write qualitative research methodology

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.

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What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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 organisation?
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, frequently asked questions about qualitative research.

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

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

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 organisations to understand their cultures.
	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.

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

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

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

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

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

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

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

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

Qualitative data analysis
Approach	When to use	Example
	To describe and categorise 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 analysing and interpreting their data. Qualitative research suffers from:

Unreliability

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

Subjectivity

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

Limited generalisability

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

Labour-intensive

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

The type of research you conducted
How you collected and analyzed your data
Any tools or materials you used in the research
How you mitigated or avoided research biases
Why you chose these methods
Your methodology section should generally be written in the past tense .
Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

Aim to describe the characteristics of something?
Explore an under-researched topic?
Establish a causal relationship?

And what type of data did you need to achieve this aim?

Quantitative data , qualitative data , or a mix of both?
Primary data collected yourself, or secondary data collected by someone else?
Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

Why is this the best way to answer your research question?
Is this a standard methodology in your field, or does it require justification?
Were there any ethical considerations involved in your choices?
What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

How did you design the questionnaire?
What form did your questions take (e.g., multiple choice, Likert scale )?
Were your surveys conducted in-person or virtually?
What sampling method did you use to select participants?
What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

How did you design the experiment ?
How did you recruit participants?
How did you manipulate and measure the variables ?
What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

Where did you source the material?
How was the data originally produced?
What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Information bias
Omitted variable bias
Regression to the mean
Survivorship bias
Undercoverage bias
Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

How did you find and select participants?
How many participants took part?
What form did the interviews take ( structured , semi-structured , or unstructured )?
How long were the interviews?
How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

What group or community did you observe? How long did you spend there?
How did you gain access to this group? What role did you play in the community?
How long did you spend conducting the research? Where was it located?
How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

What type of materials did you analyze?
How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

The Hawthorne effect
Observer bias
The placebo effect
Response bias and Nonresponse bias
The Pygmalion effect
Recall bias
Social desirability bias
Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
Which software you used (e.g., SPSS, Stata or R)
Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

Show that you followed established practice for your type of research
Discuss how you decided on your approach by evaluating existing research
Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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.

Normal distribution
Measures of central tendency
Chi square tests
Confidence interval
Quartiles & Quantiles

Methodology

Cluster sampling
Stratified sampling
Thematic analysis
Cohort study
Peer review
Ethnography

Research bias

Implicit bias
Cognitive bias
Conformity bias
Hawthorne effect
Availability heuristic
Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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.

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

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

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Qualitative Research: Characteristics, Design, Methods & Examples

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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.

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Research Methodology

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

Provide an overview of the research problem and the need for a research methodology section
Outline the main research questions and objectives

II. Research Design

Explain the research design chosen and why it is appropriate for the research question(s) and objectives
Discuss any alternative research designs considered and why they were not chosen
Describe the research setting and participants (if applicable)

III. Data Collection Methods

Describe the methods used to collect data (e.g., surveys, interviews, observations)
Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or software used for data analysis

V. Ethical Considerations

Discuss any ethical issues that may arise from the research and how they were addressed
Explain how informed consent was obtained (if applicable)
Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

Summarize the key aspects of the research methodology section
Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

Research Methodology	Research Methods
Research methodology refers to the philosophical and theoretical frameworks that guide the research process.	refer to the techniques and procedures used to collect and analyze data.
It is concerned with the underlying principles and assumptions of research.	It is concerned with the practical aspects of research.
It provides a rationale for why certain research methods are used.	It determines the specific steps that will be taken to conduct research.
It is broader in scope and involves understanding the overall approach to research.	It is narrower in scope and focuses on specific techniques and tools used in research.
It is concerned with identifying research questions, defining the research problem, and formulating hypotheses.	It is concerned with collecting data, analyzing data, and interpreting results.
It is concerned with the validity and reliability of research.	It is concerned with the accuracy and precision of data.
It is concerned with the ethical considerations of research.	It is concerned with the practical considerations of research.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is qualitative research.

Qualitative research methodologies seek to capture information that often can't be expressed numerically. These methodologies often include some level of interpretation from researchers as they collect information via observation, coded survey or interview responses, and so on. Researchers may use multiple qualitative methods in one study, as well as a theoretical or critical framework to help them interpret their data.

Qualitative research methods can be used to study:

How are political and social attitudes formed?
How do people make decisions?
What teaching or training methods are most effective?

Qualitative Research Approaches

Action research.

In this type of study, researchers will actively pursue some kind of intervention, resolve a problem, or affect some kind of change. They will not only analyze the results but will also examine the challenges encountered through the process.

Ethnography

Ethnographies are an in-depth, holistic type of research used to capture cultural practices, beliefs, traditions, and so on. Here, the researcher observes and interviews members of a culture — an ethnic group, a clique, members of a religion, etc. — and then analyzes their findings.

Grounded Theory

Researchers will create and test a hypothesis using qualitative data. Often, researchers use grounded theory to understand decision-making, problem-solving, and other types of behavior.

Narrative Research

Researchers use this type of framework to understand different aspects of the human experience and how their subjects assign meaning to their experiences. Researchers use interviews to collect data from a small group of subjects, then discuss those results in the form of a narrative or story.

Phenomenology

This type of research attempts to understand the lived experiences of a group and/or how members of that group find meaning in their experiences. Researchers use interviews, observation, and other qualitative methods to collect data.

Often used to share novel or unique information, case studies consist of a detailed, in-depth description of a single subject, pilot project, specific events, and so on.

Hossain, M.S., Runa, F., & Al Mosabbir, A. (2021). Impact of COVID-19 pandemic on rare diseases: A case study on thalassaemia patients in Bangladesh. Public Health in Practice, 2(100150), 1-3.
Nožina, M. (2021). The Czech Rhino connection: A case study of Vietnamese wildlife trafficking networks’ operations across central Europe. European Journal on Criminal Policy and Research, 27(2), 265-283.

Focus Groups

Researchers will recruit people to answer questions in small group settings. Focus group members may share similar demographics or be diverse, depending on the researchers' needs. Group members will then be asked a series of questions and have their responses recorded. While these responses may be coded and discussed numerically (e.g., 50% of group members responded negatively to a question), researchers will also use responses to provide context, nuance, and other details.

Dichabeng, P., Merat, N., & Markkula, G. (2021). Factors that influence the acceptance of future shared automated vehicles – A focus group study with United Kingdom drivers. Transportation Research: Part F, 82, 121–140.
Maynard, E., Barton, S., Rivett, K., Maynard, O., & Davies, W. (2021). Because ‘grown-ups don’t always get it right’: Allyship with children in research—From research question to authorship. Qualitative Research in Psychology, 18(4), 518–536.

Observational Study

Researchers will arrange to observe (usually in an unobtrusive way) a set of subjects in specific conditions. For example, researchers might visit a school cafeteria to learn about the food choices students make or set up trail cameras to collect information about animal behavior in the area.

He, J. Y., Chan, P. W., Li, Q. S., Li, L., Zhang, L., & Yang, H. L. (2022). Observations of wind and turbulence structures of Super Typhoons Hato and Mangkhut over land from a 356 m high meteorological tower. Atmospheric Research, 265(105910), 1-18.
Zerovnik Spela, Kos Mitja, & Locatelli Igor. (2022). Initiation of insulin therapy in patients with type 2 diabetes: An observational study. Acta Pharmaceutica, 72(1), 147–157.

Open-Ended Surveys

Unlike quantitative surveys, open-ended surveys require respondents to answer the questions in their own words.

Mujcic, A., Blankers, M., Yildirim, D., Boon, B., & Engels, R. (2021). Cancer survivors’ views on digital support for smoking cessation and alcohol moderation: a survey and qualitative study. BMC Public Health, 21(1), 1-13.
Smith, S. D., Hall, J. P., & Kurth, N. K. (2021). Perspectives on health policy from people with disabilities. Journal of Disability Policy Studies, 32(3), 224–232.

Structured or Semi-Structured Interviews

Researchers will recruit a small number of people who fit pre-determined criteria (e.g., people in a certain profession) and ask each the same set of questions, one-on-one. Semi-structured interviews will include opportunities for the interviewee to provide additional information they weren't asked about by the researcher.

Gibbs, D., Haven-Tang, C., & Ritchie, C. (2021). Harmless flirtations or co-creation? Exploring flirtatious encounters in hospitable experiences. Tourism & Hospitality Research, 21(4), 473–486.
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Here's What You Need to Understand About Research Methodology

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

Analyze and understand research methodologies faster with SciSpace Copilot

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

When someone tries to duplicate or expand upon your research after few years.
If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

Observation
Experimental
Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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What is a theoretical framework?

Developing a theoretical framework for your dissertation is one of the key elements of a qualitative research project. Through writing your literature review, you are likely to have identified either a problem that need ‘fixing’ or a gap that your research may begin to fill.

The theoretical framework is your toolbox . In the toolbox are your handy tools: a set of theories, concepts, ideas and hypotheses that you will use to build a solution to the research problem or gap you have identified.

The methodology is the instruction manual: the procedure and steps you have taken, using your chosen tools, to tackle the research problem.

Why do I need a theoretical framework?

Developing a theoretical framework shows that you have thought critically about the different ways to approach your topic, and that you have made a well-reasoned and evidenced decision about which approach will work best. theoretical frameworks are also necessary for solving complex problems or issues from the literature, showing that you have the skills to think creatively and improvise to answer your research questions. they also allow researchers to establish new theories and approaches, that future research may go on to develop., how do i create a theoretical framework for my dissertation.

First, select your tools. You are likely to need a variety of tools in qualitative research – different theories, models or concepts – to help you tackle different parts of your research question.

An overview of what to include in a theoretical framework: theories, models, ideologies, concepts, assumptions and perspectives.

When deciding what tools would be best for the job of answering your research questions or problem, explore what existing research in your area has used. You may find that there is a ‘standard toolbox’ for qualitative research in your field that you can borrow from or apply to your own research.

You will need to justify why your chosen tools are best for the job of answering your research questions, at what stage they are most relevant, and how they relate to each other. Some theories or models will neatly fit together and appear in the toolboxes of other researchers. However, you may wish to incorporate a model or idea that is not typical for your research area – the ‘odd one out’ in your toolbox. If this is the case, make sure you justify and account for why it is useful to you, and look for ways that it can be used in partnership with the other tools you are using.

You should also be honest about limitations, or where you need to improvise (for example, if the ‘right’ tool or approach doesn’t exist in your area).

This video from the Skills Centre includes an overview and example of how you might create a theoretical framework for your dissertation:

How do I choose the 'right' approach?

When designing your framework and choosing what to include, it can often be difficult to know if you’ve chosen the ‘right’ approach for your research questions. One way to check this is to look for consistency between your objectives, the literature in your framework, and your overall ethos for the research. This means ensuring that the literature you have used not only contributes to answering your research objectives, but that you also use theories and models that are true to your beliefs as a researcher.

Reflecting on your values and your overall ambition for the project can be a helpful step in making these decisions, as it can help you to fully connect your methodology and methods to your research aims.

Should I reflect on my position as a researcher?

If you feel your position as a researcher has influenced your choice of methods or procedure in any way, the methodology is a good place to reflect on this. Positionality acknowledges that no researcher is entirely objective: we are all, to some extent, influenced by prior learning, experiences, knowledge, and personal biases. This is particularly true in qualitative research or practice-based research, where the student is acting as a researcher in their own workplace, where they are otherwise considered a practitioner/professional. It's also important to reflect on your positionality if you belong to the same community as your participants where this is the grounds for their involvement in the research (ie. you are a mature student interviewing other mature learners about their experences in higher education).

The following questions can help you to reflect on your positionality and gauge whether this is an important section to include in your dissertation (for some people, this section isn’t necessary or relevant):

How might my personal history influence how I approach the topic?
How am I positioned in relation to this knowledge? Am I being influenced by prior learning or knowledge from outside of this course?
How does my gender/social class/ ethnicity/ culture influence my positioning in relation to this topic?
Do I share any attributes with my participants? Are we part of a s hared community? How might this have influenced our relationship and my role in interviews/observations?
Am I invested in the outcomes on a personal level? Who is this research for and who will feel the benefits?

One option for qualitative projects is to write an extended literature review. This type of project does not require you to collect any new data. Instead, you should focus on synthesising a broad range of literature to offer a new perspective on a research problem or question.

The main difference between an extended literature review and a dissertation where primary data is collected, is in the presentation of the methodology, results and discussion sections. This is because extended literature reviews do not actively involve participants or primary data collection, so there is no need to outline a procedure for data collection (the methodology) or to present and interpret ‘data’ (in the form of interview transcripts, numerical data, observations etc.) You will have much more freedom to decide which sections of the dissertation should be combined, and whether new chapters or sections should be added.

Here is an overview of a common structure for an extended literature review:

A structure for the extended literature review, showing the results divided into multiple themed chapters.

Introduction

Provide background information and context to set the ‘backdrop’ for your project.
Explain the value and relevance of your research in this context. Outline what do you hope to contribute with your dissertation.
Clarify a specific area of focus.
Introduce your research aims (or problem) and objectives.

Literature review

You will need to write a short, overview literature review to introduce the main theories, concepts and key research areas that you will explore in your dissertation. This set of texts – which may be theoretical, research-based, practice-based or policies – form your theoretical framework. In other words, by bringing these texts together in the literature review, you are creating a lens that you can then apply to more focused examples or scenarios in your discussion chapters.

Methodology

As you will not be collecting primary data, your methodology will be quite different from a typical dissertation. You will need to set out the process and procedure you used to find and narrow down your literature. This is also known as a search strategy.

Including your search strategy

A search strategy explains how you have narrowed down your literature to identify key studies and areas of focus. This often takes the form of a search strategy table, included as an appendix at the end of the dissertation. If included, this section takes the place of the traditional 'methodology' section.

If you choose to include a search strategy table, you should also give an overview of your reading process in the main body of the dissertation. Think of this as a chronology of the practical steps you took and your justification for doing so at each stage, such as:

Your key terms, alternatives and synonyms, and any terms that you chose to exclude.
Your choice and combination of databases;
Your inclusion/exclusion criteria, when they were applied and why. This includes filters such as language of publication, date, and country of origin;
You should also explain which terms you combined to form search phrases and your use of Boolean searching (AND, OR, NOT);
Your use of citation searching (selecting articles from the bibliography of a chosen journal article to further your search).
Your use of any search models, such as PICO and SPIDER to help shape your approach.
Search strategy template A simple template for recording your literature searching. This can be included as an appendix to show your search strategy.

The discussion section of an extended literature review is the most flexible in terms of structure. Think of this section as a series of short case studies or ‘windows’ on your research. In this section you will apply the theoretical framework you formed in the literature review – a combination of theories, models and ideas that explain your approach to the topic – to a series of different examples and scenarios. These are usually presented as separate discussion ‘chapters’ in the dissertation, in an order that you feel best fits your argument.

Think about an order for these discussion sections or chapters that helps to tell the story of your research. One common approach is to structure these sections by common themes or concepts that help to draw your sources together. You might also opt for a chronological structure if your dissertation aims to show change or development over time. Another option is to deliberately show where there is a lack of chronology or narrative across your case studies, by ordering them in a fragmentary order! You will be able to reflect upon the structure of these chapters elsewhere in the dissertation, explaining and defending your decision in the methodology and conclusion.

A summary of your key findings – what you have concluded from your research, and how far you have been able to successfully answer your research questions.

Recommendations – for improvements to your own study, for future research in the area, and for your field more widely.
Emphasise your contributions to knowledge and what you have achieved.

Alternative structure

Depending on your research aims, and whether you are working with a case-study type approach (where each section of the dissertation considers a different example or concept through the lens established in your literature review), you might opt for one of the following structures:

Splitting the literature review across different chapters:

This structure allows you to pull apart the traditional literature review, introducing it little by little with each of your themed chapters. This approach works well for dissertations that attempt to show change or difference over time, as the relevant literature for that section or period can be introduced gradually to the reader.

Whichever structure you opt for, remember to explain and justify your approach. A marker will be interested in why you decided on your chosen structure, what it allows you to achieve/brings to the project and what alternatives you considered and rejected in the planning process. Here are some example sentence starters:

In qualitative studies, your results are often presented alongside the discussion, as it is difficult to include this data in a meaningful way without explanation and interpretation. In the dsicussion section, aim to structure your work thematically, moving through the key concepts or ideas that have emerged from your qualitative data. Use extracts from your data collection - interviews, focus groups, observations - to illustrate where these themes are most prominent, and refer back to the sources from your literature review to help draw conclusions.

Here's an example of how your data could be presented in paragraph format in this section:

In analysing the interview data, two themes emerged which will be discussed in this section. These themes were: the complexity and challenges of working with families and the professional satisfaction and challenges of program planning for children in preschool or childcare.	Introduction to the key themes identified from the interviews.
For each of these graduates, their work with children was clearly the area of their professional lives that was bringing the most satisfaction, although there were some challenges identified. In the interviews, the data reveal that they were all seeking ways to improve their pedagogy and achieving success in different ways…	Summary of theme A identified from the data.
Angela suggested that in her second year of teaching she had changed in that she was programming in a "more child oriented" way. She discussed this change:	Specific example from your interviews to support this theme, embedded as a direct quotation.
These comments seem to provide evidence that Angela is growing professionally for two reasons. First, the ability to identify changes in her program suggests to me that she has deeper pedagogical knowledge gained through critical reflection on her practice, and second, there is congruence between her expressed beliefs and the practice she describes… This is supported by…(Source A; Source B).	Discussion/ interpretation of the findings, what they suggest, and what conclusions you can draw. Refer back to texts from the literature review that support your conclusions.

Example from 'Reporting and discussing your findings ', Monash University .

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Article Contents

Introduction, challenging some common methodological assumptions about online qualitative surveys, ten practical tips for designing, implementing and analysing online qualitative surveys, acknowledgements, conflict of interest statement, data availability, ethical approval.

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Methodological and practical guidance for designing and conducting online qualitative surveys in public health

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Samantha L Thomas, Hannah Pitt, Simone McCarthy, Grace Arnot, Marita Hennessy, Methodological and practical guidance for designing and conducting online qualitative surveys in public health, Health Promotion International , Volume 39, Issue 3, June 2024, daae061, https://doi.org/10.1093/heapro/daae061

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Online qualitative surveys—those surveys that prioritise qualitative questions and interpretivist values—have rich potential for researchers, particularly in new or emerging areas of public health. However, there is limited discussion about the practical development and methodological implications of such surveys, particularly for public health researchers. This poses challenges for researchers, funders, ethics committees, and peer reviewers in assessing the rigour and robustness of such research, and in deciding the appropriateness of the method for answering different research questions. Drawing and extending on the work of other researchers, as well as our own experiences of conducting online qualitative surveys with young people and adults, we describe the processes associated with developing and implementing online qualitative surveys and writing up online qualitative survey data. We provide practical examples and lessons learned about question development, the importance of rigorous piloting strategies, use of novel techniques to prompt detailed responses from participants, and decisions that are made about data preparation and interpretation. We consider reviewer comments, and some ethical considerations of this type of qualitative research for both participants and researchers. We provide a range of practical strategies to improve trustworthiness in decision-making and data interpretation—including the importance of using theory. Rigorous online qualitative surveys that are grounded in qualitative interpretivist values offer a range of unique benefits for public health researchers, knowledge users, and research participants.

Public health researchers are increasingly using online qualitative surveys.

There is still limited practical and methodological information about the design and implementation of these studies.

Building on Braun and Clarke (2013) , Terry and Braun (2017) and Braun et al . (2021) , we reflect on the methodological and practical lessons we have learnt from our own experience with conducting online qualitative surveys.

We provide guidance and practical examples about the design, implementation and analysis processes.

We argue that online qualitative surveys have rich potential for public health researchers and can be an empowering and engaging way to include diverse populations in qualitative research.

Public health researchers mostly engage in experiential (interpretive) qualitative approaches ( Braun and Clarke, 2013 ). These approaches are ‘centred on the exploration of participants’ subjective experiences and sense-making’ [( Braun and Clarke, 2021c ), p. 39]. Given the strong focus in public health on social justice, power and inequality, researchers proactively use the findings from these qualitative studies—often in collaboration with lived experience experts and others who are impacted by key decisions ( Reed et al ., 2024 )—to advocate for changes to public health policy and practice. There is also an important level of theoretical, methodological and empirical reflection that is part of the public health researcher’s role. For example, as qualitative researchers actively construct and interpret meaning from data, they constantly challenge their assumptions, their way of knowing and their way of ‘doing’ research ( Braun and Clarke, 2024 ). This reflexive practice also includes considering how to develop more inclusive opportunities for people to participate in research and to share their opinions and experiences about the issues that matter to them.

While in-depth interviews and focus groups provide rich and detailed narratives that are central to understanding people’s lives, these forms of data collection may sometimes create practical barriers for both researchers and participants. For example, they can be time consuming, and the power dynamics associated with face-to-face interviews (even in online settings) may make them less accessible for groups that are marginalized or stigmatized ( Edwards and Holland, 2020 ). While some population subgroups (and contexts) may suit (or require) face-to-face qualitative data collection approaches, others may lend themselves to different forms of data collection. Young people, for example, may be keen to be civically involved in research about the issues that matter to them, such as the climate crisis, but they may find it more convenient and comfortable using anonymized digital technologies to do so ( Arnot et al ., 2024b ). As such, part of our reflexive practice as public health researchers must be to explore, and be open to, a range of qualitative methodological approaches that could be more convenient, less intimidating and more engaging for a diverse range of population subgroups. This includes thinking about pragmatic ways of operationalizing qualitative data collection methods. How can we develop methods and engagement strategies that enable us to gain insights from a diverse range of participants about new issues or phenomenon that may pose threats to public health, or look at existing issues in new ways?

Advancements in online data collection methods have also created new options for researchers and participants about how they can be involved in qualitative studies ( Hensen et al ., 2021 ; Chen, 2023 ; Fan et al ., 2024 ). Online qualitative surveys—those surveys that prioritize qualitative values and questions—have rich potential for qualitative researchers. Braun and Clarke (2013 , p. 135) state that qualitative surveys:

…consist of a series of open-ended questions about a topic, and participants type or hand-write their responses to each question. They are self-administered; a researcher-administered qualitative survey would basically be an interview.

While these types of studies are increasingly utilized in public health, researchers have highlighted that there is still relatively limited discussion about the methodological and practical implications of these surveys ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ). This poses challenges for qualitative public health researchers, funders, ethics committees and peer reviewers in assessing the purpose, rigour and contribution of such research, and in deciding the appropriateness of the method for answering different research questions.

Using examples from online qualitative surveys that we have been involved in, this article discusses a range of methodological and practical lessons learnt from developing, implementing and analysing data from these types of surveys. While we do not claim to have all the answers, we aim to develop and extend on the methodological and practical guidance from Braun and Clarke (2013) , Terry and Braun (2017) and Braun et al . (2021) about the potential for online qualitative surveys. This includes how they can provide a rigorous ‘wide-angle picture’ [( Toerien and Wilkinson, 2004 ), p. 70] from a diverse range of participants about contemporary public health phenomena.

Figure 1 aims to develop and extend on the key points made by Braun and Clarke (2013) , Terry and Braun (2017) and Braun et al . (2021) , which provide the methodological and empirical foundation for our article.

: Methodological considerations in conducting online qualitative surveys.

Harnessing interpretivist approaches and qualitative values in online qualitative surveys

Online qualitative surveys take many forms. They may be fully qualitative or qualitative dominant—mostly qualitative with some quantitative questions ( Terry and Braun, 2017 ). There are also many different ways of conducting these studies—from using a smaller number of questions that engage specific population groups or knowledge users in understanding detailed experiences ( Hennessy and O’Donoghue, 2024 ), to a larger number of questions (which may use market research panel providers to recruit participants), that seek broader opinions and attitudes about public health issues ( Marko et al ., 2022a ; McCarthy et al ., 2023 ; Arnot et al ., 2024a ). However, based on our experiences of applying for grant funding and conducting, publishing and presenting these studies, there are still clear misconceptions and uncertainties about these types of surveys.

One of the concerns raised about online qualitative surveys is how they are situated within broader qualitative values and approaches. This includes whether they can provide empirically innovative, rigorous, rich and theoretically grounded qualitative contributions to knowledge. Our experience is that online qualitative surveys have the most potential when they harness the values of interpretivist ‘Big Q’ approaches to collect information from a diverse range of participants about their experiences, opinions and practices ( Braun et al ., 2021 ). The distinction between positivist (small q) and interpretivist (Big Q) approaches to online qualitative surveys is an important one that requires some initial methodological reflection, particularly in considering the (largely unhelpful) critiques that are made about the rigour and usefulness of these surveys. These critiques often overlook the theoretical underpinnings and qualitative values inherent in such surveys. For example, while there may be a tendency to think of surveys and survey data as atheoretical and descriptive, the use of theory is central in informing online qualitative surveys. For example, Varpio and Ellaway (2021 , p. 343) explain that theory can ‘offer explanations and detailed premises that we can wrestle with, agree with, disagree with, reject and/or accept’. This includes the research design, the approach to data collection and analysis, the interpretation of findings and the conclusions that are drawn. Theory is also important in helping researchers to engage in reflexive practice. The use of theory is essential in progressing online qualitative surveys beyond description and towards in-depth interpretation and explanations—thus facilitating a deeper understanding of the studied phenomenon ( Collins and Stockton, 2018 ; Jamie and Rathbone, 2022 ).

Considering the assumptions that online qualitative surveys can only collect ‘thin’ data

The main assumptions about online qualitative surveys are that they can only collect ‘thin’ textual data, and that they are not flexible enough as a data collection tool for researchers to prompt or ask follow-up questions or to co-create detailed and rich data with participants ( Braun and Clarke, 2013 ; Terry and Clarke, 2017 ; Braun et al ., 2021 ). While we acknowledge that the type of data that is collected in these types of studies is different from those in in-depth interview studies, these surveys may be a more accessible and engaging way to collect rich insights from a diverse range of participants who may otherwise not participate in qualitative research ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ). Despite this, peer reviewers can question the depth of information that may be collected in these studies. Assumptions about large but ‘thin’ datasets may also mean that researchers, funders and reviewers take (and perhaps expect) a more positivist approach to the design and analytical processes associated with these surveys. For example, the multiple topics and questions, larger sample sizes, and the generally smaller textual responses that online qualitative surveys generate may lead researchers to approach these surveys using more descriptive and atheoretical paradigms. This approach may focus on ‘measuring’ phenomena, using variables, developing thinner analytical description and adding numerical values to the number of responses for different categories or themes.

We have found that assumptions can also impact the review processes associated with these types of studies, receiving critiques from those with both positivist and interpretivist positions. Positivist critiques focus on matters associated with whether the samples are ‘representative’, and the flaws associated with ‘self-selecting convenience’ samples. Critiques from interpretivist colleagues question why such large sample sizes are needed for qualitative studies, seeing surveys as a less rigorous method for gaining rich and meaningful data. For example, we have had reviewers query the scope and depth of the analysis of the data that we present from these studies because they are concerned that the type of data collected lacks depth and does not fully contextualize and explain how participants think about issues. We have also had reviewers request that we should return to the study to collect quantitative data to supplement the qualitative findings of the survey. They also question how ‘representative’ the samples are of population groups. These comments, of course, are not unique to online qualitative surveys but do highlight the difficulty that reviewers may have in placing and situating these types of studies in broader qualitative approaches. With this in mind, we have also found that some reviewers can ask for additional information to justify both the use of online qualitative surveys and why we have chosen these over other qualitative approaches. For example, reviewers have asked us to justify why we have chosen an online qualitative survey and also to explain what we may have missed out on by not conducting in-depth interviews or quantitative or mixed methods surveys instead.

Requests for ‘numbers’ and ‘strategies to minimize bias’

While there is now a general understanding that attributing ‘numbers’ to qualitative data is largely unhelpful and inappropriate ( Chowdhury, 2015 ), there may be expectations that the larger sample sizes associated with online qualitative surveys enable researchers to provide numerical indicators of data. Rather than focusing on the ‘artfully interpretive’ techniques used to analyse and construct themes from the data ( Finlay, 2021 ), we have found that reviewers often ask us to provide numerical information about how many people provided different responses to different questions (or constructed themes), and the number at which ‘saturation’ was determined. Reviewer feedback that we have received about analytical processes has asked for detailed explanations about why attempts to ‘minimize bias’ (including calculations of inter-rater reliability and replicability of data quality) were not used. This demonstrates that peer reviewers may misinterpret the interpretivist values that guide online qualitative surveys, asking for information that is essentially ‘meaningless’ in qualitative paradigms in which researchers’ subjectivity ‘sculpts’ the knowledge that is produced ( Braun and Clarke, 2021a ).

The benefits and limitations of online qualitative surveys for participants, researchers and knowledge users

As well as a ‘wide-angle picture’ [( Toerien and Wilkinson, 2004 ), p. 70] on phenomenon, online qualitative surveys can also: (i) generate both rich and focused data about perceptions and practices, and (ii) have multiple participatory and practical advantages—including helping to overcome barriers to research participation ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ). For researchers , online qualitative surveys can be a more cost-effective alternative ( Braun and Clarke, 2013 ; Terry and Braun, 2017 )—they are generally more time-efficient and less labour-intensive (particularly if working with market research companies to recruit panels). They are also able to reach a broad range of participants—such as those who are geographically dispersed ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ), and those who may not have internet connectivity that is reliable enough to complete online interviews (a common issue for individuals living in regional or rural settings) ( de Villiers et al ., 2022 ). We are also more able to engage young people in qualitative research through online surveys, perhaps partly due to extensive panel company databases but also because they may be a more accessible and familiar way for young people to participate in research. The ability to quickly investigate new public health threats from the perspective of lived experience can also provide important information for researchers, providing justification for new areas of research focus, including setting agendas and advocating for the need for funding (or policy attention). Collecting data from a diverse range of participants—including from those who hold views that we may see as less ‘politically acceptable’, or inconsistent with our own public health reasoning about health and equity—is important in situating and contextualizing community attitudes towards particular issues.

For participants , benefits include having a degree of autonomy and control over their participation, including completing the survey at a time and place that suits them, and the anonymous nature of participation (that may be helpful for people from highly stigmatized groups). Participants can take time to reflect on their responses or complete the survey, and may feel more able to ‘talk back’ to the researcher about the framing of questions or the purpose of the research ( Braun et al ., 2021 ). We would also add that a benefit of these types of studies is that participants can also drop out of the study easily if the survey does not interest them or meet their expectations—something that we think might be more onerous or uncomfortable for participants in an interview or focus group.

For knowledge users, including advocates, service providers and decision-makers, qualitative research provides an important form of evidence, and the ‘wide-angle picture' [( Toerien and Wilkinson, 2004 ), p. 70] on issues from a diverse range of individuals in a community or population can be a powerful advocacy tool. Online qualitative surveys can also provide rapid insights into how changes to policy and practice may impact population subgroups in different ways.

There are, of course, some limitations associated with online qualitative surveys ( Braun et al ., 2021 ; Marko et al ., 2022b ). For example, there is no ability to engage individuals in a ‘traditional’ conversation or to prompt or probe meaning in the interactive ways that we are familiar with in interview studies. There is less ability to refine the questions that we ask participants in an iterative way throughout a study based on participant responses (particularly when working with market research panel companies). There may also be barriers associated with written literacy, access to digital technologies and stable internet connections ( Braun et al ., 2021 ). They may also not be the most suitable for individuals who have different ways of ‘knowing, being and doing’ qualitative research—including Indigenous populations [( Kennedy et al ., 2022 ), p. 1]. All of these factors should be taken into consideration when deciding whether online qualitative surveys are an appropriate way of collecting data. Finally, while these types of surveys can collect data quickly ( Marko et al ., 2022b ), there can also be additional decision-making processes related to data preparation and inclusion that can be time-consuming.

There are a range of practical considerations that can improve the rigour, trustworthiness and quality of online qualitative survey data. Again, developing and expanding on ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al ., 2021 ), Figure 2 gives an overview of some key practical considerations associated with the design, implementation and analysis of these surveys. We would also note that before starting your survey design, you should be aware that people may use different types of technology to complete the survey, and in different spaces. For example, we cannot assume that people will be sitting in front of a computer or laptop at home or in the office, with people more likely to complete surveys on a mobile phone, perhaps on a train or bus on the way to work or school.

: Top ten practical tips for conducting online qualitative surveys.

Survey design

Creating an appropriate and accessible structure

The first step in designing an online qualitative survey is to plan the structure of your survey. This step is important because the structure influences the way that participants interact with and participate through the survey. The survey structure helps to create an ‘environment’ that helps participants to share their perspectives, prompt their views and develop their ideas ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ). Similar to an interview study, the structure of the survey guides participants from one set of questions (and topics) to the next. It is important to consider the ordering of topics to enable participants to complete a survey that has a logical flow, introduces participants to concepts and allows them to develop their depth of responses.

Before participants start the survey, we provide a clear and simple lay language summary of the survey. Because many individuals will be familiar with completing quantitative surveys, we include a welcoming statement and reiterate the qualitative nature of the survey, stating that their answers can be about their own experiences:

Thank you for agreeing to take part in this survey about [topic] . This survey involves writing responses to questions rather than checking boxes.

We then clearly reiterate the purpose of the survey, providing a short description of the topic that we are investigating. We state that we do not seek to collect any data that is identifiable, that we are interested in participants perspectives, that there are no right or wrong answers, and that participants can withdraw from the survey at any time without giving a reason.

Similar to Braun et al . (2021) , we start our surveys with questions about demographic and related characteristics (which we often call ‘ participant/general characteristics ’). These can be discrete choice questions, but can also utilize open text—for example, in relation to gender identity. We have found that there is always a temptation with surveys to ask many questions about the demographic characteristics of participants. However, we caution that too many questions can be intrusive for participants and can take away valuable time from open-text questions, which are the core focus of the survey. We recommend asking participant characteristic and demographic questions that situate and contextualize the sample ( Elliott et al ., 1999 ).

We generally start the open-text sections of these surveys by asking broad introductory questions about the topic. This might include questions such as: ‘Please describe the main reasons you drink alcohol ’, and ‘W hat do you think are the main impacts of climate change on the world? ’ We have found that these types of questions get participants used to responding to open-text questions relevant to the study’s research questions and aims. For each new topic of investigation (which are based on our theoretical concepts and overall study aims and research questions), we provide a short explanation about what we will ask participants. We also use tools and text to signpost participant progress through the survey. This can be a valuable way to avoid high attrition rates where participants exit the survey because they are getting fatigued and are unclear when the survey will end:

Great! We are just over half-way through the survey.

We ask more detailed questions that are more aligned with our theoretical concepts in the middle of the survey. For example, we may start with broad questions about a harmful industry and their products (such as gambling, vaping or alcohol) and then in the middle of the survey ask more detailed questions about the commercial determinants of health and the specific tactics that these industries use (for example, about product design, political tactics, public relations strategies or how these practices may influence health and equity). In relation to these more complex questions, it is particularly important that we reiterate that there are no wrong answers and try to include encouraging text throughout the survey:

There are no right or wrong answers—we are curious to hear your opinions .

We always try to end the survey on a positive. While these types of questions depend on the study, we try to ask questions which enable participants to reflect on what could be done to address or improve an issue. This might include their attitudes about policy, or what they would say to those in positions of power:

What do you think should be done to protect young people from sports betting advertising on social media? If there was one thing that could be done to prevent young people from being exposed to the risks associated with alcohol, cigarettes, vaping, or gambling, what would it be? If you could say one thing to politicians about climate change, what would it be?

Finally, we ask participants if there is anything we have missed or if they have anything else to add, sometimes referred to as a ‘clean-up’ question ( Braun and Clarke, 2013 ). The following provides a few examples of how we have framed these questions in some of our studies:

Is there anything you would like to say about alcohol, cigarettes, vaping, and gambling products that we have not covered? Is there anything we haven’t asked you about the advertising of alcohol to women that you would like us to know?

Considering the impact of the length of the survey on responses

The length of the survey (both the number of questions and the time it takes an individual to complete the survey) is guided by a range of methodological and practical considerations and will vary between studies ( Braun and Clarke, 2013 ). Many factors will influence completion times. We try to give individuals a guide at the start of the survey about how long we think it will take to complete the survey (for example, between 20 and 30 minutes). We highlight that it may take people a little longer or shorter and that people are able to leave their browser open or save the survey and come back to finish it later. For our first few online qualitative surveys, we found that we asked lots of questions because we felt less in control of being able to prompt or ask follow-up questions from participants. However, we have learned that less is more! Asking too many questions may lead to more survey dropouts, and may significantly reduce the textual quality of the information that you receive from participants ( Braun and Clarke, 2013 ; Terry and Clarke, 2017 ). This includes considering how the survey questions might lead to repetition, which may be annoying for participants, leading to responses such as ‘like I’ve already said’ , ‘I’ve already answered that’ or ‘see above’ .

Providing clear and simple guidance

When designing an online qualitative survey, we try to think of ways to make participation in the survey engaging. We do not want individuals to feel that we are ‘mining’ them for data. Rather we want to demonstrate that we are genuinely interested in their perspectives and views. We use a range of mechanisms to do this. Because there is no opportunity to verbally explain or clarify concepts to participants, there is a particular need to ensure that the language used is clear and accessible ( Braun and Clarke, 2013 ; Terry and Clarke, 2017 ). If language or concepts are complex, you are more likely to receive ‘I don’t know’ responses to your questions. We need to remember that participants have a range of written and comprehension skills, and inclusive and accessible language is important. We also never try to assume a level of knowledge about an issue (unless we have specifically asked for participants who are aware and engaged in an issue—such as women who drink alcohol) ( Pitt et al ., 2023 ). This includes avoiding highly technical or academic language and not making assumptions that the individuals completing the survey will understand concepts in the same way that researchers do ( Braun and Clarke, 2013 ). Clearly explaining concepts or using text or images to prompt memories can help to overcome this:

Some big corporations (such as the tobacco, vaping, alcohol, junk food, or gambling industries) sponsor women's sporting teams or clubs, or other events. You might see sponsor logos on sporting uniforms, or at sporting grounds, or sponsoring a concert or arts event.

At all times, we try to centre the language that we use with the population from which we are seeking responses. Advisory groups can be particularly helpful in framing language for different population subgroups. We often use colloquial language, even if it might not be seen as the ‘correct’ academic language or terminology. Where possible, we also try to define theoretical concepts in a clear and easy to understand way. For example, in our study investigating parent perceptions of the impact of harmful products on young people, we tried to clearly define ‘normalization’:

In this section we ask you about some of the perceived health impacts of the above products on young people. We also ask you about the normalisation of these products for young people. When we talk about normalisation, we are thinking about the range of factors that might make these products more acceptable for young people to use. These factors might include individual factors, such as young people being attracted to risk, the influence of family or peers, the accessibility and availability of these products, or the way the industry advertises and promotes these products.

Using innovative approaches to improve accessibility and prompt responses

Online qualitative surveys can include features beyond traditional question-and-answer formats ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ). For example, we often use a range of photo elicitation techniques (using images or videos) to make surveys more accessible to participate in, address different levels of literacy, and overcome the assumption that we are not able to ‘prompt’ responses. These types of visual methodologies enable a collaborative and creative research experience by asking the participant to reflect on aspects of the visual materials, such as symbolic representations, and discuss these in relation to the research objectives ( Glaw et al ., 2017 ). The combination of visual images and clear descriptions helps to provide a focus for responses about different issues, as well as prompting nuanced information such as participant memories and emotions ( Glaw et al ., 2017 ). We use different types of visuals in our studies, such as photographs (including of the public health issues we’re investigating); screenshots from websites and social media posts (including newspaper headlines) and videos (including short videos from social media sites such as TikTok) ( Arnot et al ., 2024b ). For example, when talking about government responses to the climate crisis, we used a photograph of former Australian Prime Minister Scott Morrison holding a piece of coal in the Australian parliament to prompt participants’ thinking about the government’s relationship with fossil fuels and to provide a focal point for their answer. However, we would caution against using any images that may be confronting for participants or deliberately provocative. The purpose of using visuals must always be in the interests of the participants—to clarify, prompt and reflect on concepts. Ethics committees should carefully review the images used in surveys to ensure that they have a clear purpose and are unlikely to cause any discomfort.

Survey implementation

Thinking carefully about your criteria for recruitment

Determining the sample size of online qualitative studies is not an exact science. The sample sizes for recent studies have ranged from n = 46 in a study about pregnancy loss ( Hennessy and O’Donoghue, 2024 ), to n = 511 in a study with young people about the climate crisis ( Arnot et al ., 2023b ). We follow ‘rules of thumb’ [( Braun and Clarke, 2021b ), p. 211] which try to balance the needs of the research and data richness with key practical considerations (such as funding and time constraints), funder expectations, discipline-specific norms and our knowledge and experience of designing and implementing online qualitative surveys. However, we have found that peer reviewers expect much more justification of sample sizes than they do for other types of qualitative research. Robust justification of sample sizes are often needed to prevent any ‘concerns’ that reviewers may raise. Our response to these reviews often reiterates that our focus (as with all qualitative research) is not to produce a ‘generalisable’ or ‘representative’ sample but to recruit participants who will help to provide ‘rich, complex and textured data’ [( Terry and Braun, 2017 ), p. 15] about an issue. Instead of focusing on data saturation, a contested concept which is incongruent with reflexive thematic analysis in particular ( Braun and Clarke, 2021b ), we find it useful to consider information power to determine the sample size for these surveys ( Malterud et al ., 2016 ). Information power prioritizes the adequacy, quality and variability of the data collected over the number of participants.

Recruitment for online qualitative surveys can be influenced by a range of factors. Monetary and time constraints will impact the size and, if using market research company panels, the specificity of participant quotas. Recruitment strategies must be developed to ensure that the data provides enough information to answer the research questions of the study. For our research purposes, we often try to ensure that participants with a range of socio-demographic characteristics are invited to participate in the sample. We set soft quotas for age, gender and geographic location to ensure some diversity. We have found that some population subgroups may also be recruited more easily than others—although this may depend on the topic of the survey. For example, we have found that quotas for women and those living in metropolitan areas may fill more quickly. In these scenarios, the research team must weigh up the timelines associated with recruitment and data collection (e.g. How long do we want to run data collection for? How much of our budget can be spent on achieving a more equally split sample? Are quotas necessary?) versus the purpose and goals of the research (i.e. to generate ideas rather than data representativeness), and the study-specific aims and research questions.

There are, of course, concerns about not being able to ‘see’ the people that are completing these surveys. There is an increasing focus in the academic literature on ‘false’ respondents, particularly in quantitative online surveys ( Levi et al ., 2021 ; Wang et al ., 2023 ). This will be an important ongoing discussion for qualitative researchers, and we do not claim to have the answers for how to overcome these issues. For example, some individuals may say that they meet the inclusion criteria to access the survey, while others may not understand or misinterpret the inclusion criteria. There is also a level of discomfort about who and how we judge who may be a ‘legitimate’ participant or not. However, we can talk practically about some of the strategies that we use to ensure the rigour of data. For example, we find that screening questions can provide a ‘double-check’ in relation to inclusion criteria and can also help with ensuring that there is consistency between the information an individual provides about how they meet the inclusion criteria and subsequent responses. For example, in a recent survey of parents of young people, a participant stated that they were 18 years old and were a parent to a 16-year-old and 15-year-old. Their overall responses were inconsistent with being a parent of children these ages. Similarly, in our gambling studies, people may tick that they have gambled in the last year but then in subsequent questions say they have not gambled at all. This highlights the importance of checking data across all questions, although it should be noted that time and cost constraints associated with comprehensively scanning the data for such responses are not always feasible and can result in overlooking these participants.

Ensuring that there are strategies to create agency and engage participants in the research

One of the benefits of online qualitative surveys compared to traditional quantitative surveys is the scope for participants to explain their answers and to disagree with the research team’s position. An indication that participants are feeling able to do this is when they are asked for any additional comments at the end of the survey. For example, in a survey about women’s attitudes towards alcohol marketing, the following participant concluded the survey by writing: ‘I think you have covered everything. I think that you need to stop shaming women for having fun’. Other participants demonstrate their engagement and interest in the survey by reaffirming the perspectives they have shared throughout the survey. For example, in a study with young people on climate, participants responded at the end that ‘it’s one of the few things I actually care about’ , while another commented on the quality of the survey questions, stating, ‘I think this survey did a great job with probing questions to prompt all the thoughts I have on it’ .

We also think that online qualitative surveys may lead to less social desirability in participants’ responses. Participants seem less wary about communicating less politically correct opinions than they may do in a face-to-face interview. For example, at times, participants communicate attitudes that may not align with public health values (e.g. supporting personal responsibility, anti-nanny state, and neoliberal ideologies of health and wellbeing), that we rarely see communicated to us in in-depth interview or focus group studies. We would argue that these perspectives are valuable for public health researchers because they capture a different community voice that may not otherwise be represented in research. This may show where there is a lack of support for health interventions and policy reforms and may indicate where further awareness-raising needs to occur. These types of responses also contribute to reflexive practice by challenging our assumptions and positions about how we think people should think or feel about responses to particular public health issues. Examples of such responses from our surveys include:

"Like I have already said, if you try to hide it you will only make it more attractive. This nanny-state attitude of the elite drives me crazy. People must be allowed to decide for themselves."

Ethical issues for participants and researchers

Researchers should also be aware that some of the ethical issues associated with online qualitative surveys may be different from those in in-depth interviews—and it is important that these are explained in any ethical consideration of the study. Providing a clear and simply worded Plain Language Statement (in written or video form) is important in establishing informed consent and willingness to participate. While participants are given information about who to contact if they have further questions about the study, this may be an extra step for participants, and they may not feel as able to ask for clarification about the study. Because of this, we try to provide multiple examples of the types of questions that we will ask, as well as providing downloadable support details (for example, for mental health support lines). A positive aspect of surveys is that participants are able to easily ignore recruitment notices to participate in the study. They are also able to stop the survey at any time by exiting out of the browser if they feel discomfort without having to give a reason in person to a researcher.

While the anonymous nature of the survey may be empowering for some participants ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al. , 2021 ), it can also make it difficult for researchers to ascertain if people need any further support after completing the survey. Participants may also fill in surveys with someone else and may be influenced about how they should respond to questions (with the exception of some studies in which people may require assistance from someone to type their responses). Because of the above, some researchers, ethics committees and funders may be more cautious about using these studies for highly sensitive subjects. However, we would argue that the important point is that the studies follow ethical principles and take the lack of direct contact with participants into the ethical considerations of the study. It is also important to ensure that platforms used to collect survey data are trusted and secure. Here, we would argue that universities have an obligation to investigate and, where possible, approve survey providers to ensure that researchers are using platforms that meet rigorous standards for data and privacy.

It is also important to note that there may be responses from participants that may be challenging ( Terry and Braun, 2017 ; Braun and Clarke, 2021 ). Online spaces are rife with trolling due to their anonymous nature, and online surveys are not immune to this behaviour. Naturally, this leads to some silly responses—‘ Deakin University is responsible for all of this ’, but researchers should also be aware that the anonymity of surveys can (although in our experience not often) lead to responses that may cause discomfort for the researchers. For example, when asked if participants had anything else to add to a climate survey ( Arnot et al ., 2024c ), one responded ‘ nope, but you sure asked a lot of dumbass questions’ . Just as with interview-based studies, there must be processes built into the research for debriefing—particularly for students and early career researchers—as well as clear decisions about whether to include or exclude these types of responses when preparing the dataset for analysis and in writing up the results from the survey.

The importance of piloting the survey

Because of the lack of ability to explain and clarify concepts, piloting is particularly important ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ; Braun et al. , 2021 ) to ensure that: (i) the technical aspects of the survey work as intended; (ii) the survey is eliciting quality responses (with limited ‘nonsensical’ responses such as random characters); (iii) the survey responses indicate comprehension of the survey questions; and (vi) there is not a substantial number of people who ‘drop-out’ of the study. Typically, we pilot our survey with 10% of the intended sample size. After piloting, we often change question wording, particularly to address questions that elicit very small text responses, the length of the survey and sometimes refine definitions or language to ensure increased comprehension. Researchers should remember that changes to the survey questions may need to be reviewed by ethics committees before launching the full survey. It is important to build in time for piloting and the revision of the survey to ensure you get this right as once you launch the full survey, there is no going back!

Survey analysis and write-up

Preparing the dataset

Once launching the full survey, the quality of data and types of responses you receive in these types of surveys can vary. There is very limited transparency around how the dataset was prepared (more familiar to some as ‘data cleaning’) in published papers, including the decisions about which (if any) participants (or indeed responses) were excluded from the dataset and why. Nonsensical responses can be common—and can take a range of forms ( Figure 3 ). These can include random numbers or letters, a chunk of text that has been copied and pasted from elsewhere, predictive text or even repeat emojis. In one study, we had a participant quote the script of The Bee Movie in response to questions.

: Visual examples of nonsensical responses in online qualitative surveys.

Part of our familiarization with the dataset [Phase One in Braun and Clarke’s reflexive approach to thematic analysis ( Braun and Clarke, 2013 ; Braun et al ., 2021 )] includes preparing the dataset for analysis. We use this phase to help make decisions about what to include and exclude from the final dataset. While a row of emojis in the data file can easily be spotted and removed from the dataset, sometimes responses can look robust until you read, become familiar and engage with the data. For example, when asked about what they thought about collective climate action ( Arnot et al ., 2023a , 2024c ), some participants entered random yet related terms such as ‘ plastic ’, or repeated similar phrases across multiple questions:

“ why do we need paper straws ”, “ paper straws are terrible ”, “ papers straws are bad for you ”, “ paper straws are gross .”

Participants can also provide comprehensive answers for the first few questions and then nonsensical responses for the rest, which may also be due to question fatigue [( Braun and Clarke, 2013 ), p. 138]. Therefore, it is important to closely go through each participant’s response to ensure they have attempted to provide bone-fide responses. For example, in one of our young people and climate surveys ( Arnot et al ., 2023a , 2024c ), one participant responded genuinely to the first half of the survey before their quality dropped dramatically:

“I can’t even be bothered to read that question ”, “ why so many questions ”, “ bro too many sections. ”

Some market research panel providers may complete an initial quality screen of data. However, this does not replace the need for the research teams’ own data preparation processes. Researchers should ensure they are checking that responses are coherent—for example, not giving information that contradicts or is not credible. In our more recent studies, we have increasingly seen responses cut and pasted from ChatGPT and other AI tools—providing a new challenge in assessing the quality of responses. If you are seeing these types of responses, it might be an opportunity to think about the style and suitability of the questions being asked. For example, the use of AI tools might suggest that people are finding it difficult to answer questions or may feel that they have to present a ‘correct’ answer. We would also note that because of the volume of data in these surveys, the preparation of data involves multiple members of the team. In many cases, decisions need to be made about participants who may not have provided authentic responses across the survey. The research team should make clear in any paper their decisions about their choices to include or exclude participants from the study. There is a careful balancing act that can require assessing the quality of the participants’ responses across the whole dataset to determine if the overall quality of responses contributes to the research.

Navigating the volume of data and writing up results

Finally, discussions about how to navigate the volume of data that these types of studies produce could be a standalone paper. In general, principles of reflexive practices apply to the analysis of data from these studies. However, as a starting point, here are a few considerations when approaching these datasets.

We would argue that online qualitative surveys lend themselves to some types of analytical approaches over others—for example, reflexive thematic analysis, as compared to grounded theory or interpretive phenomenological analysis (though it can be used with these) ( Braun and Clarke, 2013 ; Terry and Braun, 2017 ).

While initial familiarization, coding and analysis can focus on specific questions and associated responses, it is important to analyse the dataset as a whole (or as clusters associated with particular topics) as participants may provide relevant data to a topic under multiple questions ( Terry and Braun, 2017 ). We initially focus our coding on specific questions or a group of survey questions under a topic of investigation. Once we have developed and constructed preliminary themes from the data associated with these clusters of questions, we then move to looking at responses across the dataset as we review themes further.

Researchers should think carefully about how to manage the data—which may not be available as ‘individual participant transcripts’ but rather as a ‘whole’ dataset in an Excel spreadsheet. Some may prefer qualitative data analysis software (QDAS) to manage and navigate data. However, many of us find that Excel (and particularly the use of labelled Tabs) is useful in grouping data and moving from codes to constructing themes.

As with all rigorous qualitative research, coding and theme development should be guided by the research questions. A clear record of decision-making about analytical choices (and being reflexive about these) should be kept. In any write-up, we would recommend that researchers are clear about which survey questions they used in the analysis [researchers could consider providing a supplementary file of some or all of the survey questions—see, for example Hennessy and O’Donoghue (2024) ].

In writing up the results, researchers should still seek to present a rich description of the data, as demonstrated in the presentation of results in the following papers ( Marko et al ., 2022a , 2022b ; McCarthy et al ., 2023 ; Pitt et al ., 2023 ; Hennessy and O’Donoghue, 2024 ). We have found the use of tables with additional examples of quotes as they relate to themes and subthemes can be a practical way of providing the reader with further examples of the data, particularly when constrained by journal word count limits [see, for example, Table 2 in Arnot et al ., (2024c) ]. However, these tables do not replace a full and complete presentation of the interpretation of the data.

This article offers methodological reflections and practical guidance around online qualitative survey design, implementation and analysis. While online qualitative surveys engage participants in a different type of conversation, they have design features that enable the collection of rich data. We recognize that we have much to learn and that while no survey of ours has been perfect, each new experience with developing and conducting online qualitative surveys has brought new understandings and lessons for future studies. In recognizing that we are learning, we also feel that our experience to date could be valuable for progressing the conversation about the rigour of online qualitative surveys and maximizing this method for public health gains.

H.P. is funded through a VicHealth Postdoctoral Research Fellowship. S.M. is funded through a Deakin University Faculty of Health Deans Postdoctoral Fellowship. G.A. is funded by an Australian Government Research Training Program Scholarship. M.H. is funded through an Irish Research Council Government of Ireland Postdoctoral Fellowship Award [GOIPD/2023/1168].

The pregnancy loss study was funded by the Irish Research Council through its New Foundations Awards and in partnership with the Irish Hospice Foundation as civil society partner [NF/2021/27123063].

S.T. is Editor in Chief of Health Promotion International, H.P. is a member of the Editorial Board of Health Promotion International, S.M. and G.A. are Social Media Coordinators for Health Promotion International, M.H. is an Associate Editor for Health Promotion International. They were not involved in the review process or in any decision-making on the manuscript.

The data used in this study are not available.

Ethical approval for studies conducted by Deakin University include the climate crisis (HEAG-H 55_2020, HEAG-H 162_2021); parents perceptions of harmful industries on young people (HEAG-H 158_2022); women and alcohol marketing (HEAG-H 123_2022) and gambling (HEAG 227_2020).

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

Affiliations

Last Update: September 18, 2022 .

Introduction

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

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

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

Qualitative Research Approaches

Ethnography

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

Grounded theory

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

Phenomenology

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

Narrative research

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

Research Paradigm

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

Positivist versus postpositivist

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

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

Constructivist

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

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

Data Sampling

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

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

Data Collection and Analysis

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

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

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

Dissemination

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

Applications

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

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

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

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

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

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

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

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

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

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

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

Issues of Concern

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

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

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

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

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

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

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

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

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

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

Enhancing Healthcare Team Outcomes

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

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

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

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

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

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

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Data Analysis in Research

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Data analysis in research involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is a crucial step that enables researchers to identify patterns, relationships, and trends within the data, transforming raw information into valuable insights. Through methods such as descriptive statistics, inferential statistics, and qualitative analysis, researchers can interpret their findings, draw conclusions, and support decision-making processes. An effective data analysis plan and robust methodology ensure the accuracy and reliability of research outcomes, ultimately contributing to the advancement of knowledge across various fields.

What is Data Analysis in Research?

Data analysis in research involves using statistical and logical techniques to describe, summarize, and compare collected data. This includes inspecting, cleaning, transforming, and modeling data to find useful information and support decision-making. Quantitative data provides measurable insights, and a solid research design ensures accuracy and reliability. This process helps validate hypotheses, identify patterns, and make informed conclusions, making it a crucial step in the scientific method.

Examples of Data analysis in Research

Survey Analysis : Researchers collect survey responses from a sample population to gauge opinions, behaviors, or characteristics. Using descriptive statistics, they summarize the data through means, medians, and modes, and then inferential statistics to generalize findings to a larger population.
Experimental Analysis : In scientific experiments, researchers manipulate one or more variables to observe the effect on a dependent variable. Data is analyzed using methods such as ANOVA or regression analysis to determine if changes in the independent variable(s) significantly affect the dependent variable.
Content Analysis : Qualitative research often involves analyzing textual data, such as interview transcripts or open-ended survey responses. Researchers code the data to identify recurring themes, patterns, and categories, providing a deeper understanding of the subject matter.
Correlation Studies : Researchers explore the relationship between two or more variables using correlation coefficients. For example, a study might examine the correlation between hours of study and academic performance to identify if there is a significant positive relationship.
Longitudinal Analysis : This type of analysis involves collecting data from the same subjects over a period of time. Researchers analyze this data to observe changes and developments, such as studying the long-term effects of a specific educational intervention on student achievement.
Meta-Analysis : By combining data from multiple studies, researchers perform a meta-analysis to increase the overall sample size and enhance the reliability of findings. This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention.

Data analysis in Qualitative Research

Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings. Here are some key steps and methods used in qualitative data analysis:

Coding : Researchers categorize the data by assigning labels or codes to specific segments of the text. These codes represent themes or concepts relevant to the research question.
Thematic Analysis : This method involves identifying and analyzing patterns or themes within the data. Researchers review coded data to find recurring topics and construct a coherent narrative around these themes.
Content Analysis : A systematic approach to categorize verbal or behavioral data to classify, summarize, and tabulate the data. This method often involves counting the frequency of specific words or phrases.
Narrative Analysis : Researchers focus on the stories and experiences shared by participants, analyzing the structure, content, and context of the narratives to understand how individuals make sense of their experiences.
Grounded Theory : This method involves generating a theory based on the data collected. Researchers collect and analyze data simultaneously, continually refining and adjusting their theoretical framework as new data emerges.
Discourse Analysis : Examining language use and communication patterns within the data, researchers analyze how language constructs social realities and power relationships.
Case Study Analysis : An in-depth analysis of a single case or multiple cases, exploring the complexities and unique aspects of each case to gain a deeper understanding of the phenomenon under study.

Data analysis in Quantitative Research

Data analysis in quantitative research involves the systematic application of statistical techniques to numerical data to identify patterns, relationships, and trends. Here are some common methods used in quantitative data analysis:

Descriptive Statistics : This includes measures such as mean, median, mode, standard deviation, and range, which summarize and describe the main features of a data set.
Inferential Statistics : Techniques like t-tests, chi-square tests, and ANOVA (Analysis of Variance) are used to make inferences or generalizations about a population based on a sample.
Regression Analysis : This method examines the relationship between dependent and independent variables. Simple linear regression analyzes the relationship between two variables, while multiple regression examines the relationship between one dependent variable and several independent variables.
Correlation Analysis : Researchers use correlation coefficients to measure the strength and direction of the relationship between two variables.
Factor Analysis : This technique is used to identify underlying relationships between variables by grouping them into factors based on their correlations.
Cluster Analysis : A method used to group a set of objects or cases into clusters, where objects in the same cluster are more similar to each other than to those in other clusters.
Hypothesis Testing : This involves testing an assumption or hypothesis about a population parameter. Common tests include z-tests, t-tests, and chi-square tests, which help determine if there is enough evidence to reject the null hypothesis.
Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
Multivariate Analysis : Techniques like MANOVA (Multivariate Analysis of Variance) and PCA (Principal Component Analysis) are used to analyze data that involves multiple variables to understand their effect and relationships.
Structural Equation Modeling (SEM) : A multivariate statistical analysis technique that is used to analyze structural relationships. This method is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between measured variables and latent constructs.

Data analysis in Research Methodology

Data analysis in research methodology involves the process of systematically applying statistical and logical techniques to describe, condense, recap, and evaluate data. Here are the key components and methods involved:

Data Preparation : This step includes collecting, cleaning, and organizing raw data. Researchers ensure data quality by handling missing values, removing duplicates, and correcting errors.
Descriptive Analysis : Researchers use descriptive statistics to summarize the basic features of the data. This includes measures such as mean, median, mode, standard deviation, and graphical representations like histograms and pie charts.
Inferential Analysis : This involves using statistical tests to make inferences about the population from which the sample was drawn. Common techniques include t-tests, chi-square tests, ANOVA, and regression analysis.
Qualitative Data Analysis : For non-numeric data, researchers employ methods like coding, thematic analysis, content analysis, narrative analysis, and discourse analysis to identify patterns and themes.
Quantitative Data Analysis : For numeric data, researchers apply statistical methods such as correlation, regression, factor analysis, cluster analysis, and time series analysis to identify relationships and trends.
Hypothesis Testing : Researchers test hypotheses using statistical methods to determine whether there is enough evidence to reject the null hypothesis. This involves calculating p-values and confidence intervals.
Data Interpretation : This step involves interpreting the results of the data analysis. Researchers draw conclusions based on the statistical findings and relate them back to the research questions and objectives.
Validation and Reliability : Ensuring the validity and reliability of the analysis is crucial. Researchers check for consistency in the results and use methods like cross-validation and reliability testing to confirm their findings.
Visualization : Effective data visualization techniques, such as charts, graphs, and plots, are used to present the data in a clear and understandable manner, aiding in the interpretation and communication of results.
Reporting : The final step involves reporting the results in a structured format, often including an introduction, methodology, results, discussion, and conclusion. This report should clearly convey the findings and their implications for the research question.

Types of Data analysis in Research

Purpose : To summarize and describe the main features of a dataset.
Methods : Mean, median, mode, standard deviation, frequency distributions, and graphical representations like histograms and pie charts.
Example : Calculating the average test scores of students in a class.
Purpose : To make inferences or generalizations about a population based on a sample.
Methods : T-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and confidence intervals.
Example : Testing whether a new teaching method significantly affects student performance compared to a traditional method.
Purpose : To analyze data sets to find patterns, anomalies, and test hypotheses.
Methods : Visualization techniques like box plots, scatter plots, and heat maps; summary statistics.
Example : Visualizing the relationship between hours of study and exam scores using a scatter plot.
Purpose : To make predictions about future outcomes based on historical data.
Methods : Regression analysis, machine learning algorithms (e.g., decision trees, neural networks), and time series analysis.
Example : Predicting student graduation rates based on their academic performance and demographic data.
Purpose : To provide recommendations for decision-making based on data analysis.
Methods : Optimization algorithms, simulation, and decision analysis.
Example : Suggesting the best course of action for improving student retention rates based on various predictive factors.
Purpose : To identify and understand cause-and-effect relationships.
Methods : Controlled experiments, regression analysis, path analysis, and structural equation modeling (SEM).
Example : Determining the impact of a specific intervention, like a new curriculum, on student learning outcomes.
Purpose : To understand the specific mechanisms through which variables affect one another.
Methods : Detailed modeling and simulation, often used in scientific research to understand biological or physical processes.
Example : Studying how a specific drug interacts with biological pathways to affect patient health.

How to write Data analysis in Research

Data analysis is crucial for interpreting collected data and drawing meaningful conclusions. Follow these steps to write an effective data analysis section in your research.

1. Prepare Your Data

Ensure your data is clean and organized:

Remove duplicates and irrelevant data.
Check for errors and correct them.
Categorize data if necessary.

2. Choose the Right Analysis Method

Select a method that fits your data type and research question:

Quantitative Data : Use statistical analysis such as t-tests, ANOVA, regression analysis.
Qualitative Data : Use thematic analysis, content analysis, or narrative analysis.

3. Describe Your Analytical Techniques

Clearly explain the methods you used:

Software and Tools : Mention any software (e.g., SPSS, NVivo) used.
Statistical Tests : Detail the statistical tests applied, such as chi-square tests or correlation analysis.
Qualitative Techniques : Describe coding and theme identification processes.

4. Present Your Findings

Organize your findings logically:

Use Tables and Figures : Display data in tables, graphs, and charts for clarity.
Summarize Key Results : Highlight the most significant findings.
Include Relevant Statistics : Report p-values, confidence intervals, means, and standard deviations.

5. Interpret the Results

Explain what your findings mean in the context of your research:

Compare with Hypotheses : State whether the results support your hypotheses.
Relate to Literature : Compare your results with previous studies.
Discuss Implications : Explain the significance of your findings.

6. Discuss Limitations

Acknowledge any limitations in your data or analysis:

Sample Size : Note if the sample size was small.
Biases : Mention any potential biases in data collection.
External Factors : Discuss any factors that might have influenced the results.

7. Conclude with a Summary

Wrap up your data analysis section:

Restate Key Findings : Briefly summarize the main results.
Future Research : Suggest areas for further investigation.

Importance of Data analysis in Research

Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance:

Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable. By using statistical techniques, researchers can minimize errors and biases, ensuring that the results are dependable.
Facilitates Informed Decision-Making Through data analysis, researchers can make informed decisions based on empirical evidence. This is crucial in fields like healthcare, business, and social sciences, where decisions impact policies, strategies, and outcomes.
Identifies Trends and Patterns Analyzing data helps researchers uncover trends and patterns that might not be immediately visible. These insights can lead to new hypotheses and areas of study, advancing knowledge in the field.
Supports Hypothesis Testing Data analysis is vital for testing hypotheses. Researchers can use statistical methods to determine whether their hypotheses are supported or refuted, which is essential for validating theories and advancing scientific understanding.
Provides a Basis for Predictions By analyzing current and historical data, researchers can develop models that predict future outcomes. This predictive capability is valuable in numerous fields, including economics, climate science, and public health.

FAQ’s

What is the difference between qualitative and quantitative data analysis.

Qualitative analysis focuses on non-numerical data to understand concepts, while quantitative analysis deals with numerical data to identify patterns and relationships.

What is descriptive statistics?

Descriptive statistics summarize and describe the features of a data set, including measures like mean, median, mode, and standard deviation.

What is inferential statistics?

Inferential statistics use sample data to make generalizations about a larger population, often through hypothesis testing and confidence intervals.

What is regression analysis?

Regression analysis examines the relationship between dependent and independent variables, helping to predict outcomes and understand variable impacts.

What is the role of software in data analysis?

Software like SPSS, R, and Excel facilitate data analysis by providing tools for statistical calculations, visualization, and data management.

What are data visualization techniques?

Data visualization techniques include charts, graphs, and maps, which help in presenting data insights clearly and effectively.

What is data cleaning?

Data cleaning involves removing errors, inconsistencies, and missing values from a data set to ensure accuracy and reliability in analysis.

What is the significance of sample size in data analysis?

Sample size affects the accuracy and generalizability of results; larger samples generally provide more reliable insights.

How does correlation differ from causation?

Correlation indicates a relationship between variables, while causation implies one variable directly affects the other.

What are the ethical considerations in data analysis?

Ethical considerations include ensuring data privacy, obtaining informed consent, and avoiding data manipulation or misrepresentation.

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Understanding Qualitative Research: An In-Depth Study Guide
Which Qualitative Research Design Is Right For You?
How To Write Qualitative Methodology For A Research Paper (Full Tutorial + Examples)
6 Types of Qualitative Research Methods
15 Research Methodology Examples (2024)
How Is The Research Methodology Chapter In Qualitative Research Written

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Qualitative research methodology lecture 2
Qualitative Research Methodology| Types
WRITING THE CHAPTER 3|| Research Methodology (Research Design and Method)
How to Write QUALITATIVE RESEARCH QUESTIONS
Developing Qualitative Research Titles: A Guide to It's Nature
Qualitative research methodology 4 23 lecture 1

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What Is Qualitative Research?
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 ...
Chapter 1. Introduction
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.
How to use and assess qualitative research methods
Abstract. 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 of why something is (not) observed, assessing complex multi-component interventions ...
PDF A Guide to Using Qualitative Research Methodology
uinn Patton and Michael CochranExecutive SummaryThis guide to using qualitative research methodology is designed to help you think about all the steps you need to take to ens. re that you produce a good quality piece of work.The guide starts by telling you what qualitative methodology is and when to use it in the field (understand peo.
A Practical Guide to Writing Quantitative and Qualitative Research
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
PDF Introduction to Qualitative Research Methodology
It introduces qualitative methods in an interesting and hands-on way to provide you with an understanding of key concepts and methods in qualitative research as applied to the ﬁ eld of health. All three authors are trained anthropologists who have been working in health and development for many years.
Writing Across Qualitative Research
A main way qualitative researchers integrate writing across the research process is through a researcher reflexivity journal (Brown, 2021). A research journal is a safe space, that may enhance who we are as ethical beings (Lahman, 2018), to write about possible ideas for research and the first research steps—a place to trace our wonders and ...
The Oxford Handbook of Qualitative Research
Abstract. The Oxford Handbook of Qualitative Research, second edition, presents a comprehensive retrospective and prospective review of the field of qualitative research. Original, accessible chapters written by interdisciplinary leaders in the field make this a critical reference work. Filled with robust examples from real-world research ...
Introduction to qualitative research methods
INTRODUCTION. Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures.
What Is Qualitative Research?
Qualitative research methods. 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 ...
Qualitative Research
Qualitative Research. Qualitative research is a type of research methodology that focuses on exploring and understanding people's beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.
Five Steps to Writing More Engaging Qualitative Research
A-85). Successful writing requires a writer to pay quiet diligent attention to the construction of the genre they are working in. Each genre has its own sense of verisimilitude—the bearing of truth. Each places different constraints on the writer and has different goals, forms, and structure.
What Is a Research Methodology?
Qualitative methods. In qualitative research, your analysis will be based on language, images, and observations ... Note. Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your methodology. However, we strongly advise against trying to pass AI-generated text off as ...
A Front-to-Back Guide to Writing a Qualitative Research Article
Purpose - This paper aims to offer junior scholars a front-to-back guide to writing an academic, theoretically positioned, qualitative research article in the social sciences. Design/methodology ...
Characteristics of Qualitative Research
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.
Research Methodology
Qualitative Research Methodology. This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.
Qualitative Research Methods: A Practice-Oriented Introduction
The book aims at achieving e ects in three domains: (a) the. personal, (b) the scholarly, and (c) the practical. The personal goal. is to demystify qualitative methods, give readers a feel for ...
Qualitative Research Methodologies
Qualitative research methodologies seek to capture information that often can't be expressed numerically. These methodologies often include some level of interpretation from researchers as they collect information via observation, coded survey or interview responses, and so on. ... they collect information via observation, coded survey or ...
Your Step-by-Step Guide to Writing a Good Research Methodology
Instruments you could use while writing a good research methodology. As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise. ... Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains. 2 ...
PDF Elements for a qualitative methodology chapter
The goal is to describe the research process in its complexity, however keeping a clear and relatively concise style. Elements to be addressed in the chapter (the content may be presented in a different order): Study methodology and theoretical congruence. Explain why a qualitative study is the best methodology to respond to the research ...
Choosing a Qualitative Research Approach
In this Rip Out, we describe 3 different qualitative research approaches commonly used in medical education: grounded theory, ethnography, and phenomenology. Each acts as a pivotal frame that shapes the research question (s), the method (s) of data collection, and how data are analyzed. 4, 5. Go to:
Qualitative research
Developing a theoretical framework for your dissertation is one of the key elements of a qualitative research project. Through writing your literature review, you are likely to have identified either a problem that need 'fixing' or a gap that your research may begin to fill. ... The methodology is the instruction manual: the procedure and ...
Methodological and practical guidance for designing and conducting
Harnessing interpretivist approaches and qualitative values in online qualitative surveys. Online qualitative surveys take many forms. They may be fully qualitative or qualitative dominant—mostly qualitative with some quantitative questions (Terry and Braun, 2017).There are also many different ways of conducting these studies—from using a smaller number of questions that engage specific ...
Qualitative Study
Qualitative research is a type of research that explores and provides deeper insights into real-world problems.[1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences ...
Data Analysis in Research
This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention. Data analysis in Qualitative Research. Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings.
Decentering and Rezoning: Podcasts as a DisCrit Method for Doing
To address this lack, we developed podcasting as a method for DisCrit-informed qualitative research in the form of a pilot project for the first author's doctoral study on the experiences of acquired disability as intersected with policy, race, gender, sexuality, religion, and nationality. ... Using writing as method of inquiry, in this paper ...

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

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What Is Qualitative Research? | Methods & Examples

Table of contents

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Table of contents

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Option 1: Start with your “what”

Option 2: Start with your “why”

Quantitative methods

Qualitative methods

Mixed methods

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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?