qualitative research assumptions examples

Stating the Obvious: Writing Assumptions, Limitations, and Delimitations

During the process of writing your thesis or dissertation, you might suddenly realize that your research has inherent flaws. Don’t worry! Virtually all projects contain restrictions to your research. However, being able to recognize and accurately describe these problems is the difference between a true researcher and a grade-school kid with a science-fair project. Concerns with truthful responding, access to participants, and survey instruments are just a few of examples of restrictions on your research. In the following sections, the differences among delimitations, limitations, and assumptions of a dissertation will be clarified.

Delimitations

Delimitations are the definitions you set as the boundaries of your own thesis or dissertation, so delimitations are in your control. Delimitations are set so that your goals do not become impossibly large to complete. Examples of delimitations include objectives, research questions, variables, theoretical objectives that you have adopted, and populations chosen as targets to study. When you are stating your delimitations, clearly inform readers why you chose this course of study. The answer might simply be that you were curious about the topic and/or wanted to improve standards of a professional field by revealing certain findings. In any case, you should clearly list the other options available and the reasons why you did not choose these options immediately after you list your delimitations. You might have avoided these options for reasons of practicality, interest, or relativity to the study at hand. For example, you might have only studied Hispanic mothers because they have the highest rate of obese babies. Delimitations are often strongly related to your theory and research questions. If you were researching whether there are different parenting styles between unmarried Asian, Caucasian, African American, and Hispanic women, then a delimitation of your study would be the inclusion of only participants with those demographics and the exclusion of participants from other demographics such as men, married women, and all other ethnicities of single women (inclusion and exclusion criteria). A further delimitation might be that you only included closed-ended Likert scale responses in the survey, rather than including additional open-ended responses, which might make some people more willing to take and complete your survey. Remember that delimitations are not good or bad. They are simply a detailed description of the scope of interest for your study as it relates to the research design. Don’t forget to describe the philosophical framework you used throughout your study, which also delimits your study.

Limitations

Limitations of a dissertation are potential weaknesses in your study that are mostly out of your control, given limited funding, choice of research design, statistical model constraints, or other factors. In addition, a limitation is a restriction on your study that cannot be reasonably dismissed and can affect your design and results. Do not worry about limitations because limitations affect virtually all research projects, as well as most things in life. Even when you are going to your favorite restaurant, you are limited by the menu choices. If you went to a restaurant that had a menu that you were craving, you might not receive the service, price, or location that makes you enjoy your favorite restaurant. If you studied participants’ responses to a survey, you might be limited in your abilities to gain the exact type or geographic scope of participants you wanted. The people whom you managed to get to take your survey may not truly be a random sample, which is also a limitation. If you used a common test for data findings, your results are limited by the reliability of the test. If your study was limited to a certain amount of time, your results are affected by the operations of society during that time period (e.g., economy, social trends). It is important for you to remember that limitations of a dissertation are often not something that can be solved by the researcher. Also, remember that whatever limits you also limits other researchers, whether they are the largest medical research companies or consumer habits corporations. Certain kinds of limitations are often associated with the analytical approach you take in your research, too. For example, some qualitative methods like heuristics or phenomenology do not lend themselves well to replicability. Also, most of the commonly used quantitative statistical models can only determine correlation, but not causation.

Assumptions

Assumptions are things that are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis. In other words, any scholar reading your paper will assume that certain aspects of your study is true given your population, statistical test, research design, or other delimitations. For example, if you tell your friend that your favorite restaurant is an Italian place, your friend will assume that you don’t go there for the sushi. It’s assumed that you go there to eat Italian food. Because most assumptions are not discussed in-text, assumptions that are discussed in-text are discussed in the context of the limitations of your study, which is typically in the discussion section. This is important, because both assumptions and limitations affect the inferences you can draw from your study. One of the more common assumptions made in survey research is the assumption of honesty and truthful responses. However, for certain sensitive questions this assumption may be more difficult to accept, in which case it would be described as a limitation of the study. For example, asking people to report their criminal behavior in a survey may not be as reliable as asking people to report their eating habits. It is important to remember that your limitations and assumptions should not contradict one another. For instance, if you state that generalizability is a limitation of your study given that your sample was limited to one city in the United States, then you should not claim generalizability to the United States population as an assumption of your study. Statistical models in quantitative research designs are accompanied with assumptions as well, some more strict than others. These assumptions generally refer to the characteristics of the data, such as distributions, correlational trends, and variable type, just to name a few. Violating these assumptions can lead to drastically invalid results, though this often depends on sample size and other considerations.

Click here to cancel reply.

You must be logged in to post a comment.

Chapter 1. Introduction

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

Why an Open Access Textbook on Qualitative Research Methods?

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

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

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

What is Research?

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

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

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

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

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

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

What is Qualitative Research?

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

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

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

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

Examples of Qualitative Research

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

Example 1 : The End Game (ethnography + interviews)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How Is This Book Organized?

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

Chapter Descriptions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Patton’s Ten Suggestions for Doing Qualitative Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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?

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.

Qualitative research approaches
Approach	What does it involve?
Grounded theory	Researchers collect rich data on a topic of interest and develop theories .
	Researchers immerse themselves in groups or organizations to understand their cultures.
Action research	Researchers and participants collaboratively link theory to practice to drive social change.
Phenomenological research	Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences.
Narrative research	Researchers examine how stories are told to understand how participants perceive and make sense of their experiences.

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.

Prevent plagiarism. Run a free check.

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 data analysis
Approach	When to use	Example
	To describe and categorize common words, phrases, and ideas in qualitative data.	A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps.
	To identify and interpret patterns and themes in qualitative data.	A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity.
	To examine the content, structure, and design of texts.	A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade.
	To study communication and how language is used to achieve effects in specific contexts.	A political scientist could use discourse analysis to study how politicians generate trust in election campaigns.

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 5, 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, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

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

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

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

Qualitative research can be used to:

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

Examples of qualitative research questions include:

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

Characteristics

Naturalistic setting.

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

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

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

Reality is socially constructed

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

The primacy of subject matter

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

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

Variables are complex, interwoven, and difficult to measure

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

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

Emic (insider’s point of view)

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

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

Interpretive analysis

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

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

Collecting Qualitative Data

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

Observations

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

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

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

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

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

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

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

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

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

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

Focus groups

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

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

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

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

Ethnography

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

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

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

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

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

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

Qualitative Data Analysis Methods

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

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

Grounded theory

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

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

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

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

Content analysis

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

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

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

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

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

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

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

Discourse analysis

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

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

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

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

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

Thematic analysis

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

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

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

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

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

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

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

Phases of Thematic Analysis

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

Phase
1. Gather and transcribe data	Gather raw data, for example interviews or focus groups, and transcribe audio recordings fully
2. Familiarization with data	Read and reread all your data from beginning to end; note down initial ideas
3. Create initial codes	Start identifying preliminary codes which highlight important features of the data and may be relevant to the research question
4. Create new codes which encapsulate potential themes	Review initial codes and explore any similarities, differences, or contradictions to uncover underlying themes; create a map to visualize identified themes
5. Take a break then return to the data	Take a break and then return later to review themes
6. Evaluate themes for good fit	Last opportunity for analysis; check themes are supported and saturated with data

Template analysis

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

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

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

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

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

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

Frame analysis

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

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

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

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

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

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

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

Preventing Bias in Qualitative Research

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

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

Co-coding

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

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

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

Member checking

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

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

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

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

Reflexivity in qualitative research

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

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

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

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

Reflexivity Example

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

Establishing Trustworthiness in Qualitative Research

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

1. Credibility in Qualitative Research

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

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

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

2. Transferability in Qualitative Research

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

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

3. Dependability in Qualitative Research

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

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

4. Confirmability in Qualitative Research

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

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

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

Audit Trails in Qualitative Research

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

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

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

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

Discovery of unexpected data

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

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

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

Flexibility

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

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

Naturalistic settings

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

Limitations

Time-consuming .

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

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

Subjectivity

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

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

Limited generalizability

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

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

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

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

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

Extraneous variables

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

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

What sample size should qualitative research be?

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

Are surveys qualitative or quantitative?

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

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

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

What are the ethical considerations of qualitative research?

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

What is triangulation in qualitative research?

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

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

Why is qualitative research important?

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

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

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

What is coding data in qualitative research?

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

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

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

What is the difference between qualitative and quantitative research?

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

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

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

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

What is trustworthiness in qualitative research?

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

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

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

What is data saturation in qualitative research?

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

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

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

Why is sampling in qualitative research important?

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

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

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

What is narrative analysis?

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

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

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

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

Boeije, H. (2014). Analysis in qualitative research. Sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology , 3 (2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Brooks, J., McCluskey, S., Turley, E., & King, N. (2014). The utility of template analysis in qualitative psychology research. Qualitative Research in Psychology , 12 (2), 202–222. https://doi.org/10.1080/14780887.2014.955224

Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological research and practice , 2 (1), 14-14. https://doi.org/10.1186/s42466-020-00059-z

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology nursing forum , 41 (5), 545–547. https://doi.org/10.1188/14.ONF.545-547

Critical Appraisal Skills Programme. (2018). CASP Checklist: 10 questions to help you make sense of a Qualitative research. https://casp-uk.net/images/checklist/documents/CASP-Qualitative-Studies-Checklist/CASP-Qualitative-Checklist-2018_fillable_form.pdf Accessed: March 15 2023

Clarke, V., & Braun, V. (2013). Successful qualitative research: A practical guide for beginners. Successful Qualitative Research , 1-400.

Denny, E., & Weckesser, A. (2022). How to do qualitative research?: Qualitative research methods. BJOG : an international journal of obstetrics and gynaecology , 129 (7), 1166-1167. https://doi.org/10.1111/1471-0528.17150

Glaser, B. G., & Strauss, A. L. (2017). The discovery of grounded theory. The Discovery of Grounded Theory , 1–18. https://doi.org/10.4324/9780203793206-1

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18 (1), 59-82. doi:10.1177/1525822X05279903

Halpren, E. S. (1983). Auditing naturalistic inquiries: The development and application of a model (Unpublished doctoral dissertation). Indiana University, Bloomington.

Hammarberg, K., Kirkman, M., & de Lacey, S. (2016). Qualitative research methods: When to use them and how to judge them. Human Reproduction , 31 (3), 498–501. https://doi.org/10.1093/humrep/dev334

Koch, T. (1994). Establishing rigour in qualitative research: The decision trail. Journal of Advanced Nursing, 19, 976–986. doi:10.1111/ j.1365-2648.1994.tb01177.x

Lincoln, Y., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Mays, N., & Pope, C. (2000). Assessing quality in qualitative research. BMJ, 320(7226), 50–52.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16 (1). https://doi.org/10.1177/1609406917733847

Petty, N. J., Thomson, O. P., & Stew, G. (2012). Ready for a paradigm shift? part 2: Introducing qualitative research methodologies and methods. Manual Therapy , 17 (5), 378–384. https://doi.org/10.1016/j.math.2012.03.004

Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. London: Sage

Reeves, S., Kuper, A., & Hodges, B. D. (2008). Qualitative research methodologies: Ethnography. BMJ , 337 (aug07 3). https://doi.org/10.1136/bmj.a1020

Russell, C. K., & Gregory, D. M. (2003). Evaluation of qualitative research studies. Evidence Based Nursing, 6 (2), 36–40.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: exploring its conceptualization and operationalization. Quality & quantity , 52 (4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8

Scarduzio, J. A. (2017). Emic approach to qualitative research. The International Encyclopedia of Communication Research Methods, 1–2 . https://doi.org/10.1002/9781118901731.iecrm0082

Schreier, M. (2012). Qualitative content analysis in practice / Margrit Schreier.

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22 , 63–75.

Starks, H., & Trinidad, S. B. (2007). Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory. Qualitative health research , 17 (10), 1372–1380. https://doi.org/10.1177/1049732307307031

Tenny, S., Brannan, J. M., & Brannan, G. D. (2022). Qualitative Study. In StatPearls. StatPearls Publishing.

Tobin, G. A., & Begley, C. M. (2004). Methodological rigour within a qualitative framework. Journal of Advanced Nursing, 48, 388–396. doi:10.1111/j.1365-2648.2004.03207.x

Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences , 15 (3), 398-405. https://doi.org/10.1111/nhs.12048

Wood L. A., Kroger R. O. (2000). Doing discourse analysis: Methods for studying action in talk and text. Sage.

Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences. European journal of education , 48 (2), 311-325. https://doi.org/10.1111/ejed.12014

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

5.1 Assumptions underlying research

Learning objectives.

Learners will be able to…

Ground your research project and working question in the philosophical assumptions of social science
Define the terms ‘ ontology ‘ and ‘ epistemology ‘ and explain how they relate to quantitative and qualitative research methods

Pre-awareness check (Knowledge)

Thinking back on your practice experience, what types of things were dependent on a person’s own truth and more subjective? What types of things would you consider irrefutable truths and more objective?

Last chapter, we reviewed the ethical commitment that social work researchers have to protect the people and communities impacted by their research. Answering the practical questions of harm, conflicts of interest, and other ethical issues will provide clear foundation of what you can and cannot do as part of your research project. In this chapter, we will transition from the real world to the conceptual world. Together, we will discover and explore the theoretical and philosophical foundations of your project. You should complete this chapter with a better sense of how theoretical and philosophical concepts help you answer your working question, and in turn, how theory and philosophy will affect the research project you design.

Embrace philosophy

The single biggest barrier to engaging with philosophy of science, at least according to some of my students, is the word philosophy. I had one student who told me that as soon as that word came up, she tuned out because she thought it was above her head. As we discussed in Chapter 1, some students already feel like research methods is too complex of a topic, and asking them to engage with philosophical concepts within research is like asking them to tap dance while wearing ice skates.

For those students, I would first answer that this chapter is my favorite one to write because it was the most impactful for me to learn during my MSW program. Finding my theoretical and philosophical home was important for me to develop as a clinician and a researcher. Following our advice in Chapter 2, you’ve hopefully chosen a topic that is important to your interests as a social work practitioner, and consider this chapter an opportunity to find your personal roots in addition to revising your working question and designing your research study.

Exploring theoretical and philosophical questions will cause your working question and research project to become clearer. Consider this chapter as something similar to getting a nice outfit for a fancy occasion. You have to try on a lot of different theories and philosophies before you find the one that fits with what you’re going for. There’s no right way to try on clothes, and there’s no one right theory or philosophy for your project. You might find a good fit with the first one you’ve tried on, or it might take a few different outfits. You have to find ideas that make sense together because they fit with how you think about your topic and how you should study it.

qualitative research assumptions examples

As you read this section, try to think about which assumptions feel right for your working question and research project. Which assumptions match what you think and believe about your topic? The goal is not to find the “right” answer, but to develop your conceptual understanding of your research topic by finding the right theoretical and philosophical fit.

Theoretical and philosophical fluency

In addition to self-discovery, theoretical and philosophical fluency is a skill that social workers must possess in order to engage in social justice work. That’s because theory and philosophy help sharpen your perceptions of the social world. Just as social workers use empirical data to support their work, they also use theoretical and philosophical foundations. More importantly, theory and philosophy help social workers build heuristics that can help identify the fundamental assumptions at the heart of social conflict and social problems. They alert you to the patterns in the underlying assumptions that different people make and how those assumptions shape their worldview, what they view as true, and what they hope to accomplish. In the next section, we will review feminist and other critical perspectives on research, and they should help inform you of how assumptions about research can reinforce existing oppression.

Understanding these deeper structures is a true gift of social work research. Because we acknowledge the usefulness and truth value of multiple philosophies and worldviews contained in this chapter, we can arrive at a deeper and more nuanced understanding of the social world. Methods can be closely associated with particular worldviews or ideologies. There are necessarily philosophical and theoretical aspects to this, and this can be intimidating at times, but it’s important to critically engage with these questions to improve the quality of research.

A penguin on an ice float. The top of the float is labeled method, next down is methodology, theory, and philosophical foundations.

Building your ice float

Although it may not seem like it right now, your project will develop a from a strong connection to previous theoretical and philosophical ideas about your topic. It’s likely you already have some (perhaps unstated) philosophical or theoretical ideas that undergird your thinking on the topic. Moreover, the philosophical questions we review here should inform how you understand different theories and practice modalities in social work, as they deal with the bedrock questions about science and human knowledge.

Before we can dive into philosophy, we need to recall our conversation from Chapter 1 about objective truth and subjective truths. Let’s test your knowledge with a quick example. Is crime on the rise in the United States? A recent Five Thirty Eight article highlights the disparity between historical trends on crime that are at or near their lowest in the thirty years with broad perceptions by the public that crime is on the rise (Koerth & Thomson-DeVeaux, 2020). [1] Social workers skilled at research can marshal objective facts, much like the authors do, to demonstrate that people’s perceptions are not based on a rational interpretation of the world. Of course, that is not where our work ends. Subjective facts might seek to decenter this narrative of ever-increasing crime, deconstruct is racist and oppressive origins, or simply document how that narrative shapes how individuals and communities conceptualize their world.

Objective does not mean right, and subjective does not mean wrong. Researchers must understand what kind of truth they are searching for so they can choose a theory(ies), develop a theoretical framework (in quantitative research), select an appropriate methodology, and make sure the research question(s) matches them all. As we discussed in Chapter 1, researchers seeking objective truth (one of the philosophical foundations at the bottom of Figure 5.1) often employ quantitative methods (one of the methods at the top of Figure 5.1). Similarly, researchers seeking subjective truths (again, at the bottom of Figure 5.1) often employ qualitative methods (at the top of Figure 5.1). This chapter is about the connective tissue, and by the time you are done reading, you should have a first draft of a theoretical and philosophical (a.k.a. paradigmatic) framework for your study.

Ontology: Assumptions about what is real and true

In section 1.2, we reviewed the two types of truth that social work researchers seek— objective truth and subjective truths —and linked these with the methods—quantitative and qualitative—that researchers use to study the world. If those ideas aren’t fresh in your mind, you may want to navigate back to that section for an introduction.

These two types of truth rely on different assumptions about what is real in the social world—i.e., they have a different ontology . Ontology refers to the study of being (literally, it means “rational discourse about being”). In philosophy, basic questions about existence are typically posed as ontological, e.g.:

What is there?
What types of things are there?
How can we describe existence?
What kind of categories can things go into?
Are the categories of existence hierarchical?

Objective vs. subjective ontologies

At first, it may seem silly to question whether the phenomena we encounter in the social world are real. Of course you exist, your thoughts exist, your computer exists, and your friends exist. You can see them with your eyes. This is the ontological framework of realism , which simply means that the concepts we talk about in science exist independent of observation (Burrell & Morgan, 1979). [2] Obviously, when we close our eyes, the universe does not disappear. You may be familiar with the philosophical conundrum: “If a tree falls in a forest and no one is around to hear it, does it make a sound?”

The natural sciences, like physics and biology, also generally rely on the assumption of realism. Lone trees falling make a sound. We assume that gravity and the rest of physics are there, even when no one is there to observe them. Mitochondria are easy to spot with a powerful microscope, and we can observe and theorize about their function in a cell. The gravitational force is invisible, but clearly apparent from observable facts, such as watching an apple fall from a tree. Of course, our theories about gravity have changed over the years. Improvements were made when observations could not be correctly explained using existing theories and new theories emerged that provided a better explanation of the data.

As we discussed in section 1.2, culture-bound syndromes are an excellent example of where you might come to question realism. Of course, from a Western perspective as researchers in the United States, we think that the Diagnostic and Statistical Manual (DSM) classification of mental health disorders is real and that these culture-bound syndromes are aberrations from the norm. But what about if you were a person from Korea experiencing Hwabyeong? Wouldn’t you consider the Western diagnosis of somatization disorder to be incorrect or incomplete? This conflict raises the question–do either Hwabyeong or DSM diagnoses like post-traumatic stress disorder (PTSD) really exist at all…or are they just social constructs that only exist in our minds?

If your answer is “no, they do not exist,” you are adopting the ontology of anti-realism ( or relativism ), or the idea that social concepts do not exist outside of human thought. Unlike the realists who seek a single, universal truth, the anti-realists perceive a sea of truths, created and shared within a social and cultural context. Unlike objective truth, which is true for all, subjective truths will vary based on who you are observing and the context in which you are observing them. The beliefs, opinions, and preferences of people are actually truths that social scientists measure and describe. Additionally, subjective truths do not exist independent of human observation because they are the product of the human mind. We negotiate what is true in the social world through language, arriving at a consensus and engaging in debate within our socio-cultural context.

These theoretical assumptions should sound familiar if you’ve studied social constructivism or symbolic interactionism in MSW courses, most likely in human behavior in the social environment (HBSE). [3] From an anti-realist perspective, what distinguishes the social sciences from natural sciences is human thought. When we try to conceptualize trauma from an anti-realist perspective, we must pay attention to the feelings, opinions, and stories in people’s minds. In their most radical formulations, anti-realists propose that these feelings and stories are all that truly exist.

What happens when a situation is incorrectly interpreted? Certainly, who is correct about what is a bit subjective. It depends on who you ask. Even if you can determine whether a person is actually incorrect, they think they are right. Thus, what may not be objectively true for everyone is nevertheless true to the individual interpreting the situation. Furthermore, they act on the assumption that they are right. We all do. Much of our behaviors and interactions are a manifestation of our personal subjective truth. In this sense, even incorrect interpretations are truths, even though they are true only to one person or a group of misinformed people. This leads us to question whether the social concepts we think about really exist. For researchers using subjective ontologies, they might only exist in our minds; whereas, researchers who use objective ontologies which assume these concepts exist independent of thought.

How do we resolve this dichotomy? As social workers, we know that often times what appears to be an either/or situation is actually a both/and situation. Let’s take the example of trauma. There is clearly an objective thing called trauma. We can draw out objective facts about trauma and how it interacts with other concepts in the social world such as family relationships and mental health. However, that understanding is always bound within a specific cultural and historical context. Moreover, each person’s individual experience and conceptualization of trauma is also true. Much like a client who tells you their truth through their stories and reflections, when a participant in a research study tells you what their trauma means to them, it is real even though only they experience and know it that way. By using both objective and subjective analytic lenses, we can explore different aspects of trauma—what it means to everyone, always, everywhere, and what is means to one person or group of people, in a specific place and time.

Epistemology: Assumptions about how we know things

Having discussed what is true, we can proceed to the next natural question—how can we come to know what is real and true? This is epistemology . Epistemology is derived from the Ancient Greek epistēmē which refers to systematic or reliable knowledge (as opposed to doxa, or “belief”). Basically, it means “rational discourse about knowledge,” and the focus is the study of knowledge and methods used to generate knowledge. Epistemology has a history as long as philosophy, and lies at the foundation of both scientific and philosophical knowledge.

Epistemological questions include:

What is knowledge?
How can we claim to know anything at all?
What does it mean to know something?
What makes a belief justified?
What is the relationship between the knower and what can be known?

While these philosophical questions can seem far removed from real-world interaction, thinking about these kinds of questions in the context of research helps you target your inquiry by informing your methods and helping you revise your working question. Epistemology is closely connected to method as they are both concerned with how to create and validate knowledge. Research methods are essentially epistemologies – by following a certain process we support our claim to know about the things we have been researching. Inappropriate or poorly followed methods can undermine claims to have produced new knowledge or discovered a new truth. This can have implications for future studies that build on the data and/or conceptual framework used.

Research methods can be thought of as essentially stripped down, purpose-specific epistemologies. The knowledge claims that underlie the results of surveys, focus groups, and other common research designs ultimately rest on epistemological assumptions of their methods. Focus groups and other qualitative methods usually rely on subjective epistemological (and ontological) assumptions. Surveys and other quantitative methods usually rely on objective epistemological assumptions. These epistemological assumptions often entail congruent subjective or objective ontological assumptions about the ultimate questions about reality.

Objective vs. subjective epistemologies

One key consideration here is the status of ‘truth’ within a particular epistemology or research method. If, for instance, some approaches emphasize subjective knowledge and deny the possibility of an objective truth, what does this mean for choosing a research method?

We began to answer this question in Chapter 1 when we described the scientific method and objective and subjective truths. Epistemological subjectivism focuses on what people think and feel about a situation, while epistemological objectivism focuses on objective facts irrelevant to our interpretation of a situation (Lin, 2015). [4]

While there are many important questions about epistemology to ask (e.g., “How can I be sure of what I know?” or “What can I not know?” see Willis, 2007 [5] for more), from a pragmatic perspective most relevant epistemological question in the social sciences is whether truth is better accessed using numerical data or words and performances. Generally, scientists approaching research with an objective epistemology (and realist ontology) will use quantitative methods to arrive at scientific truth. Quantitative methods examine numerical data to precisely describe and predict elements of the social world. For example, while people can have different definitions for poverty, an objective measurement such as an annual income of “less than $25,100 for a family of four” provides a precise measurement that can be compared to incomes from all other people in any society from any time period, and refers to real quantities of money that exist in the world. Mathematical relationships are uniquely useful in that they allow comparisons across individuals as well as time and space. In this book, we will review the most common designs used in quantitative research: surveys and experiments. These types of studies usually rely on the epistemological assumption that mathematics can represent the phenomena and relationships we observe in the social world.

Although mathematical relationships are useful, they are limited in what they can tell you. While you can use quantitative methods to measure individuals’ experiences and thought processes, you will miss the story behind the numbers. To analyze stories scientifically, we need to examine their expression in interviews, journal entries, performances, and other cultural artifacts using qualitative methods . Because social science studies human interaction and the reality we all create and share in our heads, subjectivists focus on language and other ways we communicate our inner experience. Qualitative methods allow us to scientifically investigate language and other forms of expression—to pursue research questions that explore the words people write and speak. This is consistent with epistemological subjectivism’s focus on individual and shared experiences, interpretations, and stories.

It is important to note that qualitative methods are entirely compatible with seeking objective truth. Approaching qualitative analysis with a more objective perspective, we look simply at what was said and examine its surface-level meaning. If a person says they brought their kids to school that day, then that is what is true. A researcher seeking subjective truth may focus on how the person says the words—their tone of voice, facial expressions, metaphors, and so forth. By focusing on these things, the researcher can understand what it meant to the person to say they dropped their kids off at school. Perhaps in describing dropping their children off at school, the person thought of their parents doing the same thing. In this way, subjective truths are deeper, more personalized, and difficult to generalize.

Putting it all together

As you might guess by the structure of the next two parts of this textbook, the distinction between quantitative and qualitative is important. Because of the distinct philosophical assumptions of objectivity and subjectivity, it will inform how you define the concepts in your research question, how you measure them, and how you gather and interpret your raw data. You certainly do not need to have a final answer right now! But stop for a minute and think about which approach feels right so far. In the next section, we will consider another set of philosophical assumptions that relate to ethics and the role of research in achieving social justice.

Key Takeaways

Philosophers of science disagree on the basic tenets of what is true and how we come to know what is true.
Researchers searching for objective truth will likely have a different research design, and methods than researchers searching for subjective truths.
These differences are due to different assumptions about what is real and true (ontology) and how we can come to understand what is real and true (epistemology).

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Does an objective or subjective epistemological/ontological framework make the most sense for your research project?

Are you more concerned with how people think and feel about your topic, their subjective truths—more specific to the time and place of your project?
Or are you more concerned with objective truth, so that your results might generalize to populations beyond the ones in your study?

Using your answer to the above question, describe how either quantitative or qualitative methods make the most sense for your project.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

You are interested in researching bullying among school-aged children, and how this impacts students’ academic success.

If you are using an objective epistemological/ontological framework, what types of research questions might you ask?
If you are using a subjective epistemological/ontological framework, what types of research questions might you ask?
Koerth, M. & Thomson-DeVeaux, A. (2020, August 3). Many Americans are convinced crime is rising in the U.S. They're wrong. FiveThirtyEight . Retrieved from: https://fivethirtyeight.com/features/many-americans-are-convinced-crime-is-rising-in-the-u-s-theyre-wrong ↵
Burrell, G. & Morgan, G. (1979). Sociological paradigms and organizational analysis . Routledge. ↵
Here are links to two HBSE open textbooks, if you are unfamiliar with social work theories. https://uark.pressbooks.pub/hbse1/ and https://uark.pressbooks.pub/humanbehaviorandthesocialenvironment2/ ↵
Lin, C. T. (2016). A critique of epistemic subjectivity. Philosophia, 44 (3), 915-920. ↵
Wills, J. W. (2007). World views, paradigms and the practice of social science research. Thousand Oaks, CA: Sage. ↵

a single truth, observed without bias, that is universally applicable

one truth among many, bound within a social and cultural context

assumptions about what is real and true

assumptions about how we come to know what is real and true

quantitative methods examine numerical data to precisely describe and predict elements of the social world

qualitative methods interpret language and behavior to understand the world from the perspectives of other people

Share This Book

Search MERLOT Discipline
MERLOT Discipline Navigation
Main Navigation
Join MERLOT/
Login to MERLOT
Career Talk
Faculty Life: Teaching
Faculty Life: Research
Faculty Life: Service
About CDIP Commons
Sharing Ideas: MERLOT

Qualitative research

Overview of qualitative research Entire books are written about qualitative research methods, and whole courses are dedicated to studying various characteristics, elements and procedures used. This section is meant as a very brief overview of some of these elements for the purpose of beginning a conversation, thinking about ways to conduct your research, and interacting with colleagues about your perspectives. It may be helpful to begin with some of the assumptions behind qualitative research to get the dialogue under way.

Assumptions underlying qualitative methods Some general assumptions are characteristic of qualitative research:

multiple realities exist in any study -- the researcher’s, those of the individuals being investigated, and the reader or audience interpreting the results;
multiple perspectives including voices of informants are included in the study;
researchers interact with those under study and actively work to minimize the distance between the researcher and those being researched;
researchers explicitly recognize and acknowledge the value-laden nature of the research;
research is context-bound;
research is based on inductive forms of logic;
categories of interest may emerge from informants (internal) or be used to frame the understanding (external);
uncovering patterns or theories that help explain a phenomenon of interest is the goal;
determining accuracy involves verifying the information with informants or "triangulating" among different sources of information.

Types of qualitative methods A qualitative "approach" is a general way of thinking about conducting qualitative research. It describes, either explicitly or implicitly, the purpose of the qualitative research, the role of the researcher(s), the stages of research, and the method of data analysis. Several approaches generally are considered when undertaking qualitative research.

Case studies In a case study the researcher explores a single entity or phenomenon (‘the case’) bounded by time and activity (e.g., a program, event, institution, or social group) and collects detailed information through a variety of data sources. The case study is a descriptive record of an individual's experiences and/or behaviors kept by an outside observer.

Ethnographic studies The ethnographic approach to qualitative research comes largely from the field of anthropology. Originally, the idea of a culture was tied to the notion of ethnicity and geographic location, but it has been broadened to include virtually any group or organization. In ethnographic research the researcher studies an intact cultural group in a natural setting over a specific period of time. A cultural group can be any group of individuals who share a common social experience, location, or other social characteristic of interest. Ethnography is an extremely broad area with a great variety of practitioners and methods. However, the most common ethnographic approach is participant observation as a part of field research. Typically the ethnographer becomes immersed in the culture as an active participant and records extensive field notes.

Phenomenological studies Phenomenology is sometimes considered a philosophical perspective as well as an approach to qualitative methodology. It has a long history in several social research disciplines including psychology, sociology and social work. Phenomenology is a school of thought that emphasizes a focus on people's subjective experiences and interpretations of the world. That is, the phenomenologist wants to understand how the world appears to others. In a phenomenological study, human experiences are examined through the detailed description of the people being studied -- the goal is to understand the ‘lived experience’ of the individuals being studied. This approach involves researching a small group of people intensively over a long period of time.

Grounded theory Grounded theory is a qualitative research approach that was originally developed by Glaser and Strauss in the 1960s. The self-defined purpose of grounded theory is to develop theory about phenomena of interest. This is not just abstract theorizing, instead the theory needs to be grounded or rooted in observation -- hence the term. Grounded theory is a complex iterative process. The research begins with the raising of generative questions that help to guide the research but are not intended to be either static or confining. As the researcher begins to gather data, core theoretical concept(s) are identified. Tentative linkages are developed between the theoretical core concepts and the data. This early phase of the research tends to be very open and can take months. Later on the researcher is more engaged in verification and summary. The effort tends to evolve toward one core category that is central.

Field research Field research can also be considered either a broad approach to qualitative research or a method of gathering qualitative data. The essential idea is that the researcher goes "into the field" to observe the phenomenon in its natural state. As such, it is probably most related to the method of participant observation. The field researcher typically takes extensive field notes that are subsequently coded and analyzed in a variety of ways.

Begin the dialogue These categories are divided and described in any number of ways by different qualitative researchers. However described, the information here is designed to start a dialogue with colleagues about what qualitative method might best help understand the phenomenon that faculty members undertake to study.

Qualitative Research Design

The Sage Handbook of Qualitative Research Denzin, N.K. & Lincoln, Y. (2005). 3rd Ed., Sage Publications, Inc. This book represents the state of the art in theory and practice of qualitative inquiry. In this edition the editors and authors ask how such inquiry can be used to address issues of social justice.

Intensive Qualitative Research: Challenges, Best Uses, and Opportunities MDRC (Manpower Demonstration Research Corporation) (2003) created in 1974 by the Ford Foundation, mounts large scale evaluations of real world policies and programs targeted to low income people This paper examines the use of ethnographic interviews, focus groups, and other intensive qualitative methods in social policy research.

Challenges of Qualitative Research

Doing Sensitive Research: What Challenges Do Qualitative Researchers Face? Dickson-Swift, V. (2007). Qualitative Social Work, Vol. 7, No. 3, p. 327-353 Much of health research involves face-to-face encounters with participants. There is a growing recognition that this process can pose difficulties for researchers. This paper explores those issues through interviews with 30 qualitative health researchers.

The Veteran's Affairs Experience: Comparative Effectiveness Research in a Large Health System David Atkins, Joel Kupersmith, Seth Eisen This article explores some of the challenges of qualitative methods in management research.

Sister-to-Sister Talk: Transcending Boundaries and Challenges in Qualitative Research with Black Women Few, A.L., Stephens, D.P. & Rouse-Arnett, M. (November, 2001). Annual conference of the National Council of Family Relations, Rochester, New York This paper discusses the challenges that Black women face when doing qualitative research with Black women.

When Black + Lesbian + Woman Does Not Equal Black Lesbian Woman: The Methodological Challenges of Qualitative and Quantitative Intersectionality Research Bowleg, L. (2008). Sex Roles, Vol. 59, Iss. 5-6, p. 312-325 The notion that social identities and social inequality based on ethnicity, sexual orientation, and sex/gender are intersectional rather than additive poses a variety of thorny methodological challenges. This paper examines how these challenges shape measurement, analysis, and inter-pretation.

Challenges and Strategies for Conducting Survey and Focus Group Research with Culturally Diverse Groups Huer, M.B. & Saenz, T.I. (May, 2003). American Journal of Speech-Language Pathology, Vol. 12, p. 209-220 These California State University Fullerton faculty offer tentative guidance and invite an open dialogue about culturally sensitive research issues.

Home > JOURNALS > PIL > Vol. 2 > No. 1 (2017)

Assumptions of Qualitative Research Methods

Craig H. Walters

Many characteristics make up the collective term of qualitative research, Methods used specify a certain framework in which researchers operate, Within this framework, there are two major assumptions in which I believe take precedence, Inductive reasoning and topic specificity are the two characteristics that I believe define qualitative research, Inductive reasoning is the epitome of qualitative research design. Researchers seek information out of the data that is gathered; they do not proceed to a project looking for specific findings, Maxwell (1996) states that qualitative research allows researchers to examine explanations and data - not to prove a self-fulfilling prophecy, Inductive reasoning looks for meaning from within the subjects, Researchers have historically attempted to objectify the subjective state of the their subjects, Instead, using an inductive approach, they can genuinely learn from their subjects. They are empathetic and reflective. Their job is to learn how their subjects think (Biklen & Bogdan, 1998).

Author's Biographies

Craig H. Walters is a beginning researcher who is currently completing his doctoral degree in Educational Leadership in a joint program offered by Columbus State University / Valdosta State University. Craig is a licensed, accredited speech pathologist employed by the Sumter County School System. His current research interests are in the biographical method of qualitative research. Perspectives In

This is an original work

This work has not been previously published, irb approval verification, recommended citation.

Walters, C. H. (2001). Assumptions of Qualitative Research Methods. Perspectives In Learning, 2 (1). Retrieved from https://csuepress.columbusstate.edu/pil/vol2/iss1/14

Since September 27, 2017

Included in

Curriculum and Instruction Commons , Online and Distance Education Commons , Scholarship of Teaching and Learning Commons , Teacher Education and Professional Development Commons

Journal Home
About This Journal
Editorial Board
Submit Article
Most Popular Papers
Receive Email Notices or RSS

Advanced Search

ISSN: 2476-0625

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

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.

Building on and contributing to sustainability transitions research with qualitative system dynamics

Original Article
Open access
Published: 29 August 2024

Cite this article

You have full access to this open access article

Vincent de Gooyert ORCID: orcid.org/0000-0003-2485-6412 1 ,
Attabik Awan ORCID: orcid.org/0009-0001-4369-9427 2 ,
Cem Gürsan ORCID: orcid.org/0000-0003-2327-2458 1 ,
Floris Swennenhuis ORCID: orcid.org/0000-0003-2432-4360 1 , 3 ,
Zahra Janipour 1 , 3 &
Sara Gonella ORCID: orcid.org/0009-0004-2692-5519 1

This paper explores the novel connection between qualitative system dynamics and sustainability transitions research. As the urgency for sustainable solutions intensifies, this interdisciplinary combination offers a promising avenue for addressing complex sustainability challenges. We reflect on recent research projects to establish the value of combining the two fields. We delve into the methodological and theoretical synergies, using examples to illustrate how the two fields can mutually benefit from each other. We find that qualitative system dynamics complements other sustainability transitions research approaches by encouraging (a) more inductive research that results in a broader system boundary than traditional sustainability transitions research frameworks and (b) higher endogeneity, which leads to a better appreciation of the feedback mechanisms that determine whether transitions succeed or not. This leads to an explicit reflection on assumptions that otherwise might remain hidden, and more explicit conceptualizations of the feedback mechanisms driving and hindering sustainability transitions and recommendations on navigating seemingly opposing interests that diminish when seeing the whole system. We also propose how future research can contribute to further cross-fertilization between the two fields, including the need for explicit positioning in terms of starting points, considering different philosophical paradigms, exploring combinations with other analytical approaches to foster change, and increasing reflection on the part of researchers, particularly in participative modes. We argue that the fusion of qualitative system dynamics with sustainability transitions research can significantly enhance our understanding and ability to manage complex sustainability issues, substantially contributing to both academic discourse and practical applications in sustainability transitions.

An actionable understanding of societal transitions: the X-curve framework

Conducting sustainability research in the anthropocene: toward a relational approach

Coevolving ostrom’s social–ecological systems (ses) framework and sustainability science: four key co-benefits, explore related subjects.

Artificial Intelligence

Avoid common mistakes on your manuscript.

Introduction

Addressing current challenges, such as climate change, biodiversity loss, and poverty, requires a substantial change in all facets of our society and economy. All our systems need to change, from industry, housing, and mobility to electricity and agriculture. With the increasing urgency of these challenges, a new field of research has emerged that is now known under the label "sustainability transitions research” (Loorbach et al. 2017 ; Köhler et al. 2019 ). Although the field is very diverse, a main commonality shared by all scholars is that they consider sustainability to be a matter not just of individual technologies and innovation but also of how those technologies co-evolve with cultural, behavioral and other social aspects in broader socio-technical systems (Markard et al. 2012 ). Scholars in this field argue that the required system-level changes are to be achieved through fundamental transformation processes or transitions (Markard et al. 2012 ; van den Bergh et al. 2011 ). They strive to make sense of the dynamics of these transitions by starting from describing empirical cases, treating human behavior as a complex object of study, and going beyond traditional models of humans as rational actors (Geels et al. 2016 ), often ending with policy recommendations to augment transitional processes. According to the field of sustainability transitions research, the required transitions are complex because they involve various interdependent actors and are influenced by nonlinear interactions between technical and social elements at multiple levels. The conceptual models and frameworks to understand sustainability transitions acknowledge this dynamic complexity and have been considerably developed (Bergek et al. 2007 ; Geels 2002 ; Kemp et al. 2007 ; Rotmans et al. 2001 ).

System dynamics is a methodology developed to understand and manage complex systems through the modelling and simulation of causal structures and feedback mechanisms (Sterman 2000 ), and qualitative system dynamics uses the same feedback thinking and diagrams but then without computer simulation. System dynamics is suitable for understanding the multiple and interacting dynamic processes in sustainability transitions research (Moallemi et al. 2017a , b ; Papachristos 2018 , 2019 ). However, the combination of qualitative system dynamics and sustainability transitions research is still nascent. Several studies have connected system dynamics to the field of transitions modeling (e.g., Halbe et al. 2020 ; Holtz et al. 2015 ; Köhler et al. 2018 ; Leventon et al. 2021 ; Moallemi and de Haan 2019 ; Papachristos 2018 , 2019 ; Purvis et al. 2022 ), and transition frameworks (e.g., Papachristos 2011 ; Walrave and Raven 2016 ; Yücel and Chiong Meza 2008 ). However, these studies often either focus on quantitative system dynamics that revolves around computer simulation or they do not distinguish between qualitative and quantitative system dynamics, resulting in an underappreciation of links between sustainability transitions research and qualitative system dynamics. There is a vast debate on the value and limitations of computer simulation modelling for sustainability transitions research, including system dynamics simulation, and how it complements, integrates, and interacts with other approaches (Geels et al. 2016 ; Holtz 2011 ; Köhler et al. 2018 ; McDowall et al. 2017 ; Moallemi et al. 2017a , b , 2021 ; Moallemi and Malekpour 2018 ; Papachristos 2011 , 2014 , 2018 , 2019 ; Trutnevyte et al. 2014 ; Turnheim et al. 2015 ). Our study is different in that we strictly focus on qualitative applications of system dynamics, which hitherto has remained a gap in the literature. Through filling this gap, we contribute to a closer connection between qualitative system dynamics and sustainability transitions research to help future qualitative system dynamicists studying sustainability transitions navigate challenges and opportunities. We build on experiences of several cases where we applied qualitative system dynamics in the context of sustainability transitions (Awan 2020 ; de Gooyert et al. 2024 ; Janipour et al. 2022 ; Gonella 2021 ; Gonella and de Gooyert 2024 ; Gürsan and de Gooyert 2021 ; Gürsan et al. 2024 ; Janipour et al. 2021 ; Swennenhuis et al. 2024 ).

The article is organized as follows. We first provide a background on sustainability transitions research and qualitative system dynamics. Then, we present a section discussing the benefits of combining qualitative system dynamics and sustainability transitions research, followed by a conclusion that summarizes the main benefits in Table 1 . This is followed by a discussion linking our findings to broader debates, limitations, and future avenues for further cross-fertilization between qualitative system dynamics and sustainability transitions research.

Background sustainability transitions research and qualitative system dynamics

Before we embark on the connection between sustainability transitions research and qualitative system dynamics, we first provide background on both below.

Sustainability transitions research

Over the last half-century, sustainability transitions have become an influential paradigm for describing and studying past transitions, and projecting and examining possible future scenarios (Markard et al. 2012 ; van den Bergh et al. 2011 ). Today, the world is facing sustainability challenges in various domains. For example, the current energy system is unsustainable due to increased greenhouse gas emissions, air pollution, energy poverty, shortage of natural resources, and uncertainties over energy security in the short- and long-run. Supply systems, such as the energy sector, can be conceptualized as socio-technical systems consisting of networks of actors, institutions, materials, and knowledge. To supply services to society, different interrelated components of the system interact and co-evolve with institutions (Markard et al. 2012 ). To deal with sustainability challenges, a fundamental shift in the existing socio-technical systems needs to occur. Such transition requires substantial changes in society's technological, organizational, political, institutional, economic, and socio-cultural dimensions (Geels and Schot 2010 ). The complexity and versatility of sustainability transitions require the involvement of stakeholders (Leal Filho and Londero Brandli 2016 ), who may not share the same understanding of the problem definition and subsequent sets of actions to create sustainable socio-technical systems that do not exist yet.

Historically, sustainability transitions research emerged as an interdisciplinary subject both empirically and intellectually. On a pragmatic level, it connects the worlds of science and policy to address grand societal problems that are "unstructured" (Loorbach et al. 2017 ), "wicked" (Hazard et al. 2020 ), "tangled" (van Assche et al. 2022 ) and "messy" (Kivimaa et al. 2021 ). On an intellectual level, it can be traced back to the two significant clusters of innovation research (including science and technology studies, history of technology, evolutionary economics, and innovation policy) and environmental studies (including environmental assessment, integrated assessment, sustainability governance, and environmental policy (Arthur 2016 ; Kemp 1994 ; O’Riordan 2001 ; Rip and Kemp 1998 ; Rotmans 1998 ; van Asselt and Rotmans 1996 ; van den Bergh and Gowdy 2000 ). Thus, sustainability transitions research got its footing from the need to study the dynamics of grand societal challenges and to steer the formulation of systemic solutions for them.

Two main approaches to studying sustainability transitions are the Multilevel Perspective (MLP) and the Technological Innovation System (TIS) approach. The MLP analyses reality from three levels: regime, niches, and landscape (Geels 2002 , 2006 ; Geels and Schot 2007 ). Transitions in the MLP are characterized by the destabilization of the socio-technical regime via interactions at all three levels (Geels and Schot 2007 ). These could be the innovations happening at the niche level, e.g., new technologies or new ways of working, the pressures applied by events at the landscape level, e.g., war or internal tensions in the regime that create the window of opportunity for the niche to emerge, e.g., changing preferences of actors towards fossil-fuels. Similarly, another dominant approach in transition studies is the Technological Innovation Systems (TIS), where the focus is on the generation, diffusion, and utilization of new technologies or new fields of knowledge (Bergek 2002 ; Bergek et al. 2007 ; Hekkert et al. 2007 ; Suurs and Hekkert 2012 ). The generation, diffusion, and utilization of emerging technology in a TIS are determined by the existing system structure (static elements) and its innovation functions or 'motors of innovation' (dynamic elements) (Suurs and Hekkert 2012 ). A TIS might be contained within a sector (where the analysis is focused on a specific innovative technology, e.g., solar PV) or cross-sectoral (where the focus is on an entire technological field, e.g., renewable energy technologies). Transitions are accelerated when the visions and activities of different actor groups align within the system, and they are completed when the existing system (existing TIS) is replaced by the emerging TIS (Bergek et al. 2007 ).

Qualitative system dynamics

System dynamics is a broad approach that combines various elements, namely feedback thinking, structure, levels and rates, modeling and simulation, and policy design (System Dynamics Society n.d.). However, not all applications of system dynamics haven an equal emphasis on all elements of system dynamics (de Gooyert and Größler 2018 ). Indeed, some studies focus on the feedback thinking, structure, and policy aspects of system dynamics and do not include computer simulation in any way. In this article, we use the term qualitative system dynamics to refer to system dynamics without simulation: the adoption of all elements typically associated with system dynamics, except for computer simulation. Such studies typically use system dynamics tools like causal loop diagrams and/or stock and flow diagrams but do not provide equations and simulation runs, for example, because the study is more aimed at exploring and mapping the structure of complex issues and causal relationships between main variables and fostering shared problem definitions and a shared language to allow coordinated action across stakeholders, rather than prioritizing policies per se. Some applications of qualitative system dynamics include the participation of groups of stakeholders and/or experts, for example, using group model building (Vennix 1996 ), but for us, qualitative system dynamics does not necessarily include groups and can also rely for example on other primary qualitative data (interviews), or secondary qualitative data (any text including scientific articles, gray literature, reports, minutes of meetings, and so on).

Other terms can also refer to what we mean by qualitative system dynamics, including labels like “systems thinking” and “soft operational research” (Forrester 1994 ). However, we prefer using qualitative system dynamics because those other terms are broader and include elements not typically adopted in system dynamics. Hence, using those other terms would dilute the importance of feedback thinking, structure, and policy aspects that we find so important in the studies where we apply qualitative system dynamics. Earlier studies have already discussed how qualitative and quantitative system dynamics complement each other and how each has distinct characteristics and contributions (Coyle 2000 ; Forrester 1994 ; Richardson 2011 ; Richmond 1994 ; Wolstenholme 1999 ). In line with those articles, we acknowledge that qualitative system dynamics has limitations, as does quantitative system dynamics or any other research approach. If based on intuition alone, causal loop diagrams and stock and flow diagrams can very well lead to misleading takeaways, and it is clear that system behavior cannot be inferred from any but the simplest qualitative models (Forrester 1994 ). However, computer simulations can be misleading as well and give a false sense of accuracy, for example, if the quantitative data used in those simulations is not of high quality and the interpretation of those numbers can be misleading if the definitions used when collecting data oversimplify the rich complexity of concepts that constitute the problem at hand. We conclude from those earlier studies that different approaches all have their strengths and weaknesses. Our aim in this article is to discuss the value of adopting qualitative system dynamics in the context of sustainability transitions research.

Benefits of combining qualitative system dynamics and sustainability transitions research

Based on our experiences in recent research projects (Awan 2020 ; de Gooyert et al. 2024 ; Janipour et al. 2022 ; Gonella 2021 ; Gonella and de Gooyert 2024 ; Gürsan and de Gooyert 2021 ; Gürsan et al. 2024 ; Janipour et al. 2021 ; Swennenhuis et al. 2024 ), we see three main benefits of combining qualitative system dynamics and sustainability transitions research: adopting qualitative system dynamics encourages broadening the system boundary compared to more traditional sustainability transitions research, it encourages a higher degree of endogeneity and sustainability transitions research offers a rich collection of frameworks and theories that qualitative system dynamics can draw from. Below, we elaborate on these benefits using illustrations from our research, and we discuss the implications of these differences for research and policy recommendations.

Qualitative system dynamics encourages a broader system boundary

We find that qualitative system dynamics encourages adopting a broader system boundary compared with most sustainability transitions research, leading to more holistic approaches. Sustainability transitions research is typically organized around theoretical domains, including political science, geography, business administration, and so on (Luederitz et al. 2017 ; Köhler et al. 2019 ). Although sustainability transitions research is inherently interdisciplinary, the strong emphasis on theoretical advancements often leads to a focus that is confined to a small number of disciplines or that emphasizes one central discipline with other disciplines playing only a peripheral role, e.g., focusing on politics (Avelino et al. 2016 ), geography (Truffer et al. 2015 ), or business administration (Loorbach and Wijsman 2013 ). Sustainability transitions research strongly emphasizes theoretical contributions, requiring researchers to be very clear on the discipline to which they contribute. This is understandable from the way that academia is organized, with research institutes, journals, and careers primarily organized around disciplines, but this also led to critical reflections that sustainability transitions research should adopt more holistic, integrated perspectives to better fit the messy and interdisciplinary nature of sustainability transitions (Andersen et al. 2020 ; Luederitz et al. 2017 ; Rosenbloom 2020 ).

Qualitative system dynamics typically starts from a central problem variable, a reference mode of behavior of that problem, continued by mapping all relevant variables to understand the behavior of that problem (Vennix 1996 ). This encourages those applying the method to not confine themselves to variables associated with a certain discipline. This holds for researchers when qualitative system dynamics is applied in a non-participatory mode and for participants when applied in a participative mode (Moallemi et al. 2021 ; Moallemi and Malekpour 2018 ). It resembles a best practice amongst system dynamicists that in the community is considered of the highest importance when it comes to system conceptualization: “approach system conceptualization creatively, from different perspectives” (Martinez-Moyano and Richardson 2013 , p. 112). This characteristic of qualitative system dynamics makes the approach more inductive: the analysis starts from the problem and works its way up. Although sustainability transitions research often shares the ambition to inductively start from empirical cases to then involve the disciplines that the case requires, the focus on theoretical advancements sometimes leads studies to start from a theoretical body of knowledge, using a problem as a case to further advance knowledge in that theoretical domain. Although qualitative system dynamics can also be applied in a purely deductive fashion (de Gooyert 2019 ), and sustainability transitions research can also be applied in an inductive or retroductive fashion (McDowall and Geels 2017 ; Papachristos and Adamides 2016 ), the way that qualitative system dynamics projects are typically organized in general leads to more holistic and integrated results. We see this back in our research projects, where results typically are not associated with a single or a primary discipline. For example, de Gooyert et al. ( 2016 ) study the progress of the Dutch energy transition and connect technological diffusion with financial, social, economic, and policy aspects; Gonella and de Gooyert ( 2024 ) study sustainability in the chemical industry and include social, economic, and ecological aspects of sustainability, and Janipour et al. ( 2021 ) study the sustainability transition of industrial clusters and cover economic, legal, social, and policy aspects.

The inductive nature of qualitative system dynamics is relevant because it has substantial implications for results and recommendations. The more holistic approaches lead to a better appreciation of how stakeholders are enabled and restricted by their context. Starting sustainability transitions research from a primary discipline may assume the perspective of one type of stakeholder. For example, approaching sustainability transition from a business administration perspective leads to recommendations on what businesses can do differently to foster sustainability transitions, and that approach typically treats what other stakeholders do (e.g., governments, citizens) more or less as a given. This has the pitfall of overestimating the agency (the extent to which stakeholders have space to influence sustainability transitions) of the central stakeholder and underestimating the agency of other stakeholders, which led to a critique of sustainability transitions research of suffering from discursive fixation (Pesch 2015 ). Below, we provide an illustration from our research (Janipour et al. 2022 ) to elucidate how qualitative system dynamics leads to a more balanced approach where each stakeholder is understood as being both enabled and restricted by its context, i.e., each stakeholder having some but limited space to influence sustainability transitions [see for a positioning of system dynamics in the structure agency continuum Lane ( 2001a , b ) as well as de Gooyert et al. ( 2016 )].

In Fig. 1 , we illustrate the above with an example based on Janipour et al. ( 2022 ) and Swennenhuis et al. ( 2024 ). In those studies, the authors show how sustainability transitions can be hindered by the mutual interdependence of government, industry, and society. Industry is restricted because it can only continue to exist if they commit to investments with a positive business case: investing in sustainable technology must be profitable for the industry to survive. Whether investments in sustainable technology are profitable can be enabled by supportive policies or regulations. This suggests that governments have the potential and agency to facilitate sustainability transitions by imposing the required policies. However, governments are restricted because they can only enforce policies and regulations that require support from society. Without support, policies lack legitimacy, and committing to such policies would mean political suicide, elected officials would not be re-elected, and policies would be reversed in the next election cycle. This puts the agency in the hands of society, which determines whether there is support for policies or not. However, this societal support for policies supporting industry to invest in sustainable technology is, in turn, influenced by industry: past industry behavior determines whether society is willing to provide it with the social license to operate. Paradoxically, investing in sustainable technology, which requires societal support, is one crucial way by which industry can foster the societal support necessary for the policies that enable these investments. The interactions between these stakeholder groups form a feedback loop that reinforces the dominant sentiment. This may form a chicken-and-egg situation or a gridlock trap, where the agency is diffused amongst actors. The mutual dependencies make each individual actor both restricted and enabled to act. Qualitative system dynamics encourages research to continue to explore how each actor is enabled and restricted by others, which helps to avoid the pitfall of ascribing too much or too little agency to actors, as is sometimes seen in other approaches in sustainability transitions research (Pesch 2015 ).

Example of a stakeholder interdependence in a sustainability transition, based on Janipour et al. ( 2022 ) and Swennenhuis et al. ( 2024 )

In addition, we find that adopting a more holistic approach with qualitative system dynamics leads to a better appreciation of how stakeholders' interests are not only conflicting but also aligned. Sustainability transitions research typically emphasizes conflicting interests, conceptualizing transitions as a buildup of activities by new actors and a consequent breakdown of activities by actors that used to be dominant (Loorbach et al. 2017 ). Sustainability transitions research has its roots, amongst others, in evolutionary economics, which emphasizes the struggle for survival between actors and processes of actors replacing each other through creative destruction (Loorbach et al. 2017 ). In contrast, the inductive nature of qualitative system dynamics, resulting in more holistic analyses, shows how competing actors may have aligned interests. For example, Janipour et al. ( 2021 ) describe two contrasting narratives about carbon capture and storage. Opponents argue that carbon is too risky, while proponents plead socio-economic and climate benefits. Using qualitative system dynamics, we find an alternative narrative possible: carefully regulated carbon capture and storage can maximize socio-economic and climate benefits while minimizing carbon lock-in. Qualitative system dynamics helps by making the interactions between stakeholders and the conflicting interests of multiple stakeholder groups explicit and combining them in a holistic perspective beyond that of individual interests. If stakeholder group interests are aligned, their efforts contribute to a common goal of sustainability instead of working against each other and blocking necessary elements of the transition. Qualitative system dynamics, particularly when combined with participative modeling techniques, helps collect and address the different viewpoints of stakeholders around a sustainability challenge and helps acknowledge the interrelated nature of their stakes. This enables finding consensus on the workings of the system, even when opposing stakeholders do not have similar goals. This also translates to an improved shared understanding of the problem, creating a platform for stakeholder collaboration to find longer-term solutions.

Qualitative system dynamics encourages a higher degree of endogeneity

We find that qualitative system dynamics encourages adopting an endogenous point of view. As discussed above, sustainability transitions research is typically organized around theoretical domains, including political science, geography, business administration, and so on (Köhler et al. 2019 ). This typically leads to distinctions between independent and dependent variables: factor A influences factor B, with A being the independent variable and B being the dependent variable. For example, how do other factors influence political factors, or how do political factors shape other factors. This has led to the critique that sustainability transitions research does not always appreciate the complexity inherent in transitions (Alkemade and de Coninck 2021 ). Qualitative system dynamics puts a central focus on feedback mechanisms; for each situation where factor A influences factor B, the system mapping procedure urges you to also consider whether there is also a path through which factor B influences factor A again. Especially building causal loop diagrams is known to encourage an endogenous perspective, with feedback mechanisms as the unit of focus (Martinez-Moyano and Richardson 2013 , p. 118). More generally, mapping variables in diagrams makes it visually very clear when a variable has only arrows going out of the variable and no variables coming in, raising the question: are we omitting any factors influencing this variable that has yet no arrows coming in? This process typically leads to “endogenizing” to a higher degree compared with sustainability transitions research and is seen as the foundation of system dynamics (Richardson 2011 , p. 219).

The higher degree of endogeneity is relevant because it shifts the focus to feedback mechanisms, and understanding these feedback mechanisms is crucial for understanding and influencing the behavior of sustainability transitions. The appreciation of feedback loops results from the endogenous point of view of system dynamics (Richardson 2011 , p. 221). Also, in our research, we have seen how sustainability transitions are substantially shaped by a myriad of feedback mechanisms, including carbon lock-in, learning effects, crowd-out, and carbon leakage (Gürsan and de Gooyert 2021 ; Janipour et al. 2021 , 2022 ; Gonella and de Gooyert 2024 ). Below, we briefly elaborate on some such mechanisms with illustrations from our past and ongoing research projects. By addressing these feedback mechanisms and putting them center stage, qualitative system dynamics can contribute to sustainability transitions research (Alkemade and de Coninck 2021 ). Qualitative system dynamics can help sustainability transitions research by explicitly mapping the reinforcing and balancing feedback mechanisms that drive or hinder a sustainability transition. Sustainability transitions are characterized by path dependencies and lock-ins: path dependency is the inclination of socio-technical systems to continue and develop along existing paths, influenced by their historical evolution, previous investments, and institutional structures that provide barriers to change (Geels and Schot 2007 ). Because existing socio-technical pathways benefit from economies of scale, a lock-in effect emerges, which means that technologies and institutions reinforce the existing socio-technical pathways both through technological and institutional developments that favor the existing direction (Unruh 2000 ). Qualitative system dynamics helps elucidate the specific feedback structure of the system that is causing the path dependencies and lock-ins in the current system, thereby enabling researchers and stakeholders alike to understand the sources of policy resistance and identify leverage points for interventions that support sustainability transitions (Hjorth and Bagheri 2006 ). Comparably, qualitative system dynamic modeling in participatory settings involves stakeholders in the modeling process. This results in a more robust and widely accepted model, especially where shared understanding and consensus support policy development (Moallemi et al. 2021 ). This is illustrated by de Gooyert et al. ( 2016 ), a study on the Dutch energy transition, where policies are historically made using transition frameworks, as an example to show how qualitative system dynamics in a participatory setting can be used to conceptualize the energy system as a superset of various subsystems where technological, ecological, social, economic, and political factors dynamically influence each other. The resulting feedback loops not only assisted in achieving consensus among stakeholders on the sources of policy resistance plaguing the Dutch energy transition but also enabled them to identify leverage points for policy interventions, e.g., heavy governmental investments in renewable energy can lead to “crowding-out” of community-based renewable energy initiatives, and instead a policy mix that supports both governmental and grassroots renewable energy projects would counter the effect of a potential policy resistance.

Another illustration is given in Fig. 2 , which shows a causal loop diagram (CLD) adaptation from Gürsan and de Gooyert ( 2021 ) of a policy resistance example using natural gas as a transition fuel. Lower natural gas prices in the USA were expected to crowd-out coal usage and thus reduce carbon emissions. The USA was able to reduce its emissions with an ample supply of shale gas; however, the increasing competition in the US fossil market had also pushed the US coal export prices to drop remarkably (Ahmed and Cameron 2014 ). The affordable coal had been exported to Europe, enabling the coal plants in Europe to stay open and keep running (Baron 2013 ). This is an example of a fossil spill-over where the offset carbon in the USA had been carried or spilled over to Europe because of the competing coal export prices. The CLD in Fig. 2 shows this unexpected influence of affordable gas prices in the USA on the European energy markets. Affordable gas prices support coal investments because of the balancing loop that connects gas and coal costs. If this balancing loop is overlooked, the rest of the model structure looks highly similar to the ‘success to the successful’ system archetype (Meadows 2008 ) (with a causal link between both loops which leads to the spill-over), where we could assume that natural gas would crowd-out coal investments since gas is relatively cleaner and more affordable than coal. In reality, cheap gas prices could offset the emission reduction benefits by allowing more affordable coal prices due to the competition in the energy market. In this example, long-term market effects (economies of scale) and the interconnectedness between global energy markets (competition in US markets' influence on European markets) led to this specific policy resistance mechanism.

CLD showing the unexpected influence of affordable gas prices in the USA on the European energy markets (adapted from Gürsan and de Gooyert 2021 )

Sustainability transitions research offers a rich collection of frameworks and theories that qualitative system dynamics can build on

Based on our experiences in past and ongoing research projects, we see the main contribution of sustainability transitions research to qualitative system dynamics in offering a rich collection of frameworks and theories to build on when applying qualitative system dynamics to sustainability issues. Below, we elaborate on this point using illustrations. Transitions are large-scale socio-technical changes that are “polycentric, multi-actor, multi-factor, and multilevel with temporal and spatial scales that vary” (Geels and Schot 2007 ; Köhler et al. 2018 ; Papachristos 2019 , p. 252). They are characterized by dynamic interactions between actors, institutions, technologies, business models, organizations, products, and services. They consist of path-dependent processes that result in specific transition pathways (Geels and Schot 2007 ). Sustainability transition research studies such pathways and elucidates how different elements of a system, such as technologies, actors, and institutions, cause a “lock-in” and “path-dependency” preventing other (sustainable) pathways from emerging (Geels and Schot 2007 ), e.g., a strong dependency on fossil-fuel as primary energy source prevents the embedding of renewable energy technologies as their replacement. Applying qualitative system dynamics can draw from this rich ensemble of frameworks and theories to inform the research. System dynamics is fundamentally interdisciplinary. To study and improve complex systems, system dynamics “draws on cognitive and social psychology, economics, and other social sciences” (Sterman 2000 , p. 5). Sustainability transitions research can then be seen as another body of knowledge that system dynamics can build on.

At first glance, this observation might seem at odds with our earlier statement above that qualitative system dynamics complements sustainability transition research because qualitative system dynamics is more inductive, less restricted by schools of thought that revolve around one discipline, like political science, geography, business administration, and so on. We argued that qualitative system dynamics helps adopt a more holistic approach because it does not use existing frameworks and theories as a starting point, which seems to conflict with the suggestion that sustainability transitions research complements qualitative system dynamics by offering such frameworks and theories. Therefore, it is good to stress that we do not mean that these frameworks and theories should steer the qualitative system dynamics applications too much, but that they fit in an iterative exploration of a topic through primary qualitative data and secondary qualitative data, with existing scientific publications as one out of multiple sources of data to draw from.

To illustrate this point, we use the research design of one of our studies on a sustainability transition in the chemical industry, see Fig. 3 . That study also navigated between inductive and deductive approaches. It used existing documents on the sustainability transition of a chemical cluster and developed a seed model out of that as a starting point for group model-building workshops, as suggested by Richardson ( 2013 ). In addition, conversations with stakeholders and a review of earlier sustainability transitions research resulted in a list of topics to discuss in interviews. These interviews then further helped prepare for the group model-building workshops, as also suggested by Vennix ( 1996 ). Finally, the results of the group model-building workshops, as well as the individual interviews, were compared with earlier sustainability transitions research to provide an answer to the research question. Figure 3 below provides a more generic research design for a study combining, in this case, participatory qualitative system dynamics and sustainability transitions research, iterating between induction through system dynamics and deduction from sustainability transitions research to provide an answer to the research question that is both holistic and well-grounded in earlier research. Such an approach is also possible for qualitative system dynamics applied in a non-participatory setting, for example, as demonstrated in both Gonella and de Gooyert ( 2024 ) and Gürsan and de Gooyert ( 2021 ), where secondary qualitative data were used to iterate between various studies, including sustainability transitions research.

Qualitative system dynamics drawing from sustainability transitions research (adapted from Janipour et al. 2022 )

The above shows that qualitative system dynamics can be coupled with frameworks and theories from sustainability transition research to complement them and support a more comprehensive analysis. The application of these transition frameworks is primarily limited to qualitative case studies. Research employing formal modeling approaches to understand the transformation of socio-technical systems is still emerging, amongst others under the name transitions modeling (Halbe 2015 ; Holtz 2011 ; Holtz et al. 2015 ; Köhler et al. 2018 ; Lopolito et al. 2013 ; Moallemi and de Haan 2019 ; Papachristos 2019 ; Walrave and Raven 2016 ). While we acknowledge that modeling sustainability transitions is an arduous task riddled with complexity and interdependencies of the processes involved, we propose that qualitative system dynamics, both in participatory and non-participatory settings, can fruitfully complement other sustainability transition studies, not the least because it forces scholars to explicitly explore causal links between the dynamics of emerging systems (Raven and Walrave 2020 ; Walrave and Raven 2016 ). Transition scholars have acknowledged that modeling approaches like qualitative system dynamics can elucidate transition dynamics more precisely and tangibly by providing the value of visualizing (and communicating) the complex interrelations and feedback in socio-technical systems (Holtz et al. 2015 ). Exposing these dynamics will allow researchers and stakeholders to use qualitative system dynamics to, for example, identify sources of policy resistance in the system (de Gooyert et al. 2016 ). Transition frameworks are not always able to fully describe the complex causal relations and underlying dynamics that cause these lock-ins and path dependencies. Using the multilevel perspective as an example, we can conceptualize system pathways as an “aggregate balance of reinforcing and disrupting forces from the landscape, regime, niches, and/or other systems” (Papachristos 2019 , p. 253). For instance, these reinforcing and disrupting forces could be dynamic feedbacks between elements (such as actors, institutions, and infrastructure) at a socio-technical level (Geels 2002 ). For a sustainability transition to successfully transpire, the balance between the endogenous reinforcing and balancing mechanisms at niche, landscape, and other system levels must counteract the balance between the endogenous reinforcing and balancing mechanisms at the regime level. Policy interventions should adapt to the exogenous landscape events and reinforce the balance at the niche and other system levels while diminishing the balance at the regime level. A similar analogy could be made with the incumbent and emerging systems in the TIS framework. In this spirit, system dynamics can also bridge the two dominant transition study frameworks—an underexplored connection that can potentially have significant benefits (Markard and Truffer 2008 ; Weber and Rohracher 2012 ).

Sustainability transitions research often acknowledges the complex nature of sustainability challenges. Various frameworks have been developed in the sustainability transition literature. However, these frameworks do not always have an explicit basis for the causal mechanisms responsible for the evolution of the systems they describe. We argue that system dynamicists can benefit from the existing conceptual frameworks and contribute to the literature by using qualitative system dynamics as an explicit causal language that helps cross the boundaries between disciplines and specialists.

In this article, we have reflected on the benefits of combining two fields of research: qualitative system dynamics and sustainability transitions research. Sustainability transitions are required to preserve the needs of future generations (Markard et al. 2012 ). Sustainability transitions research acknowledges the complex nature of the socio-technical systems that need to change and aims to advance understanding of how human behavior, institutions, and technologies co-evolve over time (van den Bergh et al. 2011 ). Sustainability transitions succeed or stall because of the underlying feedback dynamics responsible for either progress or inertia (de Gooyert et al. 2016 ). By providing explicit, tractable conceptualizations of interrelated causal relationships, qualitative system dynamics helps to improve theory building in sustainability transitions research (Carter and Little 2007 ).

We identified three key benefits of combining qualitative system dynamics and sustainability transitions research and supported these findings using illustrations from earlier studies. We find that qualitative system dynamics helps sustainability transitions research by encouraging a broader system boundary and a higher degree of endogeneity, and we find that sustainability transitions research helps qualitative system dynamics by allowing advancements through the integration of earlier frameworks and theories from the rich literature that sustainability transitions research has to offer. Table 1 summarizes these key findings.

Using qualitative system dynamics for sustainability transitions research fosters a greater appreciation for how the interests of different stakeholders are interrelated. Mapping the causal feedback mechanisms enabling and hindering sustainability transitions leads to recommendations on how to navigate seemingly opposing interests. The balancing feedback underneath inertia and resistance to change can be addressed by aligning stakeholders' interests, for example, by exploring solutions and narratives where both industry and environmentalists benefit (Gürsan and de Gooyert 2021 ; Janipour et al. 2021 ). It shows how a just transition is not just a discussion on normative aspects of sustainability but a pragmatic solution for defining a solution space where the actors involved all help push the system in the same, more sustainable direction (Gonella 2021 ). Sustainability transitions research and qualitative system dynamics have a lot to benefit from the combination of the two, and we encourage future researchers to contribute to this emerging stream of literature.

Discussion, limitations, and suggestions for future research

In this section, we discuss the position of qualitative system dynamics and sustainability transitions research in broader debates, and provide avenues for future research. The first broader debate that our findings relate to is the debate on epistemological and ontological foundations of sustainability transitions research, which is receiving increasing attention, especially in the discussion about how more case-based narrative studies on the one hand and computer simulation studies, on the other hand, compare (Geels et al. 2016 ; Geels et al. 2020 ; McDowall and Geels 2017 ; Turnheim et al. 2015 ). As described earlier, qualitative system dynamics and sustainability transitions research both deal with problems that are 'messy' and complicated (Kivimaa et al. 2021 ; Vennix 1996 ). Studies start from divergent ontological and epistemological assumptions to investigate these problems. While scientists do not fully agree on how to classify these philosophical paradigms (Avenier and Thomas 2015 ), the spectrum ranges from positivism to interpretivism (Hazard et al. 2020 ). Positivistic approaches consider reality independent of the observer and aim to produce knowledge that offers an unequivocal explanation of this 'objective' reality that exists independent of us. The philosophical paradigms that do not consider reality independent of the researcher (such as pragmatism, constructivism, constructionism, relativism, etc.) can be grouped under interpretivism (Yanow and Schwartz-Shea 2015 ). Interpretivist approaches consider reality socially constructed through each individual's experience and aim to produce knowledge that offers a plausible explanation of the 'subjective' realities appropriate for the lived experience (Avenier and Thomas 2015 ). While computer simulation models are typically characterized on the positivistic side of the spectrum, case-based narrative studies in sustainability transitions are typically characterized on the interpretivist side of the spectrum. The interesting position of qualitative system dynamics here is that it falls somewhere in between and that it, to some extent, is flexible enough to be applied in different philosophical paradigms, ranging from positivistic to interpretivist [see for a discussion Lane ( 2001a , b ), as well as de Gooyert et al. ( 2019 )]. In the positivist research tradition, rigor is established using methods that bring models as close as possible to an 'objective' reality. This is primarily achieved by collecting observations dispassionately, quantifying qualitative data, and (predominantly) using deductive logic. In other words, models are "micro-hypotheses or minor content theories" (Lane 2000 , p. 12) to be verified, authenticated, or contested. On the other hand, in the constructivist tradition, multiple realities exist that are socially constructed as a cause and consequence of peoples' behaviors towards their environment. Thus, 'systems' do not need to exist in reality, and models describe "how things might be from a particular viewpoint” (Mingers and Rosenhead 2001 , p. 299). Such approaches primarily rely on collecting qualitative data through extended discourses with research subjects and (primarily) using inductive logic. Therefore, models are considered rigorous if they reach a certain confidence level and incentivize consensus. While positivistic system dynamicists triangulate to decrease subjectivity [see Homer ( 1996 ) for examples], constructivist system dynamicists investigate that very subjectivity [an example is Group Model Building (Vennix 1996 )]—perceptions are not considered as a distorted image of reality, but as meanings that an entity assigns to different aspects of reality. We argue that qualitative system dynamics is a fruitful approach to advance sustainability transitions research because it falls in between more positivistic simulation approaches and more interpretivist narrative approaches, but this benefit comes with the challenge that epistemological and ontological assumptions remain hidden, which hinders reflection. Hence, we believe that future applications of qualitative system dynamics in sustainability transitions research would benefit from a more explicit and argued positioning of their foundations. This will help avoid confusion and ensure that contributions are assessed against their intended outcomes in light of the original ambitions.

Another suggestion for future research is to explore potential combinations with other approaches that help to foster the necessary change to bring about sustainability transitions. Both qualitative system dynamics and sustainability transitions research share the explicit aim to contribute to not only generating new knowledge but also addressing problems. However, the change necessary to achieve more sustainable modes of production and consumption often requires more than what a research approach like qualitative system dynamics can offer. At its core, qualitative system dynamics is an analytical approach aimed at creating a better understanding of complex issues. However, even if a perfect understanding of all the complexity of a sustainability challenge is achieved, this may still not be enough to bring about change (de Gooyert et al. 2016 ). Sustainability transitions require analytical approaches that help identify leverage points (Leventon et al. 2021 ) and engage stakeholders on an emotional level to inspire action. Therefore, we think combining analytical approaches like qualitative system dynamics with other approaches that engage more on a personal level may prove useful. Such approaches could include shared visioning, for example, that helps individuals to believe in alternative futures, to motivate and empower them (Loorbach et al. 2017 ), or “futuring” approaches employing, for example, multi-media installations for more immersive experiences to bring about shared orientations for action (Hajer and Pelzer 2018 ).

A limitation of our article is that we presented illustrations revolving around energy transition and climate mitigation. We expect similar considerations for other sustainability transitions like those around biodiversity and poverty. Future researchers could broaden the types of cases on which they apply qualitative system dynamics for sustainability transitions research, so that it contributes to the broad agenda of sustainable development as discussed in, for example, the Global Sustainable Development Report (Miranda and Scholz 2023 ), the IPCC assessment reports (IPCC 2023 ), and the IPBES reports (IPBES 2019 ). While one approach might be to seek cases beyond energy and climate mitigation in future research, another approach would be to broaden the scope of studies so that the transitions around energy and climate are connected to other sustainable development transitions like biodiversity and poverty. This is the topic of an emerging stream of literature on multi-system transitions (Andersen and Geels 2023 ; Andersen et al. 2023 ; Papachristos et al. 2013 ). In this article, we presented the encouragement of adopting broader system boundaries as one of the key benefits of applying qualitative system dynamics to sustainability transitions research. Indeed, contributing to the study of multi-system transitions would be a logical next step.

A final suggestion is to increase the extent to which we reflect on our role as researchers. Sustainability transitions research is inherently value-laden as it involves bringing about a better world, which requires operationalizing what “better” means. Earlier studies in sustainability transitions research showed how researchers combine roles, including process facilitator, knowledge broker, reflective scientist, and change agent (Wittmayer and Schäpke 2014 ). Especially for participative modes of qualitative system dynamics, we deem it relevant to reflect more on the positions we take as a researcher, for example using an evaluation framework as the one presented by Moallemi et al. ( 2021 ). In a context like that of group model building, you interact with stakeholders and influence how they perceive the sustainability challenge at hand. Exploring leverage points with participants from incumbents who have a stake in current systems might be seen as a justification for systems that stand in the way of required radical change. There is a real danger that efforts to empower new perspectives may reinforce existing power structures (Avelino et al. 2016 ).

Abbreviations

Causal loop diagram

Multilevel perspective

Technological innovation system

Ahmed NA, Cameron M (2014) The challenges and possible solutions of horizontal axis wind turbines as a clean energy solution for the future. Renew Sustain Energy Rev 38:439–460

Article Google Scholar

Alkemade F, de Coninck H (2021) Policy mixes for sustainability transitions must embrace system dynamics. Environ Innov Soc Trans 41:24–26

Andersen AD, Geels FW (2023) Multi-system dynamics and the speed of net-zero transitions: identifying causal processes related to technologies, actors, and institutions. Energy Res Soc Sci 102:103178

Andersen AD, Steen M, Mäkitie T, Hanson J, Thune TM, Soppe B (2020) The role of inter-sectoral dynamics in sustainability transitions: a comment on the transitions research agenda. Environ Innov Soc Trans 34:348–351

Andersen AD, Geels FW, Steen M, Bugge MM (2023) Building multi-system nexuses in low-carbon transitions: conflicts and asymmetric adjustments in Norwegian ferry electrification. Proc Natl Acad Sci 120(47)

Arthur W (2016) Increasing returns and path dependence in the economy. In: Increasing returns and path dependence in the economy. https://doi.org/10.3998/mpub.10029

Avelino F, Grin J, Pel B, Jhagroe S (2016) The politics of sustainability transitions. J Environ Plan Policy Manag 18(5):557–567

Avenier M-J, Thomas C (2015) Finding one’s way around various methodological guidelines for doing rigorous case studies: a comparison of four epistemological frameworks. Systèmes D’information & Management. https://doi.org/10.3917/sim.151.0061

Awan A (2020) Missing the bullseye: systemic factors blocking the emergence of RET sustainable business models in Indonesia. Eindhoven University of Technology, Eindhoven

Google Scholar

Baron R (2013) Renewable energy: a route to decarbonisation in peril. Document préparé pour la 29e Table ronde sur le développement durable organisée au siège de l’OCDE, à Paris, pp 4–5

Bergek A (2002) Shaping and exploiting technological opportunities: the case of renewable energy technology in Sweden. Doktorsavhandlingar Vid Chalmers Tekniska Hogskola, 1826

Bergek A, Jacobsson S, Carlsson B, Lindmark S, Rickne A, Jacobsson S, Carlsson B, Lindmark S, Rickne A (2007) Analyzing the functional dynamics of technological innovation systems: a scheme of analysis. ElsevierPaperpile 3(37):407–429. https://doi.org/10.1016/j.respol.2007.12.003

Carter SM, Little M (2007) Justifying knowledge, justifying method, taking action: epistemologies, methodologies, and methods in qualitative research. Qual Health Res. https://doi.org/10.1177/1049732307306927

Coyle G (2000) Qualitative and quantitative modelling in system dynamics: some research questions. Syst Dyn Rev J Syst Dyn Soc 16(3):225–244

de Gooyert V (2019) Developing dynamic organizational theories; three system dynamics based research strategies. Qual Quant 53:653–666

de Gooyert V, Größler A (2018) On the differences between theoretical and applied system dynamics modeling. Syst Dyn Rev 34(4):575–583

de Gooyert V, Rouwette E, van Kranenburg H, Freeman E, van Breen H (2016) Sustainability transition dynamics; towards overcoming policy resistance. Technol Forecast Soc Chang 111:135–145

de Gooyert V, Bleijenbergh I, Korzilius H, Fokkinga B, Lansu M, Raaijmakers S, Rouwette E, van der Wal M (2019) Why we do not always simulate. WiSDom Blog. Available from https://systemdynamics.org/why-we-do-not-always-simulate/

de Gooyert V, de Coninck H, ter Haar B (2024) How to make climate policy more effective? The search for high leverage points by the multidisciplinary Dutch expert team ‘Energy System 2050.’ Syst Res Behav Sci. https://doi.org/10.1002/sres.3039

Forrester JW (1994) System dynamics, systems thinking, and soft OR. Syst Dyn Rev 10(2–3):245–256

Geels FW (2002) Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Res Policy 31:1257–1274

Geels FW (2006) Multi-level perspective on system innovation: relevance for industrial transformation. In: Understanding industrial transformation. https://doi.org/10.1007/1-4020-4418-6_9

Geels FW, Schot J (2007) Typology of sociotechnical transition pathways. Res Policy 36(3):399–417. https://doi.org/10.1016/j.respol.2007.01.003

Geels FW, Schot J (2010) The dynamics of transitions: a socio-technical perspective. In: Transitions to sustainable development: new directions in the study of long term transformative change

Geels FW, Berkhout F, Van Vuuren DP (2016) Bridging analytical approaches for low-carbon transitions. Nat Clim Change 6(6):576–583

Geels FW, McMeekin A, Pfluger B (2020) Socio-technical scenarios as a methodological tool to explore social and political feasibility in low-carbon transitions: bridging computer models and the multi-level perspective in UK electricity generation (2010–2050). Technol Forecast Soc Chang 151:119258–119258

Gonella S (2021) Pursuing a just transition in the power sectors of Poland and South Africa: identifying barriers and drivers using system dynamics. Eindhoven University of Technology, Eindhoven

Gonella S, de Gooyert V (2024) What are sustainable plastics? A review of interrelated problems and solutions to help avoid unintended consequences. Environ Res Lett 19(7):073001. https://doi.org/10.1088/1748-9326/ad536d

Gürsan C, de Gooyert V (2021) The systemic impact of a transition fuel: does natural gas help or hinder the energy transition? Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2020.110552

Gürsan C, de Gooyert V, de Bruijne M, Raaijmakers J (2024) District heating with complexity: Anticipating unintended consequences in the transition towards a climate-neutral city in the Netherlands. Energy Res Soc Sci 110: https://doi.org/10.1016/j.erss.2024.103450

Hajer MA, Pelzer P (2018) 2050—an energetic Odyssey: understanding ‘Techniques of Futuring’ in the transition towards renewable energy. Energy Res Soc Sci 44:222–231

Halbe J (2015) Lessons for model use in transition research. Environ Innov Soc Trans 15:194–210. https://doi.org/10.1016/j.eist.2014.10.001

Halbe J, Holtz G, Ruutu S (2020) Participatory modeling for transition governance: linking methods to process phases. Environ Innov Soc Trans 35:60–76. https://doi.org/10.1016/j.eist.2020.01.008

Hazard L, Cerf M, Lamine C, Magda D, Steyaert P (2020) A tool for reflecting on research stances to support sustainability transitions. Nat Sustain. https://doi.org/10.1038/s41893-019-0440-x

Hekkert MP, Suurs RAA, Negro SO, Kuhlmann S, Smits REHM (2007) Functions of innovation systems: a new approach for analysing technological change. Technol Forecast Soc Change 74(4):413–432. https://doi.org/10.1016/j.techfore.2006.03.002

Hjorth P, Bagheri A (2006) Navigating towards sustainable development: a system dynamics approach. Futures 38(1):74–92

Holtz G (2011) Modelling transitions: an appraisal of experiences and suggestions for research. Environ Innov Soc Trans 1(2):167–186

Holtz G, Alkemade F, de Haan F, Köhler J, Trutnevyte E, Luthe T, Halbe J, Papachristos G, Chappin E, Kwakkel J, Ruutu S (2015) Prospects of modelling societal transitions: position paper of an emerging community. Environ Innov Soc Transit 17:41–58

Homer JB (1996) Why we iterate: scientific modeling in theory and practice. Syst Dyn Rev. https://doi.org/10.1002/(sici)1099-1727(199621)12:1<1::aid-sdr93>3.3.co;2-p

IPBES (2019) Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. In: Settele J, Díaz S, Ngo HT (eds) Brondízio ES. IPBES secretariat, Bonn pp, pp 1–1082. https://doi.org/10.5281/zenodo.383167

Chapter Google Scholar

IPCC (2023) Climate change 2023: synthesis report. contribution of Working Groups I, II and III to the sixth assessment report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds)]. IPCC, Geneva, pp 35–115. https://doi.org/10.59327/IPCC/AR6-9789291691647 .

Janipour Z, Swennenhuis F, de Gooyert V, de Coninck H (2021) Understanding contrasting narratives on carbon dioxide capture and storage for Dutch industry using system dynamics. Int J Greenh Gas Control. https://doi.org/10.1016/j.ijggc.2020.103235

Janipour Z, de Gooyert V, Huijbregts M, de Coninck H (2022) Industrial clustering as a barrier and an enabler for deep emission reduction: a case study of a Dutch chemical cluster. Clim Policy 22(3):320–338. https://doi.org/10.1080/14693062.2022.2025755

Kemp R (1994) Technology and the transition to environmental sustainability. The problem of technological regime shifts. Futures. https://doi.org/10.1016/0016-3287(94)90071-X

Kemp R, Loorbach D, Rotmans J (2007) Transition management as a model for managing processes of co-evolution towards sustainable development. Int J Sustain Dev World Ecol. https://doi.org/10.1080/13504500709469709

Kivimaa P, Laakso S, Lonkila A, Kaljonen M (2021) Moving beyond disruptive innovation: a review of disruption in sustainability transitions. Environ Innov Soc Transit. https://doi.org/10.1016/j.eist.2020.12.001

Köhler J, de Haan F, Holtz G, Kubeczko K, Moallemi E, Papachristos G, Chappin E (2018) Modelling sustainability transitions: an assessment of approaches and challenges. JASSS 21(1)

Köhler J, Geels FW, Kern F, Markard J, Onsongo E, Wieczorek A, Wells P et al (2019) An agenda for sustainability transitions research: state of the art and future directions. Environ Innov Soc Transit 31:1–32

Lane DC (2000) Diagramming conventions in system dynamics. J Oper Res Soc. https://doi.org/10.1057/palgrave.jors.2600864

Lane DC (2001a) Rerum cognoscere causas: Part I—how do the ideas of system dynamics relate to traditional social theories and the voluntarism/determinism debate? Syst Dyn Rev 17(2):97–118

Lane DC (2001b) Rerum cognoscere causas: Part II—opportunities generated by the agency/structure debate and suggestions for clarifying the social theoretic position of system dynamics. Syst Dyn Rev 17(4):293–309

Leal Filho W, Londero Brandli L (2016) Engaging stakeholders for sustainable development. World Sustain Ser. https://doi.org/10.1007/978-3-319-26734-0_21

Leventon J, Abson DJ, Lang DJ (2021) Leverage points for sustainability transformations: nine guiding questions for sustainability science and practice. Sustain Sci 16(3):721–726. https://doi.org/10.1007/s11625-021-00961-8

Loorbach D, Wijsman K (2013) Business transition management: exploring a new role for business in sustainability transitions. J Clean Prod 45:20–28

Loorbach D, Frantzeskaki N, Avelino F (2017) Sustainability transitions research: transforming science and practice for societal change. Annu Rev Environ Resour. https://doi.org/10.1146/annurev-environ-102014-021340

Lopolito A, Morone P, Taylor R (2013) Emerging innovation niches: an agent based model. Res Policy 42(6–7):1225–1238

Luederitz C, Abson DJ, Audet R, Lang DJ (2017) Many pathways toward sustainability: not conflict but co-learning between transition narratives. Sustain Sci 12(3):393–407. https://doi.org/10.1007/s11625-016-0414-0

Markard J, Truffer B (2008) Technological innovation systems and the multi-level perspective: towards an integrated framework. Res Policy. https://doi.org/10.1016/j.respol.2008.01.004

Markard J, Raven R, Truffer B (2012) Sustainability transitions: an emerging field of research and its prospects. Res Policy. https://doi.org/10.1016/j.respol.2012.02.013

Martinez-Moyano IJ, Richardson GP (2013) Best practices in system dynamics modeling. Syst Dyn Rev 29(2):102–123

McDowall W, Geels FW (2017) Ten challenges for computer models in transitions research: commentary on Holtz et al. Environ Innov Soc Transit 22:41–49

Meadows DH (2008) Thinking in systems: a primer. Chelsea Green Publishing

Mingers J, Rosenhead J (2001) An overview of related methods: VSM, system dynamics and decision analysis. In: Mingers J, Rosenhead J (eds) Rational analysis for a problematic world revisited. Wiley, Chichester, pp 267–288

Miranda JJ, Scholz I (2023) Global Sustainable Development Report 2023: times of crisis, times of change: science for accelerating transformations to sustainable development. United Nations, New York

Moallemi EA, de Haan FJ (eds) (2019) Modelling transitions: virtues, vices, visions of the future. Routledge, London

Moallemi EA, Malekpour S (2018) A participatory exploratory modelling approach for long-term planning in energy transitions. Energy Res Soc Sci 35:205–216

Moallemi EA, Aye L, de Haan FJ, Webb JM (2017a) A dual narrative-modelling approach for evaluating socio-technical transitions in electricity sectors. J Clean Prod 162:1210–1224

Moallemi EA, de Haan F, Kwakkel J, Aye L (2017b) Narrative-informed exploratory analysis of energy transition pathways: a case study of India’s electricity sector. Energy Policy 110:271–287

Moallemi EA, de Haan FJ, Hadjikakou M, Khatami S, Malekpour S, Smajgl A, Smith MS, Voinov A, Bandari R, Lamichhane P, Miller KK, Nicholson E, Novalia W, Ritchie EG, Rojas AM, Shaikh MA, Szetey K, Bryan BA (2021) Evaluating participatory modeling methods for co-creating pathways to sustainability. Earth's Future 9(3)

O’Riordan T (2001) Globalism, localism, and identity: fresh perspectives on the transition to sustainability. Earthscan

Papachristos G (2011) A system dynamics model of socio-technical regime transitions. Environ Innov Soc Trans 1(2):202–233. https://doi.org/10.1016/j.eist.2011.10.001

Papachristos G (2014) Towards multi-system sociotechnical transitions: why simulate. Technol Anal Strateg Manag 26(9):1037–1055

Papachristos G (2018) A mechanism based transition research methodology: bridging analytical approaches. Futures 98:57–71

Papachristos G (2019) System dynamics modelling and simulation for sociotechnical transitions research. Environ Innov Soc Transit. https://doi.org/10.1016/j.eist.2018.10.001

Papachristos G, Adamides E (2016) A retroductive systems-based methodology for socio-technical transitions research. Technol Forecast Soc Change 108:1–14

Papachristos G, Sofianos A, Adamides E (2013) System interactions in socio-technical transitions: extending the multi-level perspective. Environ Innov Soc Trans 7:53–69

Pesch U (2015) Tracing discursive space: agency and change in sustainability transitions. Technol Forecast Soc Change 90:379–388

Purvis B, Mao Y, Robinson D (2022) A multi-scale integrated assessment model to support urban sustainability. Sustain Sci 17(1):151–169. https://doi.org/10.1007/s11625-021-01080-0

Raven R, Walrave B (2020) Overcoming transformational failures through policy mixes in the dynamics of technological innovation systems. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2018.05.008

Richardson GP (2011) Reflections on the foundations of system dynamics. Syst Dyn Rev 27(3):219–243

Richardson GP (2013) Concept models in group model building. Syst Dyn Rev 29(1):42–55

Richmond B (1994) Systems thinking/system dynamics: let’s just get on with it. Syst Dyn Rev 10(2–3):135–157

Rip A, Kemp R (1998) Technological change. Hum Choice Clim Change 2(2):327–399

Rosenbloom D (2020) Engaging with multi-system interactions in sustainability transitions: a comment on the transitions research agenda. Environ Innov Soc Trans 34:336–340

Rotmans J (1998) Methods for IA: the challenges and opportunities ahead. Environ Model Assess. https://doi.org/10.1023/A:1019019024003

Rotmans J, Kemp RPM, van Asselt M (2001) More evolution than revolution: transition management in public policy. Foresight 3(1):15–31

Sterman J (2000) Systems thinking and modeling for a complex world. Irwin Mc-Graw Hill

Suurs R, Hekkert M (2012) Motors of sustainable innovation: understanding transitions from a technological innovation system’s perspective. In: Governing the energy transition: reality, illusion or necessity? https://doi.org/10.4324/9780203126523

Swennenhuis F, de Gooyert V, de Coninck HC (2024) Socio-technical dynamics of carbon dioxide capture and storage: A systems view on enablers and barriers at North Sea Port. Int J Greenhouse Gas Control 137:104201. https://doi.org/10.1016/j.ijggc.2024.104201

Article CAS Google Scholar

System Dynamics Society (n.d.) What is system dynamics. Retrieved March 8, 2024, from https://systemdynamics.org/what-is-system-dynamics/

Truffer B, Murphy JT, Raven R (2015) The geography of sustainability transitions: contours of an emerging theme. Environ Innov Soc Trans 17:63–72

Trutnevyte E, Barton J, O’Grady Á, Ogunkunle D, Pudjianto D, Robertson E (2014) Linking a storyline with multiple models: a cross-scale study of the UK power system transition. Technol Forecast Soc Change 89:26–42

Turnheim B, Berkhout F, Geels F, Hof A, McMeekin A, Nykvist B, van Vuuren D (2015) Evaluating sustainability transitions pathways: bridging analytical approaches to address governance challenges. Glob Environ Change 35:239–253

Unruh GC (2000) Understanding carbon lock-in. Energy policy 28(12):817–830

van Assche K, Duineveld M, Beunen R, Valentinov V, Gruezmacher M (2022) Material dependencies: hidden underpinnings of sustainability transitions. J Environ Policy Plan. https://doi.org/10.1080/1523908X.2022.2049715

van Asselt MBA, Rotmans J (1996) Uncertainty in perspective. Glob Environ Change. https://doi.org/10.1016/0959-3780(96)00015-5

van den Bergh JCJM, Gowdy JM (2000) Evolutionary theories in environmental and resource economics: approaches and applications. Environ Resour Econ. https://doi.org/10.1023/A:1008317920901

van den Bergh JCJM, Truffer B, Kallis G (2011) Environmental innovation and societal transitions: introduction and overview. Environ Innov Soc Transit. https://doi.org/10.1016/j.eist.2011.04.010

Vennix J (1996) Group model building: facilitating team learning using system dynamics. Wiley, New York

Walrave B, Raven R (2016) Modelling the dynamics of technological innovation systems. Res Policy 45(9):1833–1844. https://doi.org/10.1016/j.respol.2016.05.011

Weber KM, Rohracher H (2012) Legitimizing research, technology and innovation policies for transformative change: combining insights from innovation systems and multi-level perspective in a comprehensive “failures” framework. Res Policy. https://doi.org/10.1016/j.respol.2011.10.015

Wittmayer JM, Schäpke N (2014) Action, research and participation: roles of researchers in sustainability transitions. Sustain Sci 9:483–496. https://doi.org/10.1007/s11625-014-0258-4

Wolstenholme EF (1999) Qualitative vs quantitative modelling: the evolving balance. J Oper Res Soc 50(4):422–428

Yanow D, Schwartz-Shea P (2015) Doing social science in a humanistic manner. In: Interpretation and method. Routledge, pp 433–447. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315703275-29/social-sciencehumanistic-manner-dvora-yanow-peregrine-schwartz-shea

Yücel G, Chiong Meza CM (2008) Studying transition dynamics via focusing on underlying feedback interactions. Comput Math Organ Theory. https://doi.org/10.1007/s10588-008-9032-4

Download references

Project C4U received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 884418; Project INSpECT is funded by TKI Deltatechnologie and Next Generation Infrastructures; Project EXPLORE is funded by the Dutch Research Council (Nederlandse Organisatie voor Wetenschappelijk Onderzoek—NWO); Project Sustainable Plastics is funded by Brightsite and the Institute for Management Research (Radboud University).

Author information

Authors and affiliations.

Institute for Management Research, Radboud University, Heyendaalseweg 141, 6525 AJ, Nijmegen, The Netherlands

Vincent de Gooyert, Cem Gürsan, Floris Swennenhuis, Zahra Janipour & Sara Gonella

School of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands

Attabik Awan

Radboud Institute for Biological and Environmental Sciences, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands

Floris Swennenhuis & Zahra Janipour

You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the conception and design of the study. The authors met to discuss the lessons learned from each of their projects, on which this paper builds. All authors contributed to the drafting and revision of the manuscript.

Corresponding author

Correspondence to Vincent de Gooyert .

Ethics declarations

Conflict of interest.

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's note.

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

Handled by Tobias Plieninger, University of Gottingen and University of Kassel, Germany.

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

de Gooyert, V., Awan, A., Gürsan, C. et al. Building on and contributing to sustainability transitions research with qualitative system dynamics. Sustain Sci (2024). https://doi.org/10.1007/s11625-024-01548-9

Download citation

Received : 21 March 2024

Accepted : 15 July 2024

Published : 29 August 2024

DOI : https://doi.org/10.1007/s11625-024-01548-9

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

Sustainability transitions
Systems approaches
Feedback mechanisms
Endogenous view

Find a journal
Publish with us
Track your research

IMAGES

PPT
PPT
PPT
[PDF] Assumptions underlying quantitative and qualitative research
Revised Research Assumptions for Strategic Management Research
PPT

VIDEO

Understanding the Case Study Approach in Qualitative Research
AP Statistics Chapter 12
Qualitative Research Paradigm-I
What is Qualitative Research
What are the assumptions in these requirements?
Overview of statistics

COMMENTS

Assumptions of Qualitative Research Methods
Introduction. Many characteristics make up the collective term of qualitative research, Methods used specify a certain framework in which researchers operate, Within this framework, there are two major assumptions in which I believe take precedence, Inductive reasoning and topic specificity are the two characteristics that I believe define ...
Stating the Obvious: Writing Assumptions, Limitations, and
Stating the Obvious: Writing Assumptions, Limitations, and Delimitations During the process of writing your thesis or dissertation, you might suddenly realize that your research has inherent flaws. Don't worry! Virtually all projects contain restrictions to your research. However, being able to recognize and accurately describe these problems is the difference between a true researcher and a ...
Planning Qualitative Research: Design and Decision Making for New
Abstract For students and novice researchers, the choice of qualitative approach and subsequent alignment among problems, research questions, data collection, and data analysis can be particularly tricky. Therefore, the purpose of this paper is to provide a concise explanation of four common qualitative approaches, case study, ethnography, narrative, and phenomenology, demonstrating how each ...
How to use and assess qualitative research methods
This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions ...
PDF Assumptions underlying quantitative and qualitative research
The purpose ofthis paper is to clarify and explore theunderlying assumptions contained within quantitative and qualitative research. It is important fori stitutional research- ers to understand the philosophical grounding of the two approaches so that they may reflect onthose assumptions while engaging ininstitutional research.
PDF Introduction to Qualitative Research Methodology
assumptions underlying qualitative research • Distinguish between quantitative and qualitative approaches to research and data ... examples to help you understand the ideas and reﬂ ect on their application. The chapters are also interspersed with Boxes, Examples, and Exercises to ...
Chapter 1. Introduction
I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research. Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.
Criteria for Good Qualitative Research: A Comprehensive Review
This review aims to synthesize a published set of evaluative criteria for good qualitative research. The aim is to shed light on existing standards for assessing the rigor of qualitative research encompassing a range of epistemological and ontological standpoints. Using a systematic search strategy, published journal articles that deliberate criteria for rigorous research were identified. Then ...
What Is 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.
PDF Philosophical Assumptions and Interpretive Frameworks post
Questions for Discussion • Where do philosophy and interpretive frameworks (theory) fit into the overall process of research? • Why is it important to understand the philosophical assumptions? • What four philosophical assumptions exist when you choose qualitative research?
Philosophical and theoretical underpinnings of qualitative research
Abstract. This chapter provides an overview of the philosophical assumptions that underly qualitative research, including the types of logics employed as well as the axiological, epistemological ...
Exploring the Four Core Philosophical Assumptions in Qualitative Research
Explore the critical roles of ontological, epistemological, axiological, and methodological assumptions in qualitative research, with a focus on remote work's impact on relationships and job satisfaction.
A Practical Guide to Writing Quantitative and Qualitative Research
The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development ...
Qualitative Research: Characteristics, Design, Methods & Examples
Qualitative research is a method of inquiry used in various disciplines, including social sciences, education, and health, to explore and understand human behavior, experiences, and social phenomena. It focuses on collecting non-numerical data, such as words, images, or objects, to gain in-depth insights into people's thoughts, feelings, motivations, and perspectives.
5.1 Assumptions underlying research
The knowledge claims that underlie the results of surveys, focus groups, and other common research designs ultimately rest on epistemological assumptions of their methods. Focus groups and other qualitative methods usually rely on subjective epistemological (and ontological) assumptions. Surveys and other quantitative methods usually rely on ...
Ontological Assumptions and Generalizations in Qualitative (Audience
The aim of this article is to discuss qualitative research and focus on ontological assumptions behind our methodological arguments and choices. Generalization, often seen as the Achilles heel of qualitative research, is also discussed and its relation to ontological positions clarified.
Qualitative Research: Data Collection, Analysis, and Management
In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable ...
Qualitative Research Methods: A Practice-Oriented Introduction
It does not contain a theoretical analysis or evaluation of qualitative methods from the point of view of their epistemology (their understanding of what constitutes knowledge and the quality and ...
Qualitative research
Assumptions underlying qualitative methods Some general assumptions are characteristic of qualitative research: multiple realities exist in any study -- the researcher's, those of the individuals being investigated, and the reader or audience interpreting the results; multiple perspectives including voices of informants are included in the study; researchers interact with those under study ...
"Assumptions of Qualitative Research Methods" by Craig H. Walters
Many characteristics make up the collective term of qualitative research, Methods used specify a certain framework in which researchers operate, Within this framework, there are two major assumptions in which I believe take precedence, Inductive reasoning and topic specificity are the two characteristics that I believe define qualitative research, Inductive reasoning is the epitome of ...
What is Qualitative in Qualitative Research
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.
PDF King Sample.qxp
Philosophical Assumptions Qualitative interviewing has become a prominent research method in the social sciences. Face-to-face conversation is an everyday occurrence and this has probably resulted in an assumption that interviewing is a preferred option because the researcher feels most at ease with this technique.
Full article: Philosophical Paradigms in Qualitative Research Methods
In this paper, I discuss and critically assess how the relationship between philosophy of science and qualitative methods is presented and discussed in research on qualitative research methods educ...
Building on and contributing to sustainability transitions research
This paper explores the novel connection between qualitative system dynamics and sustainability transitions research. As the urgency for sustainable solutions intensifies, this interdisciplinary combination offers a promising avenue for addressing complex sustainability challenges. We reflect on recent research projects to establish the value of combining the two fields. We delve into the ...

Stating the Obvious: Writing Assumptions, Limitations, and Delimitations

Delimitations

Limitations

Assumptions

Chapter 1. Introduction

Why an Open Access Textbook on Qualitative Research Methods?

What is Research?

What is Qualitative Research?

Examples of Qualitative Research

Example 1 : The End Game (ethnography + interviews)

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

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

How Is This Book Organized?

Chapter Descriptions

Recommended Reading: Other Qualitative Research Textbooks

Patton’s Ten Suggestions for Doing Qualitative Research

Have a language expert improve your writing

What Is Qualitative Research? | Methods & Examples

Table of contents

Prevent plagiarism. Run a free check.

Cite this Scribbr article

Is this article helpful?

Pritha Bhandari

Qualitative Research: Characteristics, Design, Methods & Examples

Qualitative research can be used to:

Examples of qualitative research questions include:

Characteristics

Reality is socially constructed

The primacy of subject matter

Variables are complex, interwoven, and difficult to measure

Emic (insider’s point of view)

Interpretive analysis

Collecting Qualitative Data

Observations

Focus groups

Ethnography

Qualitative Data Analysis Methods

Grounded theory

Content analysis

Discourse analysis

Thematic analysis

Phases of Thematic Analysis

Template analysis

Frame analysis

Preventing Bias in Qualitative Research

Co-coding

Member checking

Reflexivity in qualitative research

Reflexivity Example

Establishing Trustworthiness in Qualitative Research

1. Credibility in Qualitative Research

2. Transferability in Qualitative Research

3. Dependability in Qualitative Research

4. Confirmability in Qualitative Research

Audit Trails in Qualitative Research

Discovery of unexpected data

Flexibility

Naturalistic settings

Limitations

Subjectivity

Limited generalizability

Extraneous variables

What sample size should qualitative research be?

Are surveys qualitative or quantitative?

What are the ethical considerations of qualitative research?

What is triangulation in qualitative research?

Why is qualitative research important?

What is coding data in qualitative research?

What is the difference between qualitative and quantitative research?

What is trustworthiness in qualitative research?

What is data saturation in qualitative research?

Why is sampling in qualitative research important?

What is narrative analysis?

5.1 Assumptions underlying research

Embrace philosophy

Theoretical and philosophical fluency

Building your ice float

Ontology: Assumptions about what is real and true

Objective vs. subjective ontologies

Epistemology: Assumptions about how we know things