can you use hypothesis in qualitative research

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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Article Contents

Introduction, when to use qualitative research, how to judge qualitative research, conclusions, authors' roles, conflict of interest.

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Qualitative research methods: when to use them and how to judge them

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K. Hammarberg, M. Kirkman, S. de Lacey, Qualitative research methods: when to use them and how to judge them, Human Reproduction , Volume 31, Issue 3, March 2016, Pages 498–501, https://doi.org/10.1093/humrep/dev334

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In March 2015, an impressive set of guidelines for best practice on how to incorporate psychosocial care in routine infertility care was published by the ESHRE Psychology and Counselling Guideline Development Group ( ESHRE Psychology and Counselling Guideline Development Group, 2015 ). The authors report that the guidelines are based on a comprehensive review of the literature and we congratulate them on their meticulous compilation of evidence into a clinically useful document. However, when we read the methodology section, we were baffled and disappointed to find that evidence from research using qualitative methods was not included in the formulation of the guidelines. Despite stating that ‘qualitative research has significant value to assess the lived experience of infertility and fertility treatment’, the group excluded this body of evidence because qualitative research is ‘not generally hypothesis-driven and not objective/neutral, as the researcher puts him/herself in the position of the participant to understand how the world is from the person's perspective’.

Qualitative and quantitative research methods are often juxtaposed as representing two different world views. In quantitative circles, qualitative research is commonly viewed with suspicion and considered lightweight because it involves small samples which may not be representative of the broader population, it is seen as not objective, and the results are assessed as biased by the researchers' own experiences or opinions. In qualitative circles, quantitative research can be dismissed as over-simplifying individual experience in the cause of generalisation, failing to acknowledge researcher biases and expectations in research design, and requiring guesswork to understand the human meaning of aggregate data.

As social scientists who investigate psychosocial aspects of human reproduction, we use qualitative and quantitative methods, separately or together, depending on the research question. The crucial part is to know when to use what method.

The peer-review process is a pillar of scientific publishing. One of the important roles of reviewers is to assess the scientific rigour of the studies from which authors draw their conclusions. If rigour is lacking, the paper should not be published. As with research using quantitative methods, research using qualitative methods is home to the good, the bad and the ugly. It is essential that reviewers know the difference. Rejection letters are hard to take but more often than not they are based on legitimate critique. However, from time to time it is obvious that the reviewer has little grasp of what constitutes rigour or quality in qualitative research. The first author (K.H.) recently submitted a paper that reported findings from a qualitative study about fertility-related knowledge and information-seeking behaviour among people of reproductive age. In the rejection letter one of the reviewers (not from Human Reproduction ) lamented, ‘Even for a qualitative study, I would expect that some form of confidence interval and paired t-tables analysis, etc. be used to analyse the significance of results'. This comment reveals the reviewer's inappropriate application to qualitative research of criteria relevant only to quantitative research.

In this commentary, we give illustrative examples of questions most appropriately answered using qualitative methods and provide general advice about how to appraise the scientific rigour of qualitative studies. We hope this will help the journal's reviewers and readers appreciate the legitimate place of qualitative research and ensure we do not throw the baby out with the bath water by excluding or rejecting papers simply because they report the results of qualitative studies.

In psychosocial research, ‘quantitative’ research methods are appropriate when ‘factual’ data are required to answer the research question; when general or probability information is sought on opinions, attitudes, views, beliefs or preferences; when variables can be isolated and defined; when variables can be linked to form hypotheses before data collection; and when the question or problem is known, clear and unambiguous. Quantitative methods can reveal, for example, what percentage of the population supports assisted conception, their distribution by age, marital status, residential area and so on, as well as changes from one survey to the next ( Kovacs et al. , 2012 ); the number of donors and donor siblings located by parents of donor-conceived children ( Freeman et al. , 2009 ); and the relationship between the attitude of donor-conceived people to learning of their donor insemination conception and their family ‘type’ (one or two parents, lesbian or heterosexual parents; Beeson et al. , 2011 ).

In contrast, ‘qualitative’ methods are used to answer questions about experience, meaning and perspective, most often from the standpoint of the participant. These data are usually not amenable to counting or measuring. Qualitative research techniques include ‘small-group discussions’ for investigating beliefs, attitudes and concepts of normative behaviour; ‘semi-structured interviews’, to seek views on a focused topic or, with key informants, for background information or an institutional perspective; ‘in-depth interviews’ to understand a condition, experience, or event from a personal perspective; and ‘analysis of texts and documents’, such as government reports, media articles, websites or diaries, to learn about distributed or private knowledge.

Qualitative methods have been used to reveal, for example, potential problems in implementing a proposed trial of elective single embryo transfer, where small-group discussions enabled staff to explain their own resistance, leading to an amended approach ( Porter and Bhattacharya, 2005 ). Small-group discussions among assisted reproductive technology (ART) counsellors were used to investigate how the welfare principle is interpreted and practised by health professionals who must apply it in ART ( de Lacey et al. , 2015 ). When legislative change meant that gamete donors could seek identifying details of people conceived from their gametes, parents needed advice on how best to tell their children. Small-group discussions were convened to ask adolescents (not known to be donor-conceived) to reflect on how they would prefer to be told ( Kirkman et al. , 2007 ).

When a population cannot be identified, such as anonymous sperm donors from the 1980s, a qualitative approach with wide publicity can reach people who do not usually volunteer for research and reveal (for example) their attitudes to proposed legislation to remove anonymity with retrospective effect ( Hammarberg et al. , 2014 ). When researchers invite people to talk about their reflections on experience, they can sometimes learn more than they set out to discover. In describing their responses to proposed legislative change, participants also talked about people conceived as a result of their donations, demonstrating various constructions and expectations of relationships ( Kirkman et al. , 2014 ).

Interviews with parents in lesbian-parented families generated insight into the diverse meanings of the sperm donor in the creation and life of the family ( Wyverkens et al. , 2014 ). Oral and written interviews also revealed the embarrassment and ambivalence surrounding sperm donors evident in participants in donor-assisted conception ( Kirkman, 2004 ). The way in which parents conceptualise unused embryos and why they discard rather than donate was explored and understood via in-depth interviews, showing how and why the meaning of those embryos changed with parenthood ( de Lacey, 2005 ). In-depth interviews were also used to establish the intricate understanding by embryo donors and recipients of the meaning of embryo donation and the families built as a result ( Goedeke et al. , 2015 ).

It is possible to combine quantitative and qualitative methods, although great care should be taken to ensure that the theory behind each method is compatible and that the methods are being used for appropriate reasons. The two methods can be used sequentially (first a quantitative then a qualitative study or vice versa), where the first approach is used to facilitate the design of the second; they can be used in parallel as different approaches to the same question; or a dominant method may be enriched with a small component of an alternative method (such as qualitative interviews ‘nested’ in a large survey). It is important to note that free text in surveys represents qualitative data but does not constitute qualitative research. Qualitative and quantitative methods may be used together for corroboration (hoping for similar outcomes from both methods), elaboration (using qualitative data to explain or interpret quantitative data, or to demonstrate how the quantitative findings apply in particular cases), complementarity (where the qualitative and quantitative results differ but generate complementary insights) or contradiction (where qualitative and quantitative data lead to different conclusions). Each has its advantages and challenges ( Brannen, 2005 ).

Qualitative research is gaining increased momentum in the clinical setting and carries different criteria for evaluating its rigour or quality. Quantitative studies generally involve the systematic collection of data about a phenomenon, using standardized measures and statistical analysis. In contrast, qualitative studies involve the systematic collection, organization, description and interpretation of textual, verbal or visual data. The particular approach taken determines to a certain extent the criteria used for judging the quality of the report. However, research using qualitative methods can be evaluated ( Dixon-Woods et al. , 2006 ; Young et al. , 2014 ) and there are some generic guidelines for assessing qualitative research ( Kitto et al. , 2008 ).

Although the terms ‘reliability’ and ‘validity’ are contentious among qualitative researchers ( Lincoln and Guba, 1985 ) with some preferring ‘verification’, research integrity and robustness are as important in qualitative studies as they are in other forms of research. It is widely accepted that qualitative research should be ethical, important, intelligibly described, and use appropriate and rigorous methods ( Cohen and Crabtree, 2008 ). In research investigating data that can be counted or measured, replicability is essential. When other kinds of data are gathered in order to answer questions of personal or social meaning, we need to be able to capture real-life experiences, which cannot be identical from one person to the next. Furthermore, meaning is culturally determined and subject to evolutionary change. The way of explaining a phenomenon—such as what it means to use donated gametes—will vary, for example, according to the cultural significance of ‘blood’ or genes, interpretations of marital infidelity and religious constructs of sexual relationships and families. Culture may apply to a country, a community, or other actual or virtual group, and a person may be engaged at various levels of culture. In identifying meaning for members of a particular group, consistency may indeed be found from one research project to another. However, individuals within a cultural group may present different experiences and perceptions or transgress cultural expectations. That does not make them ‘wrong’ or invalidate the research. Rather, it offers insight into diversity and adds a piece to the puzzle to which other researchers also contribute.

In qualitative research the objective stance is obsolete, the researcher is the instrument, and ‘subjects’ become ‘participants’ who may contribute to data interpretation and analysis ( Denzin and Lincoln, 1998 ). Qualitative researchers defend the integrity of their work by different means: trustworthiness, credibility, applicability and consistency are the evaluative criteria ( Leininger, 1994 ).

Trustworthiness

A report of a qualitative study should contain the same robust procedural description as any other study. The purpose of the research, how it was conducted, procedural decisions, and details of data generation and management should be transparent and explicit. A reviewer should be able to follow the progression of events and decisions and understand their logic because there is adequate description, explanation and justification of the methodology and methods ( Kitto et al. , 2008 )

Credibility

Credibility is the criterion for evaluating the truth value or internal validity of qualitative research. A qualitative study is credible when its results, presented with adequate descriptions of context, are recognizable to people who share the experience and those who care for or treat them. As the instrument in qualitative research, the researcher defends its credibility through practices such as reflexivity (reflection on the influence of the researcher on the research), triangulation (where appropriate, answering the research question in several ways, such as through interviews, observation and documentary analysis) and substantial description of the interpretation process; verbatim quotations from the data are supplied to illustrate and support their interpretations ( Sandelowski, 1986 ). Where excerpts of data and interpretations are incongruent, the credibility of the study is in doubt.

Applicability

Applicability, or transferability of the research findings, is the criterion for evaluating external validity. A study is considered to meet the criterion of applicability when its findings can fit into contexts outside the study situation and when clinicians and researchers view the findings as meaningful and applicable in their own experiences.

Larger sample sizes do not produce greater applicability. Depth may be sacrificed to breadth or there may be too much data for adequate analysis. Sample sizes in qualitative research are typically small. The term ‘saturation’ is often used in reference to decisions about sample size in research using qualitative methods. Emerging from grounded theory, where filling theoretical categories is considered essential to the robustness of the developing theory, data saturation has been expanded to describe a situation where data tend towards repetition or where data cease to offer new directions and raise new questions ( Charmaz, 2005 ). However, the legitimacy of saturation as a generic marker of sampling adequacy has been questioned ( O'Reilly and Parker, 2013 ). Caution must be exercised to ensure that a commitment to saturation does not assume an ‘essence’ of an experience in which limited diversity is anticipated; each account is likely to be subtly different and each ‘sample’ will contribute to knowledge without telling the whole story. Increasingly, it is expected that researchers will report the kind of saturation they have applied and their criteria for recognising its achievement; an assessor will need to judge whether the choice is appropriate and consistent with the theoretical context within which the research has been conducted.

Sampling strategies are usually purposive, convenient, theoretical or snowballed. Maximum variation sampling may be used to seek representation of diverse perspectives on the topic. Homogeneous sampling may be used to recruit a group of participants with specified criteria. The threat of bias is irrelevant; participants are recruited and selected specifically because they can illuminate the phenomenon being studied. Rather than being predetermined by statistical power analysis, qualitative study samples are dependent on the nature of the data, the availability of participants and where those data take the investigator. Multiple data collections may also take place to obtain maximum insight into sensitive topics. For instance, the question of how decisions are made for embryo disposition may involve sampling within the patient group as well as from scientists, clinicians, counsellors and clinic administrators.

Consistency

Consistency, or dependability of the results, is the criterion for assessing reliability. This does not mean that the same result would necessarily be found in other contexts but that, given the same data, other researchers would find similar patterns. Researchers often seek maximum variation in the experience of a phenomenon, not only to illuminate it but also to discourage fulfilment of limited researcher expectations (for example, negative cases or instances that do not fit the emerging interpretation or theory should be actively sought and explored). Qualitative researchers sometimes describe the processes by which verification of the theoretical findings by another team member takes place ( Morse and Richards, 2002 ).

Research that uses qualitative methods is not, as it seems sometimes to be represented, the easy option, nor is it a collation of anecdotes. It usually involves a complex theoretical or philosophical framework. Rigorous analysis is conducted without the aid of straightforward mathematical rules. Researchers must demonstrate the validity of their analysis and conclusions, resulting in longer papers and occasional frustration with the word limits of appropriate journals. Nevertheless, we need the different kinds of evidence that is generated by qualitative methods. The experience of health, illness and medical intervention cannot always be counted and measured; researchers need to understand what they mean to individuals and groups. Knowledge gained from qualitative research methods can inform clinical practice, indicate how to support people living with chronic conditions and contribute to community education and awareness about people who are (for example) experiencing infertility or using assisted conception.

Each author drafted a section of the manuscript and the manuscript as a whole was reviewed and revised by all authors in consultation.

No external funding was either sought or obtained for this study.

The authors have no conflicts of interest to declare.

Beeson D , Jennings P , Kramer W . Offspring searching for their sperm donors: how family types shape the process . Hum Reprod 2011 ; 26 : 2415 – 2424 .

Google Scholar

Brannen J . Mixing methods: the entry of qualitative and quantitative approaches into the research process . Int J Soc Res Methodol 2005 ; 8 : 173 – 184 .

Charmaz K . Grounded Theory in the 21st century; applications for advancing social justice studies . In: Denzin NK , Lincoln YS (eds). The Sage Handbook of Qualitative Research . California : Sage Publications Inc. , 2005 .

Google Preview

Cohen D , Crabtree B . Evaluative criteria for qualitative research in health care: controversies and recommendations . Ann Fam Med 2008 ; 6 : 331 – 339 .

de Lacey S . Parent identity and ‘virtual’ children: why patients discard rather than donate unused embryos . Hum Reprod 2005 ; 20 : 1661 – 1669 .

de Lacey SL , Peterson K , McMillan J . Child interests in assisted reproductive technology: how is the welfare principle applied in practice? Hum Reprod 2015 ; 30 : 616 – 624 .

Denzin N , Lincoln Y . Entering the field of qualitative research . In: Denzin NK , Lincoln YS (eds). The Landscape of Qualitative Research: Theories and Issues . Thousand Oaks : Sage , 1998 , 1 – 34 .

Dixon-Woods M , Bonas S , Booth A , Jones DR , Miller T , Shaw RL , Smith JA , Young B . How can systematic reviews incorporate qualitative research? A critical perspective . Qual Res 2006 ; 6 : 27 – 44 .

ESHRE Psychology and Counselling Guideline Development Group . Routine Psychosocial Care in Infertility and Medically Assisted Reproduction: A Guide for Fertility Staff , 2015 . http://www.eshre.eu/Guidelines-and-Legal/Guidelines/Psychosocial-care-guideline.aspx .

Freeman T , Jadva V , Kramer W , Golombok S . Gamete donation: parents' experiences of searching for their child's donor siblings or donor . Hum Reprod 2009 ; 24 : 505 – 516 .

Goedeke S , Daniels K , Thorpe M , Du Preez E . Building extended families through embryo donation: the experiences of donors and recipients . Hum Reprod 2015 ; 30 : 2340 – 2350 .

Hammarberg K , Johnson L , Bourne K , Fisher J , Kirkman M . Proposed legislative change mandating retrospective release of identifying information: consultation with donors and Government response . Hum Reprod 2014 ; 29 : 286 – 292 .

Kirkman M . Saviours and satyrs: ambivalence in narrative meanings of sperm provision . Cult Health Sex 2004 ; 6 : 319 – 336 .

Kirkman M , Rosenthal D , Johnson L . Families working it out: adolescents' views on communicating about donor-assisted conception . Hum Reprod 2007 ; 22 : 2318 – 2324 .

Kirkman M , Bourne K , Fisher J , Johnson L , Hammarberg K . Gamete donors' expectations and experiences of contact with their donor offspring . Hum Reprod 2014 ; 29 : 731 – 738 .

Kitto S , Chesters J , Grbich C . Quality in qualitative research . Med J Aust 2008 ; 188 : 243 – 246 .

Kovacs GT , Morgan G , Levine M , McCrann J . The Australian community overwhelmingly approves IVF to treat subfertility, with increasing support over three decades . Aust N Z J Obstetr Gynaecol 2012 ; 52 : 302 – 304 .

Leininger M . Evaluation criteria and critique of qualitative research studies . In: Morse J (ed). Critical Issues in Qualitative Research Methods . Thousand Oaks : Sage , 1994 , 95 – 115 .

Lincoln YS , Guba EG . Naturalistic Inquiry . Newbury Park, CA : Sage Publications , 1985 .

Morse J , Richards L . Readme First for a Users Guide to Qualitative Methods . Thousand Oaks : Sage , 2002 .

O'Reilly M , Parker N . ‘Unsatisfactory saturation’: a critical exploration of the notion of saturated sample sizes in qualitative research . Qual Res 2013 ; 13 : 190 – 197 .

Porter M , Bhattacharya S . Investigation of staff and patients' opinions of a proposed trial of elective single embryo transfer . Hum Reprod 2005 ; 20 : 2523 – 2530 .

Sandelowski M . The problem of rigor in qualitative research . Adv Nurs Sci 1986 ; 8 : 27 – 37 .

Wyverkens E , Provoost V , Ravelingien A , De Sutter P , Pennings G , Buysse A . Beyond sperm cells: a qualitative study on constructed meanings of the sperm donor in lesbian families . Hum Reprod 2014 ; 29 : 1248 – 1254 .

Young K , Fisher J , Kirkman M . Women's experiences of endometriosis: a systematic review of qualitative research . J Fam Plann Reprod Health Care 2014 ; 41 : 225 – 234 .

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Month:	Total Views:
December 2016	1
January 2017	4
February 2017	41
March 2017	82
April 2017	111
May 2017	88
June 2017	71
July 2017	73
August 2017	92
September 2017	59
October 2017	76
November 2017	84
December 2017	179
January 2018	177
February 2018	235
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May 2018	914
June 2018	1,052
July 2018	2,122
August 2018	4,606
September 2018	5,764
October 2018	7,844
November 2018	11,701
December 2018	8,722
January 2019	10,884
February 2019	10,938
March 2019	13,846
April 2019	17,949
May 2019	17,333
June 2019	12,257
July 2019	12,485
August 2019	14,138
September 2019	11,868
October 2019	13,410
November 2019	17,044
December 2019	12,312
January 2020	14,664
February 2020	15,928
March 2020	16,475
April 2020	22,019
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June 2020	15,155
July 2020	13,648
August 2020	12,338
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December 2020	10,484
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March 2021	15,285
April 2021	14,133
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June 2021	8,304
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February 2024	5,875
March 2024	8,614
April 2024	8,526
May 2024	7,309
June 2024	4,280
July 2024	3,615
August 2024	2,041

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Qualitative Research

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Your ultimate guide to qualitative research (with methods and examples).

16 min read You may be already using qualitative research and want to check your understanding, or you may be starting from the beginning. Learn about qualitative research methods and how you can best use them for maximum effect.

What is qualitative research?

Qualitative research is a research method that collects non-numerical data. Typically, it goes beyond the information that quantitative research provides (which we will cover below) because it is used to gain an understanding of underlying reasons, opinions, and motivations.

Qualitative research methods focus on the thoughts, feelings, reasons, motivations, and values of a participant, to understand why people act in the way they do .

In this way, qualitative research can be described as naturalistic research, looking at naturally-occurring social events within natural settings. So, qualitative researchers would describe their part in social research as the ‘vehicle’ for collecting the qualitative research data.

Qualitative researchers discovered this by looking at primary and secondary sources where data is represented in non-numerical form. This can include collecting qualitative research data types like quotes, symbols, images, and written testimonials.

These data types tell qualitative researchers subjective information. While these aren’t facts in themselves, conclusions can be interpreted out of qualitative that can help to provide valuable context.

Because of this, qualitative research is typically viewed as explanatory in nature and is often used in social research, as this gives a window into the behavior and actions of people.

It can be a good research approach for health services research or clinical research projects.

Free eBook: The qualitative research design handbook

Quantitative vs qualitative research

In order to compare qualitative and quantitative research methods, let’s explore what quantitative research is first, before exploring how it differs from qualitative research.

Quantitative research

Quantitative research is the research method of collecting quantitative research data – data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed .

Quantitative research methods deal with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data.

Quantitative research data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

The difference between quantitative and qualitative research methodology

While qualitative research is defined as data that supplies non-numerical information, quantitative research focuses on numerical data.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative research methods. If you want to explore ideas, thoughts, and meanings, use qualitative research methods.

While qualitative research helps you to properly define, promote and sell your products, don’t rely on qualitative research methods alone because qualitative findings can’t always be reliably repeated. Qualitative research is directional, not empirical.

The best statistical analysis research uses a combination of empirical data and human experience ( quantitative research and qualitative research ) to tell the story and gain better and deeper insights, quickly.

Where both qualitative and quantitative methods are not used, qualitative researchers will find that using one without the other leaves you with missing answers.

For example, if a retail company wants to understand whether a new product line of shoes will perform well in the target market:

Qualitative research methods could be used with a sample of target customers, which would provide subjective reasons why they’d be likely to purchase or not purchase the shoes, while
Quantitative research methods into the historical customer sales information on shoe-related products would provide insights into the sales performance, and likely future performance of the new product range.

Approaches to qualitative research

There are five approaches to qualitative research methods:

Grounded theory: Grounded theory relates to where qualitative researchers come to a stronger hypothesis through induction, all throughout the process of collecting qualitative research data and forming connections. After an initial question to get started, qualitative researchers delve into information that is grouped into ideas or codes, which grow and develop into larger categories, as the qualitative research goes on. At the end of the qualitative research, the researcher may have a completely different hypothesis, based on evidence and inquiry, as well as the initial question.
Ethnographic research : Ethnographic research is where researchers embed themselves into the environment of the participant or group in order to understand the culture and context of activities and behavior. This is dependent on the involvement of the researcher, and can be subject to researcher interpretation bias and participant observer bias . However, it remains a great way to allow researchers to experience a different ‘world’.
Action research: With the action research process, both researchers and participants work together to make a change. This can be through taking action, researching and reflecting on the outcomes. Through collaboration, the collective comes to a result, though the way both groups interact and how they affect each other gives insights into their critical thinking skills.
Phenomenological research: Researchers seek to understand the meaning of an event or behavior phenomenon by describing and interpreting participant’s life experiences. This qualitative research process understands that people create their own structured reality (‘the social construction of reality’), based on their past experiences. So, by viewing the way people intentionally live their lives, we’re able to see the experiential meaning behind why they live as they do.
Narrative research: Narrative research, or narrative inquiry, is where researchers examine the way stories are told by participants, and how they explain their experiences, as a way of explaining the meaning behind their life choices and events. This qualitative research can arise from using journals, conversational stories, autobiographies or letters, as a few narrative research examples. The narrative is subjective to the participant, so we’re able to understand their views from what they’ve documented/spoken.

Qualitative research methods can use structured research instruments for data collection, like:

Surveys for individual views

A survey is a simple-to-create and easy-to-distribute qualitative research method, which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Qualitative research questions tend to be open questions that ask for more information and provide a text box to allow for unconstrained comments.

Examples include:

Asking participants to keep a written or a video diary for a period of time to document their feelings and thoughts
In-Home-Usage tests: Buyers use your product for a period of time and report their experience

Surveys for group consensus (Delphi survey)

A Delphi survey may be used as a way to bring together participants and gain a consensus view over several rounds of questions. It differs from traditional surveys where results go to the researcher only. Instead, results go to participants as well, so they can reflect and consider all responses before another round of questions are submitted.

This can be useful to do as it can help researchers see what variance is among the group of participants and see the process of how consensus was reached.

Asking participants to act as a fake jury for a trial and revealing parts of the case over several rounds to see how opinions change. At the end, the fake jury must make a unanimous decision about the defendant on trial.
Asking participants to comment on the versions of a product being developed , as the changes are made and their feedback is taken onboard. At the end, participants must decide whether the product is ready to launch .

Semi-structured interviews

Interviews are a great way to connect with participants, though they require time from the research team to set up and conduct, especially if they’re done face-to-face.

Researchers may also have issues connecting with participants in different geographical regions. The researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

Conducting a phone interview with participants to run through their feedback on a product . During the conversation, researchers can go ‘off-script’ and ask more probing questions for clarification or build on the insights.

Focus groups

Participants are brought together into a group, where a particular topic is discussed. It is researcher-led and usually occurs in-person in a mutually accessible location, to allow for easy communication between participants in focus groups.

In focus groups , the researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

Asking participants to do UX tests, which are interface usability tests to show how easily users can complete certain tasks

Direct observation

This is a form of ethnographic research where researchers will observe participants’ behavior in a naturalistic environment. This can be great for understanding the actions in the culture and context of a participant’s setting.

This qualitative research method is prone to researcher bias as it is the researcher that must interpret the actions and reactions of participants. Their findings can be impacted by their own beliefs, values, and inferences.

Embedding yourself in the location of your buyers to understand how a product would perform against the values and norms of that society

Qualitative data types and category types

Qualitative research methods often deliver information in the following qualitative research data types:

Written testimonials

Through contextual analysis of the information, researchers can assign participants to category types:

Social class
Political alignment
Most likely to purchase a product
Their preferred training learning style

Advantages of qualitative research

Useful for complex situations: Qualitative research on its own is great when dealing with complex issues, however, providing background context using quantitative facts can give a richer and wider understanding of a topic. In these cases, quantitative research may not be enough.
A window into the ‘why’: Qualitative research can give you a window into the deeper meaning behind a participant’s answer. It can help you uncover the larger ‘why’ that can’t always be seen by analyzing numerical data.
Can help improve customer experiences: In service industries where customers are crucial, like in private health services, gaining information about a customer’s experience through health research studies can indicate areas where services can be improved.

Disadvantages of qualitative research

You need to ask the right question: Doing qualitative research may require you to consider what the right question is to uncover the underlying thinking behind a behavior. This may need probing questions to go further, which may suit a focus group or face-to-face interview setting better.
Results are interpreted: As qualitative research data is written, spoken, and often nuanced, interpreting the data results can be difficult as they come in non-numerical formats. This might make it harder to know if you can accept or reject your hypothesis.
More bias: There are lower levels of control to qualitative research methods, as they can be subject to biases like confirmation bias, researcher bias, and observation bias. This can have a knock-on effect on the validity and truthfulness of the qualitative research data results.

How to use qualitative research to your business’s advantage?

Qualitative methods help improve your products and marketing in many different ways:

Understand the emotional connections to your brand
Identify obstacles to purchase
Uncover doubts and confusion about your messaging
Find missing product features
Improve the usability of your website, app, or chatbot experience
Learn about how consumers talk about your product
See how buyers compare your brand to others in the competitive set
Learn how an organization’s employees evaluate and select vendors

6 steps to conducting good qualitative research

Businesses can benefit from qualitative research by using it to understand the meaning behind data types. There are several steps to this:

Define your problem or interest area: What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
Create a hypothesis: Ask yourself what could be the causes for the situation with those qualitative research data types.
Plan your qualitative research: Use structured qualitative research instruments like surveys, focus groups, or interviews to ask questions that test your hypothesis.
Data Collection: Collect qualitative research data and understand what your data types are telling you. Once data is collected on different types over long time periods, you can analyze it and give insights into changing attitudes and language patterns.
Data analysis: Does your information support your hypothesis? (You may need to redo the qualitative research with other variables to see if the results improve)
Effectively present the qualitative research data: Communicate the results in a clear and concise way to help other people understand the findings.

Qualitative data analysis

Evaluating qualitative research can be tough when there are several analytics platforms to manage and lots of subjective data sources to compare.

Qualtrics provides a number of qualitative research analysis tools, like Text iQ , powered by Qualtrics iQ, provides powerful machine learning and native language processing to help you discover patterns and trends in text.

This also provides you with:

Sentiment analysis — a technique to help identify the underlying sentiment (say positive, neutral, and/or negative) in qualitative research text responses
Topic detection/categorisation — this technique is the grouping or bucketing of similar themes that can are relevant for the business & the industry (eg. ‘Food quality’, ‘Staff efficiency’ or ‘Product availability’)

How Qualtrics products can enhance & simplify the qualitative research process

Even in today’s data-obsessed marketplace, qualitative data is valuable – maybe even more so because it helps you establish an authentic human connection to your customers. If qualitative research doesn’t play a role to inform your product and marketing strategy, your decisions aren’t as effective as they could be.

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting qualitative research. From survey creation and data collection to textual analysis and data reporting, it can help all your internal teams gain insights from your subjective and categorical data.

Qualitative methods are catered through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of qualitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Text IQ™ and Driver IQ™ make analyzing subjective and categorical data easy and simple. Choose to highlight key findings based on topic, sentiment, or frequency. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Knowledge Base

Methodology

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

Surveys : List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

Interviews : Asking open-ended questions verbally to respondents.
Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

Use quantitative research if you want to confirm or test something (a theory or hypothesis )
Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

Average scores ( means )
The number of times a particular answer was given
The correlation or causation between two or more variables
The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
Thematic analysis : Closely examining the data to identify the main themes and patterns
Discourse analysis : Studying how communication works in social contexts

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

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

Research bias

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

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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

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

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

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

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

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

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

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Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Introduction

Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Areas	Quantitative Research	Qualitative Research
Nature of reality	Assumes there is a single reality.	Assumes existence of dynamic and multiple reality.
Goal	Test and confirm hypotheses.	Explore and understand phenomena.
Data collection methods	Highly structured methods like questionnaires, inventories and scales.	Semi structured like in-depth interviews, observations and focus group discussions.
Design	Predetermined and rigid design.	Flexible and emergent design.
Reasoning	Deductive process to test the hypothesis.	Primarily inductive to develop the theory or hypothesis.
Focus	Concerned with the outcomes and prediction of the causal relationships.	Concerned primarily with process, rather than outcomes or products.
Sampling	Rely largely on random sampling methods.	Based on purposive sampling methods.
Sample size determination	Involves a-priori sample size calculation.	Collect data until data saturation is achieved.
Sample size	Relatively large.	Small sample size but studied in-depth.
Data analysis	Variable based and use of statistical or mathematical methods.	Case based and use non statistical descriptive or interpretive methods.

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]

Reflexivity

Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.

Conclusions

Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

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Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

Surveys : List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

Interviews : Asking open-ended questions verbally to respondents.
Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

Use quantitative research if you want to confirm or test something (a theory or hypothesis)
Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

Average scores
The number of times a particular answer was given
The correlation or causation between two or more variables
The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
Thematic analysis : Closely examining the data to identify the main themes and patterns
Discourse analysis : Studying how communication works in social contexts

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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

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

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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How to Determine the Hypothesis in a Qualitative Study

Qualitative studies use data collected from participant observations, the observations of researchers, interviews, texts and similar sources of information. Unlike a quantitative study, which uses specific measurements to determine hypothesis and conclusions, a qualitative study does not measure data according to a specified measurement, which can make forming a hypothesis slightly more complicated.

Explore this article

Determine the subject for study
Write down interview questions
Interview several participants

1 Determine the subject for study

Determine the subject for study. The subject will determine the appropriate study setup. For example, a study on how different cultures view parental responsibilities would form questions around the roles of parent’s in a child’s life.

2 Write down interview questions

Write down interview questions. According to New York University Press, the main method of forming a hypothesis in qualitative research involves interviews with the subjects of the study. This means forming questions appropriate to the study. For example, a study on parent responsibilities might ask participants what a mother is responsible for when raising a child.

3 Interview several participants

Interview several participants. Gaining the most accurate data requires interviewing more than one individual from the same group. For example, interview five people from China, five people from India, five people from Japan and five people from America. This gives a broader perspective on the subject.

Form a hypothesis from the interview data. Use the answers in the interviews and determine the proposed hypothesis. For example, a hypothesis about parental responsibility might suggest areas in which cultures' viewpoints match, such as cultures that view the role of a mother as a caregiver to children.

1 NYU Press: Introducing Qualitative Hypothesis-Generating Research
2 Oklahoma State University: Qualitative

About the Author

Helen Jain has been writing online articles since December 2009 for various websites. She has studied English and psychology and hopes to get a Ph.D. in English in the future.

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Published: 16 August 2024

Going virtual: mixed methods evaluation of online versus in-person learning in the NIH mixed methods research training program retreat

Joseph J. Gallo 1 ,
Sarah M. Murray 1 ,
John W. Creswell 2 ,
Charles Deutsch 3 &
Timothy C. Guetterman 2

BMC Medical Education volume 24 , Article number: 882 ( 2024 ) Cite this article

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Despite the central role of mixed methods in health research, studies evaluating online methods training in the health sciences are nonexistent. The focused goal was to evaluate online training by comparing the self-rated skills of scholars who experienced an in-person retreat to scholars in an online retreat in specific domains of mixed methods research for the health sciences from 2015–2023.

The authors administered a scholar Mixed Methods Skills Self-Assessment instrument based on an educational competency scale that included domains on: “research questions,” “design/approach,” “sampling,” “analysis,” and “dissemination” to participants of the Mixed Methods Research Training Program for the Health Sciences (MMRTP). Self-ratings on confidence on domains were compared before and after retreat participation within cohorts who attended in person ( n = 73) or online ( n = 57) as well as comparing across in-person to online cohorts. Responses to open-ended questions about experiences with the retreat were analyzed.

Scholars in an interactive program to improve mixed methods skills reported significantly increased confidence in ability to define or explain concepts and in ability to apply the concepts to practical problems, whether the program was attended in-person or synchronously online. Scholars in the online retreat had self-rated skill improvements as good or better than scholars who participated in person. With the possible exception of networking, scholars found the online format was associated with advantages such as accessibility and reduced burden of travel and finding childcare. No differences in difficulty of learning concepts was described.

Conclusions

Keeping in mind that the retreat is only one component of the MMRTP, this study provides evidence that mixed methods training online was associated with the same increases in self-rated skills as persons attending online and can be a key component to increasing the capacity for mixed methods research in the health sciences.

Peer Review reports

Introduction

The coronavirus pandemic accelerated interest in distance or remote learning. While the acute nature of the pandemic has abated, changes in the way people work have largely remained, with hybrid conferences and trainings more commonly implemented now than during the pre-pandemic period. Studies of health-related online teaching have focused on medical students [ 1 , 2 , 3 ], health professionals [ 4 , 5 ], and medical conferences [ 6 , 7 , 8 ] and have touted the advantages of virtual training and conferences in health education, but few studies have assessed relative growth in skills and competencies in health research methods for synchronous online vs. in-person training.

The National Institutes of Health (NIH)-funded Mixed Methods Research Training Program (MMRTP) for the Health Sciences provided training to faculty-level investigators across health disciplines from 2015–2023. The NIH is a major funder of health-related research in the United States. Its institutes span diseases and conditions (e.g., mental health, environmental health) in addition to focus areas (e.g., minority health and health disparities, nursing) and developing research capacity. Scholars in the MMRTP seek to develop skills in mixed methods research through participation in a summer retreat followed by ongoing mentorship for one year from a mixed methods expert matched to the scholar to support their development of a research proposal. Webinars leading up to the retreat include didactic sessions taught by the same faculty each year, and the retreat itself contains multiple interactive small group sessions in which each scholar presents their project and receives feedback on their grant proposal. Due to pandemic restrictions on gatherings and travel, in 2020 the MMRTP retained all components of the program but transitioned the in-person retreat to a synchronous online retreat.

The number of NIH agencies funding mixed methods research increased from 23 in 1997–2008 to 36 in 2009–2014 [ 9 ]. The usefulness of mixed methods research aligns with several Institutes’ strategic priories, including improving health equity, enhancing feasibility, acceptability, and sustainability of interventions, and addressing patient-centeredness. However, there is a tension between growing interest in mixed methods for health sciences research and a lack of training for investigators to acquire mixed methods research skills. Mixed methods research is not routinely taught in doctoral programs, institutional grant-writing programs, nor research training that academic physicians receive. The relative lack of researchers trained in mixed methods research necessitates ongoing research capacity building and mentorship [ 10 ]. Online teaching has the potential to meet growing demand for training and mentoring in mixed methods, as evidenced by the growth of online offerings by the Mixed Methods International Research Association [ 11 ]. Yet, the nature of skills and attitudes required for doing mixed methods research, such as integration of quantitative and qualitative data collection, analysis, and epistemologies, may make this type of training difficult to adapt to an online format without compromising its effectiveness.

Few studies have attempted to evaluate mixed methods training [ 12 , 13 , 14 , 15 ] and none appear to have evaluated online trainings in mixed methods research. Our goal was to evaluate our online MMRTP by comparing the self-rated skills of scholars who experienced an in-person retreat to an online retreat across specific domains. While the MMRTP retreat is only one component of the program, assessment before and after the retreat among persons who experienced the synchronous retreat online compared to in-person provides an indication of the effectiveness of online instruction in mixed methods for specific domains critical to the design of research in health services. We hypothesized that scholars who attended the retreat online would exhibit improvements in self-rated skills comparable to scholars who attended in person.

Participants

Five cohorts with a total of 73 scholars participated in the MMRTP in person (2015–2019), while four cohorts with a total of 57 scholars participated online (2020–2023). Scholars are faculty-level researchers in the health sciences in the United States. The scholars are from a variety of disciplines in the health sciences; namely, pediatrics, psychiatry, general medicine, oncology, nursing, human development, music therapy, nutrition, psychology, and social work.

The mixed methods research training program

Formal program activities include two webinars leading up to a retreat followed by ongoing mentorship support. The mixed methods content taught in webinars and the retreat is informed by a widely used textbook by Creswell and Plano Clark [ 18 ] in addition to readings on methodological topics and the practice of mixed methods. The webinars introduce mixed methods research and integration concepts, with the goal of imparting foundational knowledge and ensuring a common language. Specifically, the first webinar introduces mixed methods concepts, research designs, scientific rigor, and becoming a resource at one’s institution, while the second focuses on strategies for the integration of qualitative and quantitative research. Retreats provide an active workshop blending lectures, one-on-one meetings, and interactive faculty-led small workgroups. In addition to scholars, core program faculty who serve as investigators and mentors for the MMRTP, supplemented with consultants and former scholars, lead the retreat. The retreat has covered the state-of-the-art topics within the context of mixed methods research: rationale for use of mixed methods, procedural diagrams, study aims, use of theory, integration strategies, sampling strategies, implementation science, randomized trials, ethics, manuscript and proposal writing, and becoming a resource at one’s home institution. In addition to lectures, the retreat includes multiple interactive small group sessions in which each scholar presents their project and receives feedback on their grant proposal and is expected to make revisions based on feedback and lectures.

Scholars are matched for one year with a mentor based on the Scholar’s needs, career level, and area of health research from a national list of affiliated experienced mixed methods investigators with demonstrated success in obtaining independent funding for research related to the health sciences and a track record and commitment to mentoring. The purpose of this arrangement is to provide different perspectives on mixed methods design while also providing specific feedback on the scholar's research proposal, reviewing new ideas, and together developing a strategy and timeline for submission.

From 2015–2019 (in-person cohorts) the retreat was held over 3 days at the Johns Hopkins University Bloomberg School of Public Health (in 2016 Harvard Catalyst, the Harvard Clinical and Translational Science Center, hosted the retreat at Harvard Medical School). Due to pandemic restrictions, from 2020–2023 the retreat activities were conducted via Zoom with the same number of lecture sessions (over 3 days in 2020 and 4 days thereafter). We made adaptations for the online retreat based on continuous feedback from attendees. We had to rapidly transition to online in 2020 with the same structure as in person, but feedback from scholars led us to extend the retreat to 4 days online from 2021–2023. The extra day allowed for more breaks from Zoom sessions with time for scholars to consider feedback from small groups and to have one-on-one meetings with mentors. Discussion during interactive presentations was encouraged and facilitated by using breakout rooms at breaks mid-presentation. Online resources were available to participants through CoursePlus, the teaching and learning platform used for courses at the Johns Hopkins Bloomberg School of Public Health, hosting publications, presentation materials, recordings of lectures, sharing proposals, email, and discussion boards that scholars have access to before, during, and after the retreat.

Measurement strategy

Before and after the retreat in each year, we distributed a self-administered scholar Mixed Methods Skills Self-Assessment instrument (Supplement 1) to all participating scholars [ 15 ]; we have reported results from this pre-post assessment for the first two cohorts [ 14 ]. The Mixed Methods Skills Self-Assessment instrument has been previously used and has established reliability for the total items (α = 0.95) and evidence of criterion-related validity between experiences and ability ratings [ 15 ]. In each year, the pre-assessment is completed upon entry to the program, approximately four months prior to the retreat, and the post-assessment is administered two weeks after the retreat. The instrument consists of three sections: 1) professional experiences with mixed methods, including background, software, and resource familiarity; 2) a quantitative, qualitative, and mixed methods skills self-assessment; and 3) open-ended questions focused on learning goals for the MMRTP. The skills assessment contains items for each of the following domains: “research questions,” “design/approach,” “sampling,” “analysis,” and “dissemination.” Each skill was assessed via three items drawn from an educational competency ratings scale that ask scholars to rate: [ 16 ] “My ability to define/explain,” “My ability to apply to practical problems,” and “Extent to which I need to improve my skill.” Response options were on a five-point Likert-type scale that ranged from “Not at all” (coded ‘1’) to “To a great extent” (coded ‘5’), including a mid-point [ 17 ]. We took the mean of the scholar’s item ratings over all component items within each domain (namely, “research questions,” “design/approach,” “sampling,” “analysis,” and “dissemination”).

Open-ended questions

The baseline survey included two open-ended prompts: 1) What skills and goals are most important to you?, and 2) What would you like to learn? The post-assessment survey also included two additional open-ended questions about the retreat: 1) What aspects of the retreat were helpful?, and 2) What would you like to change about the retreat? In addition, for the online cohorts (2020–2023), we wanted to understand reactions to the online training and added three questions for this purpose: (1) In general, what did you think of the online format for the MMRTP retreat?, 2) What mixed methods concepts are easier or harder to learn virtually?, and 3) What do you think was missing from having the retreat online rather than in person?

Data analysis

Our evaluation employed a convergent mixed methods design [ 18 ], integrating an analysis of ratings pre- and post-retreat with analysis of open-ended responses provided by scholars after the retreat. Our quantitative analysis proceeded in 3 steps. First, we analyzed item-by-item baseline ratings of the extent to which scholars thought they “need to improve skills,” stratified into two groups (5 cohorts who attended in-person and 4 cohorts who attended online). The purpose of comparing the two groups at baseline on learning needs was to assess how similar the scholars in the in-person or online groups were in self-assessment of learning needs before attending the program. Second, to examine the change in scholar ratings of ability to “define or explain a concept” and in their ability to “apply to practical problems,” from before to after the retreat, we conducted paired t-tests. The goal was to compare the ratings before and after the retreat among scholars who attended the program in person to scholars who attended online. Third, we compared post-retreat ratings among in-person cohorts to online cohorts to gauge the effectiveness of the online training. We set statistical significance at α < 0.05 as a guide to inference. We calculated Cohen’s d as a guide to the magnitude of differences [ 19 ]. SPSS Version 28 was employed for all analyses.

We analyzed qualitative data using a thematic analysis approach that consisted of reviewing all open-ended responses, conducting open coding based on the data, developing and refining a codebook, and identifying major themes [ 20 ]. We then compared the qualitative results for the in-person versus online cohorts to understand any thematic differences concerning retreat experiences and reactions.

Background and experiences of scholars

Scholars in the in-person ( n = 59, 81%) and online ( n = 52, 91%) cohorts reported their primary training was quantitative rather than qualitative or mixed methods, and scholars across cohorts commonly reported at least some exposure to mixed methods research (Table 1 ). However, most scholars did not have previous mixed methods training with 17 (23%) and 16 (28%) of the in-person and online cohorts, respectively, having previously completed a mixed methods course. While experiences were similar across in-person vs. online cohorts, there were two areas in which the scholars reported a statistically significant difference: a larger portion of the online cohorts reported writing a mixed methods application that received funding ( n = 35, 48% in person; n = 46, 81% online), and a smaller proportion of the online cohorts had given a local or institutional mixed methods presentation ( n = 32, 44% in person; n = 15, 26% online).

Self-identified need to improve skills in mixed methods

At baseline, scholars rated the extent to which they needed to improve specific mixed methods skills (Table 2 ). Overall, scholars endorsed a strong need to improve all mixed methods skills. The ratings between the in-person and online cohorts were not statistically significant for any item.

Change in self-ratings of skills after the retreat

Within cohorts.

For all domains, the differences in pre-post assessment scores were statistically significant for both the in-person and online cohorts in ability to define or explain concepts and to apply concepts to practical problems (left side of Table 3 ). In other words, on average scholars improved in both in-person and online cohorts.

Across cohorts

Online cohorts had significantly better self-ratings after the retreat than did in-person cohorts in ability to define or explain concepts and to apply concepts to practical problems (in sampling, data collection, analysis, and dissemination) but no significant differences in research questions and design / approach (rightmost column of Table 3 ).

Scholar reflections about online and in-person retreats

Goals of training.

In comparing in-person to online cohorts, discussions of the skills that scholars wanted to improve had no discernable differences. Scholars mentioned wanting to develop skills in the foundations of mixed methods research, how to write competitive proposals for funding, the use of the terminology of mixed methods research, and integrative analysis. In addition, some scholars expressed wanting to become a resource at their own institutions and providing training and mentoring to others.

Small group sessions

Scholars consistently reported appreciating being able to talk through their project and gaining feedback from experts in small group sessions. Some scholars expressed a preference for afternoon small group sessions, “The small group sessions felt the most helpful, but only because we can apply what we were learning from the morning lecture sessions” (online cohort 9). How participants discussed the benefits of the small group sessions or how they used the sessions did not depend on whether they had experienced the session in person or online.

Online participants described a tradeoff between the accessibility of a virtual retreat versus advantages of in-person training. One participant explained, “I liked the online format, as I do not have reliable childcare” (online cohort 8). Many of the scholars felt that there was an aspect of networking missing when the retreat was held fully online. As one scholar described, when learning online they, “miss getting to know the other fellows and forming lasting connections” (online cohort 9). However, an equal number of others reported that having a virtual retreat meant less hassle; for instance, they were able to join from their preferred location and did not have to travel. Some individuals specifically described the tradeoff of fewer networking opportunities for ease of attendance. One scholar wrote, being online “certainly loses some of the perks of in person connection building but made it equitable to attend” (online cohort 8).

Learning online

No clear difference in ease of learning concepts was described. A scholar explained: “Learning most concepts is essentially the same virtually versus in person” (online cohort 8). However, scholars described some concepts as easier to learn in one modality versus the other, for example, simpler concepts being more suited to learning virtually while complex concepts were better suited to in-person learning. There was notable variation though in the topics which scholars considered to be simple versus complex. For instance, one scholar noted that “I suppose developing the joint displays were a bit tougher virtually since you were not literally elbow to elbow” (online cohort 7) while another explained, “joint displays lend themselves to the zoom format” (online cohort 8).

Integrating survey responses and scholar reflections

In-person and online cohorts were comparable in professional experiences and ratings of the need to improve skills before attending the retreat, sharpening the focus on differences in self-rated skills associated with attendance online compared to in person. If anything, online attendees rated skills as good or better than in-person attendees. Open-ended questions revealed that, for the most part, scholar reflections on learning were similar across in-person and online cohorts. Whether learning the concept of “mixed methods integration” was more difficult online was a source of disagreement. Online attendance was associated with numerous advantages, and small group sessions were valued, regardless of format. Taken together, the evidence from nine cohorts shows that the online retreat was acceptable and as effective in improving self-rated skills as meeting in person.

Mixed methods have become indispensable to health services research from intervention development and testing [ 21 ] to implementation science [ 22 , 23 , 24 ]. We found that scholars participating in an interactive program to improve mixed methods skills reported significantly increased confidence in their ability to define or explain concepts and in their ability to apply the concepts to practical problems, whether the program was attended in-person or synchronously online. Scholars who participated in the online retreat had self-rated skill improvements as good or better than scholars who participated in person, and these improvements were relatively large as indicated by the Cohen’s d estimates. The online retreat appeared to be effective in increasing confidence in the use of mixed methods research in the health sciences and was acceptable to scholars. Our study deserves attention because the national need is so great for investigators with training in mixed methods to address complex behavioral health problems, community- and patient-centered research, and implementation research. No program has been evaluated as we have done here.

Aside from having written a funded mixed methods proposal, the online compared to earlier in person cohorts were comparable in experiences and need to improve specific skills. Within each cohort, scholars reported significant gains in self-rated skills on their ability to “define or explain” a concept and on their ability to “apply to practical problems” in domains essential to mixed methods research. However, consistent with our hypothesis that online training would be as effective as in person we found that online scholars reported better improvement in self-ratings in ability to define or explain concepts and to apply concepts to practical problems in sampling, data collection, analysis, and dissemination but no significant differences in research questions and design / approach. Better ratings in online cohorts could reflect differences in experience with mixed methods, secular changes in knowledge and availability of resources in mixed methods, and maturation of the program facilitated by continued modifications based on feedback from scholars and participating faculty [ 13 , 14 , 15 ].

Ratings related to the “analysis” domain, which includes the central concept of mixed methods integration, deserve notice since scholars rated this skill well below other domains at baseline. While both in-person and online cohorts improved after the retreat, and online cohorts improved substantially more than in-person cohorts, ratings for analysis after the retreat remained lower than for other domains. Scholars consistently have mentioned integration as a difficult concept, and our analysis here is limited to the retreat alone. Continued mentoring one year after the retreat and work on their proposal is built in to the MMRTP to enhance understanding of integration.

Several reviews point out the advantages of online training including savings in time, money, and greenhouse emissions [ 1 , 7 , 8 ]. Online conferences may increase the reach of training to international audiences, improve the diversity of speakers and attendees, facilitate attendance of persons with disabilities, and ease the burden of finding childcare [ 1 , 8 , 25 ]. Online training in health also appears to be effective [ 2 , 4 , 5 , 25 ], though studies are limited because often no skills were evaluated, no comparison groups were used, the response rate was low, or the sample size was small [ 1 , 6 ]. With the possible exception of networking, scholars found the online format was associated with advantages, including saving travel, maintaining work-family balance, and learning effectively. As scholars did discuss perceived increase in difficulty networking, deliberate effort needs to be directed at enhancing collaborations and mentorship [ 8 ]. The MMRTP was designed with components to facilitate networking during and beyond the retreat (e.g., small group sessions, one-on-one meetings, working with a consultant on a specific proposal).

Limitations of our study should be considered. First, the retreat was only one of several components of a mentoring program for faculty in the health sciences. Second, in-person and online cohorts represent different time periods spanning 9 years during which mixed methods applications to NIH and other funders have been increasing [ 9 ]. Third, the pre- and post-evaluations of ability to explain or define concepts, or to apply the concepts to practical problems, were based on self-report. Nevertheless, the pre-post retreat survey on self-rated skills uses a skills self-assessment form we developed [ 15 ], drawing from educational theory related to the epistemology of knowledge [ 26 , 27 ].

Despite the central role of mixed methods in health research, studies evaluating online methods training in the health sciences are nonexistent. Our study provides evidence that mixed methods training online was associated with the same increases in self-rated skills as persons attending online and can be a key component to increasing the capacity for mixed methods research in the health sciences.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Mixed Methods Research Training Program

Wilcha RJ. Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review. JMIR Med Educ. 2020;6(2):e20963.

Article Google Scholar

Pei L, Wu H. Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Medical Education Online. 2019;24(1) https://doi.org/10.1080/10872981.2019.1666538

Barche A, Nayak V, Pandey A, Bhandarkar A, Nayak K. Student perceptions towards online learning in medical education during the COVID-19 pandemic: a mixed-methods study. F1000Res. 2022;11:979. https://doi.org/10.12688/f1000research.123582.1 .

Ebner C, Gegenfurtner A. Learning and Satisfaction in Webinar, Online, and Face-to-Face Instruction: A Meta-Analysis. Frontiers in Education. 2019;4(92) https://doi.org/10.3389/feduc.2019.00092

Randazzo M, Preifer R, Khamis-Dakwar R. Project-Based Learning and Traditional Online Teaching of Research Methods During COVID-19: An Investigation of Research Self-Efficacy and Student Satisfaction. Frontiers in Education. 2021;6(662850) https://doi.org/10.3389/feduc.2021.662850

Chan A, Cao A, Kim L, et al. Comparison of perceived educational value of an in-person versus virtual medical conference. Can Med Educ J. 2021;12(4):65–9. https://doi.org/10.36834/cmej.71975 .

Rubinger L, Gazendam A, Ekhtiari S, et al. Maximizing virtual meetings and conferences: a review of best practices. Int Orthop. 2020;44(8):1461–6. https://doi.org/10.1007/s00264-020-04615-9 .

Sarabipour S. Virtual conferences raise standards for accessibility and interactions. Elife. Nov 4 2020;9 https://doi.org/10.7554/eLife.62668

Coyle CE, Schulman-Green D, Feder S, et al. Federal funding for mixed methods research in the health sciences in the United States: Recent trends. J Mixed Methods Res. 2018;12(3):1–20.

Poth C, Munce SEP. Commentary – preparing today’s researchers for a yet unknown tomorrow: promising practices for a synergistic and sustainable mentoring approach to mixed methods research learning. Int J Multiple Res Approaches. 2020;12(1):56–64.

Creswell JW. Reflections on the MMIRA The Future of Mixed Methods Task Force Report. J Mixed Methods Res. 2016;10(3):215–9. https://doi.org/10.1177/1558689816650298 .

Hou S. A Mixed Methods Process Evaluation of an Integrated Course Design on Teaching Mixed Methods Research. Int J Sch Teach Learn. 2021;15(2):Article 8. https://doi.org/10.20429/ijsotl.2021.150208 .

Guetterman TC, Creswell J, Deutsch C, Gallo JJ. Process Evaluation of a Retreat for Scholars in the First Cohort: The NIH Mixed Methods Research Training Program for the Health Sciences. J Mix Methods Res. 2019;13(1):52–68. https://doi.org/10.1177/1558689816674564 .

Guetterman T, Creswell JW, Deutsch C, Gallo JJ. Skills Development and Academic Productivity of Scholars in the NIH Mixed Methods Research Training Program for the Health Sciences (invited publication). Int J Multiple Res Approach. 2018;10(1):1–17.

Guetterman T, Creswell JW, Wittink MN, et al. Development of a Self-Rated Mixed Methods Skills Assessment: The NIH Mixed Methods Research Training Program for the Health Sciences. J Contin Educ Health Prof. 2017;37(2):76–82.

Harnisch D, Shope RJ. Developing technology competencies to enhance assessment literate teachers. AACE; 2007:3053–3055.

DeVellis RF. Scale development: Theory and applications. 3rd ed. Sage; 2012.

Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. 3rd ed. Sage Publications; 2017.

Cohen J. Statistical power analysis for the behavioral sciences. 3rd ed. Academic Press; 1988.

Boeije H. A purposeful approach to the constant comparative method in the analysis of qualitative interviews. Qual Quant. 2002;36:391–409.

Aschbrenner KA, Kruse G, Gallo JJ, Plano Clark VL. Applying mixed methods to pilot feasibility studies to inform intervention trials. Pilot Feasibility Stud. 2022;8(1):217–24. https://doi.org/10.1186/s40814-022-01178-x .

Palinkas LA. Qualitative and mixed methods in mental health services and implementation research. J Clin Child Adolesc Psychol. 2014;43(6):851–61.

Albright K, Gechter K, Kempe A. Importance of mixed methods in pragmatic trials and dissemination and implementation research. Acad Pediatr Sep-Oct. 2013;13(5):400–7. https://doi.org/10.1016/j.acap.2013.06.010 .

Palinkas L, Aarons G, Horwitz S, Chamberlain P, Hurlburt M, Landsverk J. Mixed methods designs in implementation research. Adm Policy Ment Health. 2011;38:44–53.

Ni AY. Comparing the Effectiveness of Classroom and Online Learning: Teaching Research Methods. J Public Affairs Educ. 2013;19(2):199–215. https://doi.org/10.1080/15236803.2013.12001730 .

Harnisch D, Shope RJ. Developing technology competencies to enhance assessment literate teachers. presented at: Society for Information Technology & Teacher Education International Conference; March 26, 2007 2007; San Antonio, Texas.

Guetterman TC. What distinguishes a novice from an expert mixed methods researcher? Qual Quantity. 2017;51:377–98.

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Acknowledgements

The Mixed Methods Research Training Program is supported by the Office of Behavioral and Social Sciences Research under Grant R25MH104660. Participating institutes are the National Institute of Mental Health, National Heart, Lung, and Blood Institute, National Institute of Nursing Research, and the National Institute on Aging.

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All authors conceptualized the design of this study. TG analyzed the scholar data in evaluation of the program. TG and JG interpreted results and were major contributors in writing the manuscript. All authors read and approved the final manuscript.

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Gallo, J.J., Murray, S.M., Creswell, J.W. et al. Going virtual: mixed methods evaluation of online versus in-person learning in the NIH mixed methods research training program retreat. BMC Med Educ 24 , 882 (2024). https://doi.org/10.1186/s12909-024-05877-2

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The Research on Critical Period Hypothesis in Pronunciation Learning in Second Language Acquisition

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Analysing near-miss incidents in construction: a systematic literature review.

1. Introduction

Q 1 —Are near-miss events in construction industry the subject of scientific research?
Q 2 —What methods have been employed thus far to obtain information on near misses and systems for recording incidents in construction companies?
Q 3 —What methods have been used to analyse the information and figures obtained?
Q 4 —What are the key aspects of near misses in the construction industry that have been of interest to the researchers?

2. Definition of Near-Miss Events

3. research methodology, 4.1. a statistical analysis of publications, 4.2. methods used to obtain information about near misses, 4.2.1. traditional methods.

Traditional registration forms
Computerized systems for the recording of events
Surveys and interviews

4.2.2. Real-Time Monitoring Systems

Employee-tracking systems
Video surveillance systems
Wearable technology
Motion sensors

4.3. Methods Used to Analyse the Information and Figures That Have Been Obtained

4.3.1. quantitative and qualitative statistical methods, 4.3.2. analysis using artificial intelligence (ai), 4.3.3. building information modelling, 4.4. key aspects of near-miss investigations in the construction industry, 4.4.1. occupational risk assessment, 4.4.2. causes of hazards in construction, 4.4.3. time series of near misses, 4.4.4. material factors of construction processes, 4.5. a comprehensive overview of the research questions and references on near misses in the construction industry, 5. discussion, 5.1. interest of researchers in near misses in construction (question 1), 5.2. methods used to obtain near-miss information (question 2), 5.3. methods used to analyse the information and data sets (question 3), 5.4. key aspects of near-miss investigations in the construction industry (question 4), 6. conclusions.

A quantitative analysis of the Q 1 question has revealed a positive trend, namely that there is a growing interest among researchers in studying near misses in construction. The greatest interest in NM topics is observed in the United States of America, China, the United Kingdom, Australia, Hong Kong, and Germany. Additionally, there has been a recent emergence of interest in Poland. The majority of articles are mainly published in journals such as Safety Science (10), Journal of Construction Engineering and Management (8), and Automation in Construction (5);
The analysis of question Q 2 illustrates that traditional paper-based event registration systems are currently being superseded by advanced IT systems. However, both traditional and advanced systems are subject to the disadvantage of relying on employee-reported data, which introduces a significant degree of uncertainty regarding in the quality of the information provided. A substantial proportion of the data and findings presented in the studies was obtained through surveys and interviews. The implementation of real-time monitoring systems is becoming increasingly prevalent in construction sites. The objective of such systems is to provide immediate alerts in the event of potential hazards, thereby preventing a significant number of near misses. Real-time monitoring systems employ a range of technologies, including ultrasonic technology, radio frequency identification (RFID), inertial measurement units (IMUs), real-time location systems (RTLSs), industrial cameras, wearable technology, motion sensors, and advanced IT technologies, among others;
The analysis of acquired near-miss data is primarily conducted through the utilisation of quantitative and qualitative statistical methods, as evidenced by the examination of the Q 3 question. In recent years, research utilising artificial intelligence (AI) has made significant advances. The most commonly employed artificial intelligence techniques include text mining, machine learning, and artificial neural networks. The growing deployment of Building Information Modelling (BIM) technology has precipitated a profound transformation in the safety management of construction sites, with the advent of sophisticated tools for the identification and management of hazardous occurrences;
In response to question Q 4 , the study of near misses in the construction industry has identified several key aspects that have attracted the attention of researchers. These include the utilisation of both quantitative and qualitative methodologies for risk assessment, the analysis of the causes of hazards, the identification of accident precursors through the creation of time series, and the examination of material factors pertaining to construction processes. Researchers are focusing on the utilisation of both databases and advanced technologies, such as real-time location tracking, for the assessment and analysis of occupational risks. Techniques such as Analytic Hierarchy Process (AHP) and clustering facilitate a comprehensive assessment and categorisation of incidents, thereby enabling the identification of patterns and susceptibility to specific types of accidents. Moreover, the impact of a company’s safety climate and organisational culture on the frequency and characteristics of near misses represents a pivotal area of investigation. The findings of this research indicate that effective safety management requires a holistic approach that integrates technology, risk management and safety culture, with the objective of reducing accidents and enhancing overall working conditions on construction sites.

7. Gaps and Future Research Directions, Limitations

Given the diversity and variability of construction sites and the changing conditions and circumstances of work, it is essential to create homogeneous clusters of near misses and to analyse the phenomena within these clusters. The formation of such clusters may be contingent upon the direct causes of the events in question;
Given the inherently dynamic nature of construction, it is essential to analyse time series of events that indicate trends in development and safety levels. The numerical characteristics of these trends may be used to construct predictive models for future accidents and near misses;
The authors have identified potential avenues for future research, which could involve the development of mathematical models using techniques such as linear regression, artificial intelligence, and machine learning. The objective of these models is to predict the probable timing of occupational accidents within defined incident categories, utilising data from near misses. Moreover, efforts are being made to gain access to the hazardous incident recording systems of different construction companies, with a view to facilitating comparison of the resulting data;
One significant limitation of near-miss research is the lack of an integrated database that encompasses a diverse range of construction sites and construction work. A data resource of this nature would be of immense value for the purpose of conducting comprehensive analyses and formulating effective risk management strategies. This issue can be attributed to two factors: firstly, the reluctance of company managers to share their databases with researchers specialising in risk assessment, and secondly, the reluctance of employees to report near-miss incidents. Such actions may result in adverse consequences for employees, including disciplinary action or negative perceptions from managers. This consequently results in the recording of only a subset of incidents, thereby distorting the true picture of safety on the site.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

Year	Source Title	DOI/ISBN/ISSN	Reference
1999	Construction Management and Economics	10.1080/014461999371691	[ ]
2002	Structural Engineer	14665123	[ ]
2009	Building a Sustainable Future—Proceedings of the 2009 Construction Research Congress	10.1061/41020(339)4	[ ]
2010	Safety Science	10.1016/j.ssci.2010.04.009	[ ]
2010	Automation in Construction	10.1016/j.autcon.2009.11.017	[ ]
2010	Safety Science	10.1016/j.ssci.2009.06.006	[ ]
2012	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0000518	[ ]
2013	ISARC 2013—30th International Symposium on Automation and Robotics in Construction and Mining, Held in Conjunction with the 23rd World Mining Congress	10.22260/isarc2013/0113	[ ]
2014	Proceedings of the Institution of Civil Engineers: Civil Engineering	10.1680/cien.14.00010	[ ]
2014	Safety Science	10.1016/j.ssci.2013.12.012	[ ]
2014	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0000795	[ ]
2014	31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014—Proceedings	10.22260/isarc2014/0115	[ ]
2014	Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress	10.1061/9780784413517.0181	[ ]
2014	Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress	10.1061/9780784413517.0235	[ ]
2014	Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress	10.1061/9780784413517.0096	[ ]
2015	Automation in Construction	10.1016/j.autcon.2015.09.003	[ ]
2015	32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings	10.22260/isarc2015/0062	[ ]
2015	ASSE Professional Development Conference and Exposition 2015	-	[ ]
2015	Congress on Computing in Civil Engineering, Proceedings	10.1061/9780784479247.019	[ ]
2016	Automation in Construction	10.1016/j.autcon.2016.03.008	[ ]
2016	Automation in Construction	10.1016/j.autcon.2016.04.007	[ ]
2016	IEEE IAS Electrical Safety Workshop	10.1109/ESW.2016.7499701	[ ]
2016	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001100	[ ]
2016	Safety Science	10.1016/j.ssci.2015.11.025	[ ]
2016	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001049	[ ]
2016	IEEE Transactions on Industry Applications	10.1109/TIA.2015.2461180	[ ]
2017	Safety Science	10.1016/j.ssci.2017.06.012	[ ]
2017	ENR (Engineering News-Record)	8919526	[ ]
2017	6th CSCE-CRC International Construction Specialty Conference 2017—Held as Part of the Canadian Society for Civil Engineering Annual Conference and General Meeting 2017	978-151087841-9	[ ]
2017	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	10.1007/978-3-319-72323-5_12	[ ]
2017	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001209	[ ]
2017	Safety Science	10.1016/j.ssci.2016.08.027	[ ]
2017	Safety Science	10.1016/j.ssci.2016.08.022	[ ]
2018	Safety Science	10.1016/j.ssci.2018.04.004	[ ]
2018	International Journal of Construction Management	10.1080/15623599.2017.1382067	[ ]
2018	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001420	[ ]
2018	Proceedings of SPIE—The International Society for Optical Engineering	10.1117/12.2296548	[ ]
2019	Automation in Construction	10.1016/j.autcon.2019.102854	[ ]
2019	Physica A: Statistical Mechanics and its Applications	10.1016/j.physa.2019.121495	[ ]
2019	Sustainability (Switzerland)	10.3390/su11051264	[ ]
2019	Computing in Civil Engineering 2019: Data, Sensing, and Analytics—Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019	978-078448243-8	[ ]
2019	Journal of Health, Safety and Environment	18379362	[ ]
2019	Computing in Civil Engineering 2019: Data, Sensing, and Analytics—Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019	978-078448243-8	[ ]
2019	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience—Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019	10.1061/9780784482445.026	[ ]
2019	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001582	[ ]
2019	Advances in Intelligent Systems and Computing	10.1007/978-3-030-02053-8_107	[ ]
2020	Accident Analysis and Prevention	10.1016/j.aap.2020.105496	[ ]
2020	Advanced Engineering Informatics	10.1016/j.aei.2020.101062	[ ]
2020	Advanced Engineering Informatics	10.1016/j.aei.2020.101060	[ ]
2020	ARCOM 2020—Association of Researchers in Construction Management, 36th Annual Conference 2020—Proceedings	978-099554633-2	[ ]
2020	International Journal of Building Pathology and Adaptation	10.1108/IJBPA-03-2020-0018	[ ]
2020	Communications in Computer and Information Science	10.1007/978-3-030-42852-5_8	[ ]
2021	Journal of Architectural Engineering	10.1061/(ASCE)AE.1943-5568.0000501	[ ]
2021	Safety Science	10.1016/j.ssci.2021.105368	[ ]
2021	ACM International Conference Proceeding Series	10.1145/3482632.3487473	[ ]
2021	Reliability Engineering and System Safety	10.1016/j.ress.2021.107687	[ ]
2021	Proceedings of the 37th Annual ARCOM Conference, ARCOM 2021	-	[ ]
2022	Buildings	10.3390/buildings12111855	[ ]
2022	Safety Science	10.1016/j.ssci.2022.105704	[ ]
2022	Sensors	10.3390/s22093482	[ ]
2022	Proceedings of International Structural Engineering and Construction	10.14455/ISEC.2022.9(2).CSA-03	[ ]
2022	Journal of Information Technology in Construction	10.36680/j.itcon.2022.045	[ ]
2022	Forensic Engineering 2022: Elevating Forensic Engineering—Selected Papers from the 9th Congress on Forensic Engineering	10.1061/9780784484555.005	[ ]
2022	Computational Intelligence and Neuroscience	10.1155/2022/4851615	[ ]
2022	International Journal of Construction Management	10.1080/15623599.2020.1839704	[ ]
2023	Journal of Construction Engineering and Management	10.1061/JCEMD4.COENG-13979	[ ]
2023	Heliyon	10.1016/j.heliyon.2023.e21607	[ ]
2023	Accident Analysis and Prevention	10.1016/j.aap.2023.107224	[ ]
2023	Safety	10.3390/safety9030047	[ ]
2023	Engineering, Construction and Architectural Management	10.1108/ECAM-09-2021-0797	[ ]
2023	Advanced Engineering Informatics	10.1016/j.aei.2023.101929	[ ]
2023	Engineering, Construction and Architectural Management	10.1108/ECAM-05-2023-0458	[ ]
2023	Intelligent Automation and Soft Computing	10.32604/iasc.2023.031359	[ ]
2023	International Journal of Construction Management	10.1080/15623599.2020.1847405	[ ]
2024	Heliyon	10.1016/j.heliyon.2024.e26410	[ ]

Occupational Risk|Safety and Health at Work EU-OSHA. Available online: https://osha.europa.eu/en/tools-and-resources/eu-osha-thesaurus/term/70194i (accessed on 28 June 2023).
Guo, S.; Zhou, X.; Tang, B.; Gong, P. Exploring the Behavioral Risk Chains of Accidents Using Complex Network Theory in the Construction Industry. Phys. A Stat. Mech. Its Appl. 2020 , 560 , 125012. [ Google Scholar ] [ CrossRef ]
Woźniak, Z.; Hoła, B. The Structure of near Misses and Occupational Accidents in the Polish Construction Industry. Heliyon 2024 , 10 , e26410. [ Google Scholar ] [ CrossRef ]
Li, X.; Sun, W.; Fu, H.; Bu, Q.; Zhang, Z.; Huang, J.; Zang, D.; Sun, Y.; Ma, Y.; Wang, R.; et al. Schedule Risk Model of Water Intake Tunnel Construction Considering Mood Factors and Its Application. Sci. Rep. 2024 , 14 , 3857. [ Google Scholar ] [ CrossRef ]
Li, X.; Huang, J.; Li, C.; Luo, N.; Lei, W.; Fan, H.; Sun, Y.; Chen, W. Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network. Period. Polytech. Civ. Eng. 2022 , 66 , 335–343. [ Google Scholar ] [ CrossRef ]
Central Statistical Office Central Statistical Office/Thematic Areas/Labor Market/Working Conditions/Accidents at Work/Accidents at Work in the 1st Quarter of 2024. Available online: https://stat.gov.pl/obszary-tematyczne/rynek-pracy/warunki-pracy-wypadki-przy-pracy/wypadki-przy-pracy-w-1-kwartale-2024-roku,3,55.html (accessed on 17 July 2024).
Manzo, J. The $ 5 Billion Cost of Construction Fatalities in the United States: A 50 State Comparison ; The Midwest Economic Policy Institute (MEPI): Saint Paul, MN, USA, 2017. [ Google Scholar ]
Sousa, V.; Almeida, N.M.; Dias, L.A. Risk-Based Management of Occupational Safety and Health in the Construction Industry—Part 1: Background Knowledge. Saf. Sci. 2014 , 66 , 75–86. [ Google Scholar ] [ CrossRef ]
Amirah, N.A.; Him, N.F.N.; Rashid, A.; Rasheed, R.; Zaliha, T.N.; Afthanorhan, A. Fostering a Safety Culture in Manufacturing through Safety Behavior: A Structural Equation Modelling Approach. J. Saf. Sustain. 2024; in press . [ Google Scholar ] [ CrossRef ]
Heinrich, H.W. Industrial Accident Prevention ; A Scientific Approach; McGraw-Hill: New York, NY, USA, 1931. [ Google Scholar ]
Near Miss Definition Per OSHA—What Is a Near Miss? Available online: https://safetystage.com/osha-compliance/near-miss-definition-osha/ (accessed on 17 August 2024).
Cambraia, F.B.; Saurin, T.A.; Formoso, C.T. Identification, Analysis and Dissemination of Information on near Misses: A Case Study in the Construction Industry. Saf. Sci. 2010 , 48 , 91–99. [ Google Scholar ] [ CrossRef ]
Tan, J.; Li, M. How to Achieve Accurate Accountability under Current Administrative Accountability System for Work Safety Accidents in Chemical Industry in China: A Case Study on Major Work Safety Accidents during 2010–2020. J. Chin. Hum. Resour. Manag. 2022 , 13 , 26–40. [ Google Scholar ] [ CrossRef ]
Wu, W.; Gibb, A.G.F.; Li, Q. Accident Precursors and near Misses on Construction Sites: An Investigative Tool to Derive Information from Accident Databases. Saf. Sci. 2010 , 48 , 845–858. [ Google Scholar ] [ CrossRef ]
Janicak, C.A. Fall-Related Deaths in the Construction Industry. J. Saf. Res. 1998 , 29 , 35–42. [ Google Scholar ] [ CrossRef ]
Li, H.; Yang, X.; Wang, F.; Rose, T.; Chan, G.; Dong, S. Stochastic State Sequence Model to Predict Construction Site Safety States through Real-Time Location Systems. Saf. Sci. 2016 , 84 , 78–87. [ Google Scholar ] [ CrossRef ]
Yang, K.; Aria, S.; Ahn, C.R.; Stentz, T.L. Automated Detection of Near-Miss Fall Incidents in Iron Workers Using Inertial Measurement Units. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19–21 May 2014; pp. 935–944. [ Google Scholar ] [ CrossRef ]
Raviv, G.; Fishbain, B.; Shapira, A. Analyzing Risk Factors in Crane-Related near-Miss and Accident Reports. Saf. Sci. 2017 , 91 , 192–205. [ Google Scholar ] [ CrossRef ]
Zhao, X.; Zhang, M.; Cao, T. A Study of Using Smartphone to Detect and Identify Construction Workers’ near-Miss Falls Based on ANN. In Proceedings of the Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII, Denver, CO, USA, 4–8 March 2018; p. 80. [ Google Scholar ] [ CrossRef ]
Santiago, K.; Yang, X.; Ruano-Herreria, E.C.; Chalmers, J.; Cavicchia, P.; Caban-Martinez, A.J. Characterising near Misses and Injuries in the Temporary Agency Construction Workforce: Qualitative Study Approach. Occup. Environ. Med. 2020 , 77 , 94–99. [ Google Scholar ] [ CrossRef ] [ PubMed ]
What Is OSHA’s Definition of a Near Miss. Available online: https://www.osha.com/blog/near-miss-definition (accessed on 4 August 2023).
Martins, I. Investigation of Occupational Accidents and Diseases a Practical Guide for Labour Inspectors ; International Labour Office: Geneva, Switzerland, 2015. [ Google Scholar ]
National Safety Council. Near Miss Reporting Systems ; National Safety Council: Singapore, 2013. [ Google Scholar ]
PKN PN-ISO 45001:2018-06 ; Occupational Health and Safety Management Systems—Requirements with Guidance for Use. CRC Press: Boca Raton, FL, USA, 2019.
PKN PN-N-18001:2004 ; Occupational Health and Safety Management Systems—Requirements. CRC Press: Boca Raton, FL, USA, 2004.
World Health Organisation. WHO Draft GuiDelines for Adverse Event Reporting and Learning Systems ; World Health Organisation: Geneva, Switzerland, 2005. [ Google Scholar ]
International Atomic Energy Agency IAEA Satety Glossary. Terminology Used in Nuclear Safety and Radiation Protection: 2007 Edition ; International Atomic Energy Agency: Vienna, Austria, 2007. [ Google Scholar ]
Marks, E.; Teizer, J.; Hinze, J. Near Miss Reporting Program to Enhance Construction Worker Safety Performance. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19 May 2014; pp. 2315–2324. [ Google Scholar ] [ CrossRef ]
Gnoni, M.G.; Saleh, J.H. Near-Miss Management Systems and Observability-in-Depth: Handling Safety Incidents and Accident Precursors in Light of Safety Principles. Saf. Sci. 2017 , 91 , 154–167. [ Google Scholar ] [ CrossRef ]
Thoroman, B.; Goode, N.; Salmon, P. System Thinking Applied to near Misses: A Review of Industry-Wide near Miss Reporting Systems. Theor. Issues Ergon. Sci. 2018 , 19 , 712–737. [ Google Scholar ] [ CrossRef ]
Gnoni, M.G.; Tornese, F.; Guglielmi, A.; Pellicci, M.; Campo, G.; De Merich, D. Near Miss Management Systems in the Industrial Sector: A Literature Review. Saf. Sci. 2022 , 150 , 105704. [ Google Scholar ] [ CrossRef ]
Bird, F. Management Guide to Loss Control ; Loss Control Publications: Houston, TX, USA, 1975. [ Google Scholar ]
Zimmermann. Bauer International Norms and Identity ; Zimmermann: Sydney, NSW, Australia, 2006; pp. 5–21. [ Google Scholar ]
Arslan, M.; Cruz, C.; Ginhac, D. Semantic Trajectory Insights for Worker Safety in Dynamic Environments. Autom. Constr. 2019 , 106 , 102854. [ Google Scholar ] [ CrossRef ]
Arslan, M.; Cruz, C.; Ginhac, D. Visualizing Intrusions in Dynamic Building Environments for Worker Safety. Saf. Sci. 2019 , 120 , 428–446. [ Google Scholar ] [ CrossRef ]
Zhou, C.; Chen, R.; Jiang, S.; Zhou, Y.; Ding, L.; Skibniewski, M.J.; Lin, X. Human Dynamics in Near-Miss Accidents Resulting from Unsafe Behavior of Construction Workers. Phys. A Stat. Mech. Its Appl. 2019 , 530 , 121495. [ Google Scholar ] [ CrossRef ]
Chen, F.; Wang, C.; Wang, J.; Zhi, Y.; Wang, Z. Risk Assessment of Chemical Process Considering Dynamic Probability of near Misses Based on Bayesian Theory and Event Tree Analysis. J. Loss Prev. Process Ind. 2020 , 68 , 104280. [ Google Scholar ] [ CrossRef ]
Wright, L.; Van Der Schaaf, T. Accident versus near Miss Causation: A Critical Review of the Literature, an Empirical Test in the UK Railway Domain, and Their Implications for Other Sectors. J. Hazard. Mater. 2004 , 111 , 105–110. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Saleh, J.H.; Saltmarsh, E.A.; Favar, F.M.; Loı¨c Brevault, L. Accident Precursors, near Misses, and Warning Signs: Critical Review and Formal Definitions within the Framework of Discrete Event Systems. Reliab. Eng. Syst. Saf. 2013 , 114 , 148–154. [ Google Scholar ] [ CrossRef ]
Fred, A. Manuele Reviewing Heinrich. Am. Soc. Saf. Prof. 2011 , 56 , 52–61. [ Google Scholar ]
Love, P.E.D.; Tenekedjiev, K. Understanding Near-Miss Count Data on Construction Sites Using Greedy D-Vine Copula Marginal Regression: A Comment. Reliab. Eng. Syst. Saf. 2022 , 217 , 108021. [ Google Scholar ] [ CrossRef ]
Jan van Eck, N.; Waltman, L. VOSviewer Manual ; Universiteit Leiden: Leiden, The Netherlands, 2015. [ Google Scholar ]
Scopus. Content Coverage Guide ; Elsevier: Amsterdam, The Netherlands, 2023; pp. 1–24. [ Google Scholar ]
Lukic, D.; Littlejohn, A.; Margaryan, A. A Framework for Learning from Incidents in the Workplace. Saf. Sci. 2012 , 50 , 950–957. [ Google Scholar ] [ CrossRef ]
Teizer, J.; Cheng, T. Proximity Hazard Indicator for Workers-on-Foot near Miss Interactions with Construction Equipment and Geo-Referenced Hazard Area. Autom. Constr. 2015 , 60 , 58–73. [ Google Scholar ] [ CrossRef ]
Zong, L.; Fu, G. A Study on Designing No-Penalty Reporting System about Enterprise Staff’s near Miss. Adv. Mater. Res. 2011 , 255–260 , 3846–3851. [ Google Scholar ] [ CrossRef ]
Golovina, O.; Teizer, J.; Pradhananga, N. Heat Map Generation for Predictive Safety Planning: Preventing Struck-by and near Miss Interactions between Workers-on-Foot and Construction Equipment. Autom. Constr. 2016 , 71 , 99–115. [ Google Scholar ] [ CrossRef ]
Zou, P.X.W.; Lun, P.; Cipolla, D.; Mohamed, S. Cloud-Based Safety Information and Communication System in Infrastructure Construction. Saf. Sci. 2017 , 98 , 50–69. [ Google Scholar ] [ CrossRef ]
Hinze, J.; Godfrey, R. An Evaluation of Safety Performance Measures for Construction Projects. J. Constr. Res. 2011 , 4 , 5–15. [ Google Scholar ] [ CrossRef ]
Construction Inspection Software|IAuditor by SafetyCulture. Available online: https://safetyculture.com/construction/ (accessed on 25 August 2023).
Incident Reporting Made Easy|Safety Compliance|Mobile EHS Solutions. Available online: https://www.safety-reports.com/lp/safety/incident/ (accessed on 25 August 2023).
Wu, F.; Wu, T.; Yuce, M.R. An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications. Sensors 2019 , 19 , 21. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Fang, W.; Luo, H.; Xu, S.; Love, P.E.D.; Lu, Z.; Ye, C. Automated Text Classification of Near-Misses from Safety Reports: An Improved Deep Learning Approach. Adv. Eng. Inform. 2020 , 44 , 101060. [ Google Scholar ] [ CrossRef ]
Gatti, U.C.; Lin, K.-Y.; Caldera, C.; Chiang, R. Exploring the Relationship between Chronic Sleep Deprivation and Safety on Construction Sites. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19–24 May 2014; pp. 1772–1781. [ Google Scholar ] [ CrossRef ]
Hon, C.K.H.; Chan, A.P.C.; Yam, M.C.H. Relationships between Safety Climate and Safety Performance of Building Repair, Maintenance, Minor Alteration, and Addition (RMAA) Works. Saf. Sci. 2014 , 65 , 10–19. [ Google Scholar ] [ CrossRef ]
Oni, O.; Olanrewaju, A.; Cheen, K.S. Accidents at construction sites and near-misses: A constant problem. Int. Struct. Eng. Constr. 2022 , 9 , 2022. [ Google Scholar ] [ CrossRef ]
Wu, W.; Yang, H.; Chew, D.A.S.; Yang, S.-H.; Gibb, A.G.F.; Li, Q. Towards an Autonomous Real-Time Tracking System of near-Miss Accidents on Construction Sites. Autom. Constr. 2010 , 19 , 134–141. [ Google Scholar ] [ CrossRef ]
Aria, S.S.; Yang, K.; Ahn, C.R.; Vuran, M.C. Near-Miss Accident Detection for Ironworkers Using Inertial Measurement Unit Sensors. In Proceedings of the International Symposium on Automation and Robotics in Construction, ISARC 2014, Sydney, Australia, 9–11 July 2014; Volume 31, pp. 854–859. [ Google Scholar ] [ CrossRef ]
Hasanzadeh, S.; Garza, J.M. de la Productivity-Safety Model: Debunking the Myth of the Productivity-Safety Divide through a Mixed-Reality Residential Roofing Task. J. Constr. Eng. Manag. 2020 , 146 , 04020124. [ Google Scholar ] [ CrossRef ]
Teizer, J. Magnetic Field Proximity Detection and Alert Technology for Safe Heavy Construction Equipment Operation. In Proceedings of the 32nd International Symposium on Automation and Robotics in Construction, Oulu, Finland, 15–18 June 2015. [ Google Scholar ] [ CrossRef ]
Mohajeri, M.; Ardeshir, A.; Banki, M.T.; Malekitabar, H. Discovering Causality Patterns of Unsafe Behavior Leading to Fall Hazards on Construction Sites. Int. J. Constr. Manag. 2022 , 22 , 3034–3044. [ Google Scholar ] [ CrossRef ]
Kisaezehra; Farooq, M.U.; Bhutto, M.A.; Kazi, A.K. Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites. Intell. Autom. Soft Comput. 2023 , 36 , 911–927. [ Google Scholar ] [ CrossRef ]
Li, C.; Ding, L. Falling Objects Detection for near Miss Incidents Identification on Construction Site. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 138–145. [ Google Scholar ] [ CrossRef ]
Jeelani, I.; Ramshankar, H.; Han, K.; Albert, A.; Asadi, K. Real-Time Hazard Proximity Detection—Localization of Workers Using Visual Data. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 281–289. [ Google Scholar ] [ CrossRef ]
Lim, T.-K.; Park, S.-M.; Lee, H.-C.; Lee, D.-E. Artificial Neural Network–Based Slip-Trip Classifier Using Smart Sensor for Construction Workplace. J. Constr. Eng. Manag. 2015 , 142 , 04015065. [ Google Scholar ] [ CrossRef ]
Yang, K.; Jebelli, H.; Ahn, C.R.; Vuran, M.C. Threshold-Based Approach to Detect Near-Miss Falls of Iron Workers Using Inertial Measurement Units. In Proceedings of the 2015 International Workshop on Computing in Civil Engineering, Austin, TX, USA, 21–23 June 2015; 2015; 2015, pp. 148–155. [ Google Scholar ] [ CrossRef ]
Yang, K.; Ahn, C.R.; Vuran, M.C.; Aria, S.S. Semi-Supervised near-Miss Fall Detection for Ironworkers with a Wearable Inertial Measurement Unit. Autom. Constr. 2016 , 68 , 194–202. [ Google Scholar ] [ CrossRef ]
Raviv, G.; Shapira, A.; Fishbain, B. AHP-Based Analysis of the Risk Potential of Safety Incidents: Case Study of Cranes in the Construction Industry. Saf. Sci. 2017 , 91 , 298–309. [ Google Scholar ] [ CrossRef ]
Saurin, T.A.; Formoso, C.T.; Reck, R.; Beck da Silva Etges, B.M.; Ribeiro JL, D. Findings from the Analysis of Incident-Reporting Systems of Construction Companies. J. Constr. Eng. Manag. 2015 , 141 , 05015007. [ Google Scholar ] [ CrossRef ]
Williams, E.; Sherratt, F.; Norton, E. Exploring the Value in near Miss Reporting for Construction Safety. In Proceedings of the 37th Annual Conference, Virtual Event, 6–10 December 2021; pp. 319–328. [ Google Scholar ]
Baker, H.; Smith, S.; Masterton, G.; Hewlett, B. Data-Led Learning: Using Natural Language Processing (NLP) and Machine Learning to Learn from Construction Site Safety Failures. In Proceedings of the 36th Annual ARCOM Conference, Online, 7–8 September 2020; pp. 356–365. [ Google Scholar ]
Jin, R.; Wang, F.; Liu, D. Dynamic Probabilistic Analysis of Accidents in Construction Projects by Combining Precursor Data and Expert Judgments. Adv. Eng. Inform. 2020 , 44 , 101062. [ Google Scholar ] [ CrossRef ]
Zhou, Z.; Li, C.; Mi, C.; Qian, L. Exploring the Potential Use of Near-Miss Information to Improve Construction Safety Performance. Sustainability 2019 , 11 , 1264. [ Google Scholar ] [ CrossRef ]
Boateng, E.B.; Pillay, M.; Davis, P. Predicting the Level of Safety Performance Using an Artificial Neural Network. Adv. Intell. Syst. Comput. 2019 , 876 , 705–710. [ Google Scholar ] [ CrossRef ]
Zhang, M.; Cao, T.; Zhao, X. Using Smartphones to Detect and Identify Construction Workers’ Near-Miss Falls Based on ANN. J. Constr. Eng. Manag. 2018 , 145 , 04018120. [ Google Scholar ] [ CrossRef ]
Gadekar, H.; Bugalia, N. Automatic Classification of Construction Safety Reports Using Semi-Supervised YAKE-Guided LDA Approach. Adv. Eng. Inform. 2023 , 56 , 101929. [ Google Scholar ] [ CrossRef ]
Zhu, Y.; Liao, H.; Huang, D. Using Text Mining and Multilevel Association Rules to Process and Analyze Incident Reports in China. Accid. Anal. Prev. 2023 , 191 , 107224. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Li, M.; Lin, Q.; Jin, H. Research on Near-Miss Incidents Monitoring and Early Warning System for Building Construction Sites Based on Blockchain Technology. J. Constr. Eng. Manag. 2023 , 149 , 04023124. [ Google Scholar ] [ CrossRef ]
Chung, W.W.S.; Tariq, S.; Mohandes, S.R.; Zayed, T. IoT-Based Application for Construction Site Safety Monitoring. Int. J. Constr. Manag. 2020 , 23 , 58–74. [ Google Scholar ] [ CrossRef ]
Liu, X.; Xu, F.; Zhang, Z.; Sun, K. Fall-Portent Detection for Construction Sites Based on Computer Vision and Machine Learning. Eng. Constr. Archit. Manag. 2023; ahead-of-print . [ Google Scholar ] [ CrossRef ]
Abbasi, H.; Guerrieri, A.; Lee, J.; Yang, K. Mobile Device-Based Struck-By Hazard Recognition in Construction Using a High-Frequency Sound. Sensors 2022 , 22 , 3482. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Wang, F.; Li, H.; Dong, C. Understanding Near-Miss Count Data on Construction Sites Using Greedy D-Vine Copula Marginal Regression. Reliab. Eng. Syst. Saf. 2021 , 213 , 107687. [ Google Scholar ] [ CrossRef ]
Bugalia, N.; Tarani, V.; Student, G.; Kedia, J.; Gadekar, H. Machine Learning-Based Automated Classification Of Worker-Reported Safety Reports In Construction. J. Inf. Technol. Constr. 2022 , 27 , 926–950. [ Google Scholar ] [ CrossRef ]
Chen, S.; Xi, J.; Chen, Y.; Zhao, J. Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification. Comput. Intell. Neurosci. 2022 , 2022 , 4851615. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Tang, S.; Golparvar-Fard, M.; Naphade, M.; Gopalakrishna, M.M. Video-Based Activity Forecasting for Construction Safety Monitoring Use Cases. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 204–210. [ Google Scholar ] [ CrossRef ]
Rashid, K.M.; Behzadan, A.H. Risk Behavior-Based Trajectory Prediction for Construction Site Safety Monitoring. J. Constr. Eng. Manag. 2018 , 144 , 04017106. [ Google Scholar ] [ CrossRef ]
Shen, X.; Marks, E. Near-Miss Information Visualization Tool in BIM for Construction Safety. J. Constr. Eng. Manag. 2016 , 142 , 04015100. [ Google Scholar ] [ CrossRef ]
Erusta, N.E.; Sertyesilisik, B. An Investigation into Improving Occupational Health and Safety Performance of Construction Projects through Usage of BIM for Lean Management. In Communications in Computer and Information Science (CCIS) ; Springer: Berlin/Heidelberg, Germany, 2020; Volume 1188, pp. 91–100. [ Google Scholar ] [ CrossRef ]
Coffland, M.M.; Kim, A.; Sadatsafavi, H.; Uber, M.M. Improved Data Storage for Better Safety Analysis and Decision Making in Large Construction Management Firms. Available online: https://www.researchgate.net/publication/320474383_Improved_Data_Storage_for_Better_Safety_Analysis_and_Decision_Making_in_Large_Construction_Management_Firms (accessed on 12 June 2024).
Zhou, Z.; Li, Q.; Wu, W. Developing a Versatile Subway Construction Incident Database for Safety Management. J. Constr. Eng. Manag. 2011 , 138 , 1169–1180. [ Google Scholar ] [ CrossRef ]
Wu, W.; Yang, H.; Li, Q.; Chew, D. An Integrated Information Management Model for Proactive Prevention of Struck-by-Falling-Object Accidents on Construction Sites. Autom. Constr. 2013 , 34 , 67–74. [ Google Scholar ] [ CrossRef ]
Hoła, B. Identification and Evaluation of Processes in a Construction Enterprise. Arch. Civ. Mech. Eng. 2015 , 15 , 419–426. [ Google Scholar ] [ CrossRef ]
Zhou, C.; Ding, L.; Skibniewski, M.J.; Luo, H.; Jiang, S. Characterizing Time Series of Near-Miss Accidents in Metro Construction via Complex Network Theory. Saf. Sci. 2017 , 98 , 145–158. [ Google Scholar ] [ CrossRef ]
Woźniak, Z.; Hoła, B. Time Series Analysis of Hazardous Events Based on Data Recorded in a Polish Construction Company. Arch. Civ. Eng. 2024; in process . [ Google Scholar ]
Drozd, W. Characteristics of Construction Site in Terms of Occupational Safety. J. Civ. Eng. Environ. Archit. 2016 , 63 , 165–172. [ Google Scholar ]
Meliá, J.L.; Mearns, K.; Silva, S.A.; Lima, M.L. Safety Climate Responses and the Perceived Risk of Accidents in the Construction Industry. Saf. Sci. 2008 , 46 , 949–958. [ Google Scholar ] [ CrossRef ]
Bugalia, N.; Maemura, Y.; Ozawa, K. A System Dynamics Model for Near-Miss Reporting in Complex Systems. Saf. Sci. 2021 , 142 , 105368. [ Google Scholar ] [ CrossRef ]
Gyi, D.E.; Gibb, A.G.F.; Haslam, R.A. The Quality of Accident and Health Data in the Construction Industry: Interviews with Senior Managers. Constr. Manag. Econ. 1999 , 17 , 197–204. [ Google Scholar ] [ CrossRef ]
Menzies, J. Structural Safety: Learning and Warnings. Struct. Eng. 2002 , 80 , 15–16. [ Google Scholar ]
Fullerton, C.E.; Allread, B.S.; Teizer, J. Pro-Active-Real-Time Personnel Warning System. In Proceedings of the Construction Research Congress 2009: Building a Sustainable Future, Seattle, WA, USA, 5–7 April 2009; pp. 31–40. [ Google Scholar ] [ CrossRef ]
Marks, E.D.; Wetherford, J.E.; Teizer, J.; Yabuki, N. Potential of Leading Indicator Data Collection and Analysis for Proximity Detection and Alert Technology in Construction. In Proceedings of the 30th ISARC—International Symposium on Automation and Robotics in Construction Conference, Montreal, QC, Canada, 11–15 August 2013; pp. 1029–1036. [ Google Scholar ] [ CrossRef ]
Martin, H.; Lewis, T.M. Pinpointing Safety Leadership Factors for Safe Construction Sites in Trinidad and Tobago. J. Constr. Eng. Manag. 2014 , 140 , 04013046. [ Google Scholar ] [ CrossRef ]
Hobson, P.; Emery, D.; Brown, L.; Bashford, R.; Gill, J. People–Plant Interface Training: Targeting an Industry Fatal Risk. Proc. Inst. Civ. Eng. Civ. Eng. 2014 , 167 , 138–144. [ Google Scholar ] [ CrossRef ]
Marks, E.; Mckay, B.; Awolusi, I. Using near Misses to Enhance Safety Performance in Construction. In Proceedings of the ASSE Professional Development Conference and Exposition, Dallas, TX, USA, 7–10 June 2015. [ Google Scholar ]
Popp, J.D.; Scarborough, M.S. Investigations of near Miss Incidents—New Facility Construction and Commissioning Activities. IEEE Trans. Ind. Appl. 2016 , 53 , 615–621. [ Google Scholar ] [ CrossRef ]
Nickel, P.; Lungfiel, A.; Trabold, R.J. Reconstruction of near Misses and Accidents for Analyses from Virtual Reality Usability Study. In Lecture Notes in Computer Science ; Springer: Berlin/Heidelberg, Germany, 2017; Volume 10700, pp. 182–191. [ Google Scholar ] [ CrossRef ]
Gambatese, J.A.; Pestana, C.; Lee, H.W. Alignment between Lean Principles and Practices and Worker Safety Behavior. J. Constr. Eng. Manag. 2017 , 143 , 04016083. [ Google Scholar ] [ CrossRef ]
Van Voorhis, S.; Korman, R. Reading Signs of Trouble. Eng. News-Rec. 2017 , 278 , 14–17. [ Google Scholar ]
Doan, D.R. Investigation of a near-miss shock incident. IEEE Trans. Ind. Appl. 2016 , 52 , 560–561. [ Google Scholar ] [ CrossRef ]
Oswald, D.; Sherratt, F.; Smith, S. Problems with safety observation reporting: A construction industry case study. Saf. Sci. 2018 , 107 , 35–45. [ Google Scholar ] [ CrossRef ]
Raviv, G.; Shapira, A. Systematic approach to crane-related near-miss analysis in the construction industry. Int. J. Constr. Manag. 2018 , 18 , 310–320. [ Google Scholar ] [ CrossRef ]
Whiteoak, J.; Appleby, J. Mate, that was bloody close! A case history of a nearmiss program in the Australian construction industry. J. Health Saf. Environ. 2019 , 35 , 31–43. [ Google Scholar ]
Duryan, M.; Smyth, H.; Roberts, A.; Rowlinson, S.; Sherratt, F. Knowledge transfer for occupational health and safety: Cultivating health and safety learning culture in construction firms. Accid. Anal. Prev. 2020 , 139 , 105496. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Shaikh, A.Y.; Osei-Kyei, R.; Hardie, M. A critical analysis of safety performance indicators in construction. Int. J. Build. Pathol. Adapt. 2020 , 39 , 547–580. [ Google Scholar ] [ CrossRef ]
Martin, H.; Mohan, N.; Ellis, L.; Dunne, S. Exploring the Role of PPE Knowledge, Attitude, and Correct Practices in Safety Outcomes on Construction Sites. J. Archit. Eng. 2021 , 27 , 05021011. [ Google Scholar ] [ CrossRef ]
Qin, Z.; Wu, S. A simulation model of engineering construction near-miss event disclosure strategy based on evolutionary game theory. In Proceedings of the 2021 4th International Conference on Information Systems and Computer Aided Education, Dalian, China, 24–26 September 2021; pp. 2572–2577. [ Google Scholar ] [ CrossRef ]
Alamoudi, M. The Integration of NOSACQ-50 with Importance-Performance Analysis Technique to Evaluate and Analyze Safety Climate Dimensions in the Construction Sector in Saudi Arabia. Buildings 2022 , 12 , 1855. [ Google Scholar ] [ CrossRef ]
Herrmann, A.W. Development of CROSS in the United States. In Proceedings of the Forensic Engineering 2022: Elevating Forensic Engineering—Selected Papers from the 9th Congress on Forensic Engineering, Denver, Colorado, 4–7 November 2022; Volume 2, pp. 40–43. [ Google Scholar ] [ CrossRef ]
Al Shaaili, M.; Al Alawi, M.; Ekyalimpa, R.; Al Mawli, B.; Al-Mamun, A.; Al Shahri, M. Near-miss accidents data analysis and knowledge dissemination in water construction projects in Oman. Heliyon 2023 , 9 , e21607. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Agnusdei, G.P.; Gnoni, M.G.; Tornese, F.; De Merich, D.; Guglielmi, A.; Pellicci, M. Application of Near-Miss Management Systems: An Exploratory Field Analysis in the Italian Industrial Sector. Safety 2023 , 9 , 47. [ Google Scholar ] [ CrossRef ]
Duan, P.; Zhou, J. A science mapping approach-based review of near-miss research in construction. Eng. Constr. Archit. Manag. 2023 , 30 , 2582–2601. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

No.	Name of Institution/Organization	Definition
1	Occupational Safety and Health Administration (OSHA) [ ]	“A near-miss is a potential hazard or incident in which no property was damaged and no personal injury was sustained, but where, given a slight shift in time or position, damage or injury easily could have occurred. Near misses also may be referred to as close calls, near accidents, or injury-free events.”
2	International Labour Organization (ILO) [ ]	“An event, not necessarily defined under national laws and regulations, that could have caused harm to persons at work or to the public, e.g., a brick that falls off scaffolding but does not hit anyone”
3	American National Safety Council (NSC) [ ]	“A Near Miss is an unplanned event that did not result in injury, illness, or damage—but had the potential to do so”
4	PN-ISO 45001:2018-06 [ ]	A near-miss incident is described as an event that does not result in injury or health issues.
5	PN-N-18001:2004 [ ]	A near-miss incident is an accident event without injury.
6	World Health Organization (WHO) [ ]	Near misses have been defined as a serious error that has the potential to cause harm but are not due to chance or interception.
7	International Atomic Energy Agency (IAEA) [ ]	Near misses have been defined as potentially significant events that could have consequences but did not due to the conditions at the time.

No.	Journal	Number of Publications
1	Safety Science	10
2	Journal of Construction Engineering and Management	8
3	Automation in Construction	5
4	Advanced Engineering Informatics	3
5	Construction Research Congress 2014 Construction in a Global Network Proceedings of the 2014 Construction Research Congress	3
6	International Journal of Construction Management	3
7	Accident Analysis and Prevention	2
8	Computing in Civil Engineering 2019 Data Sensing and Analytics Selected Papers From The ASCE International Conference	2
9	Engineering Construction and Architectural Management	2
10	Heliyon	2

Cluster Number	Colour	Basic Keywords
1	blue	construction, construction sites, decision making, machine learning, near misses, neural networks, project management, safety, workers
2	green	building industry, construction industry, construction projects, construction work, human, near miss, near misses, occupational accident, occupational safety, safety, management, safety performance
3	red	accident prevention, construction equipment, construction, safety, construction workers, hazards, human resource management, leading indicators, machinery, occupational risks, risk management, safety engineering
4	yellow	accidents, risk assessment, civil engineering, near miss, surveys

Number of Question	Question	References
Q	Are near misses in the construction industry studied scientifically?	[ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]
Q	What methods have been used to obtain information on near misses and systems for recording incidents in construction companies?	[ , , , , , , , , , , , , , , , , , , , , ]
Q	What methods have been used to analyse the information and figures that have been obtained?	[ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]
Q	What are the key aspects of near misses in the construction industry that have been of interest to the researchers?	[ , , , , , , , , , , , , ]

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Woźniak, Z.; Hoła, B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Appl. Sci. 2024 , 14 , 7260. https://doi.org/10.3390/app14167260

Woźniak Z, Hoła B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Applied Sciences . 2024; 14(16):7260. https://doi.org/10.3390/app14167260

Woźniak, Zuzanna, and Bożena Hoła. 2024. "Analysing Near-Miss Incidents in Construction: A Systematic Literature Review" Applied Sciences 14, no. 16: 7260. https://doi.org/10.3390/app14167260

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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)

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Chapter Descriptions

Recommended Reading: Other Qualitative Research Textbooks

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Qualitative Methods in Health Care Research

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Introduction

Qualitative Research

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Qualitative Research Questions and Purpose Statements

Review of the Literature

Reflexivity

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Phenomenological research

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Historical research

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