how to describe a qualitative research design

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.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

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.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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

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

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What Is a Research Design? | Definition, Types & Guide

Introduction

Parts of a research design, types of research methodology in qualitative research, narrative research designs, phenomenological research designs, grounded theory research designs.

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

A research design in qualitative research is a critical framework that guides the methodological approach to studying complex social phenomena. Qualitative research designs determine how data is collected, analyzed, and interpreted, ensuring that the research captures participants' nuanced and subjective perspectives. Research designs also recognize ethical considerations and involve informed consent, ensuring confidentiality, and handling sensitive topics with the utmost respect and care. These considerations are crucial in qualitative research and other contexts where participants may share personal or sensitive information. A research design should convey coherence as it is essential for producing high-quality qualitative research, often following a recursive and evolving process.

Theoretical concepts and research question

The first step in creating a research design is identifying the main theoretical concepts. To identify these concepts, a researcher should ask which theoretical keywords are implicit in the investigation. The next step is to develop a research question using these theoretical concepts. This can be done by identifying the relationship of interest among the concepts that catch the focus of the investigation. The question should address aspects of the topic that need more knowledge, shed light on new information, and specify which aspects should be prioritized before others. This step is essential in identifying which participants to include or which data collection methods to use. Research questions also put into practice the conceptual framework and make the initial theoretical concepts more explicit. Once the research question has been established, the main objectives of the research can be specified. For example, these objectives may involve identifying shared experiences around a phenomenon or evaluating perceptions of a new treatment.

Methodology

After identifying the theoretical concepts, research question, and objectives, the next step is to determine the methodology that will be implemented. This is the lifeline of a research design and should be coherent with the objectives and questions of the study. The methodology will determine how data is collected, analyzed, and presented. Popular qualitative research methodologies include case studies, ethnography , grounded theory , phenomenology, and narrative research . Each methodology is tailored to specific research questions and facilitates the collection of rich, detailed data. For example, a narrative approach may focus on only one individual and their story, while phenomenology seeks to understand participants' lived common experiences. Qualitative research designs differ significantly from quantitative research, which often involves experimental research, correlational designs, or variance analysis to test hypotheses about relationships between two variables, a dependent variable and an independent variable while controlling for confounding variables.

Literature review

After the methodology is identified, conducting a thorough literature review is integral to the research design. This review identifies gaps in knowledge, positioning the new study within the larger academic dialogue and underlining its contribution and relevance. Meta-analysis, a form of secondary research, can be particularly useful in synthesizing findings from multiple studies to provide a clear picture of the research landscape.

Data collection

The sampling method in qualitative research is designed to delve deeply into specific phenomena rather than to generalize findings across a broader population. The data collection methods—whether interviews, focus groups, observations, or document analysis—should align with the chosen methodology, ethical considerations, and other factors such as sample size. In some cases, repeated measures may be collected to observe changes over time.

Data analysis

Analysis in qualitative research typically involves methods such as coding and thematic analysis to distill patterns from the collected data. This process delineates how the research results will be systematically derived from the data. It is recommended that the researcher ensures that the final interpretations are coherent with the observations and analyses, making clear connections between the data and the conclusions drawn. Reporting should be narrative-rich, offering a comprehensive view of the context and findings.

Overall, a coherent qualitative research design that incorporates these elements facilitates a study that not only adds theoretical and practical value to the field but also adheres to high quality. This methodological thoroughness is essential for achieving significant, insightful findings. Examples of well-executed research designs can be valuable references for other researchers conducting qualitative or quantitative investigations. An effective research design is critical for producing robust and impactful research outcomes.

Each qualitative research design is unique, diverse, and meticulously tailored to answer specific research questions, meet distinct objectives, and explore the unique nature of the phenomenon under investigation. The methodology is the wider framework that a research design follows. Each methodology in a research design consists of methods, tools, or techniques that compile data and analyze it following a specific approach.

The methods enable researchers to collect data effectively across individuals, different groups, or observations, ensuring they are aligned with the research design. The following list includes the most commonly used methodologies employed in qualitative research designs, highlighting how they serve different purposes and utilize distinct methods to gather and analyze data.

The narrative approach in research focuses on the collection and detailed examination of life stories, personal experiences, or narratives to gain insights into individuals' lives as told from their perspectives. It involves constructing a cohesive story out of the diverse experiences shared by participants, often using chronological accounts. It seeks to understand human experience and social phenomena through the form and content of the stories. These can include spontaneous narrations such as memoirs or diaries from participants or diaries solicited by the researcher. Narration helps construct the identity of an individual or a group and can rationalize, persuade, argue, entertain, confront, or make sense of an event or tragedy. To conduct a narrative investigation, it is recommended that researchers follow these steps:

Identify if the research question fits the narrative approach. Its methods are best employed when a researcher wants to learn about the lifestyle and life experience of a single participant or a small number of individuals.

Select the best-suited participants for the research design and spend time compiling their stories using different methods such as observations, diaries, interviewing their family members, or compiling related secondary sources.

Compile the information related to the stories. Narrative researchers collect data based on participants' stories concerning their personal experiences, for example about their workplace or homes, their racial or ethnic culture, and the historical context in which the stories occur.

Analyze the participant stories and "restore" them within a coherent framework. This involves collecting the stories, analyzing them based on key elements such as time, place, plot, and scene, and then rewriting them in a chronological sequence (Ollerenshaw & Creswell, 2000). The framework may also include elements such as a predicament, conflict, or struggle; a protagonist; and a sequence with implicit causality, where the predicament is somehow resolved (Carter, 1993).

Collaborate with participants by actively involving them in the research. Both the researcher and the participant negotiate the meaning of their stories, adding a credibility check to the analysis (Creswell & Miller, 2000).

A narrative investigation includes collecting a large amount of data from the participants and the researcher needs to understand the context of the individual's life. A keen eye is needed to collect particular stories that capture the individual experiences. Active collaboration with the participant is necessary, and researchers need to discuss and reflect on their own beliefs and backgrounds. Multiple questions could arise in the collection, analysis, and storytelling of individual stories that need to be addressed, such as: Whose story is it? Who can tell it? Who can change it? Which version is compelling? What happens when narratives compete? In a community, what do the stories do among them? (Pinnegar & Daynes, 2006).

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A research design based on phenomenology aims to understand the essence of the lived experiences of a group of people regarding a particular concept or phenomenon. Researchers gather deep insights from individuals who have experienced the phenomenon, striving to describe "what" they experienced and "how" they experienced it. This approach to a research design typically involves detailed interviews and aims to reach a deep existential understanding. The purpose is to reduce individual experiences to a description of the universal essence or understanding the phenomenon's nature (van Manen, 1990). In phenomenology, the following steps are usually followed:

Identify a phenomenon of interest . For example, the phenomenon might be anger, professionalism in the workplace, or what it means to be a fighter.

Recognize and specify the philosophical assumptions of phenomenology , for example, one could reflect on the nature of objective reality and individual experiences.

Collect data from individuals who have experienced the phenomenon . This typically involves conducting in-depth interviews, including multiple sessions with each participant. Additionally, other forms of data may be collected using several methods, such as observations, diaries, art, poetry, music, recorded conversations, written responses, or other secondary sources.

Ask participants two general questions that encompass the phenomenon and how the participant experienced it (Moustakas, 1994). For example, what have you experienced in this phenomenon? And what contexts or situations have typically influenced your experiences within the phenomenon? Other open-ended questions may also be asked, but these two questions particularly focus on collecting research data that will lead to a textural description and a structural description of the experiences, and ultimately provide an understanding of the common experiences of the participants.

Review data from the questions posed to participants . It is recommended that researchers review the answers and highlight "significant statements," phrases, or quotes that explain how participants experienced the phenomenon. The researcher can then develop meaningful clusters from these significant statements into patterns or key elements shared across participants.

Write a textual description of what the participants experienced based on the answers and themes of the two main questions. The answers are also used to write about the characteristics and describe the context that influenced the way the participants experienced the phenomenon, called imaginative variation or structural description. Researchers should also write about their own experiences and context or situations that influenced them.

Write a composite description from the structural and textural description that presents the "essence" of the phenomenon, called the essential and invariant structure.

A phenomenological approach to a research design includes the strict and careful selection of participants in the study where bracketing personal experiences can be difficult to implement. The researcher decides how and in which way their knowledge will be introduced. It also involves some understanding and identification of the broader philosophical assumptions.

Grounded theory is used in a research design when the goal is to inductively develop a theory "grounded" in data that has been systematically gathered and analyzed. Starting from the data collection, researchers identify characteristics, patterns, themes, and relationships, gradually forming a theoretical framework that explains relevant processes, actions, or interactions grounded in the observed reality. A grounded theory study goes beyond descriptions and its objective is to generate a theory, an abstract analytical scheme of a process. Developing a theory doesn't come "out of nothing" but it is constructed and based on clear data collection. We suggest the following steps to follow a grounded theory approach in a research design:

Determine if grounded theory is the best for your research problem . Grounded theory is a good design when a theory is not already available to explain a process.

Develop questions that aim to understand how individuals experienced or enacted the process (e.g., What was the process? How did it unfold?). Data collection and analysis occur in tandem, so that researchers can ask more detailed questions that shape further analysis, such as: What was the focal point of the process (central phenomenon)? What influenced or caused this phenomenon to occur (causal conditions)? What strategies were employed during the process? What effect did it have (consequences)?

Gather relevant data about the topic in question . Data gathering involves questions that are usually asked in interviews, although other forms of data can also be collected, such as observations, documents, and audio-visual materials from different groups.

Carry out the analysis in stages . Grounded theory analysis begins with open coding, where the researcher forms codes that inductively emerge from the data (rather than preconceived categories). Researchers can thus identify specific properties and dimensions relevant to their research question.

Assemble the data in new ways and proceed to axial coding . Axial coding involves using a coding paradigm or logic diagram, such as a visual model, to systematically analyze the data. Begin by identifying a central phenomenon, which is the main category or focus of the research problem. Next, explore the causal conditions, which are the categories of factors that influence the phenomenon. Specify the strategies, which are the actions or interactions associated with the phenomenon. Then, identify the context and intervening conditions—both narrow and broad factors that affect the strategies. Finally, delineate the consequences, which are the outcomes or results of employing the strategies.

Use selective coding to construct a "storyline" that links the categories together. Alternatively, the researcher may formulate propositions or theory-driven questions that specify predicted relationships among these categories.

Develop and visually present a matrix that clarifies the social, historical, and economic conditions influencing the central phenomenon. This optional step encourages viewing the model from the narrowest to the broadest perspective.

Write a substantive-level theory that is closely related to a specific problem or population. This step is optional but provides a focused theoretical framework that can later be tested with quantitative data to explore its generalizability to a broader sample.

Allow theory to emerge through the memo-writing process, where ideas about the theory evolve continuously throughout the stages of open, axial, and selective coding.

The researcher should initially set aside any preconceived theoretical ideas to allow for the emergence of analytical and substantive theories. This is a systematic research approach, particularly when following the methodological steps outlined by Strauss and Corbin (1990). For those seeking more flexibility in their research process, the approach suggested by Charmaz (2006) might be preferable.

One of the challenges when using this method in a research design is determining when categories are sufficiently saturated and when the theory is detailed enough. To achieve saturation, discriminant sampling may be employed, where additional information is gathered from individuals similar to those initially interviewed to verify the applicability of the theory to these new participants. Ultimately, its goal is to develop a theory that comprehensively describes the central phenomenon, causal conditions, strategies, context, and consequences.

Ethnographic research design

An ethnographic approach in research design involves the extended observation and data collection of a group or community. The researcher immerses themselves in the setting, often living within the community for long periods. During this time, they collect data by observing and recording behaviours, conversations, and rituals to understand the group's social dynamics and cultural norms. We suggest following these steps for ethnographic methods in a research design:

Assess whether ethnography is the best approach for the research design and questions. It's suitable if the goal is to describe how a cultural group functions and to delve into their beliefs, language, behaviours, and issues like power, resistance, and domination, particularly if there is limited literature due to the group’s marginal status or unfamiliarity to mainstream society.

Identify and select a cultural group for your research design. Choose one that has a long history together, forming distinct languages, behaviours, and attitudes. This group often might be marginalized within society.

Choose cultural themes or issues to examine within the group. Analyze interactions in everyday settings to identify pervasive patterns such as life cycles, events, and overarching cultural themes. Culture is inferred from the group members' words, actions, and the tension between their actual and expected behaviours, as well as the artifacts they use.

Conduct fieldwork to gather detailed information about the group’s living and working environments. Visit the site, respect the daily lives of the members, and collect a diverse range of materials, considering ethical aspects such as respect and reciprocity.

Compile and analyze cultural data to develop a set of descriptive and thematic insights. Begin with a detailed description of the group based on observations of specific events or activities over time. Then, conduct a thematic analysis to identify patterns or themes that illustrate how the group functions and lives. The final output should be a comprehensive cultural portrait that integrates both the participants (emic) and the researcher’s (etic) perspectives, potentially advocating for the group’s needs or suggesting societal changes to better accommodate them.

Researchers engaging in ethnography need a solid understanding of cultural anthropology and the dynamics of sociocultural systems, which are commonly explored in ethnographic research. The data collection phase is notably extensive, requiring prolonged periods in the field. Ethnographers often employ a literary, quasi-narrative style in their narratives, which can pose challenges for those accustomed to more conventional social science writing methods.

Another potential issue is the risk of researchers "going native," where they become overly assimilated into the community under study, potentially jeopardizing the objectivity and completion of their research. It's crucial for researchers to be aware of their impact on the communities and environments they are studying.

The case study approach in a research design focuses on a detailed examination of a single case or a small number of cases. Cases can be individuals, groups, organizations, or events. Case studies are particularly useful for research designs that aim to understand complex issues in real-life contexts. The aim is to provide a thorough description and contextual analysis of the cases under investigation. We suggest following these steps in a case study design:

Assess if a case study approach suits your research questions . This approach works well when you have distinct cases with defined boundaries and aim to deeply understand these cases or compare multiple cases.

Choose your case or cases. These could involve individuals, groups, programs, events, or activities. Decide whether an individual or collective, multi-site or single-site case study is most appropriate, focusing on specific cases or themes (Stake, 1995; Yin, 2003).

Gather data extensively from diverse sources . Collect information through archival records, interviews, direct and participant observations, and physical artifacts (Yin, 2003).

Analyze the data holistically or in focused segments . Provide a comprehensive overview of the entire case or concentrate on specific aspects. Start with a detailed description including the history of the case and its chronological events then narrow down to key themes. The aim is to delve into the case's complexity rather than generalize findings.

Interpret and report the significance of the case in the final phase . Explain what insights were gained, whether about the subject of the case in an instrumental study or an unusual situation in an intrinsic study (Lincoln & Guba, 1985).

The investigator must carefully select the case or cases to study, recognizing that multiple potential cases could illustrate a chosen topic or issue. This selection process involves deciding whether to focus on a single case for deeper analysis or multiple cases, which may provide broader insights but less depth per case. Each choice requires a well-justified rationale for the selected cases. Researchers face the challenge of defining the boundaries of a case, such as its temporal scope and the events and processes involved. This decision in a research design is crucial as it affects the depth and value of the information presented in the study, and therefore should be planned to ensure a comprehensive portrayal of the case.

Qualitative and quantitative research designs are distinct in their approach to data collection and data analysis. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research prioritizes understanding the depth and richness of human experiences, behaviours, and interactions.

Qualitative methods in a research design have to have internal coherence, meaning that all elements of the research project—research question, data collection, data analysis, findings, and theory—are well-aligned and consistent with each other. This coherence in the research study is especially crucial in inductive qualitative research, where the research process often follows a recursive and evolving path. Ensuring that each component of the research design fits seamlessly with the others enhances the clarity and impact of the study, making the research findings more robust and compelling. Whether it is a descriptive research design, explanatory research design, diagnostic research design, or correlational research design coherence is an important element in both qualitative and quantitative research.

Finally, a good research design ensures that the research is conducted ethically and considers the well-being and rights of participants when managing collected data. The research design guides researchers in providing a clear rationale for their methodologies, which is crucial for justifying the research objectives to the scientific community. A thorough research design also contributes to the body of knowledge, enabling researchers to build upon past research studies and explore new dimensions within their fields. At the core of the design, there is a clear articulation of the research objectives. These objectives should be aligned with the underlying concepts being investigated, offering a concise method to answer the research questions and guiding the direction of the study with proper qualitative methods.

Carter, K. (1993). The place of a story in the study of teaching and teacher education. Educational Researcher, 22(1), 5-12, 18.

Charmaz, K. (2006). Constructing grounded theory. London: Sage.

Creswell, J. W., & Miller, D. L. (2000). Determining validity in qualitative inquiry. Theory Into Practice, 39(3), 124-130.

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

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Ollerenshaw, J. A., & Creswell, J. W. (2000, April). Data analysis in narrative research: A comparison of two “restoring” approaches. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. Ontario, Canada: University of Western Ontario.

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage

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Qualitative Research Design: Start

Qualitative Research Design

What is Qualitative research design?

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much . It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Research Paradigms 

• Positivist versus Post-Positivist
• Social Constructivist (this paradigm/ideology mostly birth qualitative studies)

Events Relating to the Qualitative Research and Community Engagement Workshops @ CMU Libraries

CMU Libraries is committed to helping members of our community become data experts. To that end, CMU is offering public facing workshops that discuss Qualitative Research, Coding, and Community Engagement best practices.

The following workshops are a part of a broader series on using data. Please follow the links to register for the events. 

Qualitative Coding

Using Community Data to improve Outcome (Grant Writing)

Survey Design  

Upcoming Event: March 21st, 2024 (12:00pm -1:00 pm)

Community Engagement and Collaboration Event 

Join us for an event to improve, build on and expand the connections between Carnegie Mellon University resources and the Pittsburgh community. CMU resources such as the Libraries and Sustainability Initiative can be leveraged by users not affiliated with the university, but barriers can prevent them from fully engaging.

The conversation features representatives from CMU departments and local organizations about the community engagement efforts currently underway at CMU and opportunities to improve upon them. Speakers will highlight current and ongoing projects and share resources to support future collaboration.

Event Moderators:

Taiwo Lasisi, CLIR Postdoctoral Fellow in Community Data Literacy,  Carnegie Mellon University Libraries

Emma Slayton, Data Curation, Visualization, & GIS Specialist,  Carnegie Mellon University Libraries

Nicky Agate , Associate Dean for Academic Engagement, Carnegie Mellon University Libraries

Chelsea Cohen , The University’s Executive fellow for community engagement, Carnegie Mellon University

Sarah Ceurvorst , Academic Pathways Manager, Program Director, LEAP (Leadership, Excellence, Access, Persistence) Carnegie Mellon University

Julia Poeppibg , Associate Director of Partnership Development, Information Systems, Carnegie Mellon University 

Scott Wolovich , Director of New Sun Rising, Pittsburgh 

Additional workshops and events will be forthcoming. Watch this space for updates. 

Workshop Organizer

Qualitative Research Methods

What are Qualitative Research methods?

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

What constitutes a good research question? Does the question drive research design choices?

According to Doody and Bailey (2014);

 We can only develop a good research question by consulting relevant literature, colleagues, and supervisors experienced in the area of research. (inductive interactions).

Helps to have a directed research aim and objective.

Researchers should not be “ research trendy” and have enough evidence. This is why research objectives are important. It helps to take time, and resources into consideration.

Research questions can be developed from theoretical knowledge, previous research or experience, or a practical need at work (Parahoo 2014). They have numerous roles, such as identifying the importance of the research and providing clarity of purpose for the research, in terms of what the research intends to achieve in the end.

Qualitative Research Questions

What constitutes a good Qualitative research question?

A good qualitative question answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. Qualitative research gathers participants' experiences, perceptions and behavior.

Examples of good Qualitative Research Questions:

What are people's thoughts on the new library? 

How does it feel to be a first-generation student attending college?

Difference example (between Qualitative and Quantitative research questions):

How many college students signed up for the new semester? (Quan) 

How do college students feel about the new semester? What are their experiences so far? (Qual)

• Qualitative Research Design Workshop Powerpoint

Foley G, Timonen V. Using Grounded Theory Method to Capture and Analyze Health Care Experiences. Health Serv Res. 2015 Aug;50(4):1195-210. [ PMC free article: PMC4545354 ] [ PubMed: 25523315 ]

Devers KJ. How will we know "good" qualitative research when we see it? Beginning the dialogue in health services research. Health Serv Res. 1999 Dec;34(5 Pt 2):1153-88. [ PMC free article: PMC1089058 ] [ PubMed: 10591278 ]

Huston P, Rowan M. Qualitative studies. Their role in medical research. Can Fam Physician. 1998 Nov;44:2453-8. [ PMC free article: PMC2277956 ] [ PubMed: 9839063 ]

Corner EJ, Murray EJ, Brett SJ. Qualitative, grounded theory exploration of patients' experience of early mobilisation, rehabilitation and recovery after critical illness. BMJ Open. 2019 Feb 24;9(2):e026348. [ PMC free article: PMC6443050 ] [ PubMed: 30804034 ]

Moser A, Korstjens I. Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. Eur J Gen Pract. 2018 Dec;24(1):9-18. [ PMC free article: PMC5774281 ] [ PubMed: 29199486 ]

Houghton C, Murphy K, Meehan B, Thomas J, Brooker D, Casey D. From screening to synthesis: using nvivo to enhance transparency in qualitative evidence synthesis. J Clin Nurs. 2017 Mar;26(5-6):873-881. [ PubMed: 27324875 ]

Soratto J, Pires DEP, Friese S. Thematic content analysis using ATLAS.ti software: Potentialities for researchs in health. Rev Bras Enferm. 2020;73(3):e20190250. [ PubMed: 32321144 ]

Zamawe FC. The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections. Malawi Med J. 2015 Mar;27(1):13-5. [ PMC free article: PMC4478399 ] [ PubMed: 26137192 ]

Korstjens I, Moser A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur J Gen Pract. 2018 Dec;24(1):120-124. [ PMC free article: PMC8816392 ] [ PubMed: 29202616 ]

Saldaña, J. (2021). The coding manual for qualitative researchers. The coding manual for qualitative researchers, 1-440.

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245-51. [ PubMed: 24979285 ]

Palermo C, King O, Brock T, Brown T, Crampton P, Hall H, Macaulay J, Morphet J, Mundy M, Oliaro L, Paynter S, Williams B, Wright C, E Rees C. Setting priorities for health education research: A mixed methods study. Med Teach. 2019 Sep;41(9):1029-1038. [ PubMed: 31141390 ]

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Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

Events and Workshops

• Introduction to NVivo Have you just collected your data and wondered what to do next? Come join us for an introductory session on utilizing NVivo to support your analytical process. This session will only cover features of the software and how to import your records. Please feel free to attend any of the following sessions below: April 25th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125 May 9th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125
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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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• Qualitative Research Design

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This review provides an overview of qualitative methods and designs using examples of research. Note that qualitative researchers frequently employ  several methods in a single study.

Basic Qualitative Research Characteristics

• Design is generally based on a social constructivism perspective.
• Research problems become research questions based on prior research experience.
• Sample sizes can be as small as one.
• Data collection involves interview, observation, and/or archival (content) data.
• Interpretation is based on a combination of researcher perspective and data collected.
• Transcribing is the process of converting audio or video data to text for analysis.
• Coding is the process of reviewing notes and discovering common “themes.”
• Themes describe the patterns/phenomenon as results.

Overview of Methods

1. Interview (Individual, focus groups)

What is the difference between an interview and a survey? Primarily, open-ended questions differentiate the two. Qualitative researchers are concerned with making inference based on perspective, so it is extremely important to get as much data as possible for later analysis. Researchers spend a considerable amount of time designing interview questions. Interviews are designed to generate participant perspectives about ideas, opinions, and experiences.

2. Observation (Individual, group, location)

How is data derived from an observation? The researcher may use a variety of methods for observing, including taking general notes, using checklists, or time-and-motion logs. The considerable time it takes for even a short observation deters many researchers from using this method. Also, the researcher risks his or her interpretation when taking notes, which is accepted by qualitative researchers, but meets resistance from post-positivists . Observations are designed to generate data on activities and behaviors, and are generally more focused on setting than other methods.

3. Document Analysis (Content analysis of written data)

What types of documents do qualitative researchers analyze? Virtually anything that supports the question asked. Print media has long been a staple data source for qualitative researchers, but electronic media (email, blogs, user Web pages, and even social network profiles) have extended the data qualitative researchers can collect and analyze. The greatest challenge offered by document analysis can be sifting through all of the data to make general observations.

A Few Qualitative Research Designs

1. Biographical Study

A biographical study is often the first design type that comes to mind for most people. For example, consider O’Brien’s John F. Kennedy: A Biography . The author takes a collection of archival documents (interviews, speeches, and other writings) and various media (pictures, audio, and video footage) to present a comprehensive story of JFK. In the general sense, a biographical study is considered an exhaustive account of a life experience; however, just as some studies are limited to single aspects of a phenomenon, the focus of a biographical study can be much narrower. The film Madame Curie is an example. Crawford studies the film from a biographical perspective to present the reader with an examination of how all aspects of a film (director’s perspective, actors, camera angles, historical setting) work to present a biography. Read the introduction and scan the text to get a feel for this perspective.

2. Phenomenology

Your first step should be to take this word apart – phenomenon refers to an occurrence or experience, logical refers to a path toward understanding. So, we have a occurrence and a path (let’s go with an individual’s experience), which leads to a way of looking at the phenomenon from an individual’s point of view. The reactions, perceptions, and feelings of an individual (or group of individuals) as she/he experienced an event are principally important to the phenomenologist looking to understand an event beyond purely quantitative details. Gaston-Gayles, et al.’s (2005) look at how the civil rights era changed the role of college administrators is a good example. The authors interview men and women who were administrators during that time to identify how the profession changed as a result.

3. Grounded Theory

In a grounded theory study, interpretations are continually derived from raw data. A keyword to remember is emergent . The story emerges from the data. Often, researchers will begin with a broad topic, then use qualitative methods to gather information that defines (or further refines) a research question. For example, a teacher might want to know what effects the implementation of a dress code might have on discipline. Instead of formulating specific questions, a grounded theorist would begin by interviewing students, parents, and/or teachers, and perhaps asking students to write an essay about their thoughts on a dress code. The researcher would then follow the process of developing themes from reading the text by coding specific examples (using a highlighter, maybe) of where respondents mentioned common things. Resistance might be a common pattern emerging from the text, which may then become a topic for further analysis.

A grounded theory study is dynamic, in that it can be continually revised throughout nearly all phases of the study. You can imagine that this would frustrate a quantitative researcher. However, remember that perspective is centrally important to the qualitative researcher. While the end result of a grounded theory study is to generate some broad themes, the researcher is not making an attempt to generalize the study in the same, objective way characteristic of quantitative research. Here is a link to a grounded theory article on student leadership .

4. Ethnography

Those with sociology or anthropology backgrounds will be most familiar with this design. Ethnography focuses on meaning, largely through direct field observation. Researchers generally (though not always) become part of a culture that they wish to study, then present a picture of that culture through the “eyes” of its members. One of the most famous ethnographers is Jane Goodall, who studied chimpanzees by living among them in their native East African habitat.

5. Case Study

A case study is an in-depth analysis of people, events, and relationships, bounded by some unifying factor. An example is principal leadership in middle schools. Important aspects include not only the principal’s behaviors and views on leadership, but also the perceptions of those who interact with her/him, the context of the school, outside constituents, comparison to other principals, and other quantitative “variables.” Often, you may see a case study labeled “ethnographic case study” which generally refers to a more comprehensive study focused on a person or group of people, as the above example.

Case studies do not have to be people-focused, however, as a case study to look at a program might be conducted to see how it accomplishes its intended outcomes. For example, the Department of Education might conduct a case study on a curricular implementation in a school district – examining how new curriculum moves from development to implementation to outcomes at each level of interaction (developer, school leadership, teacher, student).

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Key Concepts in Qualitative Research Design

• First Online: 14 November 2019

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This chapter provides an outline of key concepts in qualitative research design for healthcare simulation. It explores three landmarks that provide orientation to researchers: (1) a defined purpose for the research; (2) an articulation of the researcher’s worldview; and (3) an overarching approach to research design. In practical terms, these translate to: writing the qualitative research question; articulating the relationship between the researcher and the research (reflexivity); and selecting an appropriate methodology. Three methodologies – grounded theory, phenomenology and qualitative description – are outlined and contrasted with a particular emphasis on different analysis traditions. The de facto use of methods as methodology is briefly explored and the importance of coherence across the three landmarks (as opposed to simple adherence to a particular tradition) will be emphasized. Finally, research credibility is introduced as a holistic, dynamic and tacit concept that is highly dependent on researchers and research context.

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Bearman, M. (2019). Key Concepts in Qualitative Research Design. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_10

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

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Chapter 5: Qualitative descriptive research

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Identify the key terms and concepts used in qualitative descriptive research.
• Discuss the advantages and disadvantages of qualitative descriptive research.

What is a qualitative descriptive study?

The key concept of the qualitative descriptive study is description.

Qualitative descriptive studies (also known as ‘exploratory studies’ and ‘qualitative description approaches’) are relatively new in the qualitative research landscape. They emerged predominantly in the field of nursing and midwifery over the past two decades. 1 The design of qualitative descriptive studies evolved as a means to define aspects of qualitative research that did not resemble qualitative research designs to date, despite including elements of those other study designs. 2

Qualitative descriptive studies  describe  phenomena rather than explain them. Phenomenological studies, ethnographic studies and those using grounded theory seek to explain a phenomenon. Qualitative descriptive studies aim to provide a comprehensive summary of events. The approach to this study design is journalistic, with the aim being to answer the questions who, what, where and how. 3

A qualitative descriptive study is an important and appropriate design for research questions that are focused on gaining insights about a poorly understood research area, rather than on a specific phenomenon. Since qualitative descriptive study design seeks to describe rather than explain, explanatory frameworks and theories are not required to explain or ‘ground’ a study and its results. 4 The researcher may decide that a framework or theory adds value to their interpretations, and in that case, it is perfectly acceptable to use them. However, the hallmark of genuine curiosity (naturalistic enquiry) is that the researcher does not know in advance what they will be observing or describing. 4 Because a phenomenon is being described, the qualitative descriptive analysis is more categorical and less conceptual than other methods. Qualitative content analysis is usually the main approach to data analysis in qualitative descriptive studies. 4 This has led to criticism of descriptive research being less sophisticated because less interpretation is required than with other qualitative study designs in which interpretation and explanation are key characteristics (e.g. phenomenology, grounded theory, case studies).

Diverse approaches to data collection can be utilised in qualitative description studies. However, most qualitative descriptive studies use semi-structured interviews (see Chapter 13) because they provide a reliable way to collect data. 3 The technique applied to data analysis is generally categorical and less conceptual when compared to other qualitative research designs (see Section 4). 2,3 Hence, this study design is well suited to research by practitioners, student researchers and policymakers. Its straightforward approach enables these studies to be conducted in shorter timeframes than other study designs. 3 Descriptive studies are common as the qualitative component in mixed-methods research ( see Chapter 11 ) and evaluations ( see Chapter 12 ), 1 because qualitative descriptive studies can provide information to help develop and refine questionnaires or interventions.

For example, in our research to develop a patient-reported outcome measure for people who had undergone a percutaneous coronary intervention (PCI), which is a common cardiac procedure to treat heart disease, we started by conducting a qualitative descriptive study. 5 This project was a large, mixed-methods study funded by a private health insurer. The entire research process needed to be straightforward and achievable within a year, as we had engaged an undergraduate student to undertake the research tasks. The aim of the qualitative component of the mixed-methods study was to identify and explore patients’ perceptions following PCI. We used inductive approaches to collect and analyse the data. The study was guided by the following domains for the development of patient-reported outcomes, according to US Food and Drug Administration (FDA) guidelines, which included:

• Feeling: How the patient feels physically and psychologically after medical intervention
• Function: The patient’s mobility and ability to maintain their regular routine
• Evaluation: The patient’s overall perception of the success or failure of their procedure and their perception of what contributed to it. 5(p458)

We conducted focus groups and interviews, and asked participants three questions related to the FDA outcome domains:

• From your perspective, what would be considered a successful outcome of the procedure?

Probing questions: Did the procedure meet your expectations? How do you define whether the procedure was successful?

• How did you feel after the procedure?

Probing question: How did you feel one week after and how does that compare with how you feel now?

• After your procedure, tell me about your ability to do your daily activities?

Prompt for activities including gardening, housework, personal care, work-related and family-related tasks.

Probing questions: Did you attend cardiac rehabilitation? Can you tell us about your experience of cardiac rehabilitation? What impact has medication had on your recovery?

• What, if any, lifestyle changes have you made since your procedure? 5(p459)

Data collection was conducted with 32 participants. The themes were mapped to the FDA patient-reported outcome domains, with the results confirming previous research and also highlighting new areas for exploration in the development of a new patient-reported outcome measure. For example, participants reported a lack of confidence following PCI and the importance of patient and doctor communication. Women, in particular, reported that they wanted doctors to recognise how their experiences of cardiac symptoms were different to those of men.

The study described phenomena and resulted in the development of a patient-reported outcome measure that was tested and refined using a discrete-choice experiment survey, 6 a pilot of the measure in the Victorian Cardiac Outcomes Registry and a Rasch analysis to validate the measurement’s properties. 7

Advantages and disadvantages of qualitative descriptive studies

A qualitative descriptive study is an effective design for research by practitioners, policymakers and students, due to their relatively short timeframes and low costs. The researchers can remain close to the data and the events described, and this can enable the process of analysis to be relatively simple. Qualitative descriptive studies are also useful in mixed-methods research studies. Some of the advantages of qualitative descriptive studies have led to criticism of the design approach, due to a lack of engagement with theory and the lack of interpretation and explanation of the data. 2

Table 5.1. Examples of qualitative descriptive studies

Qualitative descriptive studies are gaining popularity in health and social care due to their utility, from a resource and time perspective, for research by practitioners, policymakers and researchers. Descriptive studies can be conducted as stand-alone studies or as part of larger, mixed-methods studies.

• Bradshaw C, Atkinson S, Doody O. Employing a qualitative description approach in health care research. Glob Qual Nurs Res. 2017;4. doi:10.1177/2333393617742282
• Lambert VA, Lambert CE. Qualitative descriptive research: an acceptable design. Pac Rim Int J Nurs Res Thail. 2012;16(4):255-256. Accessed June 6, 2023. https://he02.tci-thaijo.org/index.php/PRIJNR/article/download/5805/5064
• Doyle L et al. An overview of the qualitative descriptive design within nursing research. J Res Nurs. 2020;25(5):443-455. doi:10.1177/174498711988023
• Kim H, Sefcik JS, Bradway C. Characteristics of qualitative descriptive studies: a systematic review. Res Nurs Health. 2017;40(1):23-42. doi:10.1002/nur.21768
• Ayton DR et al. Exploring patient-reported outcomes following percutaneous coronary intervention: a qualitative study. Health Expect. 2018;21(2):457-465. doi:10.1111/hex.1263
• Barker AL et al. Symptoms and feelings valued by patients after a percutaneous coronary intervention: a discrete-choice experiment to inform development of a new patient-reported outcome. BMJ Open. 2018;8:e023141. doi:10.1136/bmjopen-2018-023141
• Soh SE et al. What matters most to patients following percutaneous coronary interventions? a new patient-reported outcome measure developed using Rasch analysis. PLoS One. 2019;14(9):e0222185. doi:10.1371/journal.pone.0222185
• Hiller RM et al. Coping and support-seeking in out-of-home care: a qualitative study of the views of young people in care in England. BMJ Open. 2021;11:e038461. doi:10.1136/bmjopen-2020-038461
• Backman C, Cho-Young D. Engaging patients and informal caregivers to improve safety and facilitate person- and family-centered care during transitions from hospital to home – a qualitative descriptive study. Patient Prefer Adherence. 2019;13:617-626. doi:10.2147/PPA.S201054

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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Home > HCAS > HCAS_PUBS > HCAS_JOURNALS > TQR Home > TQR > Vol. 29 > No. 6 (2024)

Humanizing the Research Process: Collaborative Reflections on Prioritizing Research Participants’ Agency, Trust, and Connection

Annie Pezalla , Macalester College Follow Alyssa Scott , Child Trends Follow Lex Nappa , Search Institute Follow Diane Hsieh , Search Institute Follow

All research is a social construction. In this paper, we work to illuminate those moments of co-constructed meaning by taking readers on a “behind the scenes” tour of a collaborative research project that explored educator relationships. We describe our priorities in and care for participant recruitment and scheduling, our post-hoc reflections on the differences in emotional tenor between interviews and focus groups, and our own roles and positionalities within the data collection and analysis process. Action-items are recommended for other group-based qualitative studies to humanize the process of research and prioritize moments of agency, trust, and connection among participants.

feminist methodologies, critical focus group research, interviewing, reconceptualizing collaboration, decolonizing the academy, pedagogy

Author Bio(s)

Dr. Anne E. Pezalla is a visiting assistant professor of psychology at Macalester College. Her research explores positive youth development, parenting dynamics and family relationships, and interpretive methodologies. Please direct correspondence to [email protected]

Alyssa Scott is a senior research analyst in the youth development program area at Child Trends. She is dedicated to community-based participatory research, youth participatory action research, racial equity in maternal and child health, justice-involved youth and young adults, and creating culturally grounded programs that meet the needs of local communities. Please direct correspondence to [email protected]

Lex Nappa’s areas of expertise include positive youth development and social emotional learning, with a focus on cultivating and improving relationships among educators, curriculum development and applying an equity lens to adapting evidence-based interventions. Please direct correspondence to [email protected]

Dr. Ta-yang “Diane” Hsieh's research focuses on motivational theories as well as applied research projects in educational settings. Diane is originally from Taiwan; her international upbringing contributes to her passion and commitment for doing research that truly celebrates diversity. Please direct correspondence to [email protected]

Acknowledgements

This study is supported by a Chan Zuckerberg Initiative Foundation grant (CZIF2021-005774; “Expanding the Vision for Developmental Relationships in Schools”) awarded to Search Institute.

Publication Date

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Pezalla, A., Scott, A., Nappa, L., & Hsieh, D. (2024). Humanizing the Research Process: Collaborative Reflections on Prioritizing Research Participants’ Agency, Trust, and Connection. The Qualitative Report , 29 (6), 1603-1620. https://doi.org/10.46743/2160-3715/2024.6631

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• Published: 07 August 2023

The rise of resilient healthcare research during COVID-19: scoping review of empirical research

• Louise A Ellis 1 ,
• Maree Saba 1 ,
• Janet C Long 1 ,
• Hilda Bø Lyng 2 ,
• Cecilie Haraldseid-Driftland 2 ,
• Kate Churruca 1 ,
• Siri Wiig 2 ,
• Elizabeth Austin 1 ,
• Robyn Clay-Williams 1 ,
• Ann Carrigan 1 &
• Jeffrey Braithwaite 1  

BMC Health Services Research volume  23 , Article number:  833 ( 2023 ) Cite this article

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The COVID-19 pandemic has presented many multi-faceted challenges to the maintenance of service quality and safety, highlighting the need for resilient and responsive healthcare systems more than ever before. This review examined empirical investigations of Resilient Health Care (RHC) in response to the COVID-19 pandemic with the aim to: identify key areas of research; synthesise findings on capacities that develop RHC across system levels (micro, meso, macro); and identify reported adverse consequences of the effort of maintaining system performance on system agents (healthcare workers, patients).

Three academic databases were searched (Medline, EMBASE, Scopus) from 1st January 2020 to 30th August 2022 using keywords pertaining to: systems resilience and related concepts; healthcare and healthcare settings; and COVID-19. Capacities that developed and enhanced systems resilience were synthesised using a hybrid inductive-deductive thematic analysis.

Fifty publications were included in this review. Consistent with previous research, studies from high-income countries and the use of qualitative methods within the context of hospitals, dominated the included studies. However, promising developments have been made, with an emergence of studies conducted at the macro-system level, including the development of quantitative tools and indicator-based modelling approaches, and the increased involvement of low- and middle-income countries in research (LMIC). Concordant with previous research, eight key resilience capacities were identified that can support, develop or enhance resilient performance, namely: structure, alignment, coordination, learning, involvement, risk awareness, leadership, and communication. The need for healthcare workers to constantly learn and make adaptations, however, had potentially adverse physical and emotional consequences for healthcare workers, in addition to adverse effects on routine patient care.

Conclusions

This review identified an upsurge in new empirical studies on health system resilience associated with COVID-19. The pandemic provided a unique opportunity to examine RHC in practice, and uncovered emerging new evidence on RHC theory and system factors that contribute to resilient performance at micro, meso and macro levels. These findings will enable leaders and other stakeholders to strengthen health system resilience when responding to future challenges and unexpected events.

Peer Review reports

Resilient Health Care (RHC) is defined as the ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions [ 1 ]. The COVID-19 pandemic presented challenges that healthcare systems must address to maintain service quality and safety, highlighting the need for resilient and responsive healthcare systems more than ever before [ 2 ]. Healthcare practitioners, managers, and policy makers had to suddenly, and dramatically, adapt in order to absorb the shock of the pandemic and coordinate the capacities needed to deal with its impact. Since the onset of the pandemic, ‘health systems resilience’ has emerged as a key concept in global public health with the World Health Organization (WHO) publishing several papers [ 3 , 4 , 5 , 6 , 7 ] on the importance of building and strengthening health emergency preparedness and responsiveness to future epidemics and shocks.

The application of resilience thinking to healthcare is however not new, with RHC being first proposed by Eric Hollnagel in 2011 [ 8 ] to describe the application of resilience engineering [ 9 ] and disaster resilience [ 10 , 11 ] to healthcare. RHC acknowledges the complex adaptive nature of healthcare, recognising the adaptive and transformative capabilities that enable healthcare systems to continue to perform their functions in the face of challenges [ 12 , 13 ]. Despite its conceptual appeal, there have been challenges in translating the principles of RHC into concrete improvements, with compelling examples remaining scarce [ 14 ].

The importance of RHC is reflected in the growing number of reviews on the topic [ 13 , 15 , 16 ]. Although these reviews identified that the RHC literature has been predominantly conceptual, rather than empirical [ 13 , 15 , 16 ], empirical applications of RHC have increased. A systematic review conducted prior to the pandemic identified 71 empirical studies on health system resilience from 2008 to 2019, with 62% of these published in the last two years of the review (i.e., from 2017 to 2019) [ 15 ]. However, much of this existing empirical literature has focused on clinical microsystems at the ‘sharp end’ and how frontline healthcare professionals within hospital settings collectively adapt, ‘work around’, or enable things to go well [ 2 , 13 ], with a lack of empirical studies particularly at the meso and macro-levels (i.e., government, national, international) [ 14 ]. Qualitative research methods have also predominated in the empirical studies [ 13 , 15 ], reflecting that priorities have been placed on gaining in-depth understanding of everyday clinical work at the micro-level.

Another noteworthy gap in the RHC literature is the limited discussions on how ‘individual agents’ (e.g., doctors, nurses) [ 17 ] within the health system may be personally affected by their efforts to maintain system resilience [ 18 ]. However, the time appears ripe for this issue to be explored in the context of RHC, particularly in light of the COVID-19 pandemic, which has caused major disruptions across all system levels and created a need for ongoing adaptation by healthcare workers, which many suggest has resulted in widespread mental health issues and burnout amongst these workers [ 19 , 20 ].

The present study

Interest in RHC has accelerated since the onset of the COVID-19 pandemic, as indicated by the sharp increase in the number of publications in ‘health systems resilience’ since 2020 (Fig.  1 ). With the growth in empirical contributions in this field, it is timely to examine the published empirical research to determine the status of the field and identify whether there is any further evidence on how to generate or strengthen resilient performance to manage future pandemics and emergencies. Understanding factors that develop or enhance RHC is critical to developing strategies and tools for strengthening their resilience [ 12 ]. For this review, we defined an empirical study as one that reports primary or secondary data gathered by means of a specific methodological approach [ 21 ]. The objective of this study was to conduct a scoping review of empirical investigations of RHC in response to the COVID-19 pandemic with four key aims:

Map out the empirical research within the resilient healthcare domain across all system levels (micro, meso, macro).

Identify the key areas of research, including study designs and research methods that have been employed.

Synthesise findings on factors (capacities, actions, or strategies) that developed or enhanced resilient performance.

Identify any reported findings on consequences of maintaining system performance on system agents (healthcare workers, patients).

Increased publications in PubMed using the search term “health systems resilience” in titles or abstracts

The review followed a pre-determined protocol, developed in accordance with the Preferred Reporting Items of Systematic Review and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) [ 22 , 23 ] (also see PRISMA-ScR in Supplementary File 1 ). A scoping review method was used; a method which is used to examine the extent, range and nature of work on this topic and to identify gaps and provide suggestions to improve future directions for RHC research [ 24 ]. Quality assessments were not undertaken, as the aim was to examine the full breadth of the empirical literature, consistent with general aims and methodology of scoping reviews [ 25 ].

Search strategy

Three academic databases (Medline, EMBASE, Scopus) were searched from 1st January 2020 to 30th August 2022. The search strategy consisted of terms pertaining to: systems resilience (e.g., resilient healthcare) and related concepts (e.g., Safety-II); healthcare (e.g., health care) and healthcare settings (e.g., primary care, hospital); and COVID-19. The search strategy was adapted for each database as necessary (see Supplementary File 2 for the complete search strategy, using Ovid MEDLINE as an example). The search strategy was developed in consultation with an academic research librarian and was reviewed by all authors prior to execution.

Inclusion and exclusion criteria

Articles were included if they were: (a) published between the onset of COVID-19 (from 1st January 2020) and 30 August 2022, (b) in the English language, (c) peer-reviewed publications, (d) had an explicit focus on healthcare or health systems resilience in the context of COVID-19, and (e) were empirical studies. Studies that only mentioned “resilience” briefly, were concerned with individual or psychological resilience (e.g., the psychological wellbeing of healthcare workers) rather than systems-resilience or were not conducted in the context of COVID-19 were excluded. Study protocols, review papers, journal commentaries, and editorials were also excluded, as were studies not in English.

Eligibility screening

Reference details (including abstracts) were downloaded into the reference management software Endnote X9 and then exported to Rayyan QCRI for title and abstract screening. Seven reviewers (LAE, MS, JCL, KC, EA, LT, DT) screened the title/abstracts to determine their inclusion against the criteria, with 5% of the retrieved publications being independently screened by the entire review team to ensure consistent inclusion. Any discrepancies among reviewers’ judgements were reviewed by two authors (LAE and MS) with JB available for consultation if and as needed.

Data extraction

Data from included studies meeting inclusion criteria were extracted into a custom workbook in Microsoft Excel. Full-text screening was conducted initially by two independent reviewers (LT, DT), with LAE and MS subsequently duplicating the full-text review process, with any discrepancies being discussed and resolved in consultation with JB. The extraction workbook included data items on: [ 1 ] publication details (paper title, year, output type); [ 2 ] study context (e.g., hospital, primary care); [ 3 ] system level (micro: healthcare practitioner; meso: management, organisation; and/or macro: government, national, international); [ 4 ] study design (quantitative, qualitative, mixed methods); [ 5 ] study data type (primary or secondary); [ 6 ] data collection method/s (quantitative, qualitative, mixed-methods); [ 7 ] conceptual framework, model, or theory used; [ 8 ] resilience measure or tool used; [ 9 ] factors (capacities, actions, or strategies) that developed and enhanced systems resilience; and [ 10 ] reported negative consequences of resilient performance on system agents (healthcare workers, patients).

Data synthesis and analysis

A data-based convergent synthesis was employed [ 26 ]; where quantitative data were transformed into categories or themes, and summarised through narrative techniques [ 27 ]. Country of the corresponding author was coded by income classification based on World Bank definitions of gross national income per capita. The three categories were low (< US$1085), middle (US$1086–13,205), and high income (> US$13,205) [ 28 ]. Data collection methods were categorised as qualitative, quantitative or mixed methods, with specific data collection methods (e.g., interviews, surveys) also extracted and examined.

The factors that supported, developed or enhanced systems resilience were initially identified through an inductive thematic approach [ 29 ] by two authors (LAE, MS). Themes and sub-themes were then discussed and agreed by the researchers using an iterative process. Upon further analysis and reflection of the themes, it was clear that a number of the themes aligned with the ‘capacities’ for resilience outlined by Lyng et al. [ 30 ]. Therefore, in the next phase, a deductive approach was taken where the themes and sub-themes were mapped to eight of the resilience ‘capacities’. Some minor amendments were made based upon differences in themes identified in the literature included in the present review compared with the capacities. Two of the ‘capacities’ outlined by Lyng et al. [ 30 ], namely ‘competence’ and ‘facilitators’, were not included owing to the lack of data mapping to these themes, as identified from the initial inductive analysis. Themes and subthemes were cross-referenced across all studies to ensure that the revised thematic map captured the meaning across all the included studies. The last phase involved defining the themes (see Table  1 for definitions as applied in this study). Consequences of maintaining resilient performance were similarly identified using an inductive thematic approach [ 29 ] by two authors (LAE, MS).

Overview of included studies

The initial search retrieved a total of 5844 publications. After removing duplicates, 4634 remained for title and/or abstract review. Following title and/or abstract screening, 4404 publications were discarded as they did not meet the inclusion criteria. Based on the full-text assessment, a further 184 publications did not meet the inclusion criteria, resulting in 50 publications included in this review (see Supplementary File 3 for included articles). Figure  2 demonstrates the inclusion and exclusion of papers at each stage of the screening process.

PRISMA flow diagram for study selection process

Summary characteristics of the included studies

A summary of the key characteristics of the included papers is provided in Table  2 . The 50 studies were spread widely, across 45 different journals, with Safety Science (n = 3, 6.0%) and the International Journal of Health Policy and Management (n = 3, 6.0%) being the most popular. The source location was also spread widely, across 25 different countries, with most corresponding authors from the United Kingdom (n = 8, 16.0%), followed by the United States (n = 6, 12.0%). Although most studies were restricted to high-income countries (n = 34, 68%), a notable number of corresponding authors were identified from low- and middle-income countries (LMIC) (n = 16, 32.0%), and with four (8%) of these being from Brazil.

Close to half (n = 20, 40%) of the studies were conducted in the context of hospitals, which generally involved hospital healthcare workers and/or hospital leaders as participants. Four studies (8%) [ 31 , 32 , 33 , 34 ] were specifically focused on supply chain issues related to medical supply availability in the context of system adaptability and resilience, and its impact on the healthcare system more broadly. Of the studies conducted in the context of community and specialised care (n = 15, 30%), a number were focused on the resilient performance of aged care services [ 35 , 36 , 37 ] or community mental health services [ 38 , 39 , 40 ]. Primary care was a setting in seven studies (14%), with a focus on the perspectives of primary care providers in relation to healthcare system resilience [ 38 , 41 , 42 , 43 , 44 , 45 , 46 ]. Over half of the studies were classified as being at the meso level (n = 29, 58%) of the healthcare system, with fewer studies being at the micro level (n = 17, 34%) or macro level (n = 18, 36%). Notably, eleven (61%) of these macro-level studies, incorporated data from multiple countries, such as a comparison study of health system resilience across six European countries, a comparison study of government actions and their relation to systems resilience between Canada and Australia, and an indicator-based analysis of risk and resilience that incorporated ‘big data’ from 11 countries.

Three-quarters of the studies were qualitative (n = 39, 78%), seven were mixed-methods (14%) and four were quantitative (8%). Although most studies utilised primary data alone (n = 39, 78%), seven studies relied on secondary datasets (14%), such as existing big data sources [ 47 ] and questionnaire data [ 48 , 49 ], and a smaller number used both primary and secondary datasets (n = 4, 8%).

Data collection methods and tools to assess RHC

Most of the studies collected data from direct sources (i.e., where participants directly express their experience of how work takes place in practice) [ 16 ], and included interviews (n = 32, 64%), surveys (n = 15, 30%) or focus groups (n = 3, 6%). A smaller number of studies included indirect sources, such as document analysis (n = 9, 18%), observations (n = 4, 8%), and/or simulation (n = 2, 4%). One-third of studies developed and/or used tools to study RHC (n = 17, 34%); of these, over half employed researcher-developed questionnaires to assess or understand resilient performance (n = 11, 65%), three adopted a ‘big data’ indicator-based approach to assess systems resilience for emergency preparedness, two studies drew on the more commonly regarded Functional Resonance Analysis Method (FRAM) [ 50 ], and one study used observation tools based on the “Mayo high performance team scale” [ 51 ] and the “Scrub Practitioners List of Intra-operative Non-Technical Skills (SPLINTS)” [ 52 ].

Over half the researcher-developed questionnaires (n = 7, 64%) were based on a conceptual framework, including Hollnagel’s [ 53 ] ‘four cornerstones of resilience’ [ 54 ], Anderson et al.’s [ 55 ] Integrated Resilience Attributes Framework [ 56 ], Bueno et al.’s [ 57 ] guidelines for coping with complexity [ 58 ], Macrae and Wiig’s [ 59 ] resilience framework [ 35 ], the WHO’s [ 60 ] fundamental ‘building blocks’ of health systems [ 61 , 62 ] and the WHO’s hospital readiness checklist [ 63 , 64 ]. Three additional survey studies lacking a conceptual framework collected predominantly open-ended questionnaire data on how everyday clinical work is being performed during the pandemic (i.e., work-as-done), via the perceptions and experiences of healthcare workers [ 32 , 43 ], using inductive content analysis, and to confirm or corroborate any emerging themes identified from interview data [ 65 ]. One final questionnaire tool was developed to assess hospital inventory management, including the impact of COVID-19 on the availability of supply and the processes established to enhance supply chain resilience [ 31 ].

Capacities that developed and enhanced resilient performance

Based on the analysis of the included studies, eight key factors or capacities were identified at different system levels to develop or enhance resilient performance, as outlined in the following section. In this section, the eight resilience capacities have been discussed sequentially from the capacity that occurred most prevalently within the included studies to the capacity that occurred least prevalently, namely: structure, alignment, coordination, learning, involvement, risk awareness, leadership, and communication. Figure  3 provides a visual summary of the eight factors and their sub-themes (also see Supplementary File 4 giving examples for each subtheme).

Resilience capacities and related sub-themes

Structure as a capacity for resilience was identified in more than four-fifths of included studies (n = 37, 74%) and referred to the structures that support work and practice within healthcare organisations. Across the included studies in this review, five sub-themes contributed to structural capacity, including: technology, physical equipment, workforce, governance systems and financial resources.

The most prevalent among the subthemes, technology (n = 27, 54%), concerned how software and hardware were utilised during the pandemic to support the continued delivery of regular healthcare services, as well as COVID-specific responses. Several studies highlighted a spike in the use of different technologies to enable the provision of patient care in different settings [ 41 , 44 , 66 , 67 ]. For example, Gifford et al. [ 66 ] reported the way in which wards and outpatient clinics rapidly converted to “digital” wards involving e-health, video and phone consultations. Alternatively, in one study from Canada [ 68 ], a lack of appropriate technology impeded resilient performance, with the rapid but “piecemeal” adoption of multiple virtual care technologies during COVID-19 resulting in systems that duplicated administrative work for healthcare professionals.

Access to physical equipment (n = 18, 36%), such as personal protective equipment (PPE), or flexible workspaces, was another prevalent subtheme across the studies. In many instances it was the lack of availability of this equipment, particularly during the early stages of the pandemic, that impeded the COVID response [ 36 , 46 , 69 ]. However, several studies reported the way in which organisations rapidly responded by adapting equipment levels, including how and where they sourced physical equipment, as well as their novel repurposing of in-house equipment [ 35 ] and wards to create additional capacity [ 66 ].

Workforce (n = 11, 22%) involved access to staff, workforce stability, and the designation of roles and responsibilities. Some of these studies highlighted challenges in recruitment, and how understaffing affected resilient performance [ 39 , 69 ], as there was both increased demand for healthcare and staff shortages due to workers contracting COVID-19. Organisational adaptations to promote resilience and address this issue included the reassignment of staff to other parts of the hospital [ 56 ] and expanding their reach in hiring new staff, which included the provision of financial incentives [ 39 ] and the re-employment of recently retired staff [ 66 ].

Governance systems and protocols (n = 19, 38%) involved the development of new policies, or modification of existing ones, to support the many changes in work practices during the pandemic. In some instances, these policies were devised at a macro-level [ 39 ], while in others they were more locally developed [ 70 ]. Along with this, financial resources (n = 5, 10%), involved funding changes wrought by the pandemic, including the allocation of funding to support COVID care delivery [ 71 ], as well as the financial implications of the pandemic in lost revenue due to a reduction in consultations, particularly identified for small healthcare providers [ 41 ].

Alignment as a capacity for resilient performance referred to the adaptation of practices in response to the ever-changing problems posed by the COVID-19 pandemic [ 30 ]. Identified in over half of the included studies (n = 30, 60%), the alignment capacity included three subthemes: role evolution; micro-level workarounds and trade-offs; and meso- to macro-level re-structuring, rescaling and compensation strategies.

Role evolution (n = 13, 26%) concerned how roles and responsibilities of healthcare workers and leaders changed or expanded in response to the ongoing challenges of the pandemic. Healthcare managers and leaders were asked to step into different functions; for example, in crisis management, communications and crisis responses [ 66 ]. Clinical staff also needed to expand their responsibilities, extend their working hours, and were redeployed to other wards to fulfill staff shortages and meet patient demands [ 66 ]. A smaller number of staff were redeployed to special COVID-19 teams, providing direct care to infected patients [ 56 , 66 , 72 ] and healthcare leaders worked from home [ 56 ], to limit further staff exposure to the virus. The change in workspace and role, as well as the pressing needs of COVID-19 infected patients, meant that staff had to be trained in new procedures and practices; for instance, redeployed physiotherapists into intensive care units and research staff into clinical roles [ 71 ]. Although redeployment sometimes caused stress and uncertainty, with the additional challenge of unfamiliar workspaces and colleagues, redeployment was also perceived as an opportunity for positive career development and empowerment [ 65 ].

The COVID-19 pandemic introduced a need for healthcare workers to improvise and develop solutions to unexpected and frequent problems, introducing workarounds and trade-offs (n = 19, 38%) at the micro-system level. Several studies highlighted how healthcare workers developed unique and creative workarounds at the front-line to help them cope with ongoing challenges [ 35 , 41 , 66 , 70 ]. For example, workarounds intended to ease the impact of the pandemic on patients and their families included: decorating PPE masks, using dance as a greeting instead of hugging, and providing outdoor concerts for patients [ 35 , 70 ]. Additionally, some studies described staff changes in prioritization, also known in the RHC literature as trade-offs, directing their capacity to where it was needed most. This meant that scheduled surgeries and regular care were scaled down to increase capacity such as in intensive care units (ICUs) and emergency departments [ 66 ]. The risk of infection also introduced trade-offs for community health workers, as home visits were no longer allowed; instead, community health workers began to take on administrative tasks at health clinics [ 43 ].

The COVID-19 pandemic also led to alignment strategies at the meso- and macro-levels, as COVID-19 provided exceptional demands for all parts of the health system. Re-organisation , rescaling and compensation (n = 19, 38%) strategies at the organizational level included arranging for COVID-19 treatment areas, wards, assessment clinics, COVID-19 teams, and new types of administration [ 71 ]. Furthermore, new emergency plans, policies, and safety standards, such as providing separate entrances and exits at nursing homes [ 35 ], were initiated to limit spread of the virus [ 69 ]. Unlike their traditional way of working, strategies for restructuring, rescaling, and compensation often had to be created “on the go” due to the unpredictability and unfamiliarity of the situation [ 39 ]. However, two studies highlighted [ 58 , 66 ] that healthcare systems can cope more effectively with future crises by factoring in “slack resources” at an organizational level and collective level (i.e., network or national), thereby ensuring the continued availability of critical medical supplies, equipment, and human resources. Likewise, supply chain resilience studies described the adoption of “buffering” and “bridging” strategies [ 34 ], along with “strategic purchasing” [ 33 ], to ensure continued healthcare supply and equipment availability across the healthcare system.

Coordination

Coordination as a capacity for resilience referred to how teams facilitated and organised work within and between teams and organisations. Identified in over half (n = 28, 56%) of studies in this review, coordination included the following five subthemes: team cohesion; multidisciplinary teamwork; team communication; inter-organisational coordination; and intra-organisational coordination. In terms of team cohesion (n = 10, 20%), building a supportive and cohesive team was regarded as an important factor in developing and sustaining resilient performance, particularly at the clinical micro-systems of care. Several studies expressed increased “connection” [ 72 ], “collaboration” [ 39 , 70 , 71 , 72 ] and a “sense of camaraderie” [ 70 ] among teams during the pandemic as they “rallied together” [ 40 ] and “worked together toward a common goal” [ 70 ]. Traditional clinical hierarchies were also reported as less important during delivery of care [ 72 ], leading to enhanced team dynamics and coordination [ 73 ]. Three studies also highlighted the role of “peer support” [ 56 , 65 , 69 ] as co-workers provided reassurance and supported staff wellbeing.

Multidisciplinary teamwork (n = 10, 20%) was also emphasised as critical in developing and sustaining resilient performance during the pandemic. Multidisciplinary teamwork was often initially made more difficult (e.g., in cases where teams were physically divided, or fewer staff on site), however, healthcare workers adapted [ 70 ] and used creative solutions to make multidisciplinary care more accessible [ 44 , 56 , 70 , 74 ]. Hodgins et al. [ 71 ] described the “breaking down of silos”, with staff from different disciplines “coming together” to support each other and sustain resilience. Ensuring that team communication (n = 5, 10%) remained open within and between teams was also critical to ensure teams remained connected and up to date with the ever-changing situation, as well as helping to facilitate the support process [ 39 , 42 , 72 , 75 ].

Along with evolving processes and workflows, inter-organisational coordination (n = 15, 30%) and teamwork evolved throughout the pandemic. Several studies outlined the establishment of multidisciplinary teams being formed at the hospital throughout various stages of the pandemic (e.g., COVID-19-management teams, emergency response teams, specialist care teams) [ 40 , 63 , 66 , 72 , 74 ] to enable rapid response and care to changing situations. Resilient performance was fostered by experienced teams and inter-organisational collaborations who adapted and worked together, with tenacity and creativity, in ways that previously had not been required [ 36 , 67 , 70 ]. Intra-organisational coordination (n = 7, 14%) was also described as critical during the pandemic, providing a buffer to combat resource shortages (e.g., workforce, equipment, knowledge). Services were reported as drawing on both new and pre-existing relationships to overcome barriers to care [ 34 , 36 , 74 ].

Learning as a capacity for resilient performance described the facilitation of knowledge acquisition, through the provision of learning activities and opportunities [ 30 ]. Learning was identified in just under half of the included studies (n = 21, 42%), and consisted of three subthemes: on-the-job learning, training, and simulation.

On-the-job learning (n = 9, 18%) became particularly important during the COVID-19 pandemic. Exposure to new situations, equipment, and regulations, forced healthcare personnel to continuously adjust and learn during everyday work; for example, the appropriate use of protective equipment [ 35 ] or the prompt need to develop decision-making and communication skills [ 69 ]. The novelty of the situation, with lack of standardized treatment plans often brought a trial-and-error approach whereby healthcare personnel became prepared through on-going daily training sessions [ 72 ], and through shared knowledge and experience [ 65 , 69 , 72 ].

Training ( n = 15, 30%) referred to more planned and scheduled efforts to increase knowledge and preparedness through organised learning efforts, such as courses, simulations, e-learning, and workshops [ 56 ]. These training efforts had different aims than those before the pandemic, ranging from technical skill development, such as medical equipment [ 69 ], to non-technical skills such as management skills [ 66 , 70 ]. The training sessions often took place at in-house-learning arenas such as simulation centres or labs, but also online learning resources were applied to reach a boarder audience and avoid spread of the virus [ 70 ].

Simulation (n = 3, 6%) as a novel training approach was identified in a small number of studies to increase preparedness to the COVID- 19 situation. Simulations allowed for interdisciplinary teams to train together and become confident in their technical and non-technical skills [ 75 ]. New simulation teams were created, and schedules developed to run consecutive training sessions, allowing for a large part of the healthcare personnel to be involved in the training [ 71 ].

Involvement

Involvement, as a key capacity for resilience in healthcare, referred to how the organisation involved and supported effective interactions between different system actors such as family, patients, and other stakeholders [ 35 ]. Meaningful involvement was evident in over one-third (n = 18, 36%) of the included studies and identified through two subthemes: communication with patients and families, and meeting patients’ needs.

Technology and roles were leveraged as a means for communication with patients and families (n = 14, 28%) and ensured patients and families continued to be engaged with care delivery during the COVID-19 pandemic. Changes to protocols and policy intending to reduce the transmission of COVID-19 (e.g., physical distancing, reduced capacity) required healthcare personnel to adjust how patients and families were meaningfully involved in care from primarily face-to-face to remote platforms. For example, teleconsultation technology was used to facilitate patient access to care services including a 24-hour helpline [ 76 ], and new systems to provide care services with the means to monitor and support patients remotely [ 41 ]. Technology was also used during the ‘no visitor policy’ to allow COVID-19 patients to connect with their family and medical staff when in isolation [ 66 ]. Volunteer networks and patient navigators were also used to extend services and connect healthcare providers with families [ 70 , 77 ], with posters and flyers on public noticeboards also used to share important health related information with families with limited literacy [ 70 ].

Practices and processes were adapted to ensure the health system was meeting patients’ needs (n = 10, 20%) during the pandemic. Changes to practices and processes were intended to mitigate unintended consequences of reduced or remote interaction service delivery methods to manage COVID-19 (e.g., postponing care, contagion fear) and ensure care delivery strategies had the capacity to address the needs of patients and that patient access to care was maintained [ 38 ]. For example, nursing specific care delivery processes were adapted to overcome difficulties in involving patients and family members to meet the immediate needs of patients [ 72 ] and practices were reorganised to comply with hygienic guidelines, thus enabling patients with acute non-COVID-19 needs to access care [ 41 ].

Risk awareness

Risk awareness as a capacity for resilient performance, enhances a system’s resilience when understanding and responding to potential adverse events [ 30 ]. Identified in over one-third of included studies (n = 18, 36%), risk awareness comprised two subthemes: emergency preparedness; and proactive responses.

From the early stages of the pandemic, emergency preparedness (n = 10, 20%) to COVID-19 was fundamental in planning and arranging strategies to meet the constant demands on the health system [ 72 ]. The development and continued “fine-tuning” of emergency preparedness plans [ 39 , 41 , 42 , 61 , 78 ] has been described as both important and necessary [ 39 ]. Emergency plans were attuned to strengthen other resilience capacities, such as streamlining communication systems [ 42 , 78 ], governance structures (78) and decision-making structures, to ensure the “continued, effective operation of the health system” [ 42 ]. One study also highlighted that the knowledge and experience gained from COVID-19 has led to ongoing conversations at a leadership level around emergency preparedness for any future crises [ 39 ].

Monitoring and proactive response (n = 16, 32%) referred to the understanding of situational risks to allow for proactive responses at all healthcare levels [ 30 ]. Early responses to the pandemic were often described as “ad-hoc”, but as the pandemic progressed, indicators and responses were monitored internationally [ 36 , 72 , 79 ] to assess risk, enabling proactive rather than reactive responses to problems [ 36 , 72 , 79 ]. Several studies outlined the implementation of an emergency taskforce [ 36 , 61 , 72 ] which met daily to evaluate emerging evidence [ 36 ], or devised new prevention strategies [ 61 ] or digital healthcare supply chain strategy [ 78 ]. Other studies discussed organisational infrastructure to prepare for the future risk of an outbreak, such as tracking COVID-19 positive individuals within hospitals, monitoring PPE levels [ 71 ] and developing plans for housing patients at alternative locations [ 39 ].

Leadership (n = 16, 32%) as a resilient capacity demonstrated the important contribution of leaders to both their employees and the broader healthcare organisation. Four subthemes were identified that contributed to the leadership capacity: transparent and open communication; visibility at the frontlines of care; supportive and empowering; and decisive leadership.

Transparent and open communication (n = 4, 8%) from leaders was noted as crucial in dealing with the pandemic. Leaders were required to distribute a continuous flow of information from national and regional authorities to the front-line staff through various channels [ 35 ], providing updates as new information became known. In general, frontline staff found this information to be both useful and supportive [ 72 ].

Increased visibility of leaders at the frontlines of care (n = 8, 16%) was also identified as important. For example, Lyng et al. [ 35 ] reported that leaders at Norwegian nursing homes heavily affected by the pandemic altered their daily work schedules so they could be present at the frontlines of care. On the other hand, where staff expressed an absence of effective and visible leadership, there was a sense of “mistrust in leaders”, generating a negative environment [ 65 ].

Resilient performance was also associated with leaders who were s upportive and empowering (n = 8, 16%). Along with visibility at the frontlines, leaders were reported as providing logistical support, expressing “appreciation of hard work”, offering “motivations and rewards” to continue, and “empowerment” to adapt to the changed conditions [ 69 ]. At one large healthcare organisation, leaders were reported as showing genuine concern for their staff’s mental and physical wellbeing [ 39 ], and at others, as providing reassurance to “frightened and exhausted” staff [ 36 ].

The value of decisive leadership (n = 10, 20%) in enabling resilient performance during the pandemic was reported in several studies. The ongoing changing nature of the pandemic required leaders to make rapid decisions [ 36 ], be flexible yet decisive [ 39 ], take proactive steps, and adopt a more hierarchical “military” style of command [ 80 ]. For example, with the constant stream of new updates and information comings to leaders, they needed to adopt a “learning mindset” to respond effectively and be willing to change course if warranted by the new information [ 66 ].

Communication

In almost one-third of included studies (n = 15, 30%), communication was identified as a key capacity for resilient performance and included the systems of communication used to translate information within and between teams and organisations. Two main systems of communication were identified: formal communication, such as information communication technology [ 72 ] and policies sent via email [ 70 ]; and informal communication, such as social media apps [ 56 , 65 , 70 ].

Several studies reported the utilisation of formal communication systems (n = 10, 20%) during the COVID-19 pandemic. It was widely accepted that the pandemic necessitated the rapid upskilling and education of staff and patients, and it was crucial that information was accurately resourced and disseminated [ 71 ]. For example, rapidly changing information from national and regional authorities was circulated, and healthcare executives provided daily COVID-19 updates via several communication platforms, such as the staff intranet and emails [ 35 , 70 , 71 , 80 ]. Providers also received regular policy and procedural updates (e.g., infection control) as more information from regulatory bodies became available [ 72 ]. However, some communication gaps were also identified; for example, a lack of communication aligned with rapidly changing protocols that increased the difficulty of remaining informed [ 56 ]. Challenges included a lack of intra-and inter professional communication between other units [ 56 ], a lack of access to technology and inconsistent information [ 81 ].

Informal communication (n = 10, 20%) was also reported among many of the included studies, commonly involving the development of group chats via social media apps, such as WhatsApp. These communication tools facilitated the sharing of information, such as policy and procedural change, and helped to provide emotional support and load sharing at the start of the pandemic among teams [ 35 , 56 , 65 , 70 , 76 ].

Consequences on system agents

It was clear from the included studies that navigating the challenges of the COVID-19 pandemic, which came with the need to constantly learn and make adaptations in response to unexpected variation and changes, came at a personal cost to healthcare workers, particularly to those at the frontlines of care. Nine (18%) of the included studies reported that the increased workload and strenuous work conditions had negative physical consequences on healthcare workers [ 54 , 56 , 61 , 67 , 68 , 69 , 79 , 81 , 82 ]. For example, nurses reported increased “tiredness”, “exhaustion”, “muscle weakness” and “loss of appetite”, during the pandemic as a result of working longer shifts, often without breaks, while being “weighed down by PPE equipment” [ 67 , 69 ].

The pandemic also exposed staff to stressful situations, which had considerable emotional consequences on staff, a theme identified in one-third of studies (n = 17, 34%). During the early stages of the pandemic, COVID-19 created an environment of uncertainty and fear among the population as a whole, but especially among front line workers [ 43 ], who expressed fear of dying from COVID-19, depression, worry, and frustration, among other psychological complaints [ 69 ]. Leaders were no different, with one study reporting that COVID-19 had also been emotionally demanding for staff in administrative and clinical leadership roles, with “constant exposure to vicarious trauma seeping into their personal and family time outside of work” [ 39 ]. Facing simultaneous pressures of physical and emotional demands, resulted in increased incidence of severe stress, emotional exhaustion, and burnout amongst healthcare workers [ 69 ]. One study further identified the cyclical nature of the problem, with burnt out healthcare workers on stress-leave causing greater staff shortages and increased workload for those remaining at work [ 56 ].

Several studies also identified that despite the healthcare system demonstrating several capacities to exhibit resilient performance in response to COVID-19, negative “spillover effects” were exhibited on routine patient care [ 44 ]. For example, Lotta et al. noted that the physical distancing requirements and mandatory use of PPE undermined everyday clinical work, with healthcare workers not being able to maintain contact with families [ 43 ]. Additionally, Akinyemi et al. [ 80 ] detailed that the COVID-19 pandemic negatively impacted service delivery in the healthcare system, for example, through disruptions to the appointment system and emergency and routine care services, which affected patient access to healthcare.

RHC broadly refers as a system’s capacity to maintain or restore its functions despite disruptions caused by external factors [ 59 ]. RHC does not focus on an individual’s coping and resilience capacity but rather on the factors and tools that enable the workers, teams, department and organisation to adapt and cope effectively in different situations [ 16 ]. RHC is a theoretically attractive concept, with its positive focus on how ‘things go right’ rather than wrong, and as evidenced by the number of reviews that have appeared on the topic in recent years [ 10 , 13 , 16 ].

Despite signs that RHC is maturing and formalising as a research paradigm [ 13 , 16 , 59 ], there have been calls for continued developments to strengthen RHC theory and research [ 13 ]. As evidenced by this review, the COVID-19 pandemic presented a unique opportunity to research and critically advance our understanding of RHC, and in particular, created a shift in focus from theoretical conceptualisations to identifying how we might understand factors or capacities that foster resilience across the health system [ 83 ]. Previously, empirical studies on RHC were rare and skewed towards the clinical microsystems of care, however, the surge of literature on RHC during the pandemic provided a unique opportunity to take stock of the empirical landscape [ 83 ]. Indeed, since the previous review by Iflaifel et al. [ 16 ], which found 71 empirical studies on RHC over an 18-year period, the present scoping review identified a further 50 studies, highlighting the unprecedented growth of empirical applications within the RHC field over the past three years.

Consistent with previous reviews [ 13 , 16 ], qualitative methods dominated the included studies, with interviews typically being used to capture healthcare workers’ perceptions and experiences during the pandemic. Although the extensive use of qualitative methods has been cited as one of the strengths of RHC [ 13 ], this review saw the application of existing tools (e.g., FRAM, SPLINTS) along with the emergence of new quantitative assessments and indicator-based modelling approaches that could have fruitful implications, particularly in terms of enhancing system preparedness and advancing measurement and monitoring of resilient performance over time. We also identified the development of new questionnaires to assess RHC; many of which were based on a conceptual framework (e.g., such as Hollnagel’s [ 53 ] ‘four cornerstones of resilience’ and Anderson et al.’s [ 55 ] Integrated Resilience Attributes Framework). In addition, we saw an increased number of studies examining RHC in LMICs. For example, the two studies of Karamaji et al. [ 48 , 49 ] presented an approach to assessing and monitoring health systems functionality in developing African countries, with a set of indicators that combine into a “resilience index”, each with varying levels of “transformation capacity”. While RHC theorists have historically resisted establishing indicators and measurement in this field, some people are expressing a need to advance our understanding of system resilience beyond the conventional health system building blocks of the WHO published 15 years ago [ 60 ]; thus, including measurement and monitoring is increasingly pressing.

A previous criticism has been that a preponderance of studies of RHC at micro and meso levels is “not sufficient to understand systems resilience” [ 84 ], and thus it was promising to see the emergence of macro level studies in this review. The macro-level study by Smaggus et al. [ 14 ], for example, examined government responses to the pandemic, by way of a document analysis of media releases, in two countries, Canada and Australia, expanding the scope of RHC research to different system levels, and incorporating a cross-country comparison [ 84 ]. Furthermore, Smaggus et al. [ 14 ] integrated several resilience theoretical frameworks to guide their study, illustrating how theory can inform research design and analysis. However, this study also highlighted some of the difficulties of researching RHC, particularly at the macro level, and that a mixed-methods approach (e.g., including interviews and observations alongside document analysis) would be likely to provide a more complex understanding on how government actions affect health system resilience, and build a better understanding of the links between actions at the macro level and other system levels.

What was clear was that the included studies reported varying degrees of preparedness and adaptive capacity across the different healthcare services. For example, a number of studies reported how well organisations or the people who work in them “evolved” to make things work [ 39 , 54 , 81 ], while others reported extreme physical and emotional demands, leading to stress and burnout amongst healthcare workers and poor clinical care [ 37 , 39 , 43 , 65 , 69 , 73 ]. This discrepancy between resilient performance and physical and emotional burnout could be explained by the extensive use of short-term adaptations, rather than long-term innovation and system change [ 35 ]. This tradeoff between short and long term adaptations can also be expressed as a tradeoff between “specified” and “general” resilience [ 85 ]. Healthcare personnel initiating short term adaptations and workarounds, such as taking on extra responsibility, working longer shifts, often without breaks to compensate for systems deficiencies, such as workforce shortages, may only have a short-term ‘firefighting’ effect on the specific situation [ 86 ]. Without long-term, general adaptations that foster organisational and system change, short term adaptations could potentially end up as a barrier for systemic resilient performance instead of a capacity [ 55 , 87 , 88 ].

This issue also reminds us of Woods [ 89 ] notion that all systems have an “envelope of performance”; a range of how much they can adapt, due to finite resources and the inherent variation in the system. When a system is pushed to the edge of its envelope, the system can either adapt and expand its performance further into “graceful extensibility” or become “brittle” and potentially lead to system collapse. Wear and Hettinger [ 90 ] also pointed to circumstances where local adaptations may become too extensive (the “tragedy of adaptability”). In the case of COVID-19, the continuous need for short-term adaptations placed the responsibility of the system’s ability for resilient performance on the sharp-end agents rather than the system itself, who over time became physically and emotionally exhausted. Although RHC has not often considered an individual’s coping and resilience capacity, how individual-level resilience interacts with team-, organizational- and broader systems resilience is a key area for future research.

An important contribution of this study is the recognition of eight key factors or capacities in the existing literature that potentially develop and enhance resilient performance. Recognising that healthcare is highly complex and unpredictable, and understanding that these factors were identified from studies in the context of COVID-19, these findings are highly concordant with the “capacities for resilient performance” identified in the qualitative study by Lyng et al. [ 30 ]. It is hoped that the capacities identified in this study can be facilitated and supported through the development of tools and interventions [ 91 ]. As identified by Lyng et al. [ 30 ] there were obvious interdependencies between the capacities; for example, between structure and leadership, given that leaders often facilitated the implementation and adherence to different structural features such as technology, guidelines or learning arenas; and between coordination and learning given that the greatest number of learning efforts related to team training and coordinating efforts to tackle the challenges related to COVID-19.

One noticeable difference, however, between our findings and those reported by Lyng et al., [ 30 ] was the emphasis placed of the the need for teamwork and collaboration during COVID-19. While Lyng et al. [ 30 ] suggested that different capacities require different levels of collaboration, higher levels of collaboration may have been required across all eight capacities during the pandemic. Again, this may reflect that many of the adaptations reported were largely reactive efforts focused on system recovery and restoring its equilibrium, particularly during the early stages of the pandemic, thus requiring short-term workarounds or solutions particularly at the front lines of care; but which are noble and important responses to handle peak activity situations [ 87 ]. Furthermore, COVID-19 prompted higher levels of collaboration, with the need to ‘rally together’ as they faced the same issues or ‘enemy’ across contexts and system levels. In the same way, two capacities presented by Lyng et al., namely ‘facilitators’ by way of champions and ‘competence’ by way of experience and knowledge, were less prominent in the present study. This is not to say that Lyng et al.’s capacities of competence and facilitators are not important for resilient performance, but rather, in the context of the pandemic, that the collaborative efforts needed to adapt to their joint challenges, may have made individual competencies and facilitators less important, or they were not reported in our included studies. Future studies should continue advancing this theoretical framework in order to integrate factors from different countries and settings and under different situations (stress, crisis, ordinary). Arguably, three of the most important capacities in advancing systems from reactive short-term adaptations at the micro-system level to longer-term “graceful extensibility” are effective leadership, communication and learning [ 92 ]. Indeed, examples of interventions promoting these three capacities are appearing in the literature [ 92 , 93 , 94 ]. For example, ‘tiered team huddles’ to enable sharing of ideas and issues from health workers at the ‘sharp end’ with middle and senior leadership, enabling communication across boundaries and enabling organizational learning [ 92 ]. A ‘learning health system’ [ 95 , 96 ], cultivated through innovative interventions like tiered team huddles, could improve communication across boundaries and facilitate long-term lasting change. Leaders also need to consider the negative impacts of short-term adaptations and workarounds on staff mental health.

The importance of system “slack” (or “buffer”) at an organizational level and collective level (i.e., network or national), was also highlighted in the study findings, to ensure that the healthcare system is prepared and enables organizational flexibility to deploy equipment and staff rapidly and effectively to where they are needed most [ 97 ]. The provision of a margin of manoeuvrability may also reduce the resulting negative effects of continuous micro-adaptations and increased staff workloads; thereby serving as a protective [ 98 ] mechanism.

Implications for research, policy and practice

Despite that the literature confirms that resilience-based efforts and analysis need to occur across system levels (i.e., micro, meso, macro), there is still relatively little understanding – both conceptually and empirically – about how the system levels interact with each other. Although the pandemic affected all system levels, presenting the perfect opportunity to study “cross-level interactions”, most of our included studies focused on one level of analysis. Yet as our review showed, there can be a “dark side or downside of resilience” [ 29 ]. What started out as resilient short-term adaptations were exhausting for the people working in the system, resulting in stress and burnout. Considerations for how individual-level resilience factors affect resilience factors at the team and organization-level is an important area for future research.

Of course, identifying the interactions between system levels is challenging, given the non-linear nature of such interactions and the time over which they may occur. Again, this issue points to the need for mixed methods (quantitative and qualitative) approaches, the dual consideration of both positive consequences (e.g., performance, efficiency, safety outcomes), and negative consequences (e.g., by including measures of stress, job satisfaction and burnout) of systems resilience, as well as the need to collect data longitudinally to increase our understanding of causal processes between the various system levels. Although quantitative resilience tools are emerging in the literature, more work is needed to establish theory driven and well validated tools for application at the various system levels.

In this study, the resilience capacities developed by Lyng et al. [ 30 ] proved to be an applicable and useful framework. Further empirical research building on this framework would be valuable, such as clarifying the degree of interrelatedness between the capacities, as well as designing and testing interventions around the capacities. One issue remains to be resolved, however; clarification is needed as to whether resilience should be studied as an “outcome, mediator, or determinant of a system’s performance” [ 83 ]. Some previous studies use these interchangeably: with resilience described as an underlying potential required to achieve a given outcome, while at the same time concluding that the system “was” or “proved” to be resilient. The capacity approach that we have taken here suggests that resilience is an underlying potential of the system, at its various levels, to adapt or restore its functions in response to disruption. We also call on researchers to be specific about whether they are referring to reactive adaptations focused on recovery or proactive efforts to minimise brittleness, with Woods’ [ 99 ] four conceptions of resilience potentially serving as a useful framework in this regard.

The results of this study, in combination with the Lyng et al.’s [ 30 ] capacities for resilient performance framework, can be used to guide interventions to support, develop or strengthen resilience. Understanding factors that develop or enhance RHC is critical to developing interventions and tools for strengthening their resilience [ 100 ]. This study thereby contributes to this work with key insights for intervention development that can be employed to enhance resilience performance.

Strengths and limitations

Data analysis and synthesis built on and strengthened the work of Lyng et al.’s [ 30 ] capacities for resilient performance framework; this framework can be further used as a basis to guide the next wave of research on RHC. The limitations of this review are primarily methodological. Due to our search strategy, we may have not identified valuable findings published in books, research reports and white papers. Future reviews of empirical studies in this field would benefit from by-hand searching particularly of books, where much of the foundational RHC literature has been identified [ 13 ]. Although we identified a relatively high proportion of articles from medium-income countries, our restriction to records in English and published works may have underestimated the true amount of literature emerging from LMIC. Our data extraction was also restricted to what was reported and discussed in the included studies. As a result, we may have under identified some important capacities and negative consequences. Using a data-based convergent synthesis approach, we transformed data from quantitative studies into categories or themes and did not analyse or report the results separately for different study types. Future research involving innovative methods for combining systematic review, concept analysis and bibliometric analysis could be used to summarise qualitative, quantitative and mixed methods RHC studies [ 101 ].

Our review identified an explosion of new empirical studies on health system resilience associated with COVID-19. The pandemic provided a unique ‘natural experiment’ and unprecedented opportunity to examine RHC theory in practice, and uncovered emerging new evidence on RHC theory and system factors that contribute to resilient performance at micro, meso and macro levels. Additionally, we identified potential unintended consequences of short-term responses to improve resilience without due consideration of the longer-term effects. These findings will facilitate strengthening of health system performance and resilience in responding to challenges and other unexpected events in the future.

Data Availability

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

Abbreviations

Functional Resonance Analysis Method

Preferred Reporting Items of Systematic Review and Meta-Analyses Extension for Scoping Reviews

Resilient Health Care

Scrub Practitioners List of Intra-operative Non-Technical Skills

World Health Organisation

Low- and middle-income countries

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Acknowledgements

The authors would like to thank and acknowledge Dylan Thomas (DT) and Lillian Tricker (LT) for their assistance with the title/abstract and full-text screening and Mr Jeremy Cullis for his help with devising the search strategy.

This work was supported by funded from NHMRC Partnership Centre in Health System Sustainability (Grant ID 9100002) and NHMRC Investigator Grant (Grant ID 1176620). HBL, CHD and SW receiving funding from the Research Council of Norway from the FRIPRO TOPPFORSK program (Grant ID 275367) to support their time on this project. These funding bodies had no role in the conception, design, data collection, analysis or decision to publish.

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Ellis, L.A., Saba, M., Long, J.C. et al. The rise of resilient healthcare research during COVID-19: scoping review of empirical research. BMC Health Serv Res 23 , 833 (2023). https://doi.org/10.1186/s12913-023-09839-0

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What is Quantitative Research Design? Definition, Types, Methods and Best Practices

By Nick Jain

Published on: July 7, 2023

Table of Contents

What is Quantitative Research Design?

Types of quantitative research design, quantitative research design methods, quantitative research design process: 10 key steps, top 11 best practices for quantitative research design.

Quantitative research design is defined as a research method used in various disciplines, including social sciences, psychology, economics, and market research. It aims to collect and analyze numerical data to answer research questions and test hypotheses.

Quantitative research design offers several advantages, including the ability to generalize findings to larger populations, the potential for statistical analysis and hypothesis testing, and the capacity to uncover patterns and relationships among variables. However, it also has limitations, such as the potential for oversimplification of complex phenomena and the reliance on predetermined categories and measurements.

Quantitative research design key elements

Quantitative research design typically follows a systematic and structured approach. It involves the following key elements:

• Research Question: The researcher formulates a clear and specific question that can be answered through quantitative research . The question should be measurable and objective
• Variables: The researcher identifies and defines the variables relevant to the research question. Variables are attributes or characteristics that can be measured or observed. They can be independent variables (factors that are manipulated or controlled) or dependent variables (outcomes or responses that are measured).
• Hypotheses: The researcher develops one or more hypotheses based on the research question. Hypotheses are verifiable statements that make predictions about the association between variables.
• Sampling: The researcher determines the target population and selects a representative sample from that population. The sample should be large enough to provide statistically significant results and should be chosen using appropriate sampling techniques.
• Data Collection: Quantitative research design relies on the collection of numerical data. This can be done through various methods such as surveys, experiments, quantitative observations , or secondary data analysis. Standardized instruments, such as questionnaires or scales, are often used to ensure consistency and reliability.
• Data Analysis: The collected data is analyzed using statistical methods and techniques. Descriptive statistics are used to summarize and describe the data, while inferential statistics are used to draw conclusions and make generalizations about the population based on the sample data.
• Results and Conclusions: The researcher interprets the findings and draws conclusions based on the analysis. The results are typically presented in the form of tables, graphs, and statistical measures, such as means, correlations, or regression coefficients.

There are several types of quantitative research designs, each suited for different research purposes and questions. Here are some common types of quantitative research designs:

• Experimental Design

Experimental design involves the manipulation of an independent variable to observe its effect on a dependent variable while controlling for other variables. Participants are typically randomly assigned to different groups, such as a control group and one or more experimental groups, to compare the outcomes. This approach enables the establishment of cause-and-effect relationships.

• Quasi-Experimental Design

Quasi-experimental design exhibits similarities to experimental design, yet it lacks the random assignment of participants to groups. The researcher takes advantage of naturally occurring groups or pre-existing conditions to compare the effects of an independent variable on a dependent variable. While it doesn’t establish causality as strongly as experimental design, it can still provide valuable insights.

• Survey Research

Survey research involves collecting data through questionnaires or interviews administered to a sample of participants. Surveys allow researchers to gather data on a wide range of variables and can be conducted in various settings, such as online surveys or face-to-face interviews. This design is particularly useful for studying attitudes, opinions, and behaviors within a population.

• Correlational Design

The correlational design investigates the association between two or more variables without engaging in their manipulation. Researchers measure variables and determine the degree and direction of their association using statistical techniques such as correlation analysis. However, correlational research cannot establish causality, only the strength and direction of the relationship.

• Longitudinal Design

Longitudinal design involves collecting data from the same individuals or groups over an extended period. This design allows researchers to study changes and patterns over time, providing insights into the stability and development of variables. Longitudinal studies can be conducted retrospectively (looking back) or prospectively (following participants into the future).

• Cross-sectional Design

Cross-sectional design collects data from a specific population at a single point in time. Researchers examine different variables simultaneously and analyze the relationships among them. This design is often used to gather data quickly and assess the prevalence of certain characteristics or behaviors within a population.

• Ex post facto Design

Ex post facto design involves studying the effects of an independent variable that is beyond the researcher’s control. The researcher selects participants based on their exposure to the independent variable, collecting data retrospectively. This design is useful when random assignment or manipulation of variables is not feasible or ethical.

Learn more: What is Quantitative Market Research?

Quantitative research design methods refer to the specific techniques and approaches used to collect and analyze numerical data in quantitative research . Below are several commonly utilized quantitative research methods:

• Surveys: Surveys involve administering questionnaires or structured interviews to gather data from a sample of participants. Surveys can be implemented through different channels, such as conducting them in person, over the phone, via mail, or utilizing online platforms. Researchers use various question types, such as multiple-choice, Likert scales, or rating scales, to collect quantitative data on attitudes, opinions, behaviors, and demographics.
• Experiments: Experiments involve manipulating one or more independent variables and measuring their effects on dependent variables. To compare outcomes, participants are assigned randomly to various groups, including control and experimental groups. Experimental designs allow researchers to establish cause-and-effect relationships by controlling for confounding factors.
• Observational Studies: Observational studies involve systematically observing and recording behavior, events, or phenomena in natural settings. Researchers can use structured or unstructured quantitative observation methods , depending on the research objectives. Quantitative data can be collected by counting the frequency of specific behaviors or by using coding systems to categorize and analyze observed data.
• Archival Research: Archival research involves analyzing existing data collected for purposes other than the current study. Researchers may use historical documents, government records, public databases, or organizational records to extract data through quantitative research . Archival research allows for large-scale data analysis and can provide insights into long-term trends and patterns.
• Secondary Data Analysis: Similar to archival research, secondary data analysis involves using existing datasets that were collected by other researchers or organizations. Researchers analyze the data to answer new research questions or test different hypotheses. Secondary data sources can include government surveys, social surveys, or market research data.
• Content Analysis: Content analysis is a method used to analyze textual or visual data to identify patterns, themes, or relationships. Researchers code and categorize the content of documents, interviews, articles, or media sources. The coded data is then quantified and statistically analyzed to draw conclusions. Content analysis can be both qualitative and quantitative , depending on the approach used.
• Psychometric Testing: Psychometric testing involves the development and administration of tests or scales to measure psychological constructs, such as intelligence, personality traits, or attitudes. Researchers use statistical techniques to analyze the test data, such as factor analysis, reliability analysis, or item response theory.

Learn more: What is Quantitative Observation?

The quantitative research design process typically involves several key steps to ensure a systematic and rigorous approach to data collection and analysis. While the specific steps may vary depending on the research context, here are the key stages commonly involved in quantitative research design:

1. Identify the Research Problem

Clearly define the research problem or objective. Determine the research question(s) and objectives that you want to address through your quantitative research study. Ensure that your research question is specific, measurable, and aligned with your research goals.

2. Review Existing Literature

Conduct a comprehensive review of existing literature and research on the topic. This helps you understand the current state of knowledge, identify gaps in the literature, and inform your research design. It also helps in selecting appropriate variables and developing hypotheses.

3. Determine Research Design

Based on your research question and objectives, determine the appropriate research design. Decide whether an experimental, quasi-experimental, correlational, or another design would best suit your research goals. Consider factors such as feasibility, ethical considerations, and resources available.

4. Define Variables and Hypotheses

Identify the variables that are pertinent to your research question. Clearly define each variable and its operational definitions (how they will be measured or observed). Develop hypotheses that state the expected relationships between variables based on existing theories or prior research.

5. Determine Sampling Strategy

Define the target population for your study and determine the sampling strategy. Decide on the sample size and the sampling method (e.g., random sampling, stratified sampling, convenience sampling). Ensure that your sample is representative of the population you want to generalize your findings to.

6. Select Data Collection Methods

Choose the appropriate data collection methods to gather data through quantitative research . This can include surveys, experiments, observations, or secondary data analysis. Develop or select validated instruments (e.g., questionnaires, scales) for data collection. Perform a pilot test on the instruments to ensure their reliability and validity.

7. Collect Data

Implement your data collection plan. Administer surveys, conduct experiments, observe participants, or extract data from existing sources. Ensure proper data management and organization to maintain accuracy and integrity. Consider ethical considerations and obtain necessary permissions or approvals.

8. Analyze Data

Perform data analysis using appropriate statistical techniques. Depending on your research design and data characteristics, apply descriptive statistics (e.g., means, frequencies) and inferential statistics (e.g., t-tests, ANOVA, regression analysis) to analyze relationships, test hypotheses, and draw conclusions. Use statistical software for efficient and accurate analysis.

9. Interpret Results

Interpret the findings of your data analysis. Examine statistical outputs, identify significant relationships or patterns, and relate them to your research question and hypotheses. Consider the limitations of your study and address any unexpected or contradictory results.

10. Communicate Findings

Prepare a research report or manuscript that summarizes your research process, findings, and conclusions. Present your results in a clear and understandable manner using appropriate visualizations (e.g., tables, graphs). Consider disseminating your findings through academic publications, conferences, or other appropriate channels.

To ensure the quality and validity of your quantitative research design, here are some best practices to consider:

1. Define Research Objectives Clearly: Initiate the process by providing a clear definition of your research objectives and formulating precise research questions. This clarity will guide your study design and data collection process.

2. Conduct a Comprehensive Literature Review: Thoroughly review existing literature and research on your topic to understand the current state of knowledge. This helps you identify research gaps, refine your research question, and avoid duplication of efforts.

3. Use Validated Measures: When selecting or developing measurement instruments, ensure that they have established validity and reliability. Use validated scales, questionnaires, or tests that have been previously tested and proven to measure the constructs of interest accurately.

4. Pilot Testing: Before implementing your data collection, conduct pilot testing to evaluate the effectiveness of your research instruments and procedures. Pilot testing helps identify any issues or shortcomings and allows for adjustments before the main data collection.

5. Ensure Sample Representativeness: Pay attention to sample selection to ensure it is representative of the target population. Use appropriate sampling techniques and consider factors such as sample size, demographics, and relevant characteristics to enhance generalizability.

6. Minimize Nonresponse Bias: Address potential nonresponse bias by employing strategies to maximize response rates, such as providing clear instructions, using follow-up reminders, and ensuring confidentiality. Analyze nonresponse patterns to assess potential bias and consider appropriate weighting techniques if needed.

7. Maintain Data Quality: Implement robust data management practices to ensure data quality and integrity. Conduct data cleaning, perform checks for outliers and missing values, and document any data transformations or manipulations. Document your data collection procedures thoroughly to facilitate replication and transparency.

8. Employ Appropriate Statistical Analysis: Choose statistical techniques that align with your research design and data characteristics. Use appropriate descriptive and inferential statistics to analyze relationships, test hypotheses, and draw valid conclusions. Ensure proper interpretation and reporting of statistical results.

9. Address Potential Confounding Factors: Identify potential confounding variables that may influence the relationship between your independent and dependent variables. Consider controlling for these factors through study design or statistical techniques to isolate the effects of the variables of interest.

10. Consider Ethical Considerations: Adhere to ethical guidelines and obtain necessary approvals or permissions before conducting your research. Protect participants’ rights, ensure informed consent, maintain confidentiality, and handle data responsibly.

11. Document and Report: Document your research design, data collection, and analysis procedures thoroughly. This helps ensure the transparency and reproducibility of your study. Prepare a comprehensive research report or manuscript that clearly presents your methodology, findings, limitations, and implications.

Learn more: What is Quantitative Research?

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• Introduction

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

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

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

Qualitative Research Approaches

Ethnography

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

Grounded theory

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

Phenomenology

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

Narrative research

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

Research Paradigm

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

Positivist versus postpositivist

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

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

Constructivist

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

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

Data Sampling 

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

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

Data Collection and Analysis

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

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

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

Dissemination

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

Applications

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

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

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

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

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

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

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

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

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

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

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

• Issues of Concern

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

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

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

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

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

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

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

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

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

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

• Enhancing Healthcare Team Outcomes

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

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

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

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

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

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

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