections for quantitative, qualitative, and mixed methods research and include how to best discuss race, ethnicity, and culture.

Introducing Journal Article Reporting Standards for Race, Ethnicity, and Culture (JARS–REC)

JARS–REC were created to develop best practices related to the manner in which race, ethnicity, and culture are discussed within scientific manuscripts in psychological science.

Quantitative research

Use JARS–Quant when you collect your study data in numerical form or report them through statistical analyses.

Qualitative research

Use JARS–Qual when you collect your study data in the form of natural language and expression.

Mixed methods research

Use JARS–Mixed when your study combines both quantitative and qualitative methods.

Race, ethnicity, culture

Use JARS–REC for all studies for guidance on how to discuss race, ethnicity, and culture.

What are APA Style JARS?

APA Style Journal Article Reporting Standards (APA Style Jars ) are a set of standards designed for journal authors, reviewers, and editors to enhance scientific rigor in peer-reviewed journal articles. Educators and students can use APA Style JARS as teaching and learning tools for conducting high quality research and determining what information to report in scholarly papers.

The standards include information on what should be included in all manuscript sections for:

• Quantitative research ( Jars –Quant)
• Qualitative research ( Jars –Qual)
• Mixed methods research ( Jars –Mixed)

Additionally, the APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture ( Jars – Rec ) provide guidance on how to discuss race, ethnicity, and culture in scientific manuscripts. Jars – Rec should be applied to all research, whether it is quantitative, qualitative, or mixed methods.

• Race, Ethnicity, and Culture ( Jars – Rec )

Using these standards will make your research clearer and more accurate as well as more transparent for readers. For quantitative research, using the standards will increase the reproducibility of science. For qualitative research, using the standards will increase the methodological integrity of research.

Jars –Quant should be used in research where findings are reported numerically (quantitative research). Jars –Qual should be used in research where findings are reported using nonnumerical descriptive data (qualitative research). Jars –Mixed should be applied to research that includes both quantitative and qualitative research (mixed methods research). JARS–REC should be applied to all research, whether it is quantitative, qualitative, or mixed methods.

For more information on APA Style JARS:

• Read Editorial: Journal Article Reporting Standards
• View an infographic (PDF, 453KB) to learn about the benefits of JARS and how they are relevant to you
• Listen to a podcast with Drs. Harris Cooper and David Frost discussing JARS and implications for research in psychology

Many aspects of research methodology warrant a close look, and journal editors can promote better methods if we encourage authors to take responsibility to report their work in clear, understandable ways. —Nelson Cowan, Editor, Journal of Experimental Psychology: General

Read more testimonials

Watch a video about JARS

This content is disabled due to your privacy settings. To re-enable, please adjust your cookie preferences.

This video describes and discusses the updated APA Style Journal Article Reporting Standards.

Related products

Reporting Qualitative Research in Psychology

Journal article reporting standards for qualitative research

Reporting Quantitative Research in Psychology

Journal article reporting standards for quantitative research

Publication Manual, 7th Edition

The official source for writing papers and creating references in seventh edition APA Style

Jars resources

• History of APA’s journal article reporting standards
• APA Style JARS supplemental glossary
• Supplemental resource on the ethic of transparency in JARS
• Frequently asked questions
• JARS-Quant Decision Flowchart (PDF, 98KB)
• JARS-Quant Participant Flowchart (PDF, 98KB)

Jars articles

• Jars –Quant article
• Jars –Qual / Mixed article
• Jars – rec executive summary

Questions / feedback

Email an APA Style Expert if you have questions, feedback, or suggestions for modules to be included in future JARS updates.

APA resources

• APA Databases and Electronic Resources
• APA Journals
• Journal Author Resource Center
• Education and Career
• Psychological Science
• Open Science at APA
• How to Review a Manuscript

From the APA Style blog

Introducing APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture

These standards are for all authors, reviewers, and editors seeking to improve manuscript quality by encouraging more racially and ethnically conscious and culturally responsive journal reporting standards for empirical studies in psychological science.

APA Style JARS for high school students

In this post, we provide an overview of APA Style JARS and resources that can be shared with high school students who want to learn more about effective communication in scholarly research.

Happy 2022, APA Stylers!

This blog post is dedicated to our awesome APA Style users. You can use the many resources on our website to help you master APA Style and improve your scholarly writing.

APA Style JARS on the EQUATOR Network

The APA Style Journal Article Reporting Standards (APA Style JARS) have been added to the EQUATOR Network. The network aims to promote accuracy and quality in reporting of research.

APA Style JARS: Resources for instructors and students

APA Style Journal Article Reporting Standards (APA Style JARS) are a set of guidelines for papers reporting quantitative, qualitative, and mixed methods research that can be used by instructors, students, and all others reading and writing research papers.

Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study

• Open access
• Published: 07 September 2024

Cite this article

You have full access to this open access article

• Riina Kleimola   ORCID: orcid.org/0000-0003-2091-2798 1 ,
• Laura Hirsto   ORCID: orcid.org/0000-0002-8963-3036 2 &
• Heli Ruokamo   ORCID: orcid.org/0000-0002-8679-781X 1  

Learning analytics provides a novel means to support the development and growth of students into self-regulated learners, but little is known about student perspectives on its utilization. To address this gap, the present study proposed the following research question: what are the perceptions of higher education students on the utilization of a learning analytics dashboard to promote self-regulated learning? More specifically, this can be expressed via the following threefold sub-question: how do higher education students perceive the use of a learning analytics dashboard and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of self-regulated learning? Data for the study were collected from students ( N  = 16) through semi-structured interviews and analyzed using a qualitative content analysis. Results indicated that the students perceived the use of the learning analytics dashboard as an opportunity for versatile learning support, providing them with a means to control and observe their studies and learning, while facilitating various performance phase processes. Insights from the analytics data could also be used in targeting the students’ development areas as well as in reflecting on their studies and learning, both individually and jointly with their educators, thus contributing to the activities of forethought and reflection phases. However, in order for the learning analytics dashboard to serve students more profoundly across myriad studies, its further development was deemed necessary. The findings of this investigation emphasize the need to integrate the use and development of learning analytics into versatile learning processes and mechanisms of comprehensive support and guidance.

Explore related subjects

• Artificial Intelligence
• Digital Education and Educational Technology

Avoid common mistakes on your manuscript.

1 Introduction

Promoting students to become autonomous, self-regulated learners is a fundamental goal of education (Lodge et al., 2019 ; Puustinen & Pulkkinen, 2001 ). The importance of doing so is particularly highlighted in higher education (HE) contexts that strive to prepare its students for highly demanding and autonomous expert tasks (Virtanen, 2019 ). In order to perform successfully in diverse educational and professional settings, students need to take an active, self-initiated role in managing their learning processes, thereby assuming primary responsibility for their educational pursuits. Self-regulated learning (SRL) invites students to actively monitor, control, and regulate their cognition, motivation, and behavior in relation to their learning goals and contextual conditions (Pintrich, 2000 ). In an effort to create a favorable foundation for the development of SRL, many HE institutions have begun to explore and exploit the potential of emerging educational technologies, such as learning analytics (LA).

Despite the growing interest in adopting LA for educational purposes (Van Leeuwen et al., 2022 ), little is known about students’ perspectives on its utilization (Jivet et al., 2020 ; Wise et al., 2016 ). Additionally, there is only limited evidence on using LA to support SRL (Heikkinen et al., 2022 ; Jivet et al., 2018 ; Matcha et al., 2020 ; Viberg et al., 2020 ). Thus, more research is inevitably needed to better understand how students themselves consider the potential of analytics applications from the perspective of SRL. Involving students in the development of LA is particularly important, as they represent primary stakeholders targeted to benefit from its utilization (Dollinger & Lodge, 2018 ; West et al., 2020 ). LA should not only be developed for users but also with them in order to adapt its potential to their needs and expectations (Dollinger & Lodge, 2018 ; Klein et al., 2019 ).

LA is thought to provide a promising means to enhance student SRL by harnessing the massive amount of data stored in educational systems and facilitating appropriate means of support (Lodge et al., 2019 ). It is generally defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (Conole et al., 2011 , para. 4). The reporting of such data is typically conducted through learning analytics dashboards (LADs) that aggregate diverse types of indicators about learners and learning processes in a visualized form (Corrin & De Barba, 2014 ; Park & Jo, 2015 ; Schwendimann et al., 2017 ). Recently, there has been a rapid movement into LADs that present analytics data directly to students themselves (Schwendimann et al., 2017 ; Teasley, 2017 ; Van Leeuwen et al., 2022 ). Such analytics applications generally aim to provide students with insights into their study progress as well as support for optimizing learning outcomes (Molenaar et al., 2019 ; Sclater et al., 2016 ; Susnjak et al., 2022 ).

The purpose of this qualitative study is to examine how HE students perceive the use and development of an LAD to promote the different phases and processes of SRL. Instead of taking a course-level approach, this study addresses a less-examined study path perspective that covers the entirety of studies, from the start of an HE degree to its completion. A specific emphasis is placed on such an LAD that students could use both independently across studies and together with their tutor teachers as a component of educational guidance. As analytics applications are largely still under development (Sclater et al., 2016 ), and mainly in the exploratory phase (Schwendimann et al., 2017 ; Susnjak et al., 2022 ), it is essential to gain an understanding of how students perceive the use of these applications as a form of learning support. Preparing students to become efficient self-regulated learners is increasingly—and simultaneously—a matter of helping them develop into efficient users of analytics data.

2 Theoretical framework

2.1 enhancing srl in he.

SRL, which has been the subject of wide research interest over the last two decades (Panadero, 2017 ), is referred to as “self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals” (Zimmerman, 1999 , p. 14). Self-regulated students are proactive in their endeavors to learn, and they engage in diverse, personally initiated metacognitive, motivational, and behavioral processes to achieve their goals (Zimmerman, 1999 ). They master their learning through covert, cognitive means but also through behavioral, social, and environmental approaches that are reciprocally interdependent and interrelated (Zimmerman, 1999 , 2015 ), thus emphasizing the sociocognitive views on SRL (Bandura, 1986 ).

When describing and modelling SRL, researchers have widely agreed on its cyclical nature and its organization into several distinct phases and processes (Panadero, 2017 ; Puustinen & Pulkkinen, 2001 ). In the well-established model by Zimmerman and Moylan ( 2009 ), SRL occurs in the cyclic phases of forethought, performance, and self-reflection that take place before, during, and after students’ efforts to learn. In the forethought phase, students prepare themselves for learning and approach the learning tasks through the processes of planning and goal setting, and the activation of self-motivation beliefs, such as self-efficacy perceptions, outcome expectations, and personal interests. Next, in the performance phase, they carry out the actual learning tasks and make use of self-control processes and strategies, such as self-instruction, time management, help-seeking, and interest enhancement. Moreover, they keep records of their performance and monitor their learning, while promoting the achievement of desired goals. In the final self-reflection phase, students participate in the processes of evaluating their learning and reflecting on the perceived causes of their successes and failures, which typically results in different types of cognitive and affective self-reactions as responses to such activity. This phase also forms the basis for the approaches to be adjusted for and applied in the subsequent forethought phase, thereby completing the SRL cycle. The model suggests that the processes in each phase influence the following ones in a cyclical and interactive manner and provide feedback for subsequent learning efforts (Zimmerman & Moylan, 2009 ; Zimmerman, 2011 ). Participation in these processes allows students to become self-aware, competent, and decisive in their learning approaches (Kramarski & Michalsky, 2009 ).

Although several other prevalent SRL models with specific emphases also exist (e.g., Pintrich, 2000 ; Winne & Hadwin, 1998 ; for a review, see Panadero, 2017 ), the one presented above provides a comprehensive yet straightforward framework for identifying and examining the key phases and processes related to SRL (Panadero & Alonso-Tapia, 2014 ). Developing thorough insights into the student SRL is especially needed in an HE context, where the increase in digitized educational settings and tools requires students to manage their learning in a way that is autonomous and self-initiated. When pursuing an HE degree, students are expected to engage in the cyclical phases and processes of SRL as a continuous effort throughout their studies. Involvement in SRL is needed not only to successfully perform a single study module, course, or task but also to actively promote the entirety of studies throughout semesters and academic years. It therefore plays a central role in the successful completion of HE studies.

From the study path perspective, the forethought phase requires HE students to be active in the directing and planning of their studies and learning—that is, setting achievable goals, making detailed plans, finding personal interests, and trusting in their abilities to complete the degree. The performance phase, in turn, invites students to participate in the control and observation of their studies and learning. While completing their studies, they must regularly track study performance, visualize relevant study information, create functional study environments, maintain motivation and interest, and seek and receive productive guidance. The reflection phase, on the other hand, involves students in evaluating and reflecting on their studies and learning—that is, analyzing their learning achievements and processing resulting responses. These activities typically occur as overlapping, cyclic, and connected processes and as a continuum across studies. Additionally, the phases may appear simultaneously, as students strive to learn and receive feedback from different processes (Pintrich, 2000 ). The processes may also emerge in more than one phase (Panadero & Alonso-Tapia, 2014 ), and boundaries between the phases are not always that precise.

SRL is shown to benefit HE students in various ways. Research has evidenced, for instance, that online students who use their time efficiently, are aware of their learning behavior, think critically, and show efforts to learn despite challenges are likely to achieve academic success when studying in online settings (Broadbent & Poon, 2015 ). SRL is also shown to contribute to many non-academic outcomes in HE blended environments (for a review, see Anthonysamy et al., 2020 ). Despite this importance, research (e.g., Azevedo et al., 2004 ; Barnard-Brak et al., 2010 ) has indicated that students differ in their ways to self-regulate, and not all are competent self-regulated learners by default. As such, many students would require and benefit from support to develop their SRL (Moos, 2018 ; Wong et al., 2019 ).

Supporting student SRL is generally considered the responsibility of a teaching staff (Callan et al., 2022 ; Kramarski, 2018 ). It can also be a specific task given to tutor teachers assigned to each student or to a group of students for particular academic years. Sometimes referred to as advisors, they are often teachers of study programs who aim to help students in decision-making, study planning, and career reflection (De Laet et al., 2020 ), while offering them guidance and support for the better management of learning. In recent years, efforts have also been made to promote student SRL with educational technologies such as LA (e.g., Marzouk et al., 2016 ; Wise et al., 2016 ). LA is used to deliver insights for students themselves to better self-regulate their learning (e.g., Jivet et al., 2021 ; Molenaar et al., 2019 ), and also to facilitate the interaction between students and guidance personnel (e.g., Charleer et al., 2018 ). It is generally thought to promote the development of future competences needed by students in education and working life (Kleimola & Leppisaari, 2022 ), and to offer novel insights into their motivational drivers (Kleimola et al., 2023 ).

2.2 LA as a potential tool to promote SRL

Much of the recent development in the field of LA has focused on the design and implementation of LADs. In general, their purpose is to support sensemaking and encourage students and teachers to make informed decisions about learning and teaching processes (Jivet et al., 2020 ; Verbert et al., 2020 ). Schwendimann and colleagues ( 2017 ) refer to an LAD as a “display that aggregates different indicators about learner(s), learning process(es) and/or learning context(s) into one or multiple visualizations” (p. 37). Such indicators may provide information, for instance, about student actions and use of learning contents on a learning platform, or the results of one’s learning performance, such as grades (Schwendimann et al., 2017 ). Data can also be extracted from educational institutions’ student information systems to provide students with snapshots of their study progress and access to learning support (Elouazizi, 2014 ). While visualizations enable intuitive and quick interpretations of educational data (Papamitsiou & Economides, 2015 ), they additionally require careful preparation, as not all users may necessarily interpret them uniformly (Aguilar, 2018 ).

LADs can target various stakeholders, and recently there has been a growing interest in their development for students’ personal use (Van Leeuwen et al., 2022 ). Such displays, also known as student-facing dashboards, are thought to increase students’ knowledge of themselves and to assist them in achieving educational goals (Eickholt et al., 2022 ). They are also believed to promote student autonomy by encouraging students to take control of their learning and by supporting their intrinsic motivation to succeed (Bodily & Verbert, 2017 ). However, simply making analytics applications available to students does not guarantee that they will be used productively in terms of learning (Wise, 2014 ; Wise et al., 2016 ). Moreover, they may not necessarily cover or address the relevant aspects of learning (Clow, 2013 ). Thus, to promote the widespread acceptance and adoption of LADs, it is crucial to consider students’ perspectives on their use as a means of learning support (Divjak et al., 2023 ; Schumacher & Ifenthaler, 2018 ). If students’ needs are not adequately examined and met, such analytics applications may fail to encourage or even hinder the process of SRL (Schumacher & Ifenthaler, 2018 ).

Although previous research on students’ perceptions of LA to enhance their SRL appears to be limited, some studies have addressed such perspectives. Schumacher and Ifenthaler ( 2018 ) found that HE students appreciated LADs that help them plan and initiate their learning activities with supporting elements such as reminders, to-do lists, motivational prompts, learning objectives, and adaptive recommendations, thus promoting the forethought phase of SRL. The students in their study also expected such analytics applications to support the performance phase by providing analyses of their current situation and progress towards goals, materials to meet their individual learning needs, and opportunities for learning exploration and social interaction. To promote the self-reflection phase, the students anticipated LADs to allow for self-assessment, real-time feedback, and future recommendations but were divided as to whether they should receive comparative information about their own or their peers’ performance (Schumacher & Ifenthaler, 2018 ). Additionally, the students desired analytics applications to be holistic and advanced, as well as adaptable to individual needs (Schumacher & Ifenthaler, 2018 ).

Somewhat similar notions were made by Divjak and colleagues ( 2023 ), who discovered that students welcomed LADs that promote short-term planning and organization of learning but were wary of making comparisons or competing with peers, as they might demotivate learners. Correspondingly, De Barba et al. ( 2022 ) noted that students perceived goal setting and monitoring of progress from a multiple-goals approach as key features in LADs, but they were hesitant to view peer comparisons, as they could promote unproductive competition between students and challenge data privacy. In a similar vein, Rets et al. ( 2021 ) reported that students favored LADs that provide them with study recommendations but did not favor peer comparison unless additional information was included. Roberts et al. ( 2017 ), in turn, stressed that LADs should be customizable by students and offer them some level of control to support their SRL. Silvola et al. ( 2023 ) found that students perceived LADs as supportive for their study planning and monitoring at a study path level but also associated some challenges with them in terms of SRL. Further, Bennett ( 2018 ) found that students’ responses to receiving analytics data varied and were highly individual. There were different views, for instance, on the potential of analytics to motivate students: although it seemed to inspire most students, not all students felt the same way (Bennett, 2018 ; see also Corrin & De Barba, 2014 ; Schumacher & Ifenthaler, 2018 ). Moreover, LADs were reported to evoke varying affective responses in students (Bennett, 2018 ; Lim et al., 2021 ).

To promote student SRL, it is imperative that LADs comprehensively address and support all phases of SRL (Schumacher & Ifenthaler, 2018 ). However, a systematic literature review conducted by Jivet et al. ( 2017 ) indicated that students were often offered only limited support for goal setting and planning, and comprehensive self-monitoring, as very few of the LADs included in their study enabled the management of self-set learning goals or the tracking of study progress over time. According to Jivet et al. ( 2017 ), this might indicate that most LADs were mainly harnessed to support the reflection and self-evaluation phase of SRL, as the other phases were mostly ignored. Somewhat contradictory results were obtained by Viberg et al. ( 2020 ), whose literature review revealed that most studies aiming to measure or support SRL with LA were primarily focused on the forethought and performance phases and less on the reflection phase. Heikkinen et al. ( 2022 ) discovered that not many of the studies combining analytics-based interventions and SRL processes covered all phases of SRL.

It appears that further development is inevitably required for LADs to better promote student SRL as a whole. Similarly, there is a demand for their tight integration into pedagogical practices and learning processes to encourage their productive use (Wise, 2014 ; Wise et al., 2016 ). One such strategy is to use these analytics applications as a part of guidance activity and as a joint tool for both students and guidance personnel. In the study by Charleer et al. ( 2018 ), the LAD was shown to trigger conversations and to facilitate dialogue between students and study advisors, improve the personalization of guidance, and provide insights into factual data for further interpretation and reflection. However, offering students access to an LAD only during the guidance meeting may not be sufficient to meet their requirements for the entire duration of their studies. For instance, Charleer and colleagues ( 2018 ) found that the students were also interested in using the LAD independently, outside of the guidance context. Also, it seems that encouraging students to actively advance their studies with such analytics applications necessitates a student-centered approach and holistic development through research. According to Rets et al. ( 2021 ), there is a particular call for qualitative insights, as many previous LAD studies that included students have primarily used quantitative approaches (e.g., Beheshitha et al., 2016 ; Divjak et al., 2023 ; Kim et al., 2016 ).

2.3 Research questions

The purpose of this qualitative study is to examine how HE students perceive the utilization of an LAD in SRL. A specific emphasis was placed on its utilization as part of the forethought, performance, and reflection phase processes, considered central to student SRL. The main research question (RQ) and the threefold sub-question are as follows:

RQ: What are the perceptions of HE students on the utilization of an LAD to promote SRL?

How do HE students perceive the use of an LAD and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of SRL?

3.1 Context

The study was conducted in a university of applied sciences (UAS) in Finland that had launched an initial version of an LAD to be piloted together with its students and tutor teachers as a part of the guidance process. The LAD was descriptive in nature and consisted of commonly available analytics data and simple analytics indicators showing an individual student’s study progress and success in a study path. As is typical for descriptive analytics, it offered insights to better understand the past and present (Costas-Jauregui et al., 2021 ) while informing the future action (Van Leeuwen et al., 2022 ). The data were extracted from the UAS’ student information system and presented using Microsoft Power BI tools. No predictive or comparative information was included. The main display of the LAD consisted of three data visualizations and an information bar (see Fig.  1 , a–d), all presented originally in Finnish. Each visualization could also be expanded into a single display for more accurate viewing.

An example of the main display of the piloted LAD with data visualizations ( a – c ) and an information bar ( d )

First, the LAD included a data visualization that illustrated a student’s study progress and success per semester using a line chart (Fig.  1 , a). It displayed the scales for total number of credit points (left) and grade point averages (right) for courses completed on a semester timeline. Data points on the chart displayed an individual student’s study performance with respect to these indicators in each semester and were connected to each other with a line. Pointing to one of these data points also opened a data box that indicated the student name and information about courses (course name, scope, grade, assessment date) from which the credit points and grade point averages were obtained.

Second, the LAD contained another type of line chart that indicated a student’s individual study progress over time in more detail (Fig.  1 , b). The chart displayed a timeline with three-month periods and illustrated a scale for the accumulated credit points. Data points on the chart indicated the accumulated number of credit points obtained from the courses and appeared in blue if the student had passed the course(s) and in red if the student had failed the course(s) at that time. As with the line chart above it, the data points in this chart also provided more detailed information about the courses behind the credit points and were intertwined with a line.

Third, the LAD offered information related to a student’s study success through a radar chart (Fig.  1 , c). The chart represented the courses taken by the student and displayed a scale for the grades received from them. The lowest grade was placed in the center of the chart and the highest one on its outer circle. The grades in between were scaled on the chart accordingly, and the courses performed with a similar grade were displayed close to each other. Data points on the chart represented the grades obtained from numerically evaluated courses and were merged with a line. Each data point also had a data box with the course name and the grade obtained.

Fourth, the LAD included an information bar (Fig.  1 , d) that displayed the student number and the student name (removed from the figure), the total number of accumulated credit points, the grade point average for passed courses, and the amount of credit points obtained from practical training.

The LAD was piloted in authentic guidance meetings in which a tutor teacher and a student discussed topical issues related to the completion of studies. Such meetings were a part of the UAS’ standard guidance discussions that were typically held 1–2 times during the academic year, or more often if needed. In the studied meetings, the students and tutor teachers collectively reviewed the LAD to support the discussion. Only the tutor teachers were commonly able to access the LAD, as it was still under development and in the pilot phase. However, the students could examine its use as presented by the tutor teacher. In addition to the LAD, the meeting focused on reviewing the student’s personal study plan, which contained information about their studies to be completed and could be viewed through the student information system. Most of the meetings were organized online, and their duration varied according to an individual student’s needs. A researcher (first author) attended the meetings as an observer.

3.2 Participants and procedures

Participants were HE students ( N  = 16) pursuing a bachelor’s degree at the Finnish University of Applied Sciences (UAS), ranging from 21 to 49 years of age (mean = 30.38, median = 29.5); 11 (68.75%) were female, and 5 (31.25%) were male. HE studies commenced between 2016 and 2020, and comprised different academic fields, including business administration, culture, engineering, humanities and education, and social services and health care. Depending on the degree, study scope ranged from 210 to 240 ECTS credit points, which take approximately three and a half to four years to complete. However, the students could also proceed at a faster or slower pace under certain conditions. The students were selected to represent different study fields and study stages, and to have studied for more than one academic year. Informed consent to participate in the study was obtained from all students, and their participation was voluntary. The research design was approved by the respective UAS.

Data for this qualitative study was collected through semi-structured, individual student interviews conducted in April–September 2022. To address certain topics in each interview, an interview guide was used. The interview questions incorporated into the guide were tested in two student test interviews to simulate a real interview situation and to assure intelligibility, as also suggested by Chenail ( 2011 ). Findings indicated that the questions were largely usable, functional, and understandable, but some had to be slightly refined to ensure their conciseness and to improve clarity and familiarity of expressions vis-à-vis the target group. Also, the order of questions was partly reshaped to support the flow of discussion.

In the interviews, the students were asked to provide information about their demographic and educational backgrounds as well as their overall opinions of educational practices and the use of LA. In particular, they were invited to share their views on the use of the piloted LAD and its development as promoting different phases and processes of SRL. Students’ perceptions were generally based on the assumption that they could use the LAD both independently during their studies and collectively with their tutor teachers as a component of the guidance process.

Interviews were conducted immediately or shortly after the guidance meeting. Interview duration ranged from 42 to 70 min. The graphical presentation of the LAD was commonly shown to the students to provide stimuli and evoke discussion, as suggested by Kwasnicka et al. ( 2015 ). The interviews were conducted by the same researcher (first author) who observed the guidance meetings. They were primarily held online, and only one was organized face-to-face. All interviews were video recorded for subsequent analysis.

3.3 Data analysis

Interview recordings were transcribed verbatim, accumulating a total of 187 pages of textual material for analysis (Times New Roman, 12-point font, line spacing 1). A qualitative content analysis method was used to analyze the data (see Mayring, 2000 ; Schreier, 2014 ) to enhance in-depth understanding of the research phenomenon and to inform practical actions (Krippendorf, 2019 ). Also, data were approached both deductively and inductively (see Elo & Kyngäs, 2008 ; Merriam & Tisdell, 2016 ), and the analysis was supported using the ATLAS.ti program.

Analysis began with a thorough familiarization with the data in order to develop a general understanding of the students’ perspectives. First, the data were deductively coded using Zimmerman and Moylan’s ( 2009 ) SRL model as a theoretical guide for analysis and as applied to the study path perspective. All relevant units of analysis—such as paragraphs, sentences, or phrases that addressed the use of the LAD or its development in relation to the processes of SRL presented in the model—were initially identified from the data, and then sorted into meaningful units with specific codes. The focus was placed on instances in the data that were applicable and similar to the processes represented in the model, but the analysis was not limited to those that fully corresponded to them. The preliminary analysis involved several rounds of coding that ultimately led to the formation of main categories, grouped into the phases of SRL. The forethought phase consisted of processes that emphasized the planning and directing of studies and learning with the LAD. The performance phase, in turn, involved processes that addressed the control and observation of studies and learning through the LAD. Finally, the reflection phase included processes that focused on evaluating and reflecting on studies and learning with the LAD.

Second, the data were approached inductively by examining the use of the LAD and its development as distinct aspects within each phase and process of SRL (i.e., the main categories). The aim was, on the one hand, to identify how the use of the LAD was considered to serve the students in the phases and processes of SRL in its current form, and on the other hand, how it should be improved to better support them. The analysis not only focused on the characteristics of the LAD but also on the practices that surrounded its use and development. The units of analysis were first condensed from the data and then organized into subcategories for similar units. As suggested by Schreier ( 2014 ), the process was continued until a saturation point was reached—that is, no additional categories could be found. As a result, subcategories for all of the main categories were identified.

Following Schreier’s ( 2014 ) recommendation, the categories were named and described with specific data examples. Additionally, some guidelines were added to highlight differences between categories and to avoid overlap. Using parts of this categorization framework as a coding scheme, a portion of the data (120 text segments) was independently coded into the main categories by the first and second authors. The results were then compared, and all disagreements were resolved through negotiation until a shared consensus was reached. After minor changes were made to the coding scheme, the first author recoded all data. The number of students who had provided responses to each subcategory was counted and added to provide details on the study. For study integrity, the results are supported by data examples with the students’ aliases and the study fields they represented. The quotations were translated from Finnish to English.

The results are reported by first answering the threefold sub-question, that is, how do HE students perceive the use of an LAD and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of SRL. The subsequent results are then summarized to address the main RQ, that is, what are the main findings on HE students’ perceptions on the utilization of an LAD to promote SRL.

4.1 LAD as a part of the forethought phase processes

The students perceived the use of the LAD and its development as related to the forethought phase processes of SRL through the categorization presented in Table  1 below.

Regarding the process of goal setting , almost all students ( n  = 15) emphasized that the use of the LAD promoted the targeting of goal-oriented study completion and competence development. Analytics indicators—such as grades, grade point averages, and accumulated credit points—adequately informed the students of areas they should aim for, further improve, or put more effort into. Only one student ( n  = 1) considered the analytics data too general for establishing goals. However, some students ( n  = 7) specifically mentioned their desire to set and enter individual goals in the LAD. The students were considered to have individual intentions, which should also be made visible in the LAD:

For example, someone might complete [their studies] in four years, someone might do [them] even faster, so maybe in a way that there is the possibility…to set…that, well, I want or my goal is to graduate in this time, and then it would kind of show in it. (Sophia, Humanities and Education student)

Moreover, some students ( n  = 6) wanted to obtain information on the degree program’s overall target times, study requirements, or pace recommendations through the LAD.

In relation to the process of study planning , the use of the LAD provided many students ( n  = 8) grounds to plan and structure the promotion and completion of their studies, such as which courses and types of studies to choose, and what kind of study pace and schedule to follow. However, an even greater set of students ( n  = 12) hoped that the LAD could provide them with more sophisticated tools for planning. For instance, it could inform them about studies to be completed, analyze their study performance in detail, or make predictions for the future. Moreover, it should offer them opportunities to choose courses, make enrollments, set schedules, get reminders, and take notes. One example of such an advanced analytics application was described as follows: ‟It would be a bit like a conversational tool with the student as well, that you would first put…your studies in the program, so it would [then] remind you regularly that hey, do this” (James, Humanities and Education student).

When discussing the use of the LAD, most students ( n  = 12) emphasized the critical role of personal interests and preferences , which was found to not only guide studying and learning in general but to also drive and shape the utilization of the LAD. According to the students, using such an analytics application could particularly benefit those students who, for instance, prefer monitoring of study performance, perceive information in a visualized form, are interested in analytics or themselves, or find it relevant for their studies. Prior familiarization was also considered useful: ‟Of course, there are those who use this kind of thing more and those who use this kind of thing in daily life, so they could especially benefit from this, probably more than I do” (Olivia, Social Services and Health Care student). Even though the LAD was considered to offer pertinent insights for many types of learners, it might not be suitable for all. For instance, it could be challenging for some students to comprehend analytics data or to make effective use of them in their studies. In the development of the LAD, such personal aspects should be noted. The students ( n  = 7) believed the LAD might better adapt to students’ individual needs if it allows them to customize its features and displays or to use it voluntarily based on one’s personal interests and needs.

When describing the use of LAD, half of the students ( n  = 8) discussed its connections with self-efficacy . Making use of analytics data appeared to strengthen the students’ beliefs in their abilities to study and learn in a targeted manner, even if their own feelings suggested otherwise. As one of the students stated:

It’s nice to see that progress, that it has happened although it feels that it hasn’t. So, you can probably set goals based on [an idea] that you’re likely to progress, you could set [them] that you could graduate sometime. (Emma, Engineering student)

On the other hand, the use of the LAD also seemed to require students to have sufficient self-efficacy. It was perceived as vital especially when the analytics data showed unfavorable study performance, such as failed or incomplete courses, or gaps in the study performance with respect to peers. One student ( n  = 1) suggested that the LAD could include praises as evidence of and support for appropriate study performance. Such incentives may help improve the students’ self-confidence as learners. Apart from this, however, the students had no other recommendations for developing the use of the LAD to support self-efficacy.

4.2 LAD as a part of the performance phase processes

The students discussed the use of the LAD and its development in relation to the performance phase processes of SRL according to the categories described in Table  2 below.

The students ( n  = 16) widely agreed that using the LAD benefited them in the process of metacognitive monitoring. By indicating the progress and success of study performance, the LAD was thought to be well suited for observing the course of studies and the development of competences. Moreover, it helped the students to gain awareness of their individual strengths and weaknesses, as well as successes and failures, in a study path. Tracking individual study performance was also found to contribute to purposeful study completion, as the following data example demonstrates:

It’s important especially when there is a target time to graduate, so of course you must follow and stay on track in many ways as there are many such pitfalls to easily fall into, [and] as I’ve fallen quite many times, it’s good [to monitor]. (Sarah, Culture student)

Additionally, the insights of monitoring could be used in future job searches to provide information about acquired competences to potential employers. The successful promotion of studies was generally perceived to require regular monitoring by both students and their educators. However, one of the students considered it a particular responsibility of the students themselves, as the studies were completed at an HE level and were thus voluntary for them. To provide more in-depth insights, many students ( n  = 12) recommended the incorporation of a course-level monitoring opportunity in the LAD. More detailed information was needed, for instance, about course descriptions, assignments completed, and grades received. The rest of the students ( n  = 4), however, wanted to keep the course-level monitoring within the learning management system. One of them stated that it could also be a place through which the students could use the LAD. Some students ( n  = 6) emphasized the need to reconsider current assessment practices to enable better tracking of study performance. Specifically, assessments could be made in greater detail and grades given immediately after course completion. The variation in scales and time points of assessments between the courses and degree programs posed potential challenges for monitoring, thus prompting the need to unify educational practices at the organizational level.

As an activity closely related to metacognitive monitoring, the process of imaging and visualizing was emphasized by the students as helping them to advance in their educational pursuits. Most students ( n  = 15) mentioned that using the LAD allowed them to easily image their study path and clarify their study situation. As one of them stated, ‟This is quite clear, this like, that you can see the overall situation with a quick glance” (Anna, Business Administration student). The visualizations were perceived as informative, tangible, and understandable. However, they were also thought to carry the risk of students neglecting some other relevant aspects of studying and learning in the course of attracting such focused attention. Although the visualizations were generally considered clear, some students ( n  = 11) noted that they could be further improved to better organize the analytics data. For instance, the students suggested the attractive use of colors and the categorization of different types of courses. Visual symbols, in turn, may be particularly effective in course-level data. Technical aspects should also be carefully considered to avoid false visualizations.

Regarding the process of environmental structuring , the LAD appeared to be a welcome addition to the study toolkit and overall study environment. A few students ( n  = 4) considered it appropriate to utilize the LAD as a separate PowerBI application alongside other (Microsoft O365) study tools, but they also felt that it could be utilized through other systems if necessary. However, one student ( n  = 1) raised the need for overall system integrations and some students ( n  = 8) expressed a specific wish to use the LAD as an integrated part of the student information system that was thought to improve its accessibility. A few students ( n  = 6) also wanted to receive some additional analytics data as related to the information stored in such a system. For instance, the students could be informed about their study progress or offered feedback on their overall performance in relation to the personal study plan. Other students ( n  = 10), in turn, did not consider the need for this or did not mention it. It was generally emphasized that the LAD should remain sufficiently clear and simple, as too much information can make its use ineffective:

I think there is just enough information in this. Of course, if you would want to add something small, you could, but I don’t know how much, because I feel that when there is too much information, so it’s a bit like you can’t get as much out of it as you could get. (Olivia, Social Services and Health Care student)

Moreover, the analytics data must be kept private and protected. The students generally desired personal access to the LAD; if given such an opportunity, almost all ( n  = 15) believed they would utilize it in the future, and only one ( n  = 1) was unsure about this prospect. The analytics data were believed to be of particular use when studies were actively promoted. Hence they should be made available to the students from the start of their studies.

Regarding the process of interest and motivation enhancement , all students ( n  = 16) mentioned that using the LAD stimulated their interest or enhanced their motivation, although to varying degrees. For some students, a general tracking of studies was enough to encourage them to continue their pursuits, while others were particularly inspired by seeing either high or low study performance. The development of motivation and interest was generally thought to be a hindrance if the students perceived the analytics data as unfavorable or lacking essential information. As one of students mentioned, ‟If your [chart] line was downward, and if there were only ones and zeros or something like that, it could in a way decrease the motivation” (Helen, Humanities and Education student). It appeared that enhancing interest and motivation was mainly dependent on the students’ own efforts to succeed in their course of study and thus to generate favorable analytics data. However, some students ( n  = 7) felt that it could be additionally enhanced by diversifying and improving the analytics tools in the LAD. For example, the opportunities for more detailed analyses and future study planning or comparisons of study performance with that of peers might further increase these students’ motivation and interest in their studies. Even so, it was also considered possible that especially comparisons between students might have the opposite, demotivating and discouraging effect.

All students ( n  = 16) mentioned that using the LAD facilitated the process of seeking and accessing help . It enabled the identification of potential support needs—for instance, if several courses were failed or left unfinished. As noted, they were perceived as alarming signals for the students themselves to seek help and for the guidance personnel to provide targeted support. As one of the students emphasized, it was important that not only ‟a teacher [tutor] gets interested in looking at what the situation is but also that a student would understand to communicate regarding the promotion of studies and situations” (Emily, Social Services and Health Care student). Some students ( n  = 9) suggested that the students, tutor teachers, or both could receive automated alerts if concerns were to arise. On the other hand, the impact of such automated notifications on changing the course of study was considered somewhat questionable. Above all, the students ( n  = 16) preferred human contact and personal support by the guidance personnel, who would use a sensitive approach to address possibly delicate issues. Support would be important to include in existing practices, as the tutor teachers should not be overburdened. One of the students also stated that the automated alerts could be sufficient if they just worked effectively.

4.3 LAD as a part of the reflection phase and processes

The students addressed the use of the LAD and its development as a part of the reflection phase processes of SRL through categories outlined in Table  3 .

The students widely appreciated the support provided by the use of the LAD for the process of evaluation and reflection. The majority ( n  = 15) mentioned that it allowed them to individually reflect on the underlying aspects of their study performance, such as what kind of learners they are, what type of teaching or learning methods suit them, and what factors impact their learning. Similarly, the students ( n  = 16) valued the possibility of examining the analytics data together with the guidance personnel, such as tutor teachers, and commonly expressed a desire to revisit the LAD in future guidance meetings. It was thought to promote the interpretation of analytics data and to facilitate collective reflection on the reasons behind one’s study success or failure. However, this might require a certain orientation from the guidance personnel, as the student describes below:

I feel that it’s possible to address such themes that what may perhaps cause this. Of course, a lot depends on how amenable the teacher [tutor] is, like are we focusing on how the studies are going but in a way, not so much on what may cause it. (Sophia, Humanities and Education student)

Some students ( n  = 8) proposed incorporating familiarization with analytics insights into course implementations of the degree programs. Additionally, many students ( n  = 11) expressed a desire to examine the student group’s general progress in tutoring classes together with the tutor teacher and peers, particularly if the results were properly targeted and anonymized, and presented in a discreet manner. However, some students ( n  = 5) found this irrelevant. The students were generally wary to evaluate and compare an individual student’s study performance in relation to the peer average through the LAD. While some students ( n  = 4) welcomed such an opportunity, others ( n  = 6) considered it unnecessary. A few students ( n  = 5) emphasized that such comparisons between students should be optional and visible if desired, and one student ( n  = 1) did not have a definite view about it. Rather than competing with others, the students stressed the importance of challenging themselves and evaluating study performance against their own goals or previous achievements.

According to the students ( n  = 16), the use of the LAD was associated with a wide range of affective reactions . Positive responses such as joy, relief, and satisfaction were considered to emerge if the analytics data displayed by the LAD was perceived as favorable and expected, and supportive of future learning. Similarly, negative responses such as anxiety, pressure, or stress were likely to occur if such data indicated poor performance, thus challenging the learning process. On the other hand, such self-reactions could also appear as neutral or indifferent, depending on the student and the situation. Individual responses were related not only to the current version of the LAD but also to its further development targets. Some students ( n  = 3) pointed out the importance of guidance and support, through which the affective reactions could be processed together with professionals. As one of the students underlined, it is important “that there is also that support for the studies, that it isn’t just like you have this chart, and it looks bad, that try to manage. Perhaps there is that support, support in a significant role as well” (Sophia, Humanities and Education student). It seemed critical that the students were not left alone with the LAD but rather were given assistance to deal with the various responses its use may elicit.

4.4 Summary of findings on LAD utilization to promote SRL among HE students

In summary, HE students’ perceptions on the utilization of an LAD to promote SRL phases and processes were largely congruent, but nonetheless partly varied. In particular, the students agreed on the support provided by the LAD during the performance phase and for the purpose of metacognitive monitoring. Such activity was thought to not only enable the students to observe their studies and learning, but to also create the basis for the emergence of all other processes, which were facilitated by the monitoring. That is, while the students familiarized themselves with the course of their studies via the analytics data, they could further apply these insights—for instance, to visualize study situations, enhance motivation, and identify possible support needs. Monitoring with the LAD was also perceived to partly promote the students to the forethought and reflection phases and processes by giving them grounds to target their development areas as well as to reflect on their studies and learning individually and jointly with their tutor teachers. However, it was clear that less emphasis was placed on using the LAD for study planning, addressing individual interests, activating self-efficacy, and supporting environmental structuring, thus giving incentives for their further investigation and future improvement.

Although the LAD used in this study seemed to serve many functions as such, its holistic development was deemed necessary for more thorough SRL support. In particular, the students agreed on the need to improve such an analytics application to further strengthen the performance phase processes—particularly monitoring—by, for instance, developing it for the students’ independent use, and by integrating it with instructional and guidance practices provided by their educators. Moreover, the students commonly wished for more advanced analytics tools that could more directly contribute to the planning of studies and joint reflection of group-level analytics data. To better support the various processes of SRL, new features were generally welcomed into the LAD, although the students’ views and emphases on them also varied. Mixed perspectives were related, for instance, to the need to enrich data or compare students within the LAD. Thus, it seemed important to develop the LAD to conform to the preferences of its users. Along with improving the LAD, students also paid attention to the development of pedagogical practices and guidance processes that together could create appropriate conditions for the emergence of SRL.

5 Discussion

The purpose of this study was to gain insights into HE students’ perceptions on the utilization of an LAD to promote their SRL. The investigation extended the previous research by offering in-depth descriptions of the specific phases and processes of SRL associated with the use of an LAD and its development targets. By applying a study path perspective, it also provided novel insights into how to promote students to become self-regulated learners and effective users of analytics data as an integral part of their studies in HE.

The students’ perspectives on the use of LAD and its development were initially explored as a part of the forethought phase processes of SRL, with a particular focus on the planning and directing of studies and learning. In line with previous research (e.g., Divjak et al., 2023 ; Schumacher & Ifenthaler, 2018 ; Silvola et al., 2023 ), the students in this study appreciated an analytics application that helped them prepare for their future learning endeavors—that is, the initial phase of the SRL cycle (see Zimmerman & Moylan, 2009 ). Using the LAD specifically allowed the students to recognize their development areas and offered a basis to organize their future coursework. However, improvements to allow students to set individual goals and make plans directly within the LAD, as well as to increase awareness of general degree goals, were also desired. These seem to be pertinent avenues for development, as goals may inspire the students not only to invest greater efforts in learning but also to track their achievements against these goals (Wise, 2014 ; Wise et al., 2016 ). While education is typically entered with individual starting points, it is important to allow the students to set personal targets and routes for their learning (Wise, 2014 ; Wise et al., 2016 ).

The results of this study indicate that the use of LADs is primarily driven and shaped by students’ personal interests and preferences, which commonly play a crucial role in the development of SRL (see Zimmerman & Moylan, 2009 ; Panadero & Alonso-Tapia, 2014 ). It might particularly benefit those students for whom analytics-related activities are characteristic and of interest, and who consider them personally meaningful for their studies. It has been argued that if students consider analytics applications serve their learning, they are also willing to use them (Schumacher & Ifenthaler, 2018 ; Wise et al., 2016 ). On the other hand, it has also been stated that not all students are necessarily able to maximize its possible benefits on their own and might need support in understanding its purpose (Wise, 2014 ) and in finding personal relevance for its use. The findings of this study suggest that a more individual fit of LADs could be promoted by allowing students to customize its functionalities and displays. Comparable results have also been obtained from other studies (e.g., Bennett, 2018 ; Rets et al., 2021 ; Roberts et al., 2017 ; Schumacher & Ifenthaler, 2018 ), thus highlighting the need to develop customized LADs that better meet the needs of diverse students and that empower them to control their analytics data. More attention may also be needed to promote the use and development of LADs to support self-efficacy, as it appeared to be an unrecognized potential still for many students in this study. According to Rets et al. ( 2021 ), using LADs for such a purpose might particularly benefit online learners and part-time students, who often face various requirements and thus may forget the efforts put into learning and giving themselves enough credit. By facilitating students’ self-confidence, it could also promote the necessary changes in study behavior, at least for those students with low self-efficacy (Rets et al., 2021 ).

Second, the students’ views on the use of the LAD and its development were investigated in terms of the performance phase processes of SRL, with an emphasis on the control and observation of studies and learning. In line with the results of other studies (De Barba et al., 2022 ; Rets et al., 2021 ; Schumacher & Ifenthaler, 2018 ; Silvola et al., 2023 ), the students preferred using the LAD to monitor their study performance—they wanted to follow their progress and success over time and keep themselves and their educators up to date. According to Jivet et al. ( 2017 ), such functionality directly promotes the performance phase of SRL. Moreover, it seemed to serve as a basis for other activities under SRL, all of which were heavily dependent and built on the monitoring. The results of this study, however, imply that monitoring opportunities should be further expanded to provide even more detailed insights. Moreover, they indicate the need to develop and refine pedagogical practices at the organizational level in order to better serve student monitoring. As monitoring plays a crucial role in SRL (Zimmerman & Moylan, 2009 ), it is essential to examine how it is related to other SRL processes and how it can be effectively promoted with analytics applications (Viberg et al., 2020 ).

In this study, the students used the LAD not only to monitor but also to image and visualize their learning. In accordance with the views of Papamitsiou and Economides ( 2015 ), the visualizations transformed the analytics data into an easily interpretable visual form. The visualizations were not considered to generate information overload, although such a concern has sometimes been associated with the use of LADs (e.g., Susnjak et al., 2022 ). However, the students widely preferred even more descriptive and nuanced illustrations to clarify and structure the analytics data. At the same time, care must be taken to ensure that the visualizations do not divert too much attention from other relevant aspects of learning, as was also found important in prior research (e.g., Charleer et al., 2018 ; Wise, 2014 ). It seems critical that an LAD inform but not overwhelm its users (Susnjak et al., 2022 ). As argued by Klein et al. ( 2019 ), confusing visualizations may not only generate mistrust but also lead to their complete nonuse.

Although the LAD piloted in the study was considered to be a relatively functional application, it could be even more accessible and usable if it was incorporated into the student information system and enriched with the data from it. Even then, however, the LAD should remain simple to use and its data privacy ensured. It has been argued that more information is not always better (Aguilar, 2018 ), and the analytics indicators must be carefully considered to truly optimize learning (Clow, 2013 ). While developing their SRL, students would particularly benefit from a well-structured environment with fewer distractions and more facilitators for learning (Panadero & Alonso-Tapia, 2014 ). The smooth promotion of studies also seems to require personal access to the analytics data. Similar to the learners in Charleer and colleagues’ ( 2018 ) study, the students in this study desired to take advantage of the LAD autonomously, beyond the guidance context. It was believed to be especially used when they were actively promoting their studies. This is seen as a somewhat expected finding given the significant role of study performance indicators in the LAD. However, the question is also raised as to whether such an analytics application would be used mainly by those students who progress diligently but would be ignored by those who advance only a little or not at all. Ideally, the LAD would serve students in different situations and at various stages of studies.

Using the LAD offered the students a promising means to enhance motivation and interest in their studies through the monitoring of analytics data. However, not all students were inspired in the same manner or similar analytics data displayed by the LAD. Although the LAD was seen as inspiring and interesting in many ways, it also had the potential to demotivate or even discourage. This finding corroborates the results of other studies reporting mixed results on the power of LADs to motivate students (e.g., Bennett, 2018 ; Corrin & de Barba, 2014 ; Schumacher & Ifenthaler, 2018 ). As such, it would be essential that the analytics applications consider and address students with different performance levels and motivational factors (Jivet et al., 2017 ). Based on the results of this study, diversifying the tools included in the LAD might also be necessary. On the other hand, the enhancement of motivation was also found to be the responsibility of the students themselves—that is, if the students wish the analytics application to display favorable analytics data and thus motivate them, they must first display concomitant effort in their studies.

The use of the LAD provided a convenient way to intervene if the students’ study performance did not meet expectations. With the LAD, both the students and their tutor teachers could detect signs of possible support needs and address them with guidance. In the future, such needs could also be reported through automated alerts. Overall, however, the students in this study preferred human contact and personal support over automated interventions, contrary to the findings obtained by Roberts and colleagues ( 2017 ). Being identified to their educators did not seem to be a particular concern for them, although it has been found to worry students in other contexts (e.g., Roberts et al., 2017 ). Rather, the students felt they would benefit more from personal support that was specifically targeted to them and sensitive in its approach. The students generally demanded delicate, ethical consideration when acting upon analytics data and in the provision of support, which was also found to be important in prior research (e.g., Kleimola & Leppisaari, 2022 ). Additionally, Wise and colleagues ( 2016 ) underlined the need to foster student agency and to prevent students from becoming overly reliant on analytics-based interventions: if all of the students’ mistakes are pointed out to them, they may no longer learn to recognize mistakes on their own. Therefore, to support SRL, it is essential to know when to intervene and when to let students solve challenges independently (Kramarski & Michalsky, 2009 ).

Lastly, the students’ perceptions on the use and development of the LAD were examined from the perspective of the reflection phase processes of SRL, with particular attention given to evaluation and reflection on studies and learning. The use of the LAD provided the students with a basis to individually reflect on the potential causes behind their study performance, for better or worse. Moreover, they could address such issues together with guidance personnel and thus make better sense of the analytics data. Corresponding to the results of Charleer et al.’s ( 2018 ) study, collective reflection on analytical data provided the students with new insights and supported their understanding. Engaging in such reflective practices offered the students the opportunity to complete the SRL cycle and draw the necessary conclusions regarding their performance for subsequent actions (see Zimmerman & Moylan, 2009 ). In the future, analytics-based reflection could also be implemented in joint tutoring classes and courses included in the degree programs. This would likely promote the integration of LADs into the activity flow of educational environments, as recommended by Wise and colleagues ( 2016 ). In sum, using LADs should be a regular part of pedagogical practices and learning processes (Wise et al., 2016 ).

When evaluating and reflecting on their studies and learning, the students preferred to focus on themselves and their own development as learners. Similar to earlier findings (e.g., Divjak et al., 2023 ; Rets et al., 2021 ; Roberts et al., 2017 ; Schumacher & Ifenthaler, 2018 ), the students felt differently about the need to develop LADs to compare their study performance with that of other students. Although this function could help some of the students to position themselves in relation to their peers, others thought it should be optional or completely avoided. In agreement with the findings of Divjak et al. ( 2023 ), it seemed that the students wanted to avoid mutual competition comparisons; however, it might not be harmful for everyone and in every case. Consequently, care is required when considering the kind of features in the LAD that offer real value to students in a particular context (Divjak et al., 2023 ). Rather than limiting the point of reference only to peers, it might be useful to also offer students other targets for comparative activity, such as individual students’ previous progress or goals set for the activity (Wise, 2014 ; Wise et al., 2016 ; see also Bandura, 1986 ). In addition, it is important that students not be left alone to face and cope with the various reactions that may be elicited by such evaluation and reflection with analytics data (Kleimola & Leppisaari, 2022 ). As the results of this study and those of others (e.g., Bennett, 2018 ; Lim et al., 2021 ) generally indicate, affective responses evoked by LADs may vary and are not always exclusively positive. Providing a safe environment for students to reflect on successes and failures and to process the resulting responses might not only encourage necessary changes in future studies but also promote the use of an LAD as a learning support.

In summary, the results of this study imply that making an effective use of an analytics application—even with a limited amount of analytics data and functionality available—may facilitate the growth of students into self-regulated learners. That is, even if the LAD principally addresses some particular phase or process of SRL, it can act as a catalyst to encourage students in the development of SRL on a wider scale. This finding also emphasizes the interdependent and interactive nature of SRL (see Zimmerman, 2011 ; Zimmerman & Moylan, 2009 ) that similarly seems to characterize the use of an LAD. However, the potential of LADs to promote SRL may be lost unless students themselves are (pro)active in initiating and engaging with such activity or receive appropriate pedagogical support for it. There appears to be a specific need for guidance that is sensitive to the students’ affective reactions and would help students learn and develop with analytics data. Providing the students with adequate support is particularly critical if their studies have not progressed favorably or as planned. It seems important that the LAD would not only target those students who are already self-regulated learners but, with appropriate support and guidance, would also serve those students who are gradually growing in that direction.

5.1 Limitations and further research

This study has some limitations. First, it involved a relatively small number of HE students who were examined in a pilot setting. Although the sample was sufficient to provide in-depth insights and the saturation point was reached, it might be useful in further research to use quantitative approaches and diverse groups of students to improve the generalizability of results to a larger student population. Also, addressing the perspectives of guidance personnel, specifically tutor teachers, could provide additional insights into the use and development of LADs to promote SRL.

Second, the LAD piloted and investigated in this study was not yet widely in use or accessible by the students. Moreover, it was examined for a relatively brief time, so the students’ perceptions were shaped not only by their experiences but also by their expectations of its potential. Future research on students and tutor teachers with more extensive user experience could build an even more profound picture of the possibilities and limitations of the LAD from a study path perspective. Such investigation might also benefit from trace data collected from the students’ and tutor teachers’ interactions with the LAD. It would be valuable to examine how the students and tutor teachers make use of the LAD in the long term and how it is integrated into learning activities and pedagogical practices.

Third, due to the emphasis on an HE institution and the analytics application used in this specific context, the transferability of results may be limited. However, the results of this study offer many important and applicable perspectives to consider in various educational environments where LADs are implemented and aimed at supporting students across their studies.

6 Conclusions

The results of this study offer useful insights for the creation of LADs that are closely related to the theoretical aspects of learning and that meet the particular needs of their users. In particular, the study increases the understanding of how such analytics applications should be connected to the entirety of studies—that is, what kind of learning processes and pedagogical support are needed alongside them to best serve students in their learning. Consequently, it encourages a comprehensive consideration and promotion of pedagogy, educational technology, and related practices in HE. The role of LA in supporting learning and guidance seems significant, so investments must be made in its appropriate use and development. In particular, the voice of the students must be listened to, as it promotes their commitment to the joint development process and fosters the productive use of analytics applications in learning. At its best, LA becomes an integral part of HE settings, one that helps students to complete their studies and contributes to their development into self-regulated learners.

Data availability

Not applicable.

Abbreviations

Higher education

• Learning analytics
• Learning analytics dashboard

Research question

• Self-regulated learning

Aguilar, S. J. (2018). Examining the relationship between comparative and self-focused academic data visualizations in at-risk college students’ academic motivation. Journal of Research on Technology in Education , 50 (1), 84–103. https://doi.org/10.1080/15391523.2017.1401498

Article   Google Scholar  

Anthonysamy, L., Koo, A-C., & Hew, S-H. (2020). Self-regulated learning strategies and non-academic outcomes in higher education blended learning environments: A one decade review. Education and Information Technologies , 25 (5), 3677–3704. https://doi.org/10.1007/s10639-020-10134-2

Azevedo, R., Guthrie, J. T., & Seibert, D. (2004). The role of self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research , 30 (1–2), 87–111. https://doi.org/10.2190/DVWX-GM1T-6THQ-5WC7

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory . Prentice-Hall.

Barnard-Brak, L., Paton, V. O., & Lan, W. Y. (2010). Profiles in self-regulated learning in the online learning environment. The International Review of Research in Open and Distributed Learning , 11 (1), 61–80. https://doi.org/10.19173/irrodl.v11i1.769

Beheshitha, S. S., Hatala, M., Gašević, D., & Joksimović, S. (2016). The role of achievement goal orientations when studying effect of learning analytics visualizations. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (pp. 54–63). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883904

Bennett, E. (2018). Students’ learning responses to receiving dashboard data: Research report . Huddersfield Centre for Research in Education and Society, University of Huddersfield.

Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies , 10 (4), 405–418. https://doi.org/10.1109/TLT.2017.2740172

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education , 27 , 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

Callan, G., Longhurst, D., Shim, S., & Ariotti, A. (2022). Identifying and predicting teachers’ use of practices that support SRL. Psychology in the Schools , 59 (11), 2327–2344. https://doi.org/10.1002/pits.22712

Charleer, S., Moere, A. V., Klerkx, J., Verbert, K., & De Laet, T. (2018). Learning analytics dashboards to support adviser-student dialogue. IEEE Transactions on Learning Technologies , 11 (3), 389–399. https://doi.org/10.1109/TLT.2017.2720670

Chenail, R. J. (2011). Interviewing the investigator: Strategies for addressing instrumentation and researcher bias concerns in qualitative research. The Qualitative Report , 16 (1), 255–262. https://doi.org/10.46743/2160-3715/2011.1051

Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education , 18 (6), 683–695. https://doi.org/10.1080/13562517.2013.827653

Conole, G., Gašević, D., Long, P., & Siemens, G. (2011). Message from the LAK 2011 general & program chairs. Proceedings of the 1st International Conference on Learning Analytics and Knowledge . Association for Computing Machinery. https://doi.org/10.1145/2090116

Corrin, L., & De Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In B. Hegarty, J. McDonald, & S-K. Loke (Eds.), ASCILITE 2014 conference proceedings—Rhetoric and reality: Critical perspectives on educational technology (pp. 629–633). Australasian Society for Computers in Learning in Tertiary Education (ASCILITE). https://www.ascilite.org/conferences/dunedin2014/files/concisepapers/223-Corrin.pdf

Costas-Jauregui, V., Oyelere, S. S., Caussin-Torrez, B., Barros-Gavilanes, G., Agbo, F. J., Toivonen, T., Motz, R., & Tenesaca, J. B. (2021). Descriptive analytics dashboard for an inclusive learning environment. 2021 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/FIE49875.2021.9637388

De Barba, P., Oliveira, E. A., & Hu, X. (2022). Same graph, different data: A usability study of a student-facing dashboard based on self-regulated learning theory. In S. Wilson, N. Arthars, D. Wardak, P. Yeoman, E. Kalman, & D. Y. T. Liu (Eds.), ASCILITE 2022 conference proceedings: Reconnecting relationships through technology (Article e22168). Australasian Society for Computers in Learning in Tertiary Education (ASCILITE). https://doi.org/10.14742/apubs.2022.168

De Laet, T., Millecamp, M., Ortiz-Rojas, M., Jimenez, A., Maya, R., & Verbert, K. (2020). Adoption and impact of a learning analytics dashboard supporting the advisor: Student dialogue in a higher education institute in Latin America. British Journal of Educational Technology , 51 (4), 1002–1018. https://doi.org/10.1111/bjet.12962

Divjak, B., Svetec, B., & Horvat, D. (2023). Learning analytics dashboards: What do students actually ask for? Proceedings of the 13th International Learning Analytics and Knowledge Conference (pp. 44–56). Association for Computing Machinery. https://doi.org/10.1145/3576050.3576141

Dollinger, M., & Lodge, J. M. (2018). Co-creation strategies for learning analytics. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 97–101). Association for Computing Machinery. https://doi.org/10.1145/3170358.3170372

Eickholt, J., Weible, J. L., & Teasley, S. D. (2022). Student-facing learning analytics dashboard: Profiles of student use. IEEE Frontiers in Education Conference (FIE) (1–9). IEEE. https://doi.org/10.1109/FIE56618.2022.9962531

Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing , 62 (1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x

Elouazizi, N. (2014). Critical factors in data governance for learning analytics. Journal of Learning Analytics , 1 (3), 211–222. https://doi.org/10.18608/jla.2014.13.25

Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2022). Supporting self-regulated learning with learning analytics interventions: A systematic literature review. Education and Information Technologies , 28 (3), 3059–3088. https://doi.org/10.1007/s10639-022-11281-4

Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Lecture notes in computer science: Vol. 10474. Data driven approaches in digital education (pp. 82–96). Springer. https://doi.org/10.1007/978-3-319-66610-5_7

Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 31–40). Association for Computing Machinery. https://doi.org/10.1145/3170358.3170421

Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education. The Internet and Higher Education , 47 , 100758. https://doi.org/10.1016/j.iheduc.2020.100758

Jivet, I., Wong, J., Scheffel, M., Valle Torre, M., Specht, M., & Drachsler, H. (2021). Quantum of choice: How learners’ feedback monitoring decisions, goals and self-regulated learning skills are related. Proceedings of 11th International Conference on Learning Analytics and Knowledge (pp. 416–427). Association for Computing Machinery. https://doi.org/10.1145/3448139.3448179

Kim, J., Jo, I-H., & Park, Y. (2016). Effects of learning analytics dashboard: Analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review , 17 (1), 13–24. https://doi.org/10.1007/s12564-015-9403-8

Kleimola, R., & Leppisaari, I. (2022). Learning analytics to develop future competences in higher education: A case study. International Journal of Educational Technology in Higher Education , 19 (1), 17. https://doi.org/10.1186/s41239-022-00318-w

Kleimola, R., López-Pernas, S., Väisänen, S., Saqr, M., Sointu, E., & Hirsto, L. (2023). Learning analytics to explore the motivational profiles of non-traditional practical nurse students: A mixed-methods approach. Empirical Research in Vocational Education and Training , 15 (1), 11. https://doi.org/10.1186/s40461-023-00150-0

Klein, C., Lester, J., Rangwala, H., & Johri, A. (2019). Technological barriers and incentives to learning analytics adoption in higher education: Insights from users. Journal of Computing in Higher Education , 31 (3), 604–625. https://doi.org/10.1007/s12528-019-09210-5

Kramarski, B. (2018). Teachers as agents in promoting students’ SRL and performance: Applications for teachers’ dual-role training program. In D. H. Schunk, & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 223–239). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781315697048-15

Kramarski, B., & Michalsky, T. (2009). Investigating preservice teachers’ professional growth in self-regulated learning environments. Journal of Educational Psychology , 101 (1), 161–175. https://doi.org/10.1037/a0013101

Krippendorff, K. (2019). Content analysis (4th ed.). SAGE Publications. https://doi.org/10.4135/9781071878781

Kwasnicka, D., Dombrowski, S. U., White, M., & Sniehotta, F. F. (2015). Data-prompted interviews: Using individual ecological data to stimulate narratives and explore meanings. Health Psychology , 34 (12), 1191–1194. https://doi.org/10.1037/hea0000234

Lim, L-A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2021). Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: An exploratory study of four courses. Assessment & Evaluation in Higher Education , 46 (3), 339–359. https://doi.org/10.1080/02602938.2020.1782831

Lodge, J. M., Panadero, E., Broadbent, J., & De Barba, P. G. (2019). Supporting self-regulated learning with learning analytics. In J. M. Lodge, J. Horvath, & L. Corrin (Eds.), Learning analytics in the classroom: Translating learning analytics research for teachers (pp. 45–55). Routledge. https://doi.org/10.4324/9781351113038-4

Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., Ram, I., Woloshen, S., Winne, P. H., & Nesbit, J. C. (2016). What if learning analytics were based on learning science? Australasian Journal of Educational Technology , 32 (6), 1–18. https://doi.org/10.14742/ajet.3058

Matcha, W., Uzir, N. A., Gašević, D., & Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies , 13 (2), 226–245. https://doi.org/10.1109/TLT.2019.2916802

Mayring, P. (2000). Qualitative content analysis. Forum Qualitative Sozialforschung Forum: Qualitative Social Research , 1 (2). https://doi.org/10.17169/fqs-1.2.1089

Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). JosseyBass.

Molenaar, I., Horvers, A., & Baker, R. S. (2019). Towards hybrid human-system regulation: Understanding children’ SRL support needs in blended classrooms. Proceedings of the 9th International Conference on Learning Analytics and Knowledge (pp. 471–480). Association for Computing Machinery. https://doi.org/10.1145/3303772.3303780

Moos, D. C. (2018). Emerging classroom technology: Using self-regulation principles as a guide for effective implementation. In D. H. Schunk, & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 243–253). Routledge. https://doi.org/10.4324/9781315697048-16

Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology , 8 ., Article 422. https://doi.org/10.3389/fpsyg.2017.00422

Panadero, E., & Alonso-Tapia, J. (2014). How do students self-regulate? Review of Zimmerman’s cyclical model of self-regulated learning. Anales De Psicología , 30 (2), 450–462. https://doi.org/10.6018/analesps.30.2.167221

Papamitsiou, Z., & Economides, A. A. (2015). Temporal learning analytics visualizations for increasing awareness during assessment. RUSC Universities and Knowledge Society Journal , 12 (3), 129–147. https://doi.org/10.7238/rusc.v12i3.2519

Park, Y., & Jo, I. (2015). Development of the learning analytics dashboard to support students’ learning performance. Journal of Universal Computer Science , 21 (1), 110–133. https://doi.org/10.3217/jucs-021-01-0110

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3

Puustinen, M., & Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research , 45 (3), 269–286. https://doi.org/10.1080/00313830120074206

Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education , 18 (1). https://doi.org/10.1186/s41239-021-00284-9

Roberts, L. D., Howell, J. A., & Seaman, K. (2017). Give me a customizable dashboard: Personalized learning analytics dashboards in higher education. Technology Knowledge and Learning , 22 (3), 317–333. https://doi.org/10.1007/s10758-017-9316-1

Schreier, M. (2014). Qualitative content analysis. In U. Flick (Ed.), The SAGE handbook of qualitative data analysis (pp. 170–183). SAGE. https://doi.org/10.4135/9781446282243

Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in Human Behavior , 78 , 397–407. https://doi.org/10.1016/j.chb.2017.06.030

Schwendimann, B. A., Rodríguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., & Dillenbourg, P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies , 10 (1), 30–41. https://doi.org/10.1109/TLT.2016.2599522

Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning analytics in higher education—A review of UK and international practice: Full report . Jisc. https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v2_0.pdf

Silvola, A., Sjöblom, A., Näykki, P., Gedrimiene, E., & Muukkonen, H. (2023). Learning analytics for academic paths: Student evaluations of two dashboards for study planning and monitoring. Frontline Learning Research , 11 (2), 78–98. https://doi.org/10.14786/flr.v11i2.1277

Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education , 19 (1). https://doi.org/10.1186/s41239-021-00313-7

Teasley, S. D. (2017). Student facing dashboards: One size fits all? Technology Knowledge and Learning , 22 (3), 377–384. https://doi.org/10.1007/s10758-017-9314-3

Van Leeuwen, A., Teasley, S., & Wise, A. (2022). Teacher and student facing analytics. In C. Lang, G. Siemens, A. Wise, D. Gašević, & A. Merceron (Eds.), Handbook of learning analytics (2nd ed., pp. 130–140). Society for Learning Analytics Research. https://doi.org/10.18608/hla22.013

Verbert, K., Ochoa, X., De Croon, R., Dourado, R. A., & De Laet, T. (2020). Learning analytics dashboards: The past, the present and the future. Proceedings of the 10th International Conference on Learning Analytics and Knowledge (pp. 35–40). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375504

Viberg, O., Khalil, M., & Baars, M. (2020). Self-regulated learning and learning analytics in online learning environments: A review of empirical research. Proceedings of the 10th International Conference on Learning Analytics and Knowledge (pp. 524–533). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375483

Virtanen, P. (2019). Self-regulated learning in higher education: Basic dimensions, individual differences, and relationship with academic achievement (Helsinki Studies in Education, 1798–8322) [Doctoral dissertation, University of Helsinki]. University of Helsinki Open Repository. https://urn.fi/URN:ISBN:978-951-51-5681-5

West, D., Luzeckyj, A., Toohey, D., Vanderlelie, J., & Searle, B. (2020). Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics. Australasian Journal of Educational Technology , 36 (2), 60–70. https://doi.org/10.14742/ajet.4653

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Erlbaum.

Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. Proceedings of the 4th International Conference on Learning Analytics and Knowledge (pp. 203–211). Association for Computing Machinery. https://doi.org/10.1145/2567574.2567588

Wise, A. F., Vytasek, J. M., Hausknecht, S., & Zhao, Y. (2016). Developing learning analytics design knowledge in the middle space: The student tuning model and align design framework for learning analytics use. Online Learning , 20 (2), 155–182. https://doi.org/10.24059/olj.v20i2.783

Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G-J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction , 35 (4–5), 356–373. https://doi.org/10.1080/10447318.2018.1543084

Zimmerman, B. J. (1999). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7

Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and performance. In B. J. Zimmerman, & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 49–64). Routledge/Taylor & Francis Group.

Zimmerman, B. J. (2015). Self-regulated learning: Theories, measures, and outcomes. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (2nd ed., pp. 541–546), Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.26060-1

Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 299–315). Routledge/Taylor & Francis Group.

Download references

Acknowledgements

Language process: In the preparation process of the manuscript, the Quillbot Paraphraser tool was used to improve language clarity in some parts of the text (e.g., word choice). The manuscript was also proofread by a professional. After using this tool and service, the authors reviewed and revised the text as necessary, taking full responsibility for the content of this manuscript.

The authors also thank the communications and information technology specialists of the UAS under study for their support in editing Fig. 1 for publication.

This research was partly funded by Business Finland through the European Regional Development Fund (ERDF) project “Utilization of learning analytics in the various educational levels for supporting self-regulated learning (OAHOT)” (Grant no. 5145/31/2019). The article was completed with grants from the Finnish Cultural Foundation’s Central Ostrobothnia Regional Fund (Grant no. 25221232) and The Emil Aaltonen Foundation (Grant no. 230078), which were awarded to the first author.

Open Access funding provided by University of Lapland.

Author information

Authors and affiliations.

Faculty of Education, University of Lapland, Rovaniemi, Finland

Riina Kleimola & Heli Ruokamo

School of Applied Educational Science and Teacher Education, University of Eastern Finland, Joensuu, Finland

Laura Hirsto

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: RK, LH. Data collection: RK. Formal analysis: RK, LH. Writing—original draft: RK. Writing—review and editing: RK, LH, HR. Supervision: LH, HR. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Riina Kleimola .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Kleimola, R., Hirsto, L. & Ruokamo, H. Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12978-4

Download citation

Received : 14 February 2024

Accepted : 09 August 2024

Published : 07 September 2024

DOI : https://doi.org/10.1007/s10639-024-12978-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Higher education student
• Qualitative study
• Find a journal
• Publish with us
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 07 September 2024

Place identity: a generative AI’s perspective

• Kee Moon Jang 1 ,
• Junda Chen 2 ,
• Yuhao Kang   ORCID: orcid.org/0000-0003-3810-9450 1 , 3 , 4 ,
• Junghwan Kim   ORCID: orcid.org/0000-0002-7275-769X 5 ,
• Jinhyung Lee 6 ,
• Fabio Duarte 1 &
• Carlo Ratti 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1156 ( 2024 ) Cite this article

1 Altmetric

Metrics details

• Science, technology and society

Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.

Similar content being viewed by others

AI-generated visuals of car-free US cities help improve support for sustainable policies

Ingrid KG: A FAIR Knowledge Graph of Graffiti

Identifying levers of urban neighbourhood transformation using serious games

Introduction.

Place identity, as introduced by Proshansky et al. ( 1983 : p.59), refers to the “sub-structure of the self-identity of the person consisting of broadly conceived cognitions about the physical world in which the individual lives”. Emerging from an environmental psychology standpoint, such a traditional definition emphasizes an individual’s socialization with the physical environment through a complex interaction of cognition, perception, and behavior to form an identity within their surroundings. Since its introduction, the notion of place identity has expanded to describe the people-place relationship, resulting in parallel terms such as place attachment, place uniqueness or sense of place. In particular, an important distinction has been made between people’s identity with place and identity of place, which refers to properties that distinguish a place from others (Peng et al. 2020 ; Relph 1976 ). Further, a shifted focus toward the latter has offered insights into what features construct distinctive place identities in fields of urban design, geography and tourism (Larsen 2004 ; Lewicka 2008 ; Paasi 2003 ; Wang & Chen 2015 ). Despite the inherent vagueness in formalizing these concepts, prior studies have pointed out that physical settings, events that take in space, and associated individual (or group) meanings are key elements that shape distinctive place identities (Relph 1976 ; Seamon & Sowers 2008 ).

As an attempt to establish the theoretical foundation of place research, the notion of place has been discussed in contrast to space . Tuan defined the distinction between the two important concepts in human geography; space is an abstract physical environment that lacks substantial meaning, whereas place is a “center of felt value” (Tuan 1977 ) that is given meaning through human experience. Consequently, recognizing such place characteristics has been crucial to link individual behaviors to their surrounding environment and offered indicators for measuring urban form, function, emotion, and quality of life in cities (Gao et al. 2022 ; Nasar 1990 ). Prior studies have highlighted the benefits of understanding place identity in facilitating planning processes to create livable and legible places. By designing such places, individuals may develop a sense of attachment to their urban communities and cultivate environmentally friendly attitudes that are conducive to sustainability (Hernandez et al. 2010 ; Manzo & Perkins 2006 ). Thus, an important challenge in placemaking is to build physical as well as visual features that can trigger stronger subjective attachments to a place.

Despite its significance, measuring place identity has been a difficult task due to its intrinsically obscure and subjective nature (Goodchild 2010 ; Peng et al. 2020 ). Conventional studies attempted to capture built environment characteristics and human perceptions through qualitative research techniques. For instance, Hull et al. ( 1994 ) conducted a phone interview on the damaged place identity of Charleston, South Carolina after Hurricane Hugo, and Stewart et al. ( 2004 ) employed photo-elicitation, participant-employed photography followed by interviews to understand how residents’ representation of their community identity can help shape visions for landscape change. Another stream of research explored the role of identity markers, such as towers, street signs, region names and (non)commercial establishments, in reflecting the unique identities of a place (Peng et al. 2020 ). However, such qualitative approaches pose limitations in terms of time and cost efficiency, where limited sample sizes may lead to biased results.

With the emergence of various user-generated contents, researchers have been leveraging these new data sources to understand the meaningful collective place identity of cities (Jang & Kim 2017 ). In particular, text and images have been the two most widely used data formats to advance our knowledge of place identity. Previous studies have employed natural language processing (NLP) methods such as sentiment analysis and topic modeling to process text-based datasets and understand individuals’ opinions and emotions of places from online text corpora (Gao et al. 2017 ; Hu et al. 2019 ). In parallel, computer vision (CV) approaches have been effectively used to extract visual information about places from street-level images and geotagged photos (Kang et al. 2019 ; Liu et al. 2017 ; Zhang et al. 2018 , 2019 ), which offer valuable insights to advance our understanding of place.

Recently, advancements in generative artificial intelligence (GenAI) have received significant attention due to their capabilities to generate realistic text and image outputs supported by large language models (LLM). Built on billions of inputs and parameters, researchers have noted that users can overcome the language barriers through GenAI by obtaining results that can be applied across diverse populations and settings (Gottlieb et al. 2023 ; Sajjad & Saleem 2023 ). The current advancements of GenAI have enabled people to communicate and interact with ChatGPT (OpenAI 2023 ) naturally and can generate vivid images given certain prompts with DALL·E2 (Mishkin et al. 2022 ). These GenAI models have been highlighted as powerful tools with potential for a wide variety of applications in different domains, including, transportation (Kim & Lee 2023 ), education (Latif et al. 2023 ), climate literacy (Atkins et al. 2024 ) and geospatial artificial intelligence (Mai et al. 2023 ).

In the meantime, researchers are wary of the inattentive use of GenAI tools despite its potential benefits and versatility across fields. As Shen et al. ( 2023 ) describes, LLMs may become a double-edged sword that produces plausible but logically incorrect results. For such misinformation being produced, Van Dis et al. ( 2023 ) pointed out the absence of relevant data in the training set of LLMs. The output quality in terms of accuracy and bias may heavily rely on the information that was included for training. Therefore, it is essential to acknowledge and address the ethical and societal concerns of these models that stem from the lack of transparency (Dwivedi et al. 2023 ; Kang et al. 2023 ).

While creative jobs were considered safe from technological innovations until now, compared to those of routine and repetitive tasks (Ford 2015 ), the emergence of GenAI is turning things around. Although concerns remain about the ethics and disruptive impact of their usage, generative models would inevitably replace or, at least, assist content generation in creative industries (Anantrasirichai & Bull 2022 ; Lee 2022 ; Turchi et al. 2023 ). Design fields are not an exception—architectural firms are nowadays utilizing AI-assisted tools to generate 100,000 designs per day for their building projects (see Supplementary Note). Researchers have also investigated the capability of various text-to-image generators to assist the initial process of architectural design (Paananen et al. 2023 ). Additionally, recent urban studies have explored the potential of GenAI in evaluating design qualities of the built environment scenes and obtaining optimal land-use configuration through automated urban planning process (Seneviratne et al. 2022 ; Sun & Dogan 2023 ; Wang et al. 2023 ).

Creating design alternatives, however, has been a space -making, rather than a place -making, approach; it has leaned towards the simulation of physical forms of the built environment with less consideration of the surrounding contexts. Paananen et al. ( 2023 ) argued that generative systems have mostly been used to represent the geometry of architecture, such as façade, form, and layout, while its conceptual creativity remains to be studied. DALLE-URBAN has demonstrated the potential of GenAI for effectively creating urban scenes, but fell short in depicting composition and locales for specific conditions (Seneviratne et al. 2022 ). Furthermore, Bolojan et al. ( 2022 ) called for the need to consider how human perception works in the computational design workflows with GenAI models. Motivated by their potential, we raise the question: Can GenAI contribute to our understanding of place-specific contexts in a trustworthy manner?

GenAI has the potential to revolutionize the way we perceive the world and offer a new paradigm for urban studies. In particular, we intend to suggest a more proper use of GenAI in urban studies for creating place by bringing the people and meanings intertwined with human experience to the fore. To this end, we aim to examine the potential of GenAI as new tools for understanding the place identity of different cities. In this study, we ask the following two research questions: (1) How does generative AI illustrate place identity? (2) To what extent can we trust generative models in terms of their place identity results when compared with fact-based descriptions? To address these questions, we propose a computational framework to collect place identity with GenAI and evaluate the quality and trustworthiness of the data. We first asked a mixture of questions about the place identity of 64 global cities using two GenAI models, namely, ChatGPT for texts, and DALL·E2 for images. The cities were selected across 6 continents and 49 countries that represent diverse spatial coverages and contexts in order to better evaluate the performance of GenAI models at a global scale. Then, we collected two fact-based datasets as ground-truth data, including Wikipedia texts and images retrieved from Google search for comparison. Finally, we comprehensively evaluated the similarity between the AI-generated results and their fact-based counterparts.

We present a computational framework of this study in Fig. 1 . The framework primarily involves two steps: exploring place identity with GenAI and validating results by comparing with real-world settings. For each step, two types of datasets, namely, text-based, and image-based datasets were created to investigate the potential of GenAI models in capturing place identity. In particular, we employed ChatGPT to generate text descriptions of cities; and we leveraged DALL·E2 to generate images of representative streetscapes of different cities. We further collected two datasets including a text dataset from Wikipedia and an image dataset from Google search for validating the results produced by the generative models. After that, we performed cross-validation to compare similarities among these datasets, analyzed the characteristics of place identity produced by GenAI, and evaluated whether the results provided can be trusted.

The computational framework of this paper.

Understanding place identity with generative AI

Place identity from chatgpt.

We first asked ChatGPT with a prompt “What is the meaning of place identity” to confirm that its understanding of place identity is consistent with the notion of identity of place that is to be explored from the generated outputs. Then, we created a text-based dataset by asking ChatGPT to generate descriptions of the place identity of various cities around the world. To accomplish this, we developed a set of prompts using the following format:

­ “What is the place identity of { city }? Give me in ten bullet points.”

­ “What is the urban identity of { city }? Give me in ten bullet points.”

­ “What is the place identity of streetscapes in { city }? Give me in ten bullet points.”

The { city } includes a list of 64 global cities around the world. A full list of cities is in Table 1 . The prompts we used allowed us to retrieve the specific place identity information we sought to generate from the AI model for each city. It should be noted that responses generated by ChatGPT may vary in length and style, despite using the same prompt format. To ensure consistency and comparability across different cities included in our dataset, we limited the responses to ten bullet points. By doing so, the generated outputs are concise and well-structured and can be easily analyzed and compared.

Place identity from DALL·E2

Similar to the text-based datasets, we created an image-based dataset using DALL·E2 to understand place identity. We aim to capture the visual representations of the built environment and streetscapes of each city, which are essential components of its place identity. To achieve this, we input the following prompt into DALL·E2 to generate representative streetscapes for each city:

­ “What is the place identity of streetscapes of { city }?”

We generated 20 images for each city, where each image has a size of 256*256 pixels. By combining the image-based dataset with the text-based dataset, we aim to provide a comprehensive and multi-modal understanding of the place identity of each city captured by GenAI models.

Collecting real-world settings

Text-based dataset from wikipedia.

Despite the high performance of ChatGPT in generating texts, researchers and the public have raised concerns regarding its reliability and trustworthiness (Shen et al. 2023 ). However, the subjective nature of place identity, which is intrinsically related to human experience and may vary across different individuals, poses a significant challenge in validating responses generated by ChatGPT. Moreover, the absence of a large-scale ground-truth place identity dataset further complicates the validation process. To address these challenges, we collected data from Wikipedia on the full list of cities as a source of textual introduction to each case. As Jenkins et al. describes, it is plausible to consider Wikipedia entries that are created through users’ collaborative efforts as a collective perception of places with contents on main characteristics of different locations (Jenkins et al. 2016 ).

Image-based dataset from Google search

We further employed a Python web scraper to collect images of each city via Google Images ( https://images.google.com/ ) search engine. Performing content analysis on images sampled from Google has been approved as an effective method to retrieve visual information on various places and thus represent place-specific meanings (Choi et al. 2007 ; Coghlan et al. 2017 ). In this study, this was accomplished by entering a search query in the format of “{ city }”, such as “ Singapore ”. The top returned images appear based on their relevance to the search query, which we assume to reflect the place identity of that city. We then collected the top 30 image search results among all returned images for each query. By doing so, we were able to collect a representative set of images for each city, allowing us to compare with the outputs generated by DALL·E2.

Validation of place identity by generative AI

Measuring text similarity.

To validate the place identity results generated by ChatGPT, we utilized a cross-validation approach after collecting the two text-based datasets from ChatGPT and Wikipedia. More specifically, we assessed the similarity between sentences generated by ChatGPT and the sentences in Wikipedia to determine whether the AI-generated results may capture and reflect place identity.

To achieve this, we first conducted data cleaning of the Wikipedia data to ensure that the text was in a clean format and could be processed further. We utilized the tokenizer function in the Natural Language Toolkit (NLTK) Python library to segment the corpus into individual sentences. To analyze the two text-based datasets and extract their semantics, we leveraged a sentence transformer BERT model (Devlin et al. 2018 ) based on a modified version of MiniLM (Wang et al. 2020 ). Such a model has been widely used in prior studies to convert each sentence in the Wikipedia corpus and each bullet point in ChatGPT responses into word embeddings to capture their underlying semantics. This model has been distilled for efficiency and fine-tuned on 1 trillion triples of annotated data, making it highly accurate in measuring short sentence topic similarity. By inputting each sentence from ChatGPT responses and the Wikipedia corpus into such a sentence transformer BERT model, we transformed them into embedding vectors. Then, we measured cosine similarity for sentence embeddings from ChatGPT responses and Wikipedia corpuses to assess the relevance between the two datasets. This similarity score indicates the degree of relatedness between sentences through values ranging from 0 to 1. Specifically, for a sentence pair (one from ChatGPT and one from the Wikipedia corpus), higher similarity scores indicate that the ChatGPT response is highly relevant to a particular topic within the Wikipedia corpus, while lower scores denote that the response is not closely aligned with any topic in the corpus. To quantify the similarity, we iterated each bullet point in the ChatGPT responses and compared it to every sentence in the Wikipedia corpus. We identified the sentence in the Wikipedia corpus that had the highest similarity score in response to each bullet point. This allowed us to further quantify the overall similarity between the ChatGPT responses and the Wikipedia corpus.

In addition to the text similarity measurements, we also created word cloud images of each city based on ChatGPT-generated responses and the introduction from Wikipedia. A word cloud image offers a vivid graphical representation of text data, where the size of each word corresponds to its frequency in the given text. These word cloud images serve as visual representations of the topics covered in the texts of place identity, allowing for a comparison between outputs generated by ChatGPT and their corresponding Wikipedia introductions of each city.

Measuring image similarity

Similar to the comparison between ChatGPT-generated sentences with Wikipedia corpus, we also compared images generated by DALL·E2 and those collected from Google image search. We aim to evaluate the reliability and generative capability of the text-to-image model in producing realistic representations of place-specific scenes of cities. For this purpose, we adopt the Learned Perceptual Image Patch Similarity (LPIPS) to assess the perceptual similarity between AI-generated and real-world images (Zhang et al. 2018 ). This metric was evaluated against a large-scale dataset of human judgments on image pair similarity and found to outperform other perceptual similarity metrics. LPIPS computes the Euclidean distance between feature vectors of images extracted from a pretrained deep convolutional network for image classification. We employed AlexNet as the feature extractor for LPIPS calculation, which was tested to output the best performance. Noting that a lower LPIPS score indicates greater similarity, and vice versa, we defined the image similarity score ( \({S}_{i,j}\) ) between any two images \(i\) and \(j\) as follows:

Subsequently, we compare each image generated by DALL·E2 with all images from the Google image search in the same city, and identify the three most similar images based on the image similarity scores. This allows us to quantitatively compare and determine whether the results generated by the text-to-image model are consistent with the real-world urban settings of each city.

In addition, considering the subjectivity of place identity, it is necessary to keep human-in-the-loop and involve human evaluations. Therefore, we conduct a survey specifically designed to collect human ratings on the similarity between DALL·E2-generated images and Google images. We aim to invite humans to evaluate whether the two images are similar or not. An image pair that is nearest to the mean of \({S}_{i,j}\) image similarity scores for each city is selected as the representative case to be included in the survey. Hence, respondents were provided with a total of 64 questions that asked about the similarity of a given pair rated using a 7-point Likert Scale. Then we ordered the 64 cities based on the mean values of human-rated similarity to see whether the GenAI-based images might be similar to those representative images.

Last, we measure city-by-city similarity to test whether GenAI can identify cities that are visually distinctive or similar. In order to perform this experiment, we calculate the normalized Chamfer distance (CD) between DALL·E2 generated outputs of two cities. CD is a similarity metric that measures the distance between point clouds of latent representations of images. The normalized CD value ranges between [0,1], and is subtracted from 1 so that higher value indicates higher similarity, and vice versa. Recollecting that place identity refers to properties that distinguish one place from another, we attempt to examine two hypotheses from the similarity of each city pair:

Similarity between generated outputs of the same city is greater than that of different cities;

Similarity between generated outputs is greater in cities that are geographically and culturally close than in cities that are geographically and culturally distant.

This pairwise comparison enables us to explore whether the represented traits captured by GenAI are place identity-related or generic characteristics of cities, providing valuable insights for urban design research and practice.

In the following section, we will first showcase several example results created by GenAI to illustrate its potential in capturing place identity in urban studies. Although GenAI models have demonstrated outstanding capabilities in several tasks such as communication with people and painting creation, their trustworthiness has been questioned by researchers. The nature of GenAI models is that they do not produce outputs based on explicit logic but fit statistical distributions present in the training dataset. Consequently, there is a need to evaluate the trustworthiness of GenAI outputs, particularly with respect to place identity in this study, as they may contain ‘fake’ information and lead to erroneous decisions. However, the absence of a ground-truth place identity dataset at a global level poses a challenge.

To address this issue, we employed two datasets including Wikipedia introductions and Google images of cities, under the assumption that they reflect place identity as found from previous studies (Choi et al. 2007 ; Coghlan et al. 2017 ; Jenkins et al. 2016 ). We further tested whether outputs generated by GenAI models align with information retrieved from these datasets, which could be considered as a reliable representation of a city’s place identity.

Results of place identity generated by ChatGPT

First, we explored whether ChatGPT understands place identity as the identity of place, as suggested by Relph ( 1976 ), which provides the conceptual foundation of this study to explore the place-specific meanings from generated outputs of different cities. In response to the prompt, “What is the meaning of place identity?”, we obtained an answer (see Supplementary Fig. S2 ):

“Place identity refers to the unique characteristics and meanings that individuals or groups associate with a particular location. It encompasses the social, cultural, historical, and environmental elements that shape people’s perceptions and emotions about a place. Place identity is formed through experiences, interactions, and attachments to a specific geographical area, such as a neighborhood, city, or region .

This concept suggests that places have distinct personalities or identities that are constructed through the collective experiences and memories of the people who inhabit or interact with them. These identities can influence how individuals relate to and interact with their surroundings, shaping their sense of belonging, attachment, and pride in a place .

Place identity is often closely linked to notions of community, heritage, and cultural identity, as well as to broader issues such as urban planning, environmental conservation, and tourism development. Understanding and preserving place identity is important for maintaining the unique character and authenticity of different locations and for fostering a sense of connection and belonging among residents and visitors alike.”

From this response, we were able to verify that ChatGPT introduces place identity as unique characteristics and meanings of a particular location constructed by collective experiences of people. Also, it successfully described the benefits of place identity in fostering a sense of belonging and attachment among people, which coincides with our specific focus in the definition of place identity in this study.

Then, we retrieved place identity responses generated by ChatGPT through asking three different prompt keywords, { place }, { urban } and { street } identity. Table 2 presents examples of responses in two large metropolitan cities, namely, Beijing and New York. Intuitively, GenAI models capture various aspects that align with people’s general perceptions of these cities, ranging from the economy, education, culture, and history of cities. Moreover, several proper nouns that signify unique names are highlighted, such as Olympics, Tsinghua/Peking University and Tiananmen Square for Beijing or Yankees, Broadway and Empire State Building for New York, which further demonstrates ChatGPT’s ability to generate contextually relevant place identity descriptions. To gain a better understanding into the characteristics of ChatGPT responses, we offer several basic statistics of the generated outputs. On average, each bullet point contains 11.98 words, with a standard deviation of 6.43. Descriptions of urban identity tend to be lengthier, with an average of 15.86 words per bullet point and a standard deviation of 5.83. Street identity, on the other hand, is typically presented in a paragraph format with an average of 19.65 words and a standard deviation of 5.05.

Results of place identity generated by DALL·E2

Figure 2 also demonstrates examples of place identity image outputs generated by DALL·E2 in Beijing and New York. These provide visual representations that align with people’s general perceptions and common knowledge about these cities. For instance, in images depicting Beijing in Fig. 2a , we observe a combination of metropolitan cityscapes and classic Chinese architectural styles, such as hutong and siheyuan . Regarding images of New York in Fig. 2b , they reflect high density buildings, yellow traffic lights or fire escapes that align with our common perceptions of “The Big Apple”. These differences between the two groups of images clearly illustrate the ability of GenAI models in capturing unique visual features of place identity in these cities.

a Beijing. b New York.

Comparing place identity generated by ChatGPT with Wikipedia Corpus

To assess the accuracy and reliability of place identity generated by ChatGPT, we conducted a cross-validation with Wikipedia. Here, we intend to test whether AI-generated texts can provide a reliable representation of a city’s place identity. This involves computing the cosine similarity between sentence embeddings of ChatGPT responses and Wikipedia corpuses, and presenting visual comparisons between pairs of word clouds. Overall, the average text similarity scores for { place }, { urban } and { street } identity responses were 0.59, 0.58, and 0.56, respectively. This suggests that the similarity between ChatGPT and Wikipedia descriptions of a place are non-varying with respect to the prompt used for the generative model. In this section, we particularly focus on results for the { place } prompt case while discussing the results of this study.

We first investigate the relevance between two datasets. Figure 3a is a box plot showing the distribution of cosine similarity scores, where each point denotes a comparison of each bullet point in ChatGPT responses with the most relevant match within Wikipedia. Also, note that cities are arranged in descending order of mean similarity, from left to right. Here, we observe a wide range of similarities, which reflect both similar and dissimilar descriptions of place identity by ChatGPT. Several examples of high and low similarity cases are further listed in Fig. 3b . For instance, Munich and Busan were cities with the two highest mean scores, whose contexts related to either its political importance or geographical conditions were successfully generated. In contrast, however, descriptions of Rome and Prague resulted with similarity levels that were far lower than the global average. While we requested ChatGPT to generate “in ten bullet points” and conducted a sentence-by-sentence comparison with the Wikipedia corpus to obtain uniformity in length, its descriptions for both cases were much shorter than sentences from Wikipedia. The examples suggest that low similarity results may be partially due to the length of texts being compared, and therefore, a more concrete way to minimize the discrepancy in length is crucial for the effectiveness of GenAI models in capturing the complex nuances of place identity.

a Box plot of cosine similarity scores between { place } identity responses generated by ChatGPT and Wikipedia corpuses. Each city includes ten points, each indicating the highest cosine similarity per ChatGPT sentence. From left to right, cities are in descending order of their mean cosine similarity. Red line indicates the mean similarity level of individual cities. For box plots based on { urban } and { street } prompts, see Supplementary Fig. S1 . b Examples of high (Munich and Busan) and low (Rome and Prague) text similarity scores. c Comparison of word clouds between ChatGPT’s outputs (left) and Wikipedia corpuses (right): from top to bottom, Seoul, Singapore, Barcelona and Almaty.

We also present a visual comparison between pairs of word clouds created for ChatGPT answers and Wikipedia to understand the primary contents from both textual sources. Figure 3c shows example results for four different cases: Seoul, Singapore, Barcelona, and Almaty. First, ChatGPT described Seoul’s place identity through topics including culture, vibrant , and modern , while Wikipedia introduction of Seoul covered keywords including soul, life, human, spirit and belief . We find that both results emphasize intangible aspects of the capital of South Korea, which correspond to the ‘meaning’ element of place identity models as defined in the fields of environmental psychology and geography (Canter 1977 ; Relph 1976 ). Recalling that ‘meaning’ refers to individual or group sentiments created through people’s experiences, this indicates that ChatGPT captures the subjective atmosphere and cultural values as the most salient characteristics of Seoul. From word cloud comparison for Singapore, we observe keywords such as diverse , multiculturalism and melting pot from ChatGPT responses. These are supported by keywords such as Singaporean , Malaysia , British and Chinese in Wikipedia word cloud, implying that the text-to-text model identified Singapore’s diverse and polyethnic culture. Barcelona and Almaty are the cases whose identities are described in relation to broader ethnographic or national contexts. The most notable keywords in word clouds generated based on their ChatGPT responses are Catalan and Kazakhstan , respectively. Likewise, word clouds of Wikipedia corpus also highlight both keywords, from which we infer that the place identity of Barcelona and Almaty are deeply intertwined with either the ethnographic or national contexts.

Comparing place identity generated by DALL·E2 with Google images

We measured the image similarity between images generated by DALL·E2 and those collected via Google search. Parallel to the text similarity analysis, here, we examined the generative capability of GenAI in producing realistic representation of place-specific scenes of cities. In particular, we computed the Learned Perceptual Image Patch Similarity (LPIPS) that evaluates the distance between image patches and has been widely used in previous studies for aligning well with human judgment (Cheon et al. 2021 ; Zhang et al. 2018 ). A value equivalent to 1 – LPIPS is defined as an image similarity score ( \({S}_{i,j}\) ) to quantitatively assess the perceptual similarity of images, where a higher score indicates greater similarity, and vice versa.

Figure 4a provides a box plot showing the distribution of image similarity score ( \({S}_{i,j}\) ) in ascending order, from left to right. Here, we observe variability in image similarity across different cities. Overall, the average is 0.575 and the standard deviation is 0.066. We further explore specific examples selected from two contrasting cases identified with the highest and lowest mean perceptual similarities between their generated and real-world scenes. In Fig. 4b , it is evident that DALL·E2 successfully depicted the decorative Baroque-style guildhalls on the Grand-Place in Brussels. In contrast, images generated for the place identity of Tokyo were dissimilar from real-world scenes shown in Google images. As shown in Fig. 4c , the repetitive generation of mundane streets without strong visual cues may be a sign of placelessness in the urban landscapes of Tokyo. Yet, we also point out that lighting conditions may have influenced the outcome. While DALL·E2 is strongly inclined to generate daytime images, certain cities include more images of night scenes in their Google search data. This tendency is more apparent in cities that are well known for their vibrant nighttime economy. Such differences in the time of day being illustrated in DALL·E2 outputs and Google images may contribute to low perceptual similarity.

a Box plot of LPIPS scores between DALL·E2 generated and Google search images by cities. Each city includes twenty points, each indicating the highest image similarity score (equivalent to lowest LPIPS) per DALL·E2 generated image. From left to right, cities are in descending order of their mean perceptual similarity. Red line indicates the mean similarity level of individual cities. b High image similarity example: Brussels. c Low image similarity example: Tokyo.

Furthermore, we aimed to verify if this computational approach corresponds with human responses, by conducting a survey where a total of 30 respondents rated the similarity between a given pair of generated and Google search images using a 7-point Likert Scale (see Supplementary Table S1 ). The average similarity score of all image pairs was 3.406 with a standard deviation of 0.606. At an individual city level, the top three similarities rated by human responses were Chicago (4.967), Madrid (4.867) and Montreal (4.267), whereas the bottom three results were Seoul (2.367), Auckland (2.467) and Kobe (2.467). This coincides with the previous finding in Fig. 4 , in that Chicago and Montreal are among the fourth quarter (above the third quartile) in their LPIPS-based similarity, while Seoul is among the first (below the first quartile). Yet, we also noted contrasting cases such as Madrid, Auckland and Kobe, which presented mid-level similarities in Fig. 4 . The Pearson correlation between the two similarities was 0.229, with a p-value of 0.071. While this result is not statistically significant at the conventional 0.05 level, it is significant at the 0.1 level. Given the exploratory nature and the inherent subjectivity in human survey responses with a relatively small sample size, we consider a significance level of 0.1 to be appropriate (Jackson 2006 ; Stevens 2002 ). The correlation result warrants further investigation. Despite the positive relationship, the weak correlation indicates a disparity between the two similarity scores, suggesting that LPIPS-based evaluation may not fully capture the nuances of human perception of how well GenAI represented the identity of cities. Therefore, it is necessary to involve more human opinions rather than relying on machine-based metrics. This discrepancy could be due to sample variability; the given pair might not represent the entire scenes of cities, while the 30 respondents might not represent the entire population. Meanwhile, this provides a valuable attempt to bridge the gap between quantitative and qualitative assessments of GenAI. We note that our goal was to provide a preliminary insight into the relationship between computational and human evaluations, not to conduct a comprehensive human study. Further research should incorporate a larger sample size and alternative computational techniques for a more robust estimation of the reliability of GenAI models based on human perception.

City-by-city pairwise similarity between DALL·E2 generated place identity

Finally, we compared the DALL·E2-generated outputs across different cities to examine whether GenAI can identify them distinctively. We aim to test two hypotheses throughout such comparisons: (1) Similarity between generated outputs of the same city is greater than that of different cities; and (2) similarity between generated outputs is greater in cities that are geographically and culturally close than in cities that are geographically and culturally distant. Figure 5a illustrates the similarity matrix constructed based on normalized Chamfer distance (CD) between sets of DALL·E2 generated images of a given city pair. Each cell is assigned with a value of 1–CD, so that higher value indicates higher similarity, and vice versa. We also note that cities were sorted by decreasing longitude to reveal geographical patterns of similarities represented by GenAI.

a Pairwise similarity matrix. Normalized Chamfer distance (CD) is measured for sets of DALL·E2 images of a given city pair. Cities are sorted in orders of longitude. Each cell is colored based on a value of 1-CD, where red indicates strong and blue indicates weak similarity between the generated place identity of two cities. b The West (left) vs. the non-West (right). For each city, similarity with cities in different regions are plotted against that with cities in the same region. Symbol denotes the continent in which the city is located. c Pearson correlation between 1-CD for DALL·E2 and Google images of a given city pair.

Overall, we observe two distinct results. First, relative high similarity scores (in red) appear along the diagonal. This shows that DALL·E2 outputs were more similar within itself than compared across cities, which corroborates the first hypothesis. In other words, the generative model produced images that may successfully represent the place identity of individual cities. For example, Abu Dhabi, Amsterdam, Dublin, Cairo, Johannesburg, Brussels, Kyoto, Caracas, Paris and Dubai are top 10 cities with strongest identity captured by DALL·E2. In particular, the contrast between on- and off-diagonal values is most apparent for Kyoto, Abu Dhabi, Cairo and Johannesburg, indicating that DALL·E2 identified these cases as the most visually distinctive cities.

Another notable observation is the grouping of high similarity scores in the lower-right section which consists of Moscow, Istanbul, and cities from Bucharest and to the west. We view this as an indication of the dichotomy between place identity in the Western and non-Western worlds. On the one hand, cities in American and European countries are found to share visual similarities among themselves, where Amsterdam-Brussels is identified as the highest similarity pair (0.7) in all 4,160 pairwise comparisons. On the other, cities in Asia-Pacific, Middle East and African countries present relatively low similarities across most comparisons (except for the Abu Dhabi-Dubai pair with a similarity score of 0.66). This coincides with the lack of local identity in urban developments in non-Western megacities during the past decades (Choi & Reeve 2015 ; Shim & Santos 2014 ). Previous findings have pointed out the tendency of these cities to copy imported Western design, resulting in a chaotic mixture of urban and rural landscapes and failing to achieve the intended level of success (Al-Kodmany & Ali 2012 ; Yokohari et al. 2000 ). This contrast is further verified when similarity within the same region is compared against that with different regions. As illustrated in Fig. 5(b) , all cities in Americas and Europe were presented with clear intraregional similarities (plotted above the reference line), whereas their non-Western counterparts showed irregular patterns across cities. Therefore, we conclude that our second hypothesis ---pairs of cities that are geographically and culturally closer are more similar --- is partially true for American and European cities, while DALL·E2 captures evidence of placelessness (Relph 1976 ) for the rest of the world.

These findings are supported by the positive correlation in Fig. 5c , which demonstrates that the similarity between generated images of a given city pair is consistent with that between the actual urban scenes shown through Google images. This provides empirical evidence of the effectiveness of GenAI in capturing the visual distinctiveness of cities through such pairwise comparisons and verifies its capabilities in representing place identity in response to place-related prompts.

In the previous sections, we presented a computational framework that employed GenAI models to generate place identity results. We further computed text and image similarity scores between generative model responses and corresponding Wikipedia and Google image search data to test the reliability of their outputs for representing place identity in different cities. GenAI models capture salient characteristics of cities and could be utilized as a valuable data resource to advance our knowledge of place. However, their future directions as well as ethical issues and limitations should also be discussed. Here, we list several takeaways to offer implications for the future use of GenAI in urban studies pertaining to understanding place identity.

Generative AI for urban studies

In this study, we attempted to provide GenAI with prompts on place concepts that contain subjective meanings and verify its reliability in generating textual and visual outputs that capture place identity of cities. Future studies may extend this by using GenAI to construct a valuable dataset of place meanings at a larger spatiotemporal scale. For instance, we conducted a comparison among cities that best represent the countries in which they are located in. In addition, the results were obtained based on data before September 2021, the knowledge cutoff date officially announced by OpenAI ( 2023 ). Therefore, the approach in this paper can be revisited by adding more cities within the same country for an intranational study or rerunning in different years with updated data to reveal how place identity changes over time. These not only allow researchers to model the subjective nature of urban experiences (i.e., place identity, cognition, perception, etc.) but also provide a promising baseline for the use of GenAI tools in future urban studies.

GenAI can enhance our urban imagination and simulation by incorporating socioeconomic and subjective aspects of the urban environment in future studies. For instance, we can prompt GenAI to render urban scenes (or place identity) of different demographic attributes, such as age, education and race/ethnicity, leading to a question of how well the generated outputs align with different communities’ perception of the urban landscape or whether they are skewed towards certain social classes or culture. Figure 6 presents examples of generated streetscapes of Boston using the same prompts except for one keyword. In Fig. 6a , residential areas of the “white community” include brownstone houses along roads whose pavement and streetlights are well-maintained; while Fig. 6b illustrates a degraded built environment for the “black community” with bumps and cracks on the road, overgrown bushes, and building architecture that is simple to the bare minimum. This indicates that what GenAI models predict is based on social stigmatization about certain urban populations as well, with risk of reinforcing this discriminatory lens, although there is no legal or infrastructural ground for such narratives. Future studies can examine cities from low- and middle-income cities (LMICs) that often lack quality data to train GenAI models. This enables discussing the fairness of GenAI models, particularly for the social context of marginalized areas that have disproportionately low representation in the training datasets. Moreover, using query keywords that specify the perceptual qualities of the urban environment can help us understand the defining characteristics of safe, lively, wealthy, active, beautiful, and friendly cities (Dubey et al. 2016 ). While existing applications of generative models have mostly focused on automating the planning processes on a two-dimensional plane (Park et al. 2023 ; Wang et al. 2023 ), the proper use of GenAI models can help planners and designers obtain more realistic and imaginative urban scenes that are more relevant to human perception and experience.

a “white community”. b “black community”.

Finally, we also raise concerns regarding the “black-box” deep learning approaches. Our results indicated that GenAI models possess varying capabilities in representing place-specific characteristics of cities depending on their output format. However, we have minimal information on the data used for training generative models at the current stage. Wikipedia is known to be one of the sources of training data for ChatGPT (Shen et al. 2023 ), which may overlap with that used in this study, raising concerns regarding circularity in evaluating GenAI results using its own training data. Despite such limitations, Wikipedia and Google Images have been considered valuable sources of collective place-specific meanings, considering the lack of large-scale ground-truth dataset about the identity of global cities (Choi et al. 2007 ; Coghlan et al. 2017 ; Jenkins et al. 2016 ). Therefore, their usage can still be informative when particularly focused on specific domains that require qualitative assessment of generated outputs. For instance, one of our main objects of interest in this study was to identify varying degrees of similarity in representations, through which we revealed intrinsic biases and errors for different global city cases. This provides a consistent baseline for assessing the reliability of GenAI results against commonly accepted and easily accessible information. In the meantime, it remains necessary to develop more explainable AI approaches that can better elucidate the reasoning behind the generated outputs. This can be addressed in future studies in two ways. First, data from different sources that is less likely to be included in the training of generative models might be considered for their real-world counterparts. Social media or automated online surveys are two alternative platforms to crowdsource direct opinions of people at scale (Dubey et al. 2016 ; Jang & Kim 2019 ). Second, it is necessary to customize the models for domain-specific applications. Although large language models have been effective in producing general human-like responses, researchers have recently demonstrated that ‘smaller’ language models could achieve high performance with greater efficiency when fine-tuned for a particular domain or context (Fu et al. 2023 ; Schick & Schütze 2020 ; Turc et al. 2019 ).

Place-specific Scenes vs. Generic City View

By asking DALL·E2 with prompts regarding place identity of streetscapes of cities, we obtained a collection of images that depicted various street scenes. These images were then assessed to measure their similarity with images of the real-world. We could observe subtle differences among different cities regarding the architectural style, street design, or vegetation type. For instance, as shown in Fig. 7 , New York images created by DALL·E2 primarily showed prewar apartment buildings in Manhattan with wrought-iron fire escapes; images in Paris are represented by its Haussmannian architecture with stone facades, balconies, and double windows; and images in Singapore are emphasized by either its typical high-rise apartments or shophouses along with rain trees that grow in this region. All of these indicate that GenAI could capture the unique place identity, particularly related to architectural style, of each city.

a New York. b Paris. c Singapore.

However, it is worth noting that DALL·E2 has also generated a series of images that depict generic city views rather than specific to any particular place, thus failing to capture the unique characteristics of individual cities. Figure 8a shows a collection of images for New York, Tokyo, Seoul, London, Sydney, and Melbourne generated by DALL·E2. Generated images for different cities mostly depicted common urban features such as buildings, road signs, streetlights and pavements. These reflect the generic concept of a city , rather than identity , and fall short in representing the attributes that distinguish a particular city from the rest. As shown in the Sydney example in Fig. 8b , the generated place identity images do not capture landmarks of the city (Opera House and Harbour Bridge) or its scenic waterfront. Instead, a generic landscape of an urban environment is rendered, which makes it difficult to tell what the salient characteristics are from the generated images. Moreover, a pseudoword on a signpost, Hork Str Sox , hardly functions as a visual cue for the identity of streetscape in Sydney. These observations pose questions regarding the reliability of these generated images. Researchers need to carefully evaluate the quality of these AI-generated images before considering their practical use in research and real-world applications.

a Generated images for New York, Tokyo, Seoul, London, Sydney, and Melbourne. b Sydney example of comparison with Google images.

The observation of both generic and place-specific from generated images connects to the discourse of space and place that constructs the nature of geographical disciplines. As opposed to space which is an abstract and undifferentiated physical setting, place is given unique personalities over time to become locations with visual impact that brings sense of place among people (Tuan 1977 ). On the one hand, DALL·E2 produced scenes and images of placeless urban landscapes (see Fig. 8 ); “a scene may be of a place but the scene itself is not a place” (Tuan 1979 , p. 411). On the other, results in Fig. 7 showed its promising capabilities in representing the place of different cities. This is particularly intriguing because unlike places such as monument buildings, religious spaces or public plazas that are easily identifiable as ‘public symbols’ of the city, places as ‘fields of care’ in an everyday setting (e.g., park, home, drugstore street corner, marketplace) have been discussed to lack visual identity and be barely discernible through physical or structural appearances without repeated experience of the place (Tuan 1979 ; Wild 1965 ). Yet, we were able to distinguish DALL·E2-generated streetscape scenes of New York, Paris, and Singapore from elements such as streetlights, vegetation or architectural style, implying the possibility of uncovering inconspicuous places with the use of GenAI without repetitive interaction with the physical environment. This can further contribute to urban planning and design practice, considering the importance of cultural heritage and identity of a place to foster as sense of belonging among city dwellers (Hernandez et al. 2010 ; Manzo & Perkins 2006 ). Particularly, GenAI tools can be effective in collecting multiple development scenarios or design options instantly from the public that better reflect the preferences and priorities of the community. Thus, we may expect GenAI to assist in not only generating visual representations rooted in the cultural contexts of a place but also in facilitating community engagement in the urban design process and developing placemaking strategies that enhance the sense of place and attachment. Returning to Tuan’s ( 1979 ) conclusion, spatial analysis from the positivist perspective tends to simplify the underlying assumptions of people, space and place, whereas the humanist must take into account the intricacy of human nature—so must, and can, GenAI.

Opportunities and Challenges

Looking forward, we close by outlining technical challenges and opportunities to be further explored for the application of GenAI in future urban research. First, to obtain more reliable results that represent place-specific attributes of different cities, researchers may develop more careful prompt engineering. The importance of appropriate prompt designs has been commonly emphasized in previous research to enhance the consistency of GenAI models for domain-specific applications (Hase et al. 2021 ; Kang et al. 2023 ). By discussing the results of this study, we found this is more imperative for the text-to-image model compared to its text-to-text counterpart. As suggestions to design effective prompts for DALL·E2 to yield relevant responses to the place identity of cities, we can specify the point of view (POV), perspective, and captured objects in output images as in the following format:

“ What is the place identity of {city}? Show me a {perspective} focused on {object} with point of view pitch angle at {pitch} .”

As DALL·E2 produced image results with different directions and angles, parameters to set specific POV headings and pitch, { heading } and { pitch }, can be added to provide consistent viewpoints. Also, clarifying whether to show a bird-eye view or street-level scene using a { perspective } parameter can reduce variation in terms of the image perspective. Moreover, to minimize unpredictability in scenes being rendered, an { object } parameter would let resulting images focus on specific urban elements of interest. As discussed earlier in the previous section, whether to generate either day or nighttime image may also be an effective parameter to control the lighting conditions being rendered. Examples of DALL·E2 results when different parameters were used in this prompt format are shown in Fig. 9 .

a { pitch } ( b ) { perspective }. c { object }.

Another future direction lies in the improvement of methods for evaluating the reliability of generative model outputs. Here, we suggest two potential approaches for this purpose, multi-source data fusion and advanced similarity analysis. The AI-generated outputs are not always consistent with Wikipedia corpus and Google image search results as found in this study. We could incorporate social media texts and images as valuable data sources in capturing users’ various information related to places. Such data enable us to compare generative model outputs with people’s direct opinions that can better represent the identity of places (Jang & Kim 2019 ). In the meantime, we observed uncertainties in the similarity analysis results led by the subjective nature of perception. That is, why differences in similarity scores are observed, what contributes to high or low similarity results, and which scene is more relevant to the place identity of specific cities. This can be further refined by defining a more concrete threshold for interpreting the cosine similarity and LPIPS metric used in this study. Furthermore, different methods can be adopted for comparison purposes. For instance, more advanced algorithms can be applied, such as object detection and image segmentation, to retrieve object occurrences from DALL·E2 outputs and verify their correspondence with real-world urban scenes.

It is also noteworthy that prompts and outputs in this study were created only in English, overlooking the performance of GenAI models in other linguistic settings. While a few previous studies have highlighted the potential of GenAI in overcoming language barriers from being built on billions of inputs and parameters (Gottlieb et al. 2023 ; Sajjad & Saleem 2023 ), it remains important to examine the generalizability of outputs through a critical lens when conducting a multicity comparison. In its technical report, OpenAI ( 2023 ) has demonstrated the outperformance of GPT models when using English or major European languages, likely because they were designed and built primarily with data from English sources without robust multilingual testing. In addition, the English Wikipedia has both the most number articles and page views, making non-English speakers less capable of contributing to the online encyclopedia. This disproportionate representation could be a plausible explanation for the high intraregional similarities between DALL·E2-generated images of cities in the Americas and Europe in contrast to those among non-Western cities (see Fig. 5 ). Hence, this raises the question of from whose perspective are outputs being generated. For instance, in Table 2 , it is plausible to interpret that ChatGPT’s description of Beijing has a nuance toward a foreign audience, whereas that of New York assumes a US-centric audience with prior knowledge about American culture. Considering the subjective nature of place identity, we offer future research directions to inquire whether GenAI outputs paint us a picture of the local people’s knowledge, of foreign tourists and journalists’ experience, or the local authorities’ official statements by testing variations of multicultural and multilanguage prompts.

Last, acknowledging the difficulty in overcoming the limitation regarding the “black-box” nature of the generative models, a potential solution could involve comparing their outputs with actual human responses. This could be achieved by conducting a survey to how individuals assess the quality of the GenAI descriptions of different cities. GenAI outputs could be graded in terms of to what extent they are representative of people’s place identity for a certain place. Also, a focus-group interview could be helpful to gather more detailed opinions on how participants from similar demographic or experiential backgrounds perceive the validity of generated results. Meanwhile, the rapid advancements in the development of new GenAI models call for regular updates to the results for improved relevancy and contribution of the work. Potentially repeating the experiments with the latest GPT-4 or GPT-4o models and DALL·E3 may help us reveal the up-to-date performance of GenAI models in understanding and depicting place identity without relying on deliberate efforts of OpenAI targeted on these particular abilities.

Conclusions

We have recently witnessed the capabilities of GenAI models in various domains. Their capabilities in generating realistic texts and image outputs with only simple prompts have enabled collecting human-like responses in an efficient and cost-effective manner. In this study, we attempted to investigate the potential of using generative models in understanding place identity , an important concept in the field of urban design and geography. While place identity is subjective and closely tied with an individual’s perception of cities, many studies have attempted to discover the collective identity that better explains both the physical and non-physical attributes of the urban environment. We departed from two aspects, languages and visual representation, and asked two GenAI models, ChatGPT and DALL·E2, with prompts related to the place identity of different cities. We further tested the reliability of their responses by measuring their similarity with fact-based datasets, Wikipedia and Google images, that depict the real urban settings. Moreover, we conducted a pairwise comparison to verify if GenAI can also capture the visual distinctiveness or similarity between cities. Our results indicate that GenAI models have the potential to generate outputs that represent salient characteristics of cities that make them distinguishable and can serve as a valuable data source and tool for urban studies. This study is among the pioneering attempts to investigate GenAI in urban design research before applying them into planning and design practices. While exploring the capabilities of GenAI in representing the place identity of cities, we contribute to existing literature by discussing potential limitations and future research opportunities for further studies. The overall framework is expected to aid planners and designers in utilizing such tools to evaluate characteristics of cities for placemaking and city branding purposes, and in turn, shaping more imageable cities.

Data availability

All relevant data used and generated in the research are publicly available in the Figshare Repository at: https://doi.org/10.6084/m9.figshare.25041452.v1 . The detailed data management information can be found in the supplementary information.

Al-Kodmany K, Ali MM (2012) Skyscrapers and placemaking: supporting local culture and identity. Archnet-IJAR: Int J Archit Res 6(2):43

Google Scholar  

Anantrasirichai N, Bull D (2022) Artificial intelligence in the creative industries: a review. Artif Intell Rev 55(1):589–656

Article   Google Scholar  

Atkins C, Girgente G, Shirzaei M, Kim J (2024) Generative AI tools can enhance climate literacy but must be checked for biases and inaccuracies. Commun Earth Environ 5(1):226

Article   ADS   Google Scholar  

Bolojan D, Vermisso E, Yousif S (2022) Is language all we need? a query into architectural semantics using a multimodal generative workflow. POST-CARBON . Proc 27th Int Conf Assoc Computer-Aided Architectural Des Res Asia (CAADRIA) 1:353–362

Canter D (1977) The psychology of place . Architectural Press

Cheon M, Yoon S-J, Kang B, Lee J (2021) Perceptual image quality assessment with transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 433–442

Choi HS, Reeve A (2015) Local identity in the form-production process, using as a case study the multifunctional administrative city project (Sejong) in South Korea. Urban Des Int 20:66–78

Choi S, Lehto XY, Morrison AM (2007) Destination image representation on the web: Content analysis of Macau travel related websites. Tour Manag 28(1):118–129

Coghlan A, McLennan C-L, Moyle B (2017) Contested images, place meaning and potential tourists’ responses to an iconic nature-based attraction ‘at risk’: the case of the Great Barrier Reef. Tour Recreat Res 42(3):299–315

Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint ArXiv:1810.04805

Dubey A, Naik N, Parikh D, Raskar R, Hidalgo CA (2016) Deep learning the city: Quantifying urban perception at a global scale. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 , 196–212

Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, Baabdullah AM, Koohang A, Raghavan V, Ahuja M (2023) “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manag 71:102642

Ford M (2015) Rise of the Robots: Technology and the Threat of a Jobless Future . Basic Books

Fu Y, Peng H, Ou L, Sabharwal A, Khot T (2023) Specializing Smaller Language Models towards Multi-Step Reasoning. In Proceedings of the 40th International Conference on Machine Learning , PMLR, 202, p 10421–10430

Gao S, Janowicz K, Montello DR, Hu Y, Yang J-A, McKenzie G, Ju Y, Gong L, Adams B, Yan B (2017) A data-synthesis-driven method for detecting and extracting vague cognitive regions. Int J Geogr Inf Sci 31(6):1245–1271

Gao Y, Chen Y, Mu L, Gong S, Zhang P, Liu Y (2022) Measuring urban sentiments from social media data: a dual-polarity metric approach. J Geogr Syst 24(2):199–221

Goodchild MF (2010) Formalizing place in geographic information systems. In Communities, neighborhoods, and health: Expanding the boundaries of place (pp. 21–33). Springer

Gottlieb M, Kline JA, Schneider AJ, Coates WC (2023) ChatGPT and conversational artificial intelligence: Friend, foe, or future of research? Am J Emerg Med 70:81–83

Article   PubMed   Google Scholar  

Hase P, Diab M, Celikyilmaz A, Li X, Kozareva Z, Stoyanov V, Bansal M, Iyer S (2021) Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs. ArXiv Preprint ArXiv:2111.13654

Hernandez B, Martin AM, Ruiz C, del Carmen Hidalgo M (2010) The role of place identity and place attachment in breaking environmental protection laws. J Environ Psychol 30(3):281–288

Hu Y, Deng C, Zhou Z (2019) A semantic and sentiment analysis on online neighborhood reviews for understanding the perceptions of people toward their living environments. Ann Am Assoc Geogr 109(4):1052–1073

Hull RBIV, Lam M, Vigo G (1994) Place identity: symbols of self in the urban fabric. Landsc Urban Plan 28(2–3):109–120

Jackson D (2006) The power of the standard test for the presence of heterogeneity in meta‐analysis. Stat Med 25(15):2688–2699

Article   MathSciNet   PubMed   Google Scholar  

Jang KM, Kim Y (2017) Collective Place Identity. 2017 International Conference of Asian-Pacific Planning Societies (ICAPPS 2017) , 096

Jang KM, Kim Y (2019) Crowd-sourced cognitive mapping: A new way of displaying people’s cognitive perception of urban space. Plos One 14(6):e0218590

Article   CAS   PubMed   PubMed Central   Google Scholar  

Jenkins A, Croitoru A, Crooks AT, Stefanidis A (2016) Crowdsourcing a collective sense of place. PloS One 11(4):e0152932

Article   PubMed   PubMed Central   Google Scholar  

Kang Y, Jia Q, Gao S, Zeng X, Wang Y, Angsuesser S, Liu Y, Ye X, Fei T (2019) Extracting human emotions at different places based on facial expressions and spatial clustering analysis. Trans GIS 23(3):450–480

Kang Y, Zhang Q, Roth R (2023) The ethics of AI-Generated maps: A study of DALLE 2 and implications for cartography. ArXiv Preprint ArXiv:2304.10743

Kim J, Lee J (2023) How does ChatGPT Introduce transport problems and solutions in North America? Findings. https://doi.org/10.32866/001c.72634

Larsen SC (2004) Place identity in a resource-dependent area of northern British Columbia. Ann Assoc Am Geogr 94(4):944–960

Latif E, Mai G, Nyaaba M, Wu X, Liu N, Lu G, Li S, Liu T, Zhai X (2023) Artificial general intelligence (AGI) for education. ArXiv Preprint ArXiv:2304.12479

Lee H-K (2022) Rethinking creativity: creative industries, AI and everyday creativity. Media, Cult Soc 44(3):601–612

Lewicka M (2008) Place attachment, place identity, and place memory: Restoring the forgotten city past. J Environ Psychol 28(3):209–231. https://doi.org/10.1016/j.jenvp.2008.02.001

Liu L, Silva EA, Wu C, Wang H (2017) A machine learning-based method for the large-scale evaluation of the qualities of the urban environment. Computers, Environ Urban Syst 65:113–125

Mai G, Huang W, Sun J, Song S, Mishra D, Liu N, Gao S, Liu T, Cong G, Hu Y (2023) On the opportunities and challenges of foundation models for geospatial artificial intelligence. ArXiv Preprint ArXiv:2304.06798

Manzo LC, Perkins DD (2006) Finding common ground: The importance of place attachment to community participation and planning. J Plan Lit 20(4):335–350

Mishkin P, Ahmad L, Brundage M, Krueger G, Sastry G (2022) DALL· E 2 Preview-Risks and Limitations. Noudettu 28:2022

Nasar JL (1990) The evaluative image of the city. J Am Plan Assoc 56(1):41–53

OpenAI. (2023) GPT-4 Technical Report. ArXiv E-Prints , arXiv:2303.08774. https://doi.org/10.48550/arXiv.2303.08774

Paananen V, Oppenlaender J, Visuri A (2023) Using text-to-image generation for architectural design ideation. Int J Archit Comput, 14780771231222783. https://doi.org/10.1177/14780771231222783

Paasi A (2003) Region and place: regional identity in question. Prog Hum Geogr 27(4):475–485

Park C, No W, Choi J, Kim Y (2023) Development of an AI advisor for conceptual land use planning. Cities 138:104371

Peng J, Strijker D, Wu Q (2020) Place identity: how far have we come in exploring its meanings? Front Psychol 11:294

Proshansky HM, Fabian AK, Kaminoff R (1983) Place-identity: Physical world socialization of the self. J Environ Psychol 3(1):57–83

Relph E (1976) Place and placelessness (Vol. 67). Pion London

Sajjad M, Saleem R (2023) Evolution of healthcare with ChatGPT: A Word of Caution. Annals Biomed Eng, 1–2

Schick T, Schütze H (2020) It’s not just size that matters: Small language models are also few-shot learners. ArXiv Preprint ArXiv:2009.07118

Seamon D, Sowers J (2008) Place and placelessness, Edward Relph. Key Texts Hum Geogr 43:51

Seneviratne S, Senanayake D, Rasnayaka S, Vidanaarachchi R, Thompson J (2022) DALLE-URBAN: Capturing the urban design expertise of large text to image transformers. In 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) , IEEE, pp 1–9. https://doi.org/10.1109/DICTA56598.2022.10034603

Shen X, Chen Z, Backes M, Zhang Y (2023) In chatgpt we trust? measuring and characterizing the reliability of chatgpt. ArXiv Preprint ArXiv:2304.08979

Shen Y, Heacock L, Elias J, Hentel KD, Reig B, Shih G, Moy L (2023) ChatGPT and other large language models are double-edged swords. In Radiology (Vol. 307, Issue 2, p. e230163). Radiological Society of North America

Shim C, Santos CA (2014) Tourism, place and placelessness in the phenomenological experience of shopping malls in Seoul. Tour Manag 45:106–114

Stevens J (2002) Applied multivariate statistics for the social sciences (Vol. 4). Mahwah, NJ: Lawrence Erlbaum Associates

Stewart WP, Liebert D, Larkin KW (2004) Community identities as visions for landscape change. Landsc Urban Plan 69(2–3):315–334

Sun Y, Dogan T (2023) Generative methods for Urban design and rapid solution space exploration. Environ Plan B: Urban Analytics City Sci 50(6):1577–1590

Tuan Y-F (1977) Space and place: The perspective of experience . U of Minnesota Press

Tuan YF (1979) Space and place: humanistic perspective. In Philosophy in geography (pp. 387-427). Dordrecht: Springer Netherlands

Turc I, Chang M-W, Lee K, Toutanova K (2019) Well-read students learn better: On the importance of pre-training compact models. ArXiv Preprint ArXiv:1908.08962

Turchi T, Carta S, Ambrosini L, Malizia A (2023) Human-AI Co-creation: Evaluating the Impact of Large-Scale Text-to-Image Generative Models on the Creative Process. International Symposium on End User Development , 35–51

Van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL (2023) ChatGPT: five priorities for research. Nature 614(7947):224–226

Article   ADS   PubMed   Google Scholar  

Wang D, Lu C-T, Fu Y (2023) Towards automated urban planning: When generative and chatgpt-like ai meets urban planning. ArXiv Preprint ArXiv:2304.03892

Wang S, Chen JS (2015) The influence of place identity on perceived tourism impacts. Ann Tour Res 52:16–28

Wang W, Wei F, Dong L, Bao H, Yang N, Zhou M (2020) Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Adv Neural Inf Process Syst 33:5776–5788

Wild J (1965) Existence and the World of Freedom

Yokohari M, Takeuchi K, Watanabe T, Yokota S (2000) Beyond greenbelts and zoning: A new planning concept for the environment of Asian mega-cities. Landsc Urban Plan 47(3–4):159–171

Zhang F, Zhang D, Liu Y, Lin H (2018) Representing place locales using scene elements. Comput Environ Urban Syst 71:153–164

Zhang F, Zhou B, Ratti C, Liu Y (2019) Discovering place-informative scenes and objects using social media photos. R Soc Open Sci 6(3):181375

Article   ADS   PubMed   PubMed Central   Google Scholar  

Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 586–595

Download references

Acknowledgements

The authors would like to thank the members of MIT Senseable City Lab who provided feedback on this project. The authors would like to acknowledge the financial support of open access from MIT Libraries. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.

Author information

Authors and affiliations.

Senseable City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA

Kee Moon Jang, Yuhao Kang, Fabio Duarte & Carlo Ratti

Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA

GISense Lab, Department of Geography and the Environment, The University of Texas at Austin, Austin, TX, USA

Department of Geography, University of South Carolina, Columbia, SC, USA

Department of Geography, Virginia Tech, Blacksburg, VA, USA

Junghwan Kim

Department of Geography and Environment, Western University, London, ON, Canada

Jinhyung Lee

You can also search for this author in PubMed   Google Scholar

Contributions

KJ: Conceptualization, Methodology, Data Curation, Formal analysis, Writing—Original Draft. JC: Methodology, Software, Data Curation, Formal analysis. YK: Conceptualization, Methodology, Data Curation, Writing—Original Draft, Corresponding author. JK: Conceptualization, Resources. JL: Conceptualization, Validation. FD: Conceptualization, Supervision, Funding acquisition, Corresponding author. CR: Resources, Supervision, Funding acquisition. Writing—Review & Editing: all authors.

Corresponding author

Correspondence to Yuhao Kang .

Ethics declarations

Competing interests.

FD was a Collection Guest Editor for this journal at the time of acceptance for publication. The manuscript was assessed in line with the journal’s standard editorial processes, including its policy on competing interests. Other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary file, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Jang, K.M., Chen, J., Kang, Y. et al. Place identity: a generative AI’s perspective. Humanit Soc Sci Commun 11 , 1156 (2024). https://doi.org/10.1057/s41599-024-03645-7

Download citation

Received : 02 January 2024

Accepted : 20 August 2024

Published : 07 September 2024

DOI : https://doi.org/10.1057/s41599-024-03645-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies