qualitative research empirical

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Qualitative and Quantitative Research

What is "empirical research".

empirical research
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Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief.

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology." Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

Specific research questions to be answered
Definition of the population, behavior, or phenomena being studied
Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools
Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies
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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.

University of Memphis Libraries
Research Guides

Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

Introduction
Database Tools
Search Terms
Image Descriptions

Calfee & Chambliss (2005) (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions." Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43).

The evidence collected during empirical research is often referred to as "data."

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research:

A research question , which will determine research objectives.
A particular and planned design for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
The gathering of primary data , which is then analysed.
A particular methodology for collecting and analysing the data, such as an experiment or survey.
The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
The ability to recreate the study and test the results. This is known as reliability .
The ability to generalize from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element.

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016) (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

Quantitative research -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
Qualitative research -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study.

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology:

Characteristics of Quantitative Research

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes.

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.), Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge. http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing. https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained [video]. YouTube. https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016). Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell. http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .

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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and qualitative market research methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

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Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

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There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

It is used to authenticate traditional research through various experiments and observations.
This research methodology makes the research being conducted more competent and authentic.
It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
The level of control in such a research is high so the researcher can control multiple variables.
It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Qualitative, Quantitative & Empirical Research

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Empirical Studies

An empirical study is research derived from actual observation or experimentation.
The written articles resulting from empirical studies undergo a rigorous review by experts in the field of study prior to being published in journals.
After passing this review the articles are published in a scholarly, peer-reviewed, or academic journal.
Empirical study articles will generally contain the following features: Abstract - This is a summary of the article. Introduction - This is often identified as the hypothesis of the study and describes the researcher's intent. Method - A description of how the research was conducted. Results - A description of the findings obtained as a result of the research. Most often answers the hypothesis. Conclusion - A description of how/if the findings were successful and the impact made as a result. References - A detailed listing of all resources cited in the article that support the written work.

Mixed Methods Research

Mixed Methods Research uses strategies from both qualitative and quantitative research processes to provide a greater understanding of the subject matter.

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Qualitative vs Empirical Research: Difference and Comparison

Empirical research draws its conclusions strictly from ’empirical’ or ‘verifiable’ evidence.

This research analysis mode can be further bifurcated into two subsets known as qualitative and quantitative research, where the former deals with a general description of the studied phenomenon. In contrast, the latter is based on numerical analysis.

Key Takeaways Data type: Qualitative research focuses on non-numerical data like opinions, emotions, and experiences, while empirical research emphasizes measurable, numerical data. Methods: Qualitative research employs interviews, observations, and content analysis, while empirical research uses experiments, surveys, and quantitative data analysis. Goal: Qualitative research aims to understand human behavior and underlying motivations, whereas empirical research seeks to establish cause-and-effect relationships and test hypotheses.

Qualitative vs Empirical Research

The difference between qualitative and empirical research is misinterpreted as the practical method being the independent research method dealing with numerical data and facts. In contrast, the qualitative approach is concerned with the descriptive analysis and opinions of the subjects.

But, empirical research is not an independent category and entails both qualitative and quantitative methods as subcategories.

The empirical method gathers information from the numerical data by working on the predetermined notions. The qualitative approach also incorporates non-numerical data, such as interviews, focus groups, etc., to get a clearer idea of the subject.

Comparison Table

What is qualitative research.

The qualitative method is the research practice that considers the human subject to develop an understanding of human and social behaviour and to find a way to feel and react.

The advantages of Qualitative Research are as follows:

It allows for exploring the hidden research approaches, which numerical data alone cannot do.
Along with the research analysis, it allows for the contradictions to suggest the social reality of individuals.
It follows no conventional standards; therefore, data duplication is impossible.

Along with the advantages, this research method has certain drawbacks as well, such as:

Sampling size is not enough to provide the true reflection of ideas that the researcher wants to carry forward.
A personal bias towards certain notions consciously or unconsciously influences it.

What is Empirical Research?

Empirical research is the analytical form of research method which collects information based on numerical data by employing statistical, mathematical, and numerical techniques from descriptive (qualitative) sources.

Surveys, focus groups, polls, experimental research, etc., are some of the elements of the analysis that it considers.

The advantages of Empirical research are as follows:

One can easily collect more samples in less time by doing it quantitatively.
Researchers can focus on specified cultural realities they want to explore.
Most of the time, it doesn’t require direct interactions with the subjects.
It can be performed in a lab-like setting or on some naturalistic grounds.

Along with the advantages, this research method also has certain drawbacks, such as:

This kind of research is very time-consuming and demands patience.
It can also prove expensive because the researcher will conduct the study at different locations.

Main Differences Between Qualitative and Empirical Research

The elements of analysis in Qualitative research are interviews, group discussions, case studies, etc., whereas, in Empirical research, the key elements are surveys, focus groups, interviews, etc.
The qualitative mode of analysis is multi-method in focus. Therefore, it allows ambiguities and contradictions, but the Empirical method works by measuring things and drawing explanatory inferences. Therefore, it doesn’t always provide space for contradictions.

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Empirical Research: Quantitative & Qualitative

Quantitative vs. Qualitative
Empirical Research

What's the Difference Between Qualitative and Quantitative?

Distinguishing quantitative & qualitative methods, word clues to identify methods.

Reference Works for Social Sciences Research
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What’s the Difference Between Qualitative and Quantitative Methods?

From: Suter, W. N. (2012). Qualitative Data, Analysis, and Design. In Introduction to educational research: A critical thinking approach . SAGE Publications, Inc., www.galileo.usg.edu/redirect?inst=pie1&url=https://dx.doi.org/10.4135/9781483384443

The words in this table can be used to evaluate whether an article tends more toward the quantitative or qualitative domain. Well-written article abstracts will contain words like these to succinctly characterize the article's content.

Adapted from: McMillan, J. H. (2012). Educational research: Fundamentals for the consumer (6th ed.). Boston, MA: Pearson.

Search SAGE Research Methods for resources about qualitative methods

Search SAGE Research Methods for resources about quantitative methods

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

EXAMPLE 1. Descriptive research question (quantitative research)
- Presents research variables to be assessed (distinct phenotypes and subphenotypes)
“BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
EXAMPLE 2. Relationship research question (quantitative research)
- Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
“Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
EXAMPLE 3. Comparative research question (quantitative research)
- Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
“BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
EXAMPLE 4. Exploratory research question (qualitative research)
- Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
“Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
EXAMPLE 5. Relationship research question (quantitative research)
- Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
“Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

EXAMPLE 1. Working hypothesis (quantitative research)
- A hypothesis that is initially accepted for further research to produce a feasible theory
“As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
“In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
EXAMPLE 2. Exploratory hypothesis (qualitative research)
- Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
“We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
“Conclusion
Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
“We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
EXAMPLE 4. Statistical hypothesis (quantitative research)
- An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
“Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
“Statistical Analysis
( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

EXAMPLE 1. Background, hypotheses, and aims are provided
“Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
“ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
“This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
EXAMPLE 2. Background, hypotheses, and aims are provided
“We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
“ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
EXAMPLE 3. Background, aim, and hypothesis are provided
“In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
“This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
“ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Barroga E, Matanguihan GJ.
Methodology: Barroga E, Matanguihan GJ.
Writing - original draft: Barroga E, Matanguihan GJ.
Writing - review & editing: Barroga E, Matanguihan GJ.

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CRO Guide > Chapter 3.1

Qualitative Research: Definition, Methodology, Limitation & Examples

Qualitative research is a method focused on understanding human behavior and experiences through non-numerical data. Examples of qualitative research include:

One-on-one interviews,
Focus groups, Ethnographic research,
Case studies,
Record keeping,
Qualitative observations

In this article, we’ll provide tips and tricks on how to use qualitative research to better understand your audience through real world examples and improve your ROI. We’ll also learn the difference between qualitative and quantitative data.

Marketers often seek to understand their customers deeply. Qualitative research methods such as face-to-face interviews, focus groups, and qualitative observations can provide valuable insights into your products, your market, and your customers’ opinions and motivations. Understanding these nuances can significantly enhance marketing strategies and overall customer satisfaction.

What is Qualitative Research

Qualitative research is a market research method that focuses on obtaining data through open-ended and conversational communication. This method focuses on the “why” rather than the “what” people think about you. Thus, qualitative research seeks to uncover the underlying motivations, attitudes, and beliefs that drive people’s actions.

Let’s say you have an online shop catering to a general audience. You do a demographic analysis and you find out that most of your customers are male. Naturally, you will want to find out why women are not buying from you. And that’s what qualitative research will help you find out.

In the case of your online shop, qualitative research would involve reaching out to female non-customers through methods such as in-depth interviews or focus groups. These interactions provide a platform for women to express their thoughts, feelings, and concerns regarding your products or brand. Through qualitative analysis, you can uncover valuable insights into factors such as product preferences, user experience, brand perception, and barriers to purchase.

Types of Qualitative Research Methods

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience regarding a particular topic.

The most frequently used qualitative analysis methods are one-on-one interviews, focus groups, ethnographic research, case study research, record keeping, and qualitative observation.

1. One-on-one interviews

Conducting one-on-one interviews is one of the most common qualitative research methods. One of the advantages of this method is that it provides a great opportunity to gather precise data about what people think and their motivations.

Spending time talking to customers not only helps marketers understand who their clients are, but also helps with customer care: clients love hearing from brands. This strengthens the relationship between a brand and its clients and paves the way for customer testimonials.

A company might conduct interviews to understand why a product failed to meet sales expectations.
A researcher might use interviews to gather personal stories about experiences with healthcare.

These interviews can be performed face-to-face or on the phone and usually last between half an hour to over two hours.

When a one-on-one interview is conducted face-to-face, it also gives the marketer the opportunity to read the body language of the respondent and match the responses.

2. Focus groups

Focus groups gather a small number of people to discuss and provide feedback on a particular subject. The ideal size of a focus group is usually between five and eight participants. The size of focus groups should reflect the participants’ familiarity with the topic. For less important topics or when participants have little experience, a group of 10 can be effective. For more critical topics or when participants are more knowledgeable, a smaller group of five to six is preferable for deeper discussions.

The main goal of a focus group is to find answers to the “why”, “what”, and “how” questions. This method is highly effective in exploring people’s feelings and ideas in a social setting, where group dynamics can bring out insights that might not emerge in one-on-one situations.

A focus group could be used to test reactions to a new product concept.
Marketers might use focus groups to see how different demographic groups react to an advertising campaign.

One advantage that focus groups have is that the marketer doesn’t necessarily have to interact with the group in person. Nowadays focus groups can be sent as online qualitative surveys on various devices.

Focus groups are an expensive option compared to the other qualitative research methods, which is why they are typically used to explain complex processes.

3. Ethnographic research

Ethnographic research is the most in-depth observational method that studies individuals in their naturally occurring environment.

This method aims at understanding the cultures, challenges, motivations, and settings that occur.

A study of workplace culture within a tech startup.
Observational research in a remote village to understand local traditions.

Ethnographic research requires the marketer to adapt to the target audiences’ environments (a different organization, a different city, or even a remote location), which is why geographical constraints can be an issue while collecting data.

This type of research can last from a few days to a few years. It’s challenging and time-consuming and solely depends on the expertise of the marketer to be able to analyze, observe, and infer the data.

4. Case study research

The case study method has grown into a valuable qualitative research method. This type of research method is usually used in education or social sciences. It involves a comprehensive examination of a single instance or event, providing detailed insights into complex issues in real-life contexts.

Analyzing a single school’s innovative teaching method.
A detailed study of a patient’s medical treatment over several years.

Case study research may seem difficult to operate, but it’s actually one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

Record keeping is similar to going to the library: you go over books or any other reference material to collect relevant data. This method uses already existing reliable documents and similar sources of information as a data source.

Historical research using old newspapers and letters.
A study on policy changes over the years by examining government records.

This method is useful for constructing a historical context around a research topic or verifying other findings with documented evidence.

6. Qualitative observation

Qualitative observation is a method that uses subjective methodologies to gather systematic information or data. This method deals with the five major sensory organs and their functioning, sight, smell, touch, taste, and hearing.

Sight : Observing the way customers visually interact with product displays in a store to understand their browsing behaviors and preferences.
Smell : Noting reactions of consumers to different scents in a fragrance shop to study the impact of olfactory elements on product preference.
Touch : Watching how individuals interact with different materials in a clothing store to assess the importance of texture in fabric selection.
Taste : Evaluating reactions of participants in a taste test to identify flavor profiles that appeal to different demographic groups.
Hearing : Documenting responses to changes in background music within a retail environment to determine its effect on shopping behavior and mood.

Below we are also providing real-life examples of qualitative research that demonstrate practical applications across various contexts:

Qualitative Research Real World Examples

Let’s explore some examples of how qualitative research can be applied in different contexts.

1. Online grocery shop with a predominantly male audience

Method used: one-on-one interviews.

Let’s go back to one of the previous examples. You have an online grocery shop. By nature, it addresses a general audience, but after you do a demographic analysis you find out that most of your customers are male.

One good method to determine why women are not buying from you is to hold one-on-one interviews with potential customers in the category.

Interviewing a sample of potential female customers should reveal why they don’t find your store appealing. The reasons could range from not stocking enough products for women to perhaps the store’s emphasis on heavy-duty tools and automotive products, for example. These insights can guide adjustments in inventory and marketing strategies.

2. Software company launching a new product

Method used: focus groups.

Focus groups are great for establishing product-market fit.

Let’s assume you are a software company that wants to launch a new product and you hold a focus group with 12 people. Although getting their feedback regarding users’ experience with the product is a good thing, this sample is too small to define how the entire market will react to your product.

So what you can do instead is holding multiple focus groups in 20 different geographic regions. Each region should be hosting a group of 12 for each market segment; you can even segment your audience based on age. This would be a better way to establish credibility in the feedback you receive.

3. Alan Pushkin’s “God’s Choice: The Total World of a Fundamentalist Christian School”

Method used: ethnographic research.

Moving from a fictional example to a real-life one, let’s analyze Alan Peshkin’s 1986 book “God’s Choice: The Total World of a Fundamentalist Christian School”.

Peshkin studied the culture of Bethany Baptist Academy by interviewing the students, parents, teachers, and members of the community alike, and spending eighteen months observing them to provide a comprehensive and in-depth analysis of Christian schooling as an alternative to public education.

The study highlights the school’s unified purpose, rigorous academic environment, and strong community support while also pointing out its lack of cultural diversity and openness to differing viewpoints. These insights are crucial for understanding how such educational settings operate and what they offer to students.

Even after discovering all this, Peshkin still presented the school in a positive light and stated that public schools have much to learn from such schools.

Peshkin’s in-depth research represents a qualitative study that uses observations and unstructured interviews, without any assumptions or hypotheses. He utilizes descriptive or non-quantifiable data on Bethany Baptist Academy specifically, without attempting to generalize the findings to other Christian schools.

4. Understanding buyers’ trends

Method used: record keeping.

Another way marketers can use quality research is to understand buyers’ trends. To do this, marketers need to look at historical data for both their company and their industry and identify where buyers are purchasing items in higher volumes.

For example, electronics distributors know that the holiday season is a peak market for sales while life insurance agents find that spring and summer wedding months are good seasons for targeting new clients.

5. Determining products/services missing from the market

Conducting your own research isn’t always necessary. If there are significant breakthroughs in your industry, you can use industry data and adapt it to your marketing needs.

The influx of hacking and hijacking of cloud-based information has made Internet security a topic of many industry reports lately. A software company could use these reports to better understand the problems its clients are facing.

As a result, the company can provide solutions prospects already know they need.

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Qualitative Research Approaches

Once the marketer has decided that their research questions will provide data that is qualitative in nature, the next step is to choose the appropriate qualitative approach.

The approach chosen will take into account the purpose of the research, the role of the researcher, the data collected, the method of data analysis , and how the results will be presented. The most common approaches include:

Narrative : This method focuses on individual life stories to understand personal experiences and journeys. It examines how people structure their stories and the themes within them to explore human existence. For example, a narrative study might look at cancer survivors to understand their resilience and coping strategies.
Phenomenology : attempts to understand or explain life experiences or phenomena; It aims to reveal the depth of human consciousness and perception, such as by studying the daily lives of those with chronic illnesses.
Grounded theory : investigates the process, action, or interaction with the goal of developing a theory “grounded” in observations and empirical data.
Ethnography : describes and interprets an ethnic, cultural, or social group;
Case study : examines episodic events in a definable framework, develops in-depth analyses of single or multiple cases, and generally explains “how”. An example might be studying a community health program to evaluate its success and impact.

How to Analyze Qualitative Data

Analyzing qualitative data involves interpreting non-numerical data to uncover patterns, themes, and deeper insights. This process is typically more subjective and requires a systematic approach to ensure reliability and validity.

1. Data Collection

Ensure that your data collection methods (e.g., interviews, focus groups, observations) are well-documented and comprehensive. This step is crucial because the quality and depth of the data collected will significantly influence the analysis.

2. Data Preparation

Once collected, the data needs to be organized. Transcribe audio and video recordings, and gather all notes and documents. Ensure that all data is anonymized to protect participant confidentiality where necessary.

3. Familiarization

Immerse yourself in the data by reading through the materials multiple times. This helps you get a general sense of the information and begin identifying patterns or recurring themes.

Develop a coding system to tag data with labels that summarize and account for each piece of information. Codes can be words, phrases, or acronyms that represent how these segments relate to your research questions.

Descriptive Coding : Summarize the primary topic of the data.
In Vivo Coding : Use language and terms used by the participants themselves.
Process Coding : Use gerunds (“-ing” words) to label the processes at play.
Emotion Coding : Identify and record the emotions conveyed or experienced.

5. Thematic Development

Group codes into themes that represent larger patterns in the data. These themes should relate directly to the research questions and form a coherent narrative about the findings.

6. Interpreting the Data

Interpret the data by constructing a logical narrative. This involves piecing together the themes to explain larger insights about the data. Link the results back to your research objectives and existing literature to bolster your interpretations.

7. Validation

Check the reliability and validity of your findings by reviewing if the interpretations are supported by the data. This may involve revisiting the data multiple times or discussing the findings with colleagues or participants for validation.

8. Reporting

Finally, present the findings in a clear and organized manner. Use direct quotes and detailed descriptions to illustrate the themes and insights. The report should communicate the narrative you’ve built from your data, clearly linking your findings to your research questions.

Limitations of qualitative research

The disadvantages of qualitative research are quite unique. The techniques of the data collector and their own unique observations can alter the information in subtle ways. That being said, these are the qualitative research’s limitations:

1. It’s a time-consuming process

The main drawback of qualitative study is that the process is time-consuming. Another problem is that the interpretations are limited. Personal experience and knowledge influence observations and conclusions.

Thus, qualitative research might take several weeks or months. Also, since this process delves into personal interaction for data collection, discussions often tend to deviate from the main issue to be studied.

2. You can’t verify the results of qualitative research

Because qualitative research is open-ended, participants have more control over the content of the data collected. So the marketer is not able to verify the results objectively against the scenarios stated by the respondents. For example, in a focus group discussing a new product, participants might express their feelings about the design and functionality. However, these opinions are influenced by individual tastes and experiences, making it difficult to ascertain a universally applicable conclusion from these discussions.

3. It’s a labor-intensive approach

Qualitative research requires a labor-intensive analysis process such as categorization, recording, etc. Similarly, qualitative research requires well-experienced marketers to obtain the needed data from a group of respondents.

4. It’s difficult to investigate causality

Qualitative research requires thoughtful planning to ensure the obtained results are accurate. There is no way to analyze qualitative data mathematically. This type of research is based more on opinion and judgment rather than results. Because all qualitative studies are unique they are difficult to replicate.

5. Qualitative research is not statistically representative

Because qualitative research is a perspective-based method of research, the responses given are not measured.

Comparisons can be made and this can lead toward duplication, but for the most part, quantitative data is required for circumstances that need statistical representation and that is not part of the qualitative research process.

While doing a qualitative study, it’s important to cross-reference the data obtained with the quantitative data. By continuously surveying prospects and customers marketers can build a stronger database of useful information.

Quantitative vs. Qualitative Research

Qualitative and quantitative research side by side in a table

Image source

Quantitative and qualitative research are two distinct methodologies used in the field of market research, each offering unique insights and approaches to understanding consumer behavior and preferences.

As we already defined, qualitative analysis seeks to explore the deeper meanings, perceptions, and motivations behind human behavior through non-numerical data. On the other hand, quantitative research focuses on collecting and analyzing numerical data to identify patterns, trends, and statistical relationships.

Let’s explore their key differences:

Nature of Data:

Quantitative research : Involves numerical data that can be measured and analyzed statistically.
Qualitative research : Focuses on non-numerical data, such as words, images, and observations, to capture subjective experiences and meanings.

Research Questions:

Quantitative research : Typically addresses questions related to “how many,” “how much,” or “to what extent,” aiming to quantify relationships and patterns.
Qualitative research: Explores questions related to “why” and “how,” aiming to understand the underlying motivations, beliefs, and perceptions of individuals.

Data Collection Methods:

Quantitative research : Relies on structured surveys, experiments, or observations with predefined variables and measures.
Qualitative research : Utilizes open-ended interviews, focus groups, participant observations, and textual analysis to gather rich, contextually nuanced data.

Analysis Techniques:

Quantitative research: Involves statistical analysis to identify correlations, associations, or differences between variables.
Qualitative research: Employs thematic analysis, coding, and interpretation to uncover patterns, themes, and insights within qualitative data.

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Last modified: January 3, 2023
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A systematic literature review of empirical research on ChatGPT in education

Open access
Published: 26 May 2024
Volume 3 , article number 60 , ( 2024 )

Cite this article

You have full access to this open access article

Yazid Albadarin ORCID: orcid.org/0009-0005-8068-8902 1 ,
Mohammed Saqr 1 ,
Nicolas Pope 1 &
Markku Tukiainen 1

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli’s (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell’s (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Avoid common mistakes on your manuscript.

1 Introduction

Educational technology, a rapidly evolving field, plays a crucial role in reshaping the landscape of teaching and learning [ 82 ]. One of the most transformative technological innovations of our era that has influenced the field of education is Artificial Intelligence (AI) [ 50 ]. Over the last four decades, AI in education (AIEd) has gained remarkable attention for its potential to make significant advancements in learning, instructional methods, and administrative tasks within educational settings [ 11 ]. In particular, a large language model (LLM), a type of AI algorithm that applies artificial neural networks (ANNs) and uses massively large data sets to understand, summarize, generate, and predict new content that is almost difficult to differentiate from human creations [ 79 ], has opened up novel possibilities for enhancing various aspects of education, from content creation to personalized instruction [ 35 ]. Chatbots that leverage the capabilities of LLMs to understand and generate human-like responses have also presented the capacity to enhance student learning and educational outcomes by engaging students, offering timely support, and fostering interactive learning experiences [ 46 ].

The ongoing and remarkable technological advancements in chatbots have made their use more convenient, increasingly natural and effortless, and have expanded their potential for deployment across various domains [ 70 ]. One prominent example of chatbot applications is the Chat Generative Pre-Trained Transformer, known as ChatGPT, which was introduced by OpenAI, a leading AI research lab, on November 30th, 2022. ChatGPT employs a variety of deep learning techniques to generate human-like text, with a particular focus on recurrent neural networks (RNNs). Long short-term memory (LSTM) allows it to grasp the context of the text being processed and retain information from previous inputs. Also, the transformer architecture, a neural network architecture based on the self-attention mechanism, allows it to analyze specific parts of the input, thereby enabling it to produce more natural-sounding and coherent output. Additionally, the unsupervised generative pre-training and the fine-tuning methods allow ChatGPT to generate more relevant and accurate text for specific tasks [ 31 , 62 ]. Furthermore, reinforcement learning from human feedback (RLHF), a machine learning approach that combines reinforcement learning techniques with human-provided feedback, has helped improve ChatGPT’s model by accelerating the learning process and making it significantly more efficient.

This cutting-edge natural language processing (NLP) tool is widely recognized as one of today's most advanced LLMs-based chatbots [ 70 ], allowing users to ask questions and receive detailed, coherent, systematic, personalized, convincing, and informative human-like responses [ 55 ], even within complex and ambiguous contexts [ 63 , 77 ]. ChatGPT is considered the fastest-growing technology in history: in just three months following its public launch, it amassed an estimated 120 million monthly active users [ 16 ] with an estimated 13 million daily queries [ 49 ], surpassing all other applications [ 64 ]. This remarkable growth can be attributed to the unique features and user-friendly interface that ChatGPT offers. Its intuitive design allows users to interact seamlessly with the technology, making it accessible to a diverse range of individuals, regardless of their technical expertise [ 78 ]. Additionally, its exceptional performance results from a combination of advanced algorithms, continuous enhancements, and extensive training on a diverse dataset that includes various text sources such as books, articles, websites, and online forums [ 63 ], have contributed to a more engaging and satisfying user experience [ 62 ]. These factors collectively explain its remarkable global growth and set it apart from predecessors like Bard, Bing Chat, ERNIE, and others.

In this context, several studies have explored the technological advancements of chatbots. One noteworthy recent research effort, conducted by Schöbel et al. [ 70 ], stands out for its comprehensive analysis of more than 5,000 studies on communication agents. This study offered a comprehensive overview of the historical progression and future prospects of communication agents, including ChatGPT. Moreover, other studies have focused on making comparisons, particularly between ChatGPT and alternative chatbots like Bard, Bing Chat, ERNIE, LaMDA, BlenderBot, and various others. For example, O’Leary [ 53 ] compared two chatbots, LaMDA and BlenderBot, with ChatGPT and revealed that ChatGPT outperformed both. This superiority arises from ChatGPT’s capacity to handle a wider range of questions and generate slightly varied perspectives within specific contexts. Similarly, ChatGPT exhibited an impressive ability to formulate interpretable responses that were easily understood when compared with Google's feature snippet [ 34 ]. Additionally, ChatGPT was compared to other LLMs-based chatbots, including Bard and BERT, as well as ERNIE. The findings indicated that ChatGPT exhibited strong performance in the given tasks, often outperforming the other models [ 59 ].

Furthermore, in the education context, a comprehensive study systematically compared a range of the most promising chatbots, including Bard, Bing Chat, ChatGPT, and Ernie across a multidisciplinary test that required higher-order thinking. The study revealed that ChatGPT achieved the highest score, surpassing Bing Chat and Bard [ 64 ]. Similarly, a comparative analysis was conducted to compare ChatGPT with Bard in answering a set of 30 mathematical questions and logic problems, grouped into two question sets. Set (A) is unavailable online, while Set (B) is available online. The results revealed ChatGPT's superiority in Set (A) over Bard. Nevertheless, Bard's advantage emerged in Set (B) due to its capacity to access the internet directly and retrieve answers, a capability that ChatGPT does not possess [ 57 ]. However, through these varied assessments, ChatGPT consistently highlights its exceptional prowess compared to various alternatives in the ever-evolving chatbot technology.

The widespread adoption of chatbots, especially ChatGPT, by millions of students and educators, has sparked extensive discussions regarding its incorporation into the education sector [ 64 ]. Accordingly, many scholars have contributed to the discourse, expressing both optimism and pessimism regarding the incorporation of ChatGPT into education. For example, ChatGPT has been highlighted for its capabilities in enriching the learning and teaching experience through its ability to support different learning approaches, including adaptive learning, personalized learning, and self-directed learning [ 58 , 60 , 91 ]), deliver summative and formative feedback to students and provide real-time responses to questions, increase the accessibility of information [ 22 , 40 , 43 ], foster students’ performance, engagement and motivation [ 14 , 44 , 58 ], and enhance teaching practices [ 17 , 18 , 64 , 74 ].

On the other hand, concerns have been also raised regarding its potential negative effects on learning and teaching. These include the dissemination of false information and references [ 12 , 23 , 61 , 85 ], biased reinforcement [ 47 , 50 ], compromised academic integrity [ 18 , 40 , 66 , 74 ], and the potential decline in students' skills [ 43 , 61 , 64 , 74 ]. As a result, ChatGPT has been banned in multiple countries, including Russia, China, Venezuela, Belarus, and Iran, as well as in various educational institutions in India, Italy, Western Australia, France, and the United States [ 52 , 90 ].

Clearly, the advent of chatbots, especially ChatGPT, has provoked significant controversy due to their potential impact on learning and teaching. This indicates the necessity for further exploration to gain a deeper understanding of this technology and carefully evaluate its potential benefits, limitations, challenges, and threats to education [ 79 ]. Therefore, conducting a systematic literature review will provide valuable insights into the potential prospects and obstacles linked to its incorporation into education. This systematic literature review will primarily focus on ChatGPT, driven by the aforementioned key factors outlined above.

However, the existing literature lacks a systematic literature review of empirical studies. Thus, this systematic literature review aims to address this gap by synthesizing the existing empirical studies conducted on chatbots, particularly ChatGPT, in the field of education, highlighting how ChatGPT has been utilized in educational settings, and identifying any existing gaps. This review may be particularly useful for researchers in the field and educators who are contemplating the integration of ChatGPT or any chatbot into education. The following research questions will guide this study:

What are students' and teachers' initial attempts at utilizing ChatGPT in education?

What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?

2 Methodology

To conduct this study, the authors followed the essential steps of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) and Okoli’s [ 54 ] steps for conducting a systematic review. These included identifying the study’s purpose, drafting a protocol, applying a practical screening process, searching the literature, extracting relevant data, evaluating the quality of the included studies, synthesizing the studies, and ultimately writing the review. The subsequent section provides an extensive explanation of how these steps were carried out in this study.

2.1 Identify the purpose

Given the widespread adoption of ChatGPT by students and teachers for various educational purposes, often without a thorough understanding of responsible and effective use or a clear recognition of its potential impact on learning and teaching, the authors recognized the need for further exploration of ChatGPT's impact on education in this early stage. Therefore, they have chosen to conduct a systematic literature review of existing empirical studies that incorporate ChatGPT into educational settings. Despite the limited number of empirical studies due to the novelty of the topic, their goal is to gain a deeper understanding of this technology and proactively evaluate its potential benefits, limitations, challenges, and threats to education. This effort could help to understand initial reactions and attempts at incorporating ChatGPT into education and bring out insights and considerations that can inform the future development of education.

2.2 Draft the protocol

The next step is formulating the protocol. This protocol serves to outline the study process in a rigorous and transparent manner, mitigating researcher bias in study selection and data extraction [ 88 ]. The protocol will include the following steps: generating the research question, predefining a literature search strategy, identifying search locations, establishing selection criteria, assessing the studies, developing a data extraction strategy, and creating a timeline.

2.3 Apply practical screen

The screening step aims to accurately filter the articles resulting from the searching step and select the empirical studies that have incorporated ChatGPT into educational contexts, which will guide us in answering the research questions and achieving the objectives of this study. To ensure the rigorous execution of this step, our inclusion and exclusion criteria were determined based on the authors' experience and informed by previous successful systematic reviews [ 21 ]. Table 1 summarizes the inclusion and exclusion criteria for study selection.

2.4 Literature search

We conducted a thorough literature search to identify articles that explored, examined, and addressed the use of ChatGPT in Educational contexts. We utilized two research databases: Dimensions.ai, which provides access to a large number of research publications, and lens.org, which offers access to over 300 million articles, patents, and other research outputs from diverse sources. Additionally, we included three databases, Scopus, Web of Knowledge, and ERIC, which contain relevant research on the topic that addresses our research questions. To browse and identify relevant articles, we used the following search formula: ("ChatGPT" AND "Education"), which included the Boolean operator "AND" to get more specific results. The subject area in the Scopus and ERIC databases were narrowed to "ChatGPT" and "Education" keywords, and in the WoS database was limited to the "Education" category. The search was conducted between the 3rd and 10th of April 2023, which resulted in 276 articles from all selected databases (111 articles from Dimensions.ai, 65 from Scopus, 28 from Web of Science, 14 from ERIC, and 58 from Lens.org). These articles were imported into the Rayyan web-based system for analysis. The duplicates were identified automatically by the system. Subsequently, the first author manually reviewed the duplicated articles ensured that they had the same content, and then removed them, leaving us with 135 unique articles. Afterward, the titles, abstracts, and keywords of the first 40 manuscripts were scanned and reviewed by the first author and were discussed with the second and third authors to resolve any disagreements. Subsequently, the first author proceeded with the filtering process for all articles and carefully applied the inclusion and exclusion criteria as presented in Table 1 . Articles that met any one of the exclusion criteria were eliminated, resulting in 26 articles. Afterward, the authors met to carefully scan and discuss them. The authors agreed to eliminate any empirical studies solely focused on checking ChatGPT capabilities, as these studies do not guide us in addressing the research questions and achieving the study's objectives. This resulted in 14 articles eligible for analysis.

2.5 Quality appraisal

The examination and evaluation of the quality of the extracted articles is a vital step [ 9 ]. Therefore, the extracted articles were carefully evaluated for quality using Fink’s [ 24 ] standards, which emphasize the necessity for detailed descriptions of methodology, results, conclusions, strengths, and limitations. The process began with a thorough assessment of each study's design, data collection, and analysis methods to ensure their appropriateness and comprehensive execution. The clarity, consistency, and logical progression from data to results and conclusions were also critically examined. Potential biases and recognized limitations within the studies were also scrutinized. Ultimately, two articles were excluded for failing to meet Fink’s criteria, particularly in providing sufficient detail on methodology, results, conclusions, strengths, or limitations. The review process is illustrated in Fig. 1 .

The study selection process

2.6 Data extraction

The next step is data extraction, the process of capturing the key information and categories from the included studies. To improve efficiency, reduce variation among authors, and minimize errors in data analysis, the coding categories were constructed using Creswell's [ 15 ] coding techniques for data extraction and interpretation. The coding process involves three sequential steps. The initial stage encompasses open coding , where the researcher examines the data, generates codes to describe and categorize it, and gains a deeper understanding without preconceived ideas. Following open coding is axial coding , where the interrelationships between codes from open coding are analyzed to establish more comprehensive categories or themes. The process concludes with selective coding , refining and integrating categories or themes to identify core concepts emerging from the data. The first coder performed the coding process, then engaged in discussions with the second and third authors to finalize the coding categories for the first five articles. The first coder then proceeded to code all studies and engaged again in discussions with the other authors to ensure the finalization of the coding process. After a comprehensive analysis and capturing of the key information from the included studies, the data extraction and interpretation process yielded several themes. These themes have been categorized and are presented in Table 2 . It is important to note that open coding results were removed from Table 2 for aesthetic reasons, as it included many generic aspects, such as words, short phrases, or sentences mentioned in the studies.

2.7 Synthesize studies

In this stage, we will gather, discuss, and analyze the key findings that emerged from the selected studies. The synthesis stage is considered a transition from an author-centric to a concept-centric focus, enabling us to map all the provided information to achieve the most effective evaluation of the data [ 87 ]. Initially, the authors extracted data that included general information about the selected studies, including the author(s)' names, study titles, years of publication, educational levels, research methodologies, sample sizes, participants, main aims or objectives, raw data sources, and analysis methods. Following that, all key information and significant results from the selected studies were compiled using Creswell’s [ 15 ] coding techniques for data extraction and interpretation to identify core concepts and themes emerging from the data, focusing on those that directly contributed to our research questions and objectives, such as the initial utilization of ChatGPT in learning and teaching, learners' and educators' familiarity with ChatGPT, and the main findings of each study. Finally, the data related to each selected study were extracted into an Excel spreadsheet for data processing. The Excel spreadsheet was reviewed by the authors, including a series of discussions to ensure the finalization of this process and prepare it for further analysis. Afterward, the final result being analyzed and presented in various types of charts and graphs. Table 4 presents the extracted data from the selected studies, with each study labeled with a capital 'S' followed by a number.

This section consists of two main parts. The first part provides a descriptive analysis of the data compiled from the reviewed studies. The second part presents the answers to the research questions and the main findings of these studies.

3.1 Part 1: descriptive analysis

This section will provide a descriptive analysis of the reviewed studies, including educational levels and fields, participants distribution, country contribution, research methodologies, study sample size, study population, publication year, list of journals, familiarity with ChatGPT, source of data, and the main aims and objectives of the studies. Table 4 presents a comprehensive overview of the extracted data from the selected studies.

3.1.1 The number of the reviewed studies and publication years

The total number of the reviewed studies was 14. All studies were empirical studies and published in different journals focusing on Education and Technology. One study was published in 2022 [S1], while the remaining were published in 2023 [S2]-[S14]. Table 3 illustrates the year of publication, the names of the journals, and the number of reviewed studies published in each journal for the studies reviewed.

3.1.2 Educational levels and fields

The majority of the reviewed studies, 11 studies, were conducted in higher education institutions [S1]-[S10] and [S13]. Two studies did not specify the educational level of the population [S12] and [S14], while one study focused on elementary education [S11]. However, the reviewed studies covered various fields of education. Three studies focused on Arts and Humanities Education [S8], [S11], and [S14], specifically English Education. Two studies focused on Engineering Education, with one in Computer Engineering [S2] and the other in Construction Education [S3]. Two studies focused on Mathematics Education [S5] and [S12]. One study focused on Social Science Education [S13]. One study focused on Early Education [S4]. One study focused on Journalism Education [S9]. Finally, three studies did not specify the field of education [S1], [S6], and [S7]. Figure 2 represents the educational levels in the reviewed studies, while Fig. 3 represents the context of the reviewed studies.

Educational levels in the reviewed studies

Context of the reviewed studies

3.1.3 Participants distribution and countries contribution

The reviewed studies have been conducted across different geographic regions, providing a diverse representation of the studies. The majority of the studies, 10 in total, [S1]-[S3], [S5]-[S9], [S11], and [S14], primarily focused on participants from single countries such as Pakistan, the United Arab Emirates, China, Indonesia, Poland, Saudi Arabia, South Korea, Spain, Tajikistan, and the United States. In contrast, four studies, [S4], [S10], [S12], and [S13], involved participants from multiple countries, including China and the United States [S4], China, the United Kingdom, and the United States [S10], the United Arab Emirates, Oman, Saudi Arabia, and Jordan [S12], Turkey, Sweden, Canada, and Australia [ 13 ]. Figures 4 and 5 illustrate the distribution of participants, whether from single or multiple countries, and the contribution of each country in the reviewed studies, respectively.

The reviewed studies conducted in single or multiple countries

The Contribution of each country in the studies

3.1.4 Study population and sample size

Four study populations were included: university students, university teachers, university teachers and students, and elementary school teachers. Six studies involved university students [S2], [S3], [S5] and [S6]-[S8]. Three studies focused on university teachers [S1], [S4], and [S6], while one study specifically targeted elementary school teachers [S11]. Additionally, four studies included both university teachers and students [S10] and [ 12 , 13 , 14 ], and among them, study [S13] specifically included postgraduate students. In terms of the sample size of the reviewed studies, nine studies included a small sample size of less than 50 participants [S1], [S3], [S6], [S8], and [S10]-[S13]. Three studies had 50–100 participants [S2], [S9], and [S14]. Only one study had more than 100 participants [S7]. It is worth mentioning that study [S4] adopted a mixed methods approach, including 10 participants for qualitative analysis and 110 participants for quantitative analysis.

3.1.5 Participants’ familiarity with using ChatGPT

The reviewed studies recruited a diverse range of participants with varying levels of familiarity with ChatGPT. Five studies [S2], [S4], [S6], [S8], and [S12] involved participants already familiar with ChatGPT, while eight studies [S1], [S3], [S5], [S7], [S9], [S10], [S13] and [S14] included individuals with differing levels of familiarity. Notably, one study [S11] had participants who were entirely unfamiliar with ChatGPT. It is important to note that four studies [S3], [S5], [S9], and [S11] provided training or guidance to their participants before conducting their studies, while ten studies [S1], [S2], [S4], [S6]-[S8], [S10], and [S12]-[S14] did not provide training due to the participants' existing familiarity with ChatGPT.

3.1.6 Research methodology approaches and source(S) of data

The reviewed studies adopted various research methodology approaches. Seven studies adopted qualitative research methodology [S1], [S4], [S6], [S8], [S10], [S11], and [S12], while three studies adopted quantitative research methodology [S3], [S7], and [S14], and four studies employed mixed-methods, which involved a combination of both the strengths of qualitative and quantitative methods [S2], [S5], [S9], and [S13].

In terms of the source(s) of data, the reviewed studies obtained their data from various sources, such as interviews, questionnaires, and pre-and post-tests. Six studies relied on interviews as their primary source of data collection [S1], [S4], [S6], [S10], [S11], and [S12], four studies relied on questionnaires [S2], [S7], [S13], and [S14], two studies combined the use of pre-and post-tests and questionnaires for data collection [S3] and [S9], while two studies combined the use of questionnaires and interviews to obtain the data [S5] and [S8]. It is important to note that six of the reviewed studies were quasi-experimental [S3], [S5], [S8], [S9], [S12], and [S14], while the remaining ones were experimental studies [S1], [S2], [S4], [S6], [S7], [S10], [S11], and [S13]. Figures 6 and 7 illustrate the research methodologies and the source (s) of data used in the reviewed studies, respectively.

Research methodologies in the reviewed studies

Source of data in the reviewed studies

3.1.7 The aim and objectives of the studies

The reviewed studies encompassed a diverse set of aims, with several of them incorporating multiple primary objectives. Six studies [S3], [S6], [S7], [S8], [S11], and [S12] examined the integration of ChatGPT in educational contexts, and four studies [S4], [S5], [S13], and [S14] investigated the various implications of its use in education, while three studies [S2], [S9], and [S10] aimed to explore both its integration and implications in education. Additionally, seven studies explicitly explored attitudes and perceptions of students [S2] and [S3], educators [S1] and [S6], or both [S10], [S12], and [S13] regarding the utilization of ChatGPT in educational settings.

3.2 Part 2: research questions and main findings of the reviewed studies

This part will present the answers to the research questions and the main findings of the reviewed studies, classified into two main categories (learning and teaching) according to AI Education classification by [ 36 ]. Figure 8 summarizes the main findings of the reviewed studies in a visually informative diagram. Table 4 provides a detailed list of the key information extracted from the selected studies that led to generating these themes.

The main findings in the reviewed studies

4 Students' initial attempts at utilizing ChatGPT in learning and main findings from students' perspective

4.1 virtual intelligent assistant.

Nine studies demonstrated that ChatGPT has been utilized by students as an intelligent assistant to enhance and support their learning. Students employed it for various purposes, such as answering on-demand questions [S2]-[S5], [S8], [S10], and [S12], providing valuable information and learning resources [S2]-[S5], [S6], and [S8], as well as receiving immediate feedback [S2], [S4], [S9], [S10], and [S12]. In this regard, students generally were confident in the accuracy of ChatGPT's responses, considering them relevant, reliable, and detailed [S3], [S4], [S5], and [S8]. However, some students indicated the need for improvement, as they found that answers are not always accurate [S2], and that misleading information may have been provided or that it may not always align with their expectations [S6] and [S10]. It was also observed by the students that the accuracy of ChatGPT is dependent on several factors, including the quality and specificity of the user's input, the complexity of the question or topic, and the scope and relevance of its training data [S12]. Many students felt that ChatGPT's answers were not always accurate and most of them believed that it requires good background knowledge to work with.

4.2 Writing and language proficiency assistant

Six of the reviewed studies highlighted that ChatGPT has been utilized by students as a valuable assistant tool to improve their academic writing skills and language proficiency. Among these studies, three mainly focused on English education, demonstrating that students showed sufficient mastery in using ChatGPT for generating ideas, summarizing, paraphrasing texts, and completing writing essays [S8], [S11], and [S14]. Furthermore, ChatGPT helped them in writing by making students active investigators rather than passive knowledge recipients and facilitated the development of their writing skills [S11] and [S14]. Similarly, ChatGPT allowed students to generate unique ideas and perspectives, leading to deeper analysis and reflection on their journalism writing [S9]. In terms of language proficiency, ChatGPT allowed participants to translate content into their home languages, making it more accessible and relevant to their context [S4]. It also enabled them to request changes in linguistic tones or flavors [S8]. Moreover, participants used it to check grammar or as a dictionary [S11].

4.3 Valuable resource for learning approaches

Five studies demonstrated that students used ChatGPT as a valuable complementary resource for self-directed learning. It provided learning resources and guidance on diverse educational topics and created a supportive home learning environment [S2] and [S4]. Moreover, it offered step-by-step guidance to grasp concepts at their own pace and enhance their understanding [S5], streamlined task and project completion carried out independently [S7], provided comprehensive and easy-to-understand explanations on various subjects [S10], and assisted in studying geometry operations, thereby empowering them to explore geometry operations at their own pace [S12]. Three studies showed that students used ChatGPT as a valuable learning resource for personalized learning. It delivered age-appropriate conversations and tailored teaching based on a child's interests [S4], acted as a personalized learning assistant, adapted to their needs and pace, which assisted them in understanding mathematical concepts [S12], and enabled personalized learning experiences in social sciences by adapting to students' needs and learning styles [S13]. On the other hand, it is important to note that, according to one study [S5], students suggested that using ChatGPT may negatively affect collaborative learning competencies between students.

4.4 Enhancing students' competencies

Six of the reviewed studies have shown that ChatGPT is a valuable tool for improving a wide range of skills among students. Two studies have provided evidence that ChatGPT led to improvements in students' critical thinking, reasoning skills, and hazard recognition competencies through engaging them in interactive conversations or activities and providing responses related to their disciplines in journalism [S5] and construction education [S9]. Furthermore, two studies focused on mathematical education have shown the positive impact of ChatGPT on students' problem-solving abilities in unraveling problem-solving questions [S12] and enhancing the students' understanding of the problem-solving process [S5]. Lastly, one study indicated that ChatGPT effectively contributed to the enhancement of conversational social skills [S4].

4.5 Supporting students' academic success

Seven of the reviewed studies highlighted that students found ChatGPT to be beneficial for learning as it enhanced learning efficiency and improved the learning experience. It has been observed to improve students' efficiency in computer engineering studies by providing well-structured responses and good explanations [S2]. Additionally, students found it extremely useful for hazard reporting [S3], and it also enhanced their efficiency in solving mathematics problems and capabilities [S5] and [S12]. Furthermore, by finding information, generating ideas, translating texts, and providing alternative questions, ChatGPT aided students in deepening their understanding of various subjects [S6]. It contributed to an increase in students' overall productivity [S7] and improved efficiency in composing written tasks [S8]. Regarding learning experiences, ChatGPT was instrumental in assisting students in identifying hazards that they might have otherwise overlooked [S3]. It also improved students' learning experiences in solving mathematics problems and developing abilities [S5] and [S12]. Moreover, it increased students' successful completion of important tasks in their studies [S7], particularly those involving average difficulty writing tasks [S8]. Additionally, ChatGPT increased the chances of educational success by providing students with baseline knowledge on various topics [S10].

5 Teachers' initial attempts at utilizing ChatGPT in teaching and main findings from teachers' perspective

5.1 valuable resource for teaching.

The reviewed studies showed that teachers have employed ChatGPT to recommend, modify, and generate diverse, creative, organized, and engaging educational contents, teaching materials, and testing resources more rapidly [S4], [S6], [S10] and [S11]. Additionally, teachers experienced increased productivity as ChatGPT facilitated quick and accurate responses to questions, fact-checking, and information searches [S1]. It also proved valuable in constructing new knowledge [S6] and providing timely answers to students' questions in classrooms [S11]. Moreover, ChatGPT enhanced teachers' efficiency by generating new ideas for activities and preplanning activities for their students [S4] and [S6], including interactive language game partners [S11].

5.2 Improving productivity and efficiency

The reviewed studies showed that participants' productivity and work efficiency have been significantly enhanced by using ChatGPT as it enabled them to allocate more time to other tasks and reduce their overall workloads [S6], [S10], [S11], [S13], and [S14]. However, three studies [S1], [S4], and [S11], indicated a negative perception and attitude among teachers toward using ChatGPT. This negativity stemmed from a lack of necessary skills to use it effectively [S1], a limited familiarity with it [S4], and occasional inaccuracies in the content provided by it [S10].

5.3 Catalyzing new teaching methodologies

Five of the reviewed studies highlighted that educators found the necessity of redefining their teaching profession with the assistance of ChatGPT [S11], developing new effective learning strategies [S4], and adapting teaching strategies and methodologies to ensure the development of essential skills for future engineers [S5]. They also emphasized the importance of adopting new educational philosophies and approaches that can evolve with the introduction of ChatGPT into the classroom [S12]. Furthermore, updating curricula to focus on improving human-specific features, such as emotional intelligence, creativity, and philosophical perspectives [S13], was found to be essential.

5.4 Effective utilization of CHATGPT in teaching

According to the reviewed studies, effective utilization of ChatGPT in education requires providing teachers with well-structured training, support, and adequate background on how to use ChatGPT responsibly [S1], [S3], [S11], and [S12]. Establishing clear rules and regulations regarding its usage is essential to ensure it positively impacts the teaching and learning processes, including students' skills [S1], [S4], [S5], [S8], [S9], and [S11]-[S14]. Moreover, conducting further research and engaging in discussions with policymakers and stakeholders is indeed crucial for the successful integration of ChatGPT in education and to maximize the benefits for both educators and students [S1], [S6]-[S10], and [S12]-[S14].

6 Discussion

The purpose of this review is to conduct a systematic review of empirical studies that have explored the utilization of ChatGPT, one of today’s most advanced LLM-based chatbots, in education. The findings of the reviewed studies showed several ways of ChatGPT utilization in different learning and teaching practices as well as it provided insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of the reviewed studies came from diverse fields of education, which helped us avoid a biased review that is limited to a specific field. Similarly, the reviewed studies have been conducted across different geographic regions. This kind of variety in geographic representation enriched the findings of this review.

In response to RQ1 , "What are students' and teachers' initial attempts at utilizing ChatGPT in education?", the findings from this review provide comprehensive insights. Chatbots, including ChatGPT, play a crucial role in supporting student learning, enhancing their learning experiences, and facilitating diverse learning approaches [ 42 , 43 ]. This review found that this tool, ChatGPT, has been instrumental in enhancing students' learning experiences by serving as a virtual intelligent assistant, providing immediate feedback, on-demand answers, and engaging in educational conversations. Additionally, students have benefited from ChatGPT’s ability to generate ideas, compose essays, and perform tasks like summarizing, translating, paraphrasing texts, or checking grammar, thereby enhancing their writing and language competencies. Furthermore, students have turned to ChatGPT for assistance in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks, which fosters a supportive home learning environment, allowing them to take responsibility for their own learning and cultivate the skills and approaches essential for supportive home learning environment [ 26 , 27 , 28 ]. This finding aligns with the study of Saqr et al. [ 68 , 69 ] who highlighted that, when students actively engage in their own learning process, it yields additional advantages, such as heightened motivation, enhanced achievement, and the cultivation of enthusiasm, turning them into advocates for their own learning.

Moreover, students have utilized ChatGPT for tailored teaching and step-by-step guidance on diverse educational topics, streamlining task and project completion, and generating and recommending educational content. This personalization enhances the learning environment, leading to increased academic success. This finding aligns with other recent studies [ 26 , 27 , 28 , 60 , 66 ] which revealed that ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities. Ultimately, fostering students' performance, engagement, and motivation, leading to increase students' academic success [ 14 , 44 , 58 ]. This ultimate outcome is in line with the findings of Saqr et al. [ 68 , 69 ], which emphasized that learning strategies are important catalysts of students' learning, as students who utilize effective learning strategies are more likely to have better academic achievement.

Teachers, too, have capitalized on ChatGPT's capabilities to enhance productivity and efficiency, using it for creating lesson plans, generating quizzes, providing additional resources, generating and preplanning new ideas for activities, and aiding in answering students’ questions. This adoption of technology introduces new opportunities to support teaching and learning practices, enhancing teacher productivity. This finding aligns with those of Day [ 17 ], De Castro [ 18 ], and Su and Yang [ 74 ] as well as with those of Valtonen et al. [ 82 ], who revealed that emerging technological advancements have opened up novel opportunities and means to support teaching and learning practices, and enhance teachers’ productivity.

In response to RQ2 , "What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?", the findings from this review provide profound insights and raise significant concerns. Starting with the insights, chatbots, including ChatGPT, have demonstrated the potential to reshape and revolutionize education, creating new, novel opportunities for enhancing the learning process and outcomes [ 83 ], facilitating different learning approaches, and offering a range of pedagogical benefits [ 19 , 43 , 72 ]. In this context, this review found that ChatGPT could open avenues for educators to adopt or develop new effective learning and teaching strategies that can evolve with the introduction of ChatGPT into the classroom. Nonetheless, there is an evident lack of research understanding regarding the potential impact of generative machine learning models within diverse educational settings [ 83 ]. This necessitates teachers to attain a high level of proficiency in incorporating chatbots, such as ChatGPT, into their classrooms to create inventive, well-structured, and captivating learning strategies. In the same vein, the review also found that teachers without the requisite skills to utilize ChatGPT realized that it did not contribute positively to their work and could potentially have adverse effects [ 37 ]. This concern could lead to inequity of access to the benefits of chatbots, including ChatGPT, as individuals who lack the necessary expertise may not be able to harness their full potential, resulting in disparities in educational outcomes and opportunities. Therefore, immediate action is needed to address these potential issues. A potential solution is offering training, support, and competency development for teachers to ensure that all of them can leverage chatbots, including ChatGPT, effectively and equitably in their educational practices [ 5 , 28 , 80 ], which could enhance accessibility and inclusivity, and potentially result in innovative outcomes [ 82 , 83 ].

Additionally, chatbots, including ChatGPT, have the potential to significantly impact students' thinking abilities, including retention, reasoning, analysis skills [ 19 , 45 ], and foster innovation and creativity capabilities [ 83 ]. This review found that ChatGPT could contribute to improving a wide range of skills among students. However, it found that frequent use of ChatGPT may result in a decrease in innovative capacities, collaborative skills and cognitive capacities, and students' motivation to attend classes, as well as could lead to reduced higher-order thinking skills among students [ 22 , 29 ]. Therefore, immediate action is needed to carefully examine the long-term impact of chatbots such as ChatGPT, on learning outcomes as well as to explore its incorporation into educational settings as a supportive tool without compromising students' cognitive development and critical thinking abilities. In the same vein, the review also found that it is challenging to draw a consistent conclusion regarding the potential of ChatGPT to aid self-directed learning approach. This finding aligns with the recent study of Baskara [ 8 ]. Therefore, further research is needed to explore the potential of ChatGPT for self-directed learning. One potential solution involves utilizing learning analytics as a novel approach to examine various aspects of students' learning and support them in their individual endeavors [ 32 ]. This approach can bridge this gap by facilitating an in-depth analysis of how learners engage with ChatGPT, identifying trends in self-directed learning behavior, and assessing its influence on their outcomes.

Turning to the significant concerns, on the other hand, a fundamental challenge with LLM-based chatbots, including ChatGPT, is the accuracy and quality of the provided information and responses, as they provide false information as truth—a phenomenon often referred to as "hallucination" [ 3 , 49 ]. In this context, this review found that the provided information was not entirely satisfactory. Consequently, the utilization of chatbots presents potential concerns, such as generating and providing inaccurate or misleading information, especially for students who utilize it to support their learning. This finding aligns with other findings [ 6 , 30 , 35 , 40 ] which revealed that incorporating chatbots such as ChatGPT, into education presents challenges related to its accuracy and reliability due to its training on a large corpus of data, which may contain inaccuracies and the way users formulate or ask ChatGPT. Therefore, immediate action is needed to address these potential issues. One possible solution is to equip students with the necessary skills and competencies, which include a background understanding of how to use it effectively and the ability to assess and evaluate the information it generates, as the accuracy and the quality of the provided information depend on the input, its complexity, the topic, and the relevance of its training data [ 28 , 49 , 86 ]. However, it's also essential to examine how learners can be educated about how these models operate, the data used in their training, and how to recognize their limitations, challenges, and issues [ 79 ].

Furthermore, chatbots present a substantial challenge concerning maintaining academic integrity [ 20 , 56 ] and copyright violations [ 83 ], which are significant concerns in education. The review found that the potential misuse of ChatGPT might foster cheating, facilitate plagiarism, and threaten academic integrity. This issue is also affirmed by the research conducted by Basic et al. [ 7 ], who presented evidence that students who utilized ChatGPT in their writing assignments had more plagiarism cases than those who did not. These findings align with the conclusions drawn by Cotton et al. [ 13 ], Hisan and Amri [ 33 ] and Sullivan et al. [ 75 ], who revealed that the integration of chatbots such as ChatGPT into education poses a significant challenge to the preservation of academic integrity. Moreover, chatbots, including ChatGPT, have increased the difficulty in identifying plagiarism [ 47 , 67 , 76 ]. The findings from previous studies [ 1 , 84 ] indicate that AI-generated text often went undetected by plagiarism software, such as Turnitin. However, Turnitin and other similar plagiarism detection tools, such as ZeroGPT, GPTZero, and Copyleaks, have since evolved, incorporating enhanced techniques to detect AI-generated text, despite the possibility of false positives, as noted in different studies that have found these tools still not yet fully ready to accurately and reliably identify AI-generated text [ 10 , 51 ], and new novel detection methods may need to be created and implemented for AI-generated text detection [ 4 ]. This potential issue could lead to another concern, which is the difficulty of accurately evaluating student performance when they utilize chatbots such as ChatGPT assistance in their assignments. Consequently, the most LLM-driven chatbots present a substantial challenge to traditional assessments [ 64 ]. The findings from previous studies indicate the importance of rethinking, improving, and redesigning innovative assessment methods in the era of chatbots [ 14 , 20 , 64 , 75 ]. These methods should prioritize the process of evaluating students' ability to apply knowledge to complex cases and demonstrate comprehension, rather than solely focusing on the final product for assessment. Therefore, immediate action is needed to address these potential issues. One possible solution would be the development of clear guidelines, regulatory policies, and pedagogical guidance. These measures would help regulate the proper and ethical utilization of chatbots, such as ChatGPT, and must be established before their introduction to students [ 35 , 38 , 39 , 41 , 89 ].

In summary, our review has delved into the utilization of ChatGPT, a prominent example of chatbots, in education, addressing the question of how ChatGPT has been utilized in education. However, there remain significant gaps, which necessitate further research to shed light on this area.

7 Conclusions

This systematic review has shed light on the varied initial attempts at incorporating ChatGPT into education by both learners and educators, while also offering insights and considerations that can facilitate its effective and responsible use in future educational contexts. From the analysis of 14 selected studies, the review revealed the dual-edged impact of ChatGPT in educational settings. On the positive side, ChatGPT significantly aided the learning process in various ways. Learners have used it as a virtual intelligent assistant, benefiting from its ability to provide immediate feedback, on-demand answers, and easy access to educational resources. Additionally, it was clear that learners have used it to enhance their writing and language skills, engaging in practices such as generating ideas, composing essays, and performing tasks like summarizing, translating, paraphrasing texts, or checking grammar. Importantly, other learners have utilized it in supporting and facilitating their directed and personalized learning on a broad range of educational topics, assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. Educators, on the other hand, found ChatGPT beneficial for enhancing productivity and efficiency. They used it for creating lesson plans, generating quizzes, providing additional resources, and answers learners' questions, which saved time and allowed for more dynamic and engaging teaching strategies and methodologies.

However, the review also pointed out negative impacts. The results revealed that overuse of ChatGPT could decrease innovative capacities and collaborative learning among learners. Specifically, relying too much on ChatGPT for quick answers can inhibit learners' critical thinking and problem-solving skills. Learners might not engage deeply with the material or consider multiple solutions to a problem. This tendency was particularly evident in group projects, where learners preferred consulting ChatGPT individually for solutions over brainstorming and collaborating with peers, which negatively affected their teamwork abilities. On a broader level, integrating ChatGPT into education has also raised several concerns, including the potential for providing inaccurate or misleading information, issues of inequity in access, challenges related to academic integrity, and the possibility of misusing the technology.

Accordingly, this review emphasizes the urgency of developing clear rules, policies, and regulations to ensure ChatGPT's effective and responsible use in educational settings, alongside other chatbots, by both learners and educators. This requires providing well-structured training to educate them on responsible usage and understanding its limitations, along with offering sufficient background information. Moreover, it highlights the importance of rethinking, improving, and redesigning innovative teaching and assessment methods in the era of ChatGPT. Furthermore, conducting further research and engaging in discussions with policymakers and stakeholders are essential steps to maximize the benefits for both educators and learners and ensure academic integrity.

It is important to acknowledge that this review has certain limitations. Firstly, the limited inclusion of reviewed studies can be attributed to several reasons, including the novelty of the technology, as new technologies often face initial skepticism and cautious adoption; the lack of clear guidelines or best practices for leveraging this technology for educational purposes; and institutional or governmental policies affecting the utilization of this technology in educational contexts. These factors, in turn, have affected the number of studies available for review. Secondly, the utilization of the original version of ChatGPT, based on GPT-3 or GPT-3.5, implies that new studies utilizing the updated version, GPT-4 may lead to different findings. Therefore, conducting follow-up systematic reviews is essential once more empirical studies on ChatGPT are published. Additionally, long-term studies are necessary to thoroughly examine and assess the impact of ChatGPT on various educational practices.

Despite these limitations, this systematic review has highlighted the transformative potential of ChatGPT in education, revealing its diverse utilization by learners and educators alike and summarized the benefits of incorporating it into education, as well as the forefront critical concerns and challenges that must be addressed to facilitate its effective and responsible use in future educational contexts. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Data availability

The data supporting our findings are available upon request.

Abbreviations

Artificial intelligence

AI in education

Large language model

Artificial neural networks

Chat Generative Pre-Trained Transformer

Recurrent neural networks

Long short-term memory

Reinforcement learning from human feedback

Natural language processing

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

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The paper is co-funded by the Academy of Finland (Suomen Akatemia) Research Council for Natural Sciences and Engineering for the project Towards precision education: Idiographic learning analytics (TOPEILA), Decision Number 350560.

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YA contributed to the literature search, data analysis, discussion, and conclusion. Additionally, YA contributed to the manuscript’s writing, editing, and finalization. MS contributed to the study’s design, conceptualization, acquisition of funding, project administration, allocation of resources, supervision, validation, literature search, and analysis of results. Furthermore, MS contributed to the manuscript's writing, revising, and approving it in its finalized state. NP contributed to the results, and discussions, and provided supervision. NP also contributed to the writing process, revisions, and the final approval of the manuscript in its finalized state. MT contributed to the study's conceptualization, resource management, supervision, writing, revising the manuscript, and approving it.

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The process of synthesizing the data presented in Table 4 involved identifying the relevant studies through a search process of databases (ERIC, Scopus, Web of Knowledge, Dimensions.ai, and lens.org) using specific keywords "ChatGPT" and "education". Following this, inclusion/exclusion criteria were applied, and data extraction was performed using Creswell's [ 15 ] coding techniques to capture key information and identify common themes across the included studies.

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Albadarin, Y., Saqr, M., Pope, N. et al. A systematic literature review of empirical research on ChatGPT in education. Discov Educ 3 , 60 (2024). https://doi.org/10.1007/s44217-024-00138-2

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Published: 22 May 2024

Preparedness for a first clinical placement in nursing: a descriptive qualitative study

Philippa H. M. Marriott 1 ,
Jennifer M. Weller-Newton 2 nAff3 &
Katharine J. Reid 4

BMC Nursing volume 23 , Article number: 345 ( 2024 ) Cite this article

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A first clinical placement for nursing students is a challenging period involving translation of theoretical knowledge and development of an identity within the healthcare setting; it is often a time of emotional vulnerability. It can be a pivotal moment for ambivalent nursing students to decide whether to continue their professional training. To date, student expectations prior to their first clinical placement have been explored in advance of the experience or gathered following the placement experience. However, there is a significant gap in understanding how nursing students’ perspectives about their first clinical placement might change or remain consistent following their placement experiences. Thus, the study aimed to explore first-year nursing students’ emotional responses towards and perceptions of their preparedness for their first clinical placement and to examine whether initial perceptions remain consistent or change during the placement experience.

The research utilised a pre-post qualitative descriptive design. Six focus groups were undertaken before the first clinical placement (with up to four participants in each group) and follow-up individual interviews ( n = 10) were undertaken towards the end of the first clinical placement with first-year entry-to-practice postgraduate nursing students. Data were analysed thematically.

Three main themes emerged: (1) adjusting and managing a raft of feelings, encapsulating participants’ feelings about learning in a new environment and progressing from academia to clinical practice; (2) sinking or swimming, comprising students’ expectations before their first clinical placement and how these perceptions are altered through their clinical placement experience; and (3) navigating placement, describing relationships between healthcare staff, patients, and peers.

Conclusions

This unique study of first-year postgraduate entry-to-practice nursing students’ perspectives of their first clinical placement adds to the extant knowledge. By examining student experience prior to and during their first clinical placement experience, it is possible to explore the consistency and change in students’ narratives over the course of an impactful experience. Researching the narratives of nursing students embarking on their first clinical placement provides tertiary education institutions with insights into preparing students for this critical experience.

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First clinical placements enable nursing students to develop their professional identity through initial socialisation, and where successful, first clinical placement experiences can motivate nursing students to persist with their studies [ 1 , 2 , 3 , 4 ]. Where the transition from the tertiary environment to learning in the healthcare workplace is turbulent, it may impact nursing students’ learning, their confidence and potentially increase attrition rates from educational programs [ 2 , 5 , 6 ]. Attrition from preregistration nursing courses is a global concern, with the COVID-19 pandemic further straining the nursing workforce; thus, the supply of nursing professionals is unlikely to meet demand [ 7 ]. The COVID-19 pandemic has also impacted nursing education, with student nurses augmenting the diminishing nursing workforce [ 7 , 8 ].

The first clinical placement often triggers immense anxiety and fear for nursing students [ 9 , 10 ]. Research suggests that among nursing students, anxiety arises from perceived knowledge deficiencies, role ambiguity, the working environment, caring for ‘real’ people, potentially causing harm, exposure to nudity and death, and ‘not fitting in’ [ 2 , 3 , 11 ]. These stressors are reported internationally and often relate to inadequate preparation for entering the clinical environment [ 2 , 10 , 12 ]. Previous research suggests that high anxiety before the first clinical placement can be related to factors likely to affect patient outcomes, such as self-confidence and efficacy [ 13 ]. High anxiety during clinical placement may impair students’ capacity to learn, thus compromising the value of the clinical environment for learning [ 10 ].

The first clinical placement often occurs soon after commencing nursing training and can challenge students’ beliefs, philosophies, and preconceived ideas about nursing. An experience of cultural or ‘reality’ shock often arises when entering the healthcare setting, creating dissonance between reality and expectations [ 6 , 14 ]. These experiences may be exacerbated by tertiary education providers teaching of ‘ideal’ clinical practice [ 2 , 6 ]. The perceived distance between theoretical knowledge and what is expected in a healthcare placement, as opposed to what occurs on clinical placement, has been well documented as the theory-practice gap or an experience of cognitive dissonance [ 2 , 3 ].

Given the pivotal role of the first clinical placement in nursing students’ trajectories to nursing practice, it is important to understand students’ experiences and to explore how the placement experience shapes initial perceptions. Existing research focusses almost entirely either on describing nursing students’ projected emotions and perceptions prior to undertaking a first clinical placement [ 3 ] or examines student perceptions of reflecting on a completed first placement [ 15 ]. We wished to examine consistency and change in student perception of their first clinical placement by tracking their experiences longitudinally. We focused on a first clinical placement undertaken in a Master of Nursing Science. This two-year postgraduate qualification provides entry-to-practice nursing training for students who have completed any undergraduate qualification. The first clinical placement component of the course aimed to orient students to the clinical environment, support students to acquire skills and develop their clinical reasoning through experiential learning with experienced nursing mentors.

This paper makes a significant contribution to understanding how nursing students’ perceptions might develop over time because of their clinical placement experiences. Our research addresses a further gap in the existing literature, by focusing on students completing an accelerated postgraduate two-year entry-to-practice degree open to students with any prior undergraduate degree. Thus, the current research aimed to understand nursing students’ emotional responses and expectations and their perceptions of preparedness before attending their first clinical placement and to contrast these initial perceptions with their end-of-placement perspectives.

Study design

A descriptive qualitative study was undertaken, utilising a pre- and post-design for data collection. Focus groups with first-year postgraduate entry-to-practice nursing students were conducted before the first clinical placement, with individual semi-structured interviews undertaken during the first clinical placement.

Setting and participants

All first-year students enrolled in the two-year Master of Nursing Science program ( n = 190) at a tertiary institution in Melbourne, Australia, were eligible to participate. There were no exclusion criteria. At the time of this study, students were enrolled in a semester-long subject focused on nursing assessment and care. They studied the theoretical underpinnings of nursing and science, theoretical and practical nursing clinical skills and Indigenous health over the first six weeks of the course. Students completed a preclinical assessment as a hurdle before commencing a three-week clinical placement in a hospital setting, a subacute or acute environment. Overall, the clinical placement aimed to provide opportunities for experiential learning, skill acquisition, development of clinical reasoning skills and professional socialisation [ 16 , 17 ].

In total, sixteen students participated voluntarily in a focus group of between 60 and 90 min duration; ten of these students also participated in individual interviews of between 30 and 60 min duration, a number sufficient to reach data saturation. Table 1 shows the questions used in the focus groups conducted before clinical placement commenced and the questions for the semi-structured interview questions conducted during clinical placement. Study participants’ undergraduate qualifications included bachelor’s degrees in science, arts and business. A small number of participants had previous healthcare experience (e.g. as healthcare assistants). The participants attended clinical placement in the Melbourne metropolitan, Victorian regional and rural hospital locations.

Data collection

The study comprised two phases. The first phase comprised six focus groups prior to the first clinical placement, and the second phase comprised ten individual semi-structured interviews towards the end of the first clinical placement. Focus groups (with a maximum of four participants) and individual interviews were conducted by the lead author online via Zoom and were audio-recorded. Capping group size to a relatively small number considered diversity of perceptions and opportunities for participants to share their insights and to confirm or contradict their peers, particularly in the online environment [ 18 , 19 ].

Focus groups and interview questions were developed with reference to relevant literature, piloted with volunteer final-year nursing students, and then verified with the coauthors. All focus groups and interviewees received the same structured questions (Table 1 ) to ensure consistency and to facilitate comparison across the placement experience in the development of themes. Selective probing of interviewees’ responses for clarification to gain in-depth responses was undertaken. Nonverbal cues, impressions, or observations were noted.

The lead author was a registered nurse who had a clinical teaching role within the nursing department and was responsible for coordinating clinical placement experiences. To ensure rigour during the data collection process, the lead author maintained a reflective account, exploring her experiences of the discussions, reflecting on her interactions with participants as a researcher and as a clinical educator, and identifying areas for improvement (for instance allowing participants to tell their stories with fewer prompts). These reflections in conjunction with regular discussion with the other authors throughout the data collection period, aided in identifying any researcher biases, feelings and thoughts that possibly influenced the research [ 20 ].

To maintain rigour during the data analysis phase, we adhered to a systematic process involving input from all authors to code the data and to identify, refine and describe the themes and subthemes reported in this work. This comprehensive analytic process, reported in detail in the following section, was designed to ensure that the findings arising from this research were derived from a rigorous approach to analysing the data.

Data analysis

Focus groups and interviews were transcribed using the online transcription service Otter ( https://otter.ai/ ) and then checked and anonymised by the first author. Preliminary data analysis was carried out simultaneously by the first author using thematic content analysis proposed by Braun and Clarke [ 21 ] using NVivo 12 software [ 22 ]. All three authors undertook a detailed reading of the first three transcripts from both the focus groups and interviews and independently identified major themes. This preliminary coding was used as the basis of a discussion session to identify common themes between authors, to clarify sources of disagreement and to establish guidelines for further coding. Subsequent coding of the complete data set by the lead author identified a total of 533 descriptive codes; no descriptive code was duplicated across the themes. Initially, the descriptive codes were grouped into major themes identified from the literature, but with further analysis, themes emerged that were unique to the current study.

The research team met frequently during data analysis to discuss the initial descriptive codes, to confirm the major themes and subthemes, to revise themes on which there was disagreement and to identify any additional themes. Samples of quotes were reviewed by the second and third authors to decide whether these quotes were representative of the identified themes. The process occurred iteratively to refine the thematic categories, to discuss the definitions of each theme and to identify exemplar quotes.

Ethical considerations

The lead author was a clinical teacher and the clinical placement coordinator in the nursing department at the time of the study. Potential risks of perceived coercion and power imbalances were identified because of the lead author’s dual roles as an academic and as a researcher. To manage these potential risks, an academic staff member who was not part of the research study informed students about the study during a face-to-face lecture and ensured that all participants received a plain language statement identifying the lead author’s role and how perceived conflicts of interest would be managed. These included the lead author not undertaking any teaching or assessment role for the duration of the study and ensuring that placement allocations were completed prior to undertaking recruitment for the study. All students who participated in the study provided informed written consent. No financial or other incentives were offered. Approval to conduct the study was granted by the University of Melbourne Human Research Ethics Committee (Ethics ID 1955997.1).

Three main themes emerged describing students’ feelings and perceptions of their first clinical placement. In presenting the findings, before or during has been assigned to participants’ quotes to clarify the timing of students’ perspectives related to the clinical placement.

Major theme 1: Adjusting and managing a raft of feelings

The first theme encompassed the many positive and negative feelings about work-integrated learning expressed by participants before and during their clinical placement. Positive feelings before clinical placement were expressed by participants who were comfortable with the unknown and cautiously optimistic.

I am ready to just go with the flow, roll with the punches (Participant [P]1 before).

Overwhelmingly, however, the majority of feelings and thoughts anticipating the first clinical placement were negatively oriented. Students who expressed feelings of fear, anxiety, lack of knowledge, lack of preparedness, uncertainty about nursing as a career, or strong concerns about being a burden were all classified as conveying negative feelings. These negative feelings were categorised into four subthemes.

Subtheme 1.1 I don’t have enough knowledge

All participants expressed some concerns and anxiety before their first clinical placement. These encompassed concerns about knowledge inadequacy and were linked to a perception of under preparedness. Participants’ fears related to harming patients, responsibility for managing ‘real’ people, medication administration, and incomplete understanding of the language and communication skills within a healthcare setting. Anxiety for many participants merged with the logistics and management of their life during the clinical placement.

I’m scared that they will assume that I have more knowledge than I do (P3 before). I feel quite similar with P10, especially when she said fear of unknown and fear that she might do something wrong (P9 before).

Subtheme 1.2 Worry about judgment, being seen through that lens

Participants voiced concerns that they would be judged negatively by patients or healthcare staff because they perceived that the student nurse belonged to specific social groups related to their cultural background, ethnicity or gender. Affiliation with these groups contributed to students’ sense of self or identity, with students often describing such groups as a community. Before the clinical placement, participants worried that such judgements would impact the support they received on placement and their ability to deliver patient care.

Some older patients might prefer to have nurses from their own background, their own ethnicity, how they would react to me, or if racism is involved (P10 before). I just don’t want to reinforce like, whatever negative perceptions people might have of that community (P16 before).

Participants’ concerns prior to the first clinical placement about judgement or poor treatment because of patients’ preconceived ideas about specific ethnic groups did not eventuate.

I mean, it didn’t really feel like very much of a thing once I was actually there. It is one of those things you stress about, and it does not really amount to anything (P16 during).

Some students’ placement experiences revealed the positive benefits of their cultural background to enhancing patient care. One student affirmed that the placement experience reinforced their commitment to nursing and that this was related to their ability to communicate with patients whose first language was not English.

Yeah, definitely. Like, I can speak a few dialects. You know, I can actually see a difference with a lot of the non-English speaking background people. As soon as you, as soon as they’re aware that you’re trying and you’re trying to speak your language, they, they just open up. Yeah, yes. And it improves the care (P10 during).

However, a perceived lack of judgement was sometimes attributed to wearing the full personal protective equipment required during the COVID-19 pandemic, which meant that their personal features were largely obscured. For this reason, it was more difficult for patients to make assumptions or attributions about students’ ethnic or gender identity based on their appearance.

People tend to assume and call us all girls, which was irritating. It was mostly just because all of us were so covered up, no one could see anyone’s faces (P16 during).

Subtheme 1.3 Is nursing really for me?

Prior to their first clinical placement experience, many participants expressed ambivalence about a nursing career and anticipated that undertaking clinical placement could determine their suitability for the profession. Once exposed to clinical placement, the majority of students were completely committed to their chosen profession, with a minority remaining ambivalent or, in rare cases, choosing to leave the course. Not yet achieving full commitment to a nursing career was related to not wishing to work in the ward they had for their clinical placement, while remaining open to trying different specialities.

I didn’t have an actual idea of what I wanted to do after arts, this wasn’t something that I was aiming towards specifically (P14 before). I think I’m still not 100%, but enough to go on, that I’m happy to continue the course as best as I can (P11 during).

Subtheme 1.4 Being a burden

Before clinical placement, participants had concerns about being burdensome and how this would affect their clinical placement experiences.

If we end up being a burden to them, an extra responsibility for them on top of their day, then we might not be treated as well (P10 before).

A sense of burden remained a theme during the clinical placement for participants for the first five to seven days, after which most participants acknowledged that their role became more active. As students contributed more productively to their placement, their feelings of being a burden reduced.

Major theme 2: Sinking or swimming

The second major theme, sinking or swimming, described participants’ expectations about a successful placement experience and identified themes related to students’ successes (‘swimming’) or difficulties (‘sinking’) during their placement experience. Prior to clinical placement, without a realistic preview of what the experience might entail, participants were uncertain of their role, hoped for ‘nice’ supervising nurses and anticipated an observational role that would keep them afloat.

I will focus on what I want to learn and see if that coincides with what is expected, I guess (P15 before).

During the clinical placement, the reality was very different, with a sense of sinking. Participants discovered, some with shock, that they were expected to participate actively in the healthcare team.

I got the sense that they were not going to muck around, and, you know, they’re ‘gonna’ use the free labour that came with me (P1 during).

Adding to the confusion about the expected placement experience, participants believed that healthcare staff were unclear about students’ scope of practice for a postgraduate entry-to-practice degree, creating misalignment between students’ and supervising nurses’ expectations.

It seems to me like the educators don’t really seem to have a clear picture of what the scope is, and what is actually required or expected of us (P10 during).

In exploring perceived expectations of the clinical placement and the modifying effect of placement on initial expectations, three subthemes were identified: translation to practice is overwhelming, trying to find the rhythm or jigsaw pieces, and individual agency.

Subtheme 2.1 Translation to practice is overwhelming

Before clinical placement, participants described concerns about insufficient knowledge to enable them to engage effectively with the placement experience.

If I am doing an assessment understanding what are those indications and why I would be doing it or not doing it at a certain time (P1 before).

Integrating and applying theoretical content while navigating an unfamiliar clinical environment created a significant gap between theory and practice during clinical placement. As the clinical placement experience proceeded and initial fears dissipated, students became more aware of applying their theoretical knowledge in the clinical context.

We’re learning all this theory and clinical stuff, but then we don’t really have a realistic idea of what it’s like until we’re kind of thrown into it for three weeks (P10 during).

Subtheme 2.2 Trying to find the rhythm or the jigsaw pieces

Before clinical placement, participants described learning theory and clinical skills with contextual unfamiliarity. They had the jigsaw pieces but did not know how to assemble it; they had the music but did not know the final song. When discussing their expectations about clinical placement, the small number of participants with a healthcare background (e.g. as healthcare assistants) proposed realistic answers, whereas others struggled to answer or cited stories from friends or television. With a lack of context, feelings of unpreparedness were exacerbated. Once in the clinical environment, participants further emphasised that they could not identify what they needed to know to successfully prepare for clinical placement.

It was never really pieced together. We’ve learned bits and pieces, and then we’re putting it together ourselves (P8 during). On this course I feel it was this is how you do it, but I did not know how it was supposed to be played, I did not know the rhythm (P4 during).

Subtheme 2.3 Individual agency

Participants’ individual agency, their attitude, self-efficacy, and self-motivation affected their clinical placement experiences. Participant perceptions in advance of the clinical placement experience remained consistent with their perspectives following clinical placement. Before clinical placement, participants who were highly motivated to learn exhibited a growth mindset [ 23 ] and planned to be proactive in delivering patient care. During their clinical placement, initially positive students remained positive and optimistic about their future. Participants who believed that their first clinical placement role would be largely observational and were less proactive about applying their knowledge and skills identified boredom and a lack of learning opportunities on clinical placement.

A shadowing position, we don’t have enough skills and authority to do any work, not do any worthwhile skills (P3 before). I thought it would be a lot busier, because we’re limited with our scope, so there’s not much we can do, it’s just a bit slower than I thought (P12 during).

Individual agency appears to influence a successful first clinical placement; other factors may also be implicated but were not the focus of this study. Further research exploring the relationships between students’ age, life experience, resilience, individual agency, and the use of coping strategies during a first clinical placement would be useful.

Major theme 3: The reality of navigating placement relationships

The third main theme emphasised the reality of navigating clinical placement relationships and explored students’ relationships with healthcare staff, patients, and peers. Before clinical placement, many participants, especially those with healthcare backgrounds, expressed fears about relationships with supervising nurses. They perceived that the dynamics of the team and the healthcare workplace might influence the support they received. Several participants were nervous about attending placement on their own without peers for support, especially if the experience was challenging. Participants identified expectations of being mistreated, believing that it was unavoidable, and prepared themselves to not take it personally.

For me it’s where we’re going to land, are we going to be in a supportive, kind of nurturing environment, or is it just kind of sink or swim? (P5 before). If you don’t really trust them, you’re nervous the entire time and you’ll be like what if I get it wrong (P16 before).

Despite these concerns, students strongly emphasised the value of relationships during their first clinical placement, with these perceptions unchanged by their clinical placement experience. Where relationships were positive, participants felt empowered to be autonomous, and their self-confidence increased.

You get that that instant reaction from the patients. And that makes you feel more confident. So that really got me through the first week (P14 during). I felt like I was intruding, then as I started to build a bit of rapport with the people, and they saw that I was around, I don’t feel that as much now (P1 during).

Such development hinged on the receptiveness and support of supervising nurses, the team on the ward, and patients and could be hindered by poor relationships.

He was the old-style buddy nurse in his fifties, every time I questioned him, he would go ssshh, just listen, no questions, it was very stressful (P10 during). It depends whether the buddy sees us as an extra pair of hands, or we’re learners (P11 during).

Where students experienced poor behaviour from supervising nurses, they described a range of emotional responses to these interactions and also coping strategies including avoiding unfriendly staff and actively seeking out those who were more inclusive.

If they weren’t very nice, it wouldn’t be very enjoyable and if they didn’t trust you, then it would be a bit frustrating, that like I can do this, but you won’t let me (P12 during). If another nurse was not nice to me, and I was their buddy, I would literally just not buddy with them and go and follow whoever was nice to me (P4 during).

Relationships with peers were equally important; students on clinical placement with peers valued the shared experience. In contrast, students who attended clinical placement alone at a regional or rural hospital felt disconnected from the opportunities that learning with peers afforded.

Our research explored the emotional responses and perceptions of preparedness of postgraduate entry-to-practice nursing students prior to and during their first clinical placement. In this study, we described how the perceptions of nursing students remained consistent or were modified by their clinical placement experiences. Our analysis of students’ experiences identified three major themes: adjusting and managing a raft of feelings; sinking or swimming; and the reality of navigating placement relationships. We captured similar themes identified in the literature; however, our study also identified novel aspects of nursing students’ experiences of their first clinical placement.

The key theme, adjusting and managing a raft of feelings, which encapsulates anxiety before clinical placement, is consistent with previous research. This theme included concerns in communicating with healthcare staff and managing registered nurses’ negative attitudes and expectations, in addition to an academic workload [ 11 , 24 ]. Concerns not previously identified in the literature included a fear of judgement or discrimination by healthcare staff or patients that might impact the reputation of marginalised communities. Fortunately, these initial fears largely dissipated during clinical placement. Some students discovered that a diverse cultural background was an asset during their clinical placement. Although these initial fears were ameliorated by clinical placement experiences, evidence of such fears before clinical placement is concerning. Further research to identify appropriate support for nursing students from culturally diverse or marginalised communities is warranted. For example, a Finnish study highlighted the importance of mentoring culturally diverse students, creating a pedagogical atmosphere during clinical placement and integrating cultural diversity into nursing education [ 25 ].

Preclinical expectations of being mistreated can be viewed as an unavoidable phenomenon for nursing students [ 26 ]. The existing literature highlights power imbalances and hierarchical differences within the healthcare system, where student nurses may be marginalised, disrespected, and ignored [ 9 , 27 , 28 ]. During their clinical placement, students in our study reported unintentional incivility by supervising nurses: feeling not wanted, ignored, or asked to remain quiet by supervising nurses who were unfriendly or highly critical. These findings were similar to those of Thomas et al.’s [ 29 ] UK study and were particularly heightened at the beginning of clinical placement. Several students acknowledged that nursing staff fatigue from a high turnover of students on their ward and the COVID-19 pandemic could be contributing factors. In response to such incivility, students reported decreased self-confidence and described becoming quiet and withdrawing from active participation with their patients. Students oriented their behaviour towards repetitive low-level tasks, aiming to please and help their supervising nurse, to the detriment of learning opportunities. Fortunately, these incidents did not appear to impact nursing students’ overall experience of clinical placement. Indeed, students found positive experiences with different supervising nurses and their own self-reflection assisted with coping. Other active strategies to combat incivility identified in the current study that were also identified by Thomas et al. [ 29 ] included avoiding nurses who were uncivil, asking to work with nurses who were ‘nice’ to them, and seeking out support from other staff as a coping strategy. The nursing students in our study were undertaking a postgraduate entry-to-practice qualification and already had an undergraduate degree. The likely greater levels of experience and maturity of this cohort may influence their resilience when working with unsupportive supervising nurses and identifying strategies to manage challenging situations.

The theory-practice gap emerged in the theme of sinking or swimming. A theory-practice gap describes the perceived dissonance between theoretical knowledge and expectations for the first clinical placement, as opposed to the reality of the experience, and has been reported in previous studies (see, for instance, 24 , 30 , 31 , 32 ). Existing research has shown that when the first clinical placement does not meet inexperienced student nurses’ expectations, a disconnect between theory and practice occurs, creating feelings of being lost and insecure within the new environment, potentially impacting students’ motivation and risk of attrition [ 19 , 33 ]. The current study identified further areas exacerbating the theory-practice gap. Before the clinical placement, students without a healthcare background lacked context for their learning. They lacked understanding of nurses’ shift work and were apprehensive about applying clinical skills learned in the classroom. Hence, some students were uncertain if they were prepared for their first clinical placement or even how to prepare, which increased their anxiety. Prior research has demonstrated that applying theoretical knowledge more seamlessly during clinical placement was supported when students knew what to expect [ 6 ]. For instance, a Canadian study exposed students as observers to the healthcare setting before starting clinical placement, enabling early theory to practice connections that minimised misconceptions and false assumptions during clinical placement [ 34 ].

In the current study, the theory-practice gap was further exacerbated during clinical placement, where healthcare staff were confused about students’ scope of practice and the course learning objectives and expectations in a postgraduate entry-to-practice nursing qualification. The central booking system for clinical placements classifies first-year nursing students who participated in this study as equivalent to second-year undergraduate nursing students. Such a classification could create a misalignment between clinical educators’ expectations and their delivery of education versus students’ actual learning needs and capacity [ 3 , 31 ]. Additional communication to healthcare partners is warranted to enhance understanding of the scope of practice and expectations of a first-year postgraduate entry-to-practice nursing student. Educating and empowering students to communicate their learning needs within their scope of practice is also required.

Our research identified a link between students’ personality traits or individual agency and their first clinical placement experience. The importance of a positive orientation towards learning and the nursing profession in preparedness for clinical placement has been highlighted in previous studies [ 31 ]. Students’ experience of their first clinical placement in our study appeared to be strongly influenced by their mindset [ 23 ]. Some students demonstrated motivation to learn, were happy to ‘roll with the punches’, yet remain active in their learning requirements, whereas others perceived their role as observational and expected supervising nurses to provide learning opportunities. Students who anticipated a passive learning approach prior to their first clinical placement reported boredom, limited activity, and lack of opportunities during their first clinical placement. These students could have a lowered sense of self-efficacy, which may lead to a greater risk of doubt, stress, and reduced commitment to the profession [ 35 ]. Self-efficacy theory explores self-perceived confidence and competence around people’s beliefs in their ability to influence events, which is associated with motivation and is key to nursing students progressing in their career path confidently [ 35 , 36 ]. In the current study, students who actively engaged in their learning process used strategies such as self-reflection and sought support from clinical educators, peers and family. Such active approaches to learning appeared to increase their resilience and motivation to learn as they progressed in their first clinical placement.

Important relationships with supervising nurses, peers, or patients were highlighted in the theme of the reality of navigating placement relationships. This theme links with previous research findings about belongingness. Belongingness is a fundamental human need and impacts students’ behaviour, emotions, cognitive processes, overall well-being, and socialisation into the profession [ 37 , 38 ]. Nursing students who experience belongingness feel part of a team and are more likely to report positive experiences. Several students in the current study described how feeling part of a team improved self-confidence and empowered work-integrated learning. Nonetheless, compared with previous literature (see for instance, 2), working as a team and belongingness were infrequent themes. Such infrequency could be related to the short duration of the clinical placement. In shorter clinical placements, nursing students learn a range of technical skills but have less time to develop teamwork skills and experience socialisation to the profession [ 29 , 39 ].

Positive relationships with supervising nurses appeared fundamental to students’ experiences. Previous research has shown that in wards with safe psycho-social climates, where the culture tolerates mistakes, regarding them as learning opportunities, a pedagogical atmosphere prevails [ 25 , 39 ]. Whereas, if nursing students experience insolent behaviours or incivility, this not only impacts learning it can also affect career progression [ 26 ]. Participants who felt safe asking questions were given responsibility, had autonomy to conduct skills within their scope of practice and thrived in their learning. This finding aligns with previous research affirming that a welcoming and supportive clinical placement environment, where staff are caring, approachable and helpful, enables student nurses to flourish [ 36 , 40 , 41 , 42 ]. Related research highlights that students’ perception of a good clinical placement is linked to participation within the community and instructor behaviour over the quality of the clinical environment and opportunities [ 27 , 28 ]. Over a decade ago, a large European study found that the single most important element for students’ clinical learning was the supervisory relationship [ 39 ]. In our study, students identified how supervising nurses impacted their emotions and this was critical to their experience of clinical placement, rather than how effective they were in their teaching, delivery of feedback, or their knowledge base.

Students’ relationships with patients were similarly important for a successful clinical placement. Before the clinical placement, students expressed anxiety and fears in communicating and interacting with patients, particularly if they were dying or acutely unwell, which is reflective of the literature [ 2 , 10 , 11 ]. However, during clinical placement, relationships with patients positively impacted nursing students’ experiences, especially at the beginning when they felt particularly vulnerable in a new environment. Towards the end of clinical placement, feelings of incompetence, nervousness and uncertainty had subsided. Students were more active in patient care, which increased self-confidence, empowerment, and independence, in turn further improving relationships with patients and creating a positive feedback loop [ 36 , 42 , 43 ].

Limitations

This study involved participants from one university and a single course, thus limiting the generalisability of the results. Thus, verification of the major themes identified in this research in future studies is needed. Nonetheless, the purpose of this study was to explore in detail the way in which the experiences of clinical placement for student nurses modified initial emotional responses towards undertaking placement and their perceptions of preparedness. Participants in this study undertook their clinical placement in a variety of different hospital wards in different specialties, which contributed to the rigour of the study in identifying similar themes in nursing students’ experiences across diverse placement contexts.

This study explored the narratives of first-year nursing students undertaking a postgraduate entry-to-practice qualification on their preparedness for clinical placement. Exploring students’ changing perspectives before and during the clinical placement adds to extant knowledge about nursing students’ emotional responses and perceptions of preparedness. Our research highlighted the role that preplacement emotions and expectations may have in shaping nursing students’ clinical placement experiences. Emerging themes from this study highlighted the importance students placed on relationships with peers, patients, and supervising nurses. Significant anxiety and other negative emotions experienced by nursing students prior to the first clinical placement suggests that further research is needed to explore the impact of contextual learning to scaffold students’ transition to the clinical environment. The findings of this research also have significant implications for educational practice. Additional educational support for nursing students prior to entering the clinical environment for the first time might include developing students’ understanding of the clinical environment, such as through increasing students’ understanding of the different roles of nurses in the clinical context through pre-recorded interviews with nurses. Modified approaches to simulated teaching prior to the first clinical placement would also be useful to increase the emphasis on students applying their learning in a team-based, student-led context, rather than emphasising discrete clinical skill competencies. Finally, increasing contact between students and university-based educators throughout the placement would provide further opportunities for students to debrief, to receive support and to manage some of the negative emotions identified in this study. Further supporting the transition to the first clinical placement could be fundamental to reducing the theory-practice gap and allaying anxiety. Such support is crucial during their first clinical placement to reduce attrition and boost the nursing workforce.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to the conditions of our ethics approval but may be available from the corresponding author on reasonable request and subject to permission from the Human Research Ethics Committee.

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Acknowledgements

The authors wish to thank the first-year nursing students who participated in this study and generously shared their experiences of undertaking their first clinical placement.

No funding was received for this study.

Author information

Jennifer M. Weller-Newton

Present address: School of Nursing and Midwifery, University of Canberra, Kirinari Drive, Bruce, Canberra, ACT, 2617, Australia

Authors and Affiliations

Department of Nursing, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

Philippa H. M. Marriott

Department of Rural Health, The University of Melbourne, Grattan St, Shepparton, VIC, 3630, Australia

Present address: Department of Medical Education, Melbourne Medical School, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

Katharine J. Reid

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Contributions

All authors made a substantial contribution to conducting the research and preparing the manuscript for publication. P.M., J.W-N. and K.R. conceptualised the research and designed the study. P.M. undertook the data collection, and all authors were involved in thematic analysis and interpretation. P.M. wrote the first draft of the manuscript, K.R. undertook a further revision and all authors contributed to subsequent versions. All authors approved the final version for submission. Each author is prepared to take public responsibility for the research.

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Correspondence to Katharine J. Reid .

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The research was undertaken in accordance with the National Health and Medical Research Council of Australia’s National Statement on Ethical Conduct in Human Research and the Australian Code for the Responsible Conduct of Research. Ethical approval to conduct the study was obtained from the University of Melbourne Human Research Ethics Committee (Ethics ID 1955997.1). All participants received a plain language statement that described the requirements of the study. All participants provided informed written consent to participate, which was affirmed verbally at the beginning of focus groups and interviews.

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Marriott, P.H.M., Weller-Newton, J.M. & Reid, K.J. Preparedness for a first clinical placement in nursing: a descriptive qualitative study. BMC Nurs 23 , 345 (2024). https://doi.org/10.1186/s12912-024-01916-x

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Empirical Research: Quantitative & Qualitative

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Glafera Janet Matanguihan

INTRODUCTION

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions in quantitative research

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Qualitative Research: Definition, Methodology, Limitation & Examples

Table of Contents

What is Qualitative Research

Types of Qualitative Research Methods

1. One-on-one interviews

2. Focus groups

3. Ethnographic research

4. Case study research

5. Record keeping

6. Qualitative observation

Qualitative Research Real World Examples

1. Online grocery shop with a predominantly male audience

2. Software company launching a new product

3. Alan Pushkin’s “God’s Choice: The Total World of a Fundamentalist Christian School”

4. Understanding buyers’ trends

5. Determining products/services missing from the market

Real-time Customer Lifetime Value (CLV) Benchmark Report

Qualitative Research Approaches

How to Analyze Qualitative Data

1. Data Collection

2. Data Preparation

3. Familiarization

5. Thematic Development

6. Interpreting the Data

7. Validation

8. Reporting

Limitations of qualitative research

1. It’s a time-consuming process