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Qualitative Variable – Types and Examples

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Qualitative Variable

Definition:

Qualitative variable, also known as a categorical variable, is a type of variable in statistics that describes an attribute or characteristic of a data point, rather than a numerical value.

Qualitative variables are typically represented by labels or categories, such as “male” or “female,” and are often used in surveys and polls to gather information about a population’s characteristics.

Types Qualitative Variable

There are two main types of qualitative variables:

Nominal Variables

A nominal variable is a Qualitative Variable where the categories are not ordered in any particular way. For example, gender (male or female), race (Asian, Black, Hispanic, etc.), or religion (Christian, Muslim, Hindu, etc.). Nominal variables can be represented using numbers, but the numbers do not have any quantitative meaning. For example, a researcher might assign the number “1” to male and “2” to female, but these numbers do not represent a quantitative difference between the categories.

Ordinal Variables

An ordinal variable is a Qualitative Variable where the categories are ordered in some way. For example, educational level (high school, college, graduate school), income level (low, medium, high), or level of agreement (strongly agree, somewhat agree, neutral, somewhat disagree, strongly disagree). Ordinal variables can be represented using numbers, and the numbers have a quantitative meaning, but the distance between the categories is not necessarily equal. For example, the difference between “high school” and “college” may not be the same as the difference between “college” and “graduate school.”

Examples of Qualitative Variables

Here are some examples of qualitative variables:

Gender : Male or female
Marital status: Married, single, divorced, widowed
Race : Asian, Black, Hispanic, White, etc.
Religious affiliation: Christian, Muslim, Hindu, Buddhist, etc.
Political affiliation : Democrat, Republican, Independent, etc.
Educational level : High school, college, graduate school
Type of employment : Full-time, part-time, self-employed, unemployed
Type of housing: Apartment, house, condo, etc.
Method of transportation : Car, bus, train, bike, etc.
Language spoken: English, Spanish, French, etc.

Applications of Qualitative Variable

Qualitative variables are used in many applications in different fields, including:

Market research : Qualitative variables are often used in market research to understand consumer behavior and preferences. For example, a company might use qualitative variables such as age, gender, and income to segment their target market and create customized marketing campaigns.
Public opinion polling : Qualitative variables are used in public opinion polling to gather information about people’s attitudes, beliefs, and opinions. Pollsters may ask questions about political affiliation, religious affiliation, or social issues to understand public opinion on a particular topic.
Social sciences research: Qualitative variables are commonly used in social sciences research to study human behavior, culture, and society. Researchers may use qualitative variables to categorize people based on their demographic information or cultural background, and to analyze patterns and trends in behavior or attitudes.
Healthcare research: Qualitative variables are used in healthcare research to identify risk factors and to understand the impact of treatments on patients. Researchers may use qualitative variables such as age, gender, or medical history to identify populations at risk for certain diseases, and to evaluate the effectiveness of different treatment options.
Education research: Qualitative variables are used in education research to study the effectiveness of different teaching methods and to identify factors that influence student learning. Researchers may use qualitative variables such as socio-economic status, educational level, or learning style to analyze patterns and trends in student performance.

When to use Qualitative Variable

Qualitative variables should be used in research when the variable being studied is categorical and does not involve numerical values. Here are some situations where qualitative variables are appropriate:

When studying demographic characteristics: Qualitative variables are useful for studying demographic characteristics such as age, gender, ethnicity, and religion. These variables can be used to segment a population into groups and to compare differences between groups.
When studying attitudes and beliefs : Qualitative variables can be used to study people’s attitudes and beliefs about various topics, such as politics, social issues, or religion. Researchers can use surveys or interviews to gather data on these variables.
When studying cultural differences: Qualitative variables are often used in cross-cultural research to study differences between cultures. Researchers may use qualitative variables such as language spoken, nationality, or cultural background to identify groups for comparison.
When studying consumer behavior : Qualitative variables can be used in market research to study consumer behavior and preferences. Researchers can use qualitative variables such as brand loyalty, product preference, or buying habits to understand consumer behavior.
When studying patient outcomes: Qualitative variables can be used in healthcare research to study patient outcomes, such as quality of life, satisfaction with treatment, or adherence to medication. Researchers can use qualitative variables to identify factors that influence patient outcomes and to develop interventions to improve patient care.

Purpose of Qualitative Variable

The purpose of a qualitative variable is to categorize data into distinct groups based on non-numerical characteristics or attributes. The use of qualitative variables allows researchers to describe and analyze non-quantifiable phenomena, such as attitudes, beliefs, behaviors, and demographic characteristics, and to identify patterns and trends in the data. The main purposes of qualitative variables are:

To describe and categorize : Qualitative variables are used to describe and categorize data into meaningful groups based on characteristics or attributes that are not numerical.
To compare and contrast: Qualitative variables allow researchers to compare and contrast different groups or categories of data, such as different demographic groups or cultural backgrounds.
To identify patterns and trends: Qualitative variables allow researchers to identify patterns and trends in data that may not be apparent with numerical data. For example, a researcher may use qualitative variables to identify cultural differences in attitudes toward healthcare.
To develop hypotheses: Qualitative variables can be used to develop hypotheses or research questions for further study. For example, a researcher may use qualitative variables to identify risk factors for a particular disease, which can then be further studied using quantitative methods.
To inform decision-making: Qualitative variables can provide important information to inform decision-making in fields such as healthcare, education, and business. For example, healthcare providers may use qualitative variables to identify patient preferences and needs, which can inform treatment decisions.

Characteristics of Qualitative Variable

Here are some of the characteristics of qualitative variables:

Categorical : Qualitative variables are categorical in nature, meaning that they describe characteristics or attributes that are not numerical. They can be nominal, ordinal or binary.
Non-numeric : Qualitative variables do not involve numerical values, but rather descriptive or categorical data such as colors, shapes, types, or names.
Limited number of categories: Qualitative variables are often limited to a small number of categories, such as male/female, married/single/divorced, or white/black/Asian.
Mutually exclusive categories : Categories in a qualitative variable must be mutually exclusive, meaning that each observation can only belong to one category.
No numerical order : Unlike quantitative variables, qualitative variables do not have a numerical order or ranking. Categories are assigned based on non-numerical criteria.
Can be used for comparison : Qualitative variables are often used for comparison purposes, such as comparing the frequency of certain behaviors or attitudes across different demographic groups.
Can be used for classification: Qualitative variables can be used to classify data into distinct groups based on common characteristics or attributes. For example, people can be classified into different racial or ethnic groups based on their ancestry.
Can be used for hypothesis testing : Qualitative variables can be used to test hypotheses about differences between groups or categories of data. For example, a researcher may hypothesize that men and women have different attitudes toward a particular social issue, and use a qualitative variable to test this hypothesis.

Advantages of Qualitative Variable

There are several advantages of using qualitative variables.

Rich data: Qualitative variables can provide rich data about complex phenomena such as attitudes, behaviors, and cultural differences. This data can be useful for gaining a deep understanding of a particular issue or topic.
Flexibility : Qualitative variables are flexible and can be used in a variety of research methods, such as interviews, focus groups, and observations. This allows researchers to choose the method that best suits their research question and participants.
Participant perspective : Qualitative variables allow researchers to capture the participant’s perspective and experience. By using open-ended questions or prompts, researchers can gain insight into how participants perceive and interpret a particular issue.
Depth of understanding: Qualitative variables allow for a depth of understanding that may not be possible with quantitative variables alone. Qualitative data can provide details and context that quantitative data may miss.
Contextualization : Qualitative variables can provide contextualization, allowing researchers to understand the cultural, social, and historical factors that shape attitudes and behaviors.
Theory development: Qualitative variables can be useful for developing new theories or refining existing ones. By gathering rich data and analyzing it using qualitative methods, researchers can identify patterns and relationships that can inform the development of new theories.
Researcher reflexivity : Qualitative variables require the researcher to be reflexive and acknowledge their own biases and assumptions. This can help to ensure that the research is ethical and inclusive, and that the data collected is valid and reliable.

Limitations of Qualitative Variable

Some Limitations of Qualitative Variable are as follows:

Subjectivity : Qualitative data is often collected through open-ended questions or prompts, which can lead to subjective responses that are difficult to quantify or compare. This can make it challenging to establish inter-rater reliability and can limit the generalizability of the findings.
Limited sample size : Qualitative research often involves small sample sizes, which can limit the generalizability of the findings. While qualitative research is typically focused on gaining a deep understanding of a particular issue, the findings may not be representative of the broader population.
Time-consuming: Qualitative research can be time-consuming, particularly when collecting and analyzing data. Researchers must spend significant amounts of time in the field, conducting interviews or focus groups, and then transcribing and analyzing the data.
Limited control: Qualitative research often involves limited control over the research environment and the participants. This can make it challenging to ensure that the data collected is valid and reliable.
Limited generalizability: Qualitative research is typically focused on gaining a deep understanding of a particular issue, rather than testing hypotheses or making generalizations about the broader population. As a result, the findings may be less generalizable than those obtained through quantitative research methods.
Ethical concerns: Qualitative research often involves collecting sensitive or personal information from participants. Researchers must take care to ensure that participants are fully informed about the research, that their privacy is protected, and that they are not harmed in any way by their participation.
Bias : Qualitative research can be subject to bias, particularly if the researcher has a vested interest in the outcome of the research. Researchers must take care to acknowledge their own biases and assumptions, and to use multiple sources of data to ensure the validity and reliability of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Chapter 1. Introduction

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

Why an Open Access Textbook on Qualitative Research Methods?

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

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

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

What is Research?

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

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

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

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

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

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

What is Qualitative Research?

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

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

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

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

Examples of Qualitative Research

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

Example 1 : The End Game (ethnography + interviews)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How Is This Book Organized?

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

Chapter Descriptions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Patton’s Ten Suggestions for Doing Qualitative Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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).

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Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

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

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

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

Qualitative research can be used to:

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

Examples of qualitative research questions include:

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

Characteristics

Naturalistic setting.

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

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

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

Reality is socially constructed

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

The primacy of subject matter

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

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

Variables are complex, interwoven, and difficult to measure

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

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

Emic (insider’s point of view)

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

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

Interpretive analysis

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

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

Collecting Qualitative Data

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

Observations

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

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

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

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

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

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

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

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

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

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

Focus groups

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

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

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

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

Ethnography

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

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

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

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

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

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

Qualitative Data Analysis Methods

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

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

Grounded theory

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

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

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

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

Content analysis

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

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

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

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

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

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

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

Discourse analysis

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

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

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

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

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

Thematic analysis

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

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

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

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

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

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

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

Phases of Thematic Analysis

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

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

Template analysis

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

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

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

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

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

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

Frame analysis

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

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

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

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

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

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

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

Preventing Bias in Qualitative Research

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

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

Co-coding

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

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

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

Member checking

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

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

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

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

Reflexivity in qualitative research

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

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

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

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

Reflexivity Example

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

Establishing Trustworthiness in Qualitative Research

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

1. Credibility in Qualitative Research

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

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

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

2. Transferability in Qualitative Research

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

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

3. Dependability in Qualitative Research

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

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

4. Confirmability in Qualitative Research

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

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

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

Audit Trails in Qualitative Research

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

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

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

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

Discovery of unexpected data

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

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

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

Flexibility

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

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

Naturalistic settings

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

Limitations

Time-consuming .

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

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

Subjectivity

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

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

Limited generalizability

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

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

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

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

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

Extraneous variables

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

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

What sample size should qualitative research be?

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

Are surveys qualitative or quantitative?

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

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

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

What are the ethical considerations of qualitative research?

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

What is triangulation in qualitative research?

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

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

Why is qualitative research important?

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

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

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

What is coding data in qualitative research?

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

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

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

What is the difference between qualitative and quantitative research?

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

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

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

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

What is trustworthiness in qualitative research?

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

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

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

What is data saturation in qualitative research?

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

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

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

Why is sampling in qualitative research important?

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

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

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

What is narrative analysis?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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29 Conceptualization in qualitative research

Chapter outline

15.1 Alternative paradigms: Interpretivism, critical paradigm, and pragmatism

15.2 Multiparadigmatic research: An example

15.3 idiographic causal relationships, 15.4 qualitative research questions.

Now let’s change things up! In the previous chapters, we explored steps to create and carry out a quantitative research study. Quantitative studies are great when we want to summarize or test relationships between ideas using numbers and the power of statistics. However, qualitative research offers us a different and equally important tool. Sometimes the aim of research projects is to explore meaning and lived experience. Instead of trying to arrive at generalizable conclusions for all people, some research projects establish a deep, authentic description of a specific time, place, and group of people.

Qualitative research relies on the power of human expression through words, pictures, movies, performance and other artifacts that represent these things. All of these tell stories about the human experience and we want to learn from them and have them be represented in our research. Generally speaking, qualitative research is about the gathering up of these stories, breaking them into pieces so we can examine the ideas that make them up, and putting them back together in a way that allows us to tell a common or shared story that responds to our research question. To do that, we need to discuss the assumptions underlying social science.

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

17.1 Alternative paradigms: Interpretivism, critical, and pragmatism

Learning objectives.

Students will be able to…

Distinguish between the assumptions of positivism, interpretivism, critical, and pragmatist research paradigms.
Use paradigm to describe how scientific thought changes over time.

In Chapter 10, we reviewed the assumptions that underly post-positivism (abbreviated hereafter as positivism for brevity). Quantitative methods are most often the choice for positivist research questions because they conform to these assumptions. Qualitative methods can conform to these assumptions; however, they are limited in their generalizability.

Kivunja & Kuyini (2017) [1] describe the essential features of positivism as:

A belief that theory is universal and law-like generalizations can be made across contexts
The assumption that context is not important
The belief that truth or knowledge is ‘out there to be discovered’ by research
The belief that cause and effect are distinguishable and analytically separable
The belief that results of inquiry can be quantified
The belief that theory can be used to predict and to control outcomes
The belief that research should follow the scientific method of investigation
Rests on formulation and testing of hypotheses
Employs empirical or analytical approaches
Pursues an objective search for facts
Believes in ability to observe knowledge
The researcher’s ultimate aim is to establish a comprehensive universal theory, to account for human and social behavior
Application of the scientific method

Because positivism is the dominant social science research paradigm, it can be easy to ignore or be confused by research that does not use these assumptions. We covered in Chapter 10 the table reprinted below when discussing the assumptions underlying positivistic social science.

As you consider your research project, keep these philosophical assumptions in mind. They are useful shortcuts to understanding the deeper ideas and assumptions behind the construction of knowledge. The purpose of exploring these philosophical assumptions isn’t to find out which is true and which is false. Instead, the goal is to identify the assumptions that fit with how you think about your research question. Choosing a paradigm helps you make those assumptions explicit.

Table 7.1 Philosophical assumptions in social science research

Ontology: assumptions about what is real
Epistemology: assumptions about how we come to know what is real
Assumptions about the researcher
Assumptions about human action
Assumptions about the social world
Assumptions about the purpose of research

Before we explore alternative paradigms, it’s important for us to review what paradigms are.

How do scientific ideas change over time?

Much like your ideas develop over time as you learn more, so does the body of scientific knowledge. Kuhn’s (1962) [2] The Structure of Scientific Revolutions is one of the most influential works on the philosophy of science, and is credited with introducing the idea of competing paradigms (or “disciplinary matrices”) in research. Kuhn investigated the way that scientific practices evolve over time, arguing that we don’t have a simple progression from “less knowledge” to “more knowledge” because the way that we approach inquiry is changing over time. This can happen gradually, but the process results in moments of change where our understanding of a phenomenon changes more radically (such as in the transition from Newtonian to Einsteinian physics; or from Lamarckian to Darwinian theories of evolution). For a social work practice example, Fleuridas & Krafcik (2019) [3] trace the development of the “four forces” of psychotherapy , from psychodynamics to behaviorism to humanism as well as the competition among emerging perspectives to establish itself as the fourth force to guide psychotherapeutic practice. But how did the problems in one paradigm inspire new paradigms? Kuhn presents us with a way of understanding the history of scientific development across all topics and disciplines.

As you can see in this video from Matthew J. Brown (CC-BY), there are four stages in the cycle of science in Kuhn’s approach. Firstly, a pre-paradigmatic state where competing approaches share no consensus. Secondly, the “normal” state where there is wide acceptance of a particular set of methods and assumptions. Thirdly, a state of crisis where anomalies that cannot be solved within the existing paradigm emerge and competing theories to address them follow. Fourthly, a revolutionary phase where some new paradigmatic approach becomes dominant and supplants the old. Shnieder (2009) [4] suggests that the Kuhnian phases are characterized by different kinds of scientific activity.

Newer approaches often build upon rather than replace older ones, but they also overlap and can exist within a state of competition. Scientists working within a particular paradigm often share methods, assumptions and values. In addition to supporting specific methods, research paradigms also influence things like the ambition and nature of research, the researcher-participant relationship and how the role of the researcher is understood.

Paradigm vs. theory

The terms ‘ paradigm ‘ and ‘ theory ‘ are often used interchangeably in social science. There is not a consensus among social scientists as to whether these are identical or distinct concepts. With that said, in this text, we will make a clear distinction between the two ideas because thinking about each concept separately is more useful for our purposes.

We define paradigm a set of common philosophical (ontological, epistemological, and axiological) assumptions that inform research. The four paradigms we describe in this section refer to patterns in how groups of researchers resolve philosophical questions. Some assumptions naturally make sense together, and paradigms grow out of researchers with shared assumptions about what is important and how to study it. Paradigms are like “analytic lenses” and a provide framework on top of which we can build theoretical and empirical knowledge (Kuhn, 1962). [5] Consider this video of an interview with world-famous physicist Richard Feynman in which he explains why “when you explain a ‘why,’ you have to be in some framework that you allow something to be true. Otherwise, you are perpetually asking why.” In order to answer basic physics question like “what is happening when two magnets attract?” or a social work research question like “what is the impact of this therapeutic intervention on depression,” you must understand the assumptions you are making about social science and the social world. Paradigmatic assumptions about objective and subjective truth support methodological choices like whether to conduct interviews or send out surveys, for example.

While paradigms are broad philosophical assumptions, theory is more specific, and refers to a set of concepts and relationships scientists use to explain the social world. Theories are more concrete, while paradigms are more abstract. Look back to Figure 7.1 at the beginning of this chapter. Theory helps you identify the concepts and relationships that align with your paradigmatic understanding of the problem. Moreover, theory informs how you will measure the concepts in your research question and the design of your project.

For both theories and paradigms, Kuhn’s observation of scientific paradigms, crises, and revolutions is instructive for understanding the history of science. Researchers inherit institutions, norms, and ideas that are marked by the battlegrounds of theoretical and paradigmatic debates that stretch back hundreds of years. We have necessarily simplified this history into four paradigms: positivism, interpretivism, critical, and pragmatism. Our framework and explanation are inspired by the framework of Guba and Lincoln (1990) [6] and Burrell and Morgan (1979). [7] while also incorporating pragmatism as a way of resolving paradigmatic questions. Most of social work research and theory can be classified as belonging to one of these four paradigms, though this classification system represents only one of many useful approaches to analyzing social science research paradigms.

Building on our discussion in section 7.1 on objective vs. subjective epistemologies and ontologies, we will start with the difference between positivism and interpretivism. Afterward, we will link our discussion of axiology in section 7.2 with the critical paradigm. Finally, we will situate pragmatism as a way to resolve paradigmatic questions strategically. The difference between positivism and interpretivism is a good place to start, since the critical paradigm and pragmatism build on their philosophical insights.

It’s important to think of paradigms less as distinct categories and more as a spectrum along which projects might fall. For example, some projects may be somewhat positivist, somewhat interpretivist, and a little critical. No project fits perfectly into one paradigm. Additionally, there is no paradigm that is more correct than the other. Each paradigm uses assumptions that are logically consistent, and when combined, are a useful approach to understanding the social world using science. The purpose of this section is to acquaint you with what research projects in each paradigm look like and how they are grounded in philosophical assumptions about social science.

You should read this section to situate yourself in terms of what paradigm feels most “at home” to both you as a person and to your project. You may find, as I have, that your research projects are more conventional and less radical than what feels most like home to you, personally. In a research project, however, students should start with their working question rather than their heart. Use the paradigm that fits with your question the best, rather than which paradigm you think fits you the best.

Interpretivism: Researcher as “empathizer”

Positivism is focused on generalizable truth. Interpretivism , by contrast, develops from the idea that we want to understand the truths of individuals, how they interpret and experience the world, their thought processes, and the social structures that emerge from sharing those interpretations through language and behavior. The process of interpretation (or social construction) is guided by the empathy of the researcher to understand the meaning behind what other people say.

Historically, interpretivism grew out of a specific critique of positivism: that knowledge in the human and social sciences cannot conform to the model of natural science because there are features of human experience that cannot objectively be “known”. The tools we use to understand objects that have no self-awareness may not be well-attuned to subjective experiences like emotions, understandings, values, feelings, socio-cultural factors, historical influences, and other meaningful aspects of social life. Instead of finding a single generalizable “truth,” the interpretivist researcher aims to generate understanding and often adopts a relativist position.

While positivists seek “the truth,” the social constructionist framework argues that “truth” varies. Truth differs based on who you ask, and people change what they believe is true based on social interactions. These subjective truths also exist within social and historical contexts, and our understanding of truth varies across communities and time periods. This is because we, according to this paradigm, create reality ourselves through our social interactions and our interpretations of those interactions. Key to the interpretivist perspective is the idea that social context and interaction frame our realities.

Researchers operating within this framework take keen interest in how people come to socially agree, or disagree, about what is real and true. Consider how people, depending on their social and geographical context, ascribe different meanings to certain hand gestures. When a person raises their middle finger, those of us in Western cultures will probably think that this person isn’t very happy (not to mention the person at whom the middle finger is being directed!). In other societies around the world, a thumbs-up gesture, rather than a middle finger, signifies discontent (Wong, 2007). [8] The fact that these hand gestures have different meanings across cultures aptly demonstrates that those meanings are socially and collectively constructed. What, then, is the “truth” of the middle finger or thumbs up? As we’ve seen in this section, the truth depends on the intention of the person making the gesture, the interpretation of the person receiving it, and the social context in which the action occurred.

Qualitative methods are preferred as ways to investigate these phenomena. Data collected might be unstructured (or “messy”) and correspondingly a range of techniques for approaching data collection have been developed. Interpretivism acknowledges that it is impossible to remove cultural and individual influence from research, often instead making a virtue of the positionality of the researcher and the socio-cultural context of a study.

One common objection positivists levy against interpretivists is that interpretivism tends to emphasize the subjective over the objective. If the starting point for an investigation is that we can’t fully and objectively know the world, how can we do research into this without everything being a matter of opinion? For the positivist, this risk for confirmation bias as well as invalid and unreliable measures makes interpretivist research unscientific. Clearly, we disagree with this assessment, and you should, too. Positivism and interpretivism have different ontologies and epistemologies with contrasting notions of rigor and validity (for more information on assumptions about measurement, see Chapter 11 for quantitative validity and reliability and Chapter 20 for qualitative rigor). Nevertheless, both paradigms apply the values and concepts of the scientific method through systematic investigation of the social world, even if their assumptions lead them to do so in different ways. Interpretivist research often embraces a relativist epistemology, bringing together different perspectives in search of a trustworthy and authentic understanding or narrative.

Kivunja & Kuyini (2017) [9] describe the essential features of interpretivism as:

The belief that truths are multiple and socially constructed
The acceptance that there is inevitable interaction between the researcher and his or her research participants
The acceptance that context is vital for knowledge and knowing
The belief that knowledge can be value laden and the researcher’s values need to be made explicit
The need to understand specific cases and contexts rather deriving universal laws that apply to everyone, everywhere.
The belief that causes and effects are mutually interdependent, and that causality may be circular or contradictory
The belief that contextual factors need to be taken into consideration in any systematic pursuit of understanding

One important clarification: it’s important to think of the interpretivist perspective as not just about individual interpretations but the social life of interpretations. While individuals may construct their own realities, groups—from a small one such as a married couple to large ones such as nations—often agree on notions of what is true and what “is” and what “is not.” In other words, the meanings that we construct have power beyond the individuals who create them. Therefore, the ways that people and communities act based on such meanings is of as much interest to interpretivists as how they were created in the first place. Theories like social constructionism, phenomenology, and symbolic interactionism are often used in concert with interpretivism.

Is interpretivism right for your project?

An interpretivist orientation to research is appropriate when your working question asks about subjective truths. The cause-and-effect relationships that interpretivist studies produce are specific to the time and place in which the study happened, rather than a generalizable objective truth. More pragmatically, if you picture yourself having a conversation with participants like an interview or focus group, then interpretivism is likely going to be a major influence for your study.

Positivists critique the interpretivist paradigm as non-scientific. They view the interpretivist focus on subjectivity and values as sources of bias. Positivists and interpretivists differ on the degree to which social phenomena are like natural phenomena. Positivists believe that the assumptions of the social sciences and natural sciences are the same, while interpretivists strongly believe that social sciences differ from the natural sciences because their subjects are social creatures.

Similarly, the critical paradigm finds fault with the interpretivist focus on the status quo rather than social change. Although interpretivists often proceed from a feminist or other standpoint theory, the focus is less on liberation than on understanding the present from multiple perspectives. Other critical theorists may object to the consensus orientation of interpretivist research. By searching for commonalities between people’s stories, they may erase the uniqueness of each individual’s story. For example, while interpretivists may arrive at a consensus definition of what the experience of “coming out” is like for people who identify as lesbian, gay, bisexual, transgender, or queer, it cannot represent the diversity of each person’s unique “coming out” experience and what it meant to them. For example, see Rosario and colleagues’ (2009) [10] critique the literature on lesbians “coming out” because previous studies did not addressing how appearing, behaving, or identifying as a butch or femme impacted the experience of “coming out” for lesbians.

From your literature search, identify an empirical article that uses qualitative methods to answer a research question similar to your working question or about your research topic.
Review the assumptions of the interpretivist research paradigm.
Discuss in a few sentences how the author’s conclusions are based on some of these paradigmatic assumptions. How might a researcher operating from a different paradigm (like positivism or the critical paradigm) critique the conclusions of this study?

Critical paradigm: Researcher as “activist”

As we’ve discussed a bit in the preceding sections, the critical paradigm focuses on power, inequality, and social change. Although some rather diverse perspectives are included here, the critical paradigm, in general, includes ideas developed by early social theorists, such as Max Horkheimer (Calhoun et al., 2007), [11] and later works developed by feminist scholars, such as Nancy Fraser (1989). [12] Unlike the positivist paradigm, the critical paradigm assumes that social science can never be truly objective or value-free. Furthermore, this paradigm operates from the perspective that scientific investigation should be conducted with the express goal of social change. Researchers in the critical paradigm foreground axiology, positionality and values . In contrast with the detached, “objective” observations associated with the positivist researcher, critical approaches make explicit the intention for research to act as a transformative or emancipatory force within and beyond the study.

Researchers in the critical paradigm might start with the knowledge that systems are biased against certain groups, such as women or ethnic minorities, building upon previous theory and empirical data. Moreover, their research projects are designed not only to collect data, but to impact the participants as well as the systems being studied. The critical paradigm applies its study of power and inequality to change those power imbalances as part of the research process itself. If this sounds familiar to you, you may remember hearing similar ideas when discussing social conflict theory in your human behavior in the social environment (HBSE) class. [13] Because of this focus on social change, the critical paradigm is a natural home for social work research. However, we fall far short of adopting this approach widely in our profession’s research efforts.

Is the critical paradigm right for your project?

Every social work research project impacts social justice in some way. What distinguishes critical research is how it integrates an analysis of power into the research process itself. Critical research is appropriate for projects that are activist in orientation. For example, critical research projects should have working questions that explicitly seek to raise the consciousness of an oppressed group or collaborate equitably with community members and clients to addresses issues of concern. Because of their transformative potential, critical research projects can be incredibly rewarding to complete. However, partnerships take a long time to develop and social change can evolve slowly on an issue, making critical research projects a more challenging fit for student research projects which must be completed under a tight deadline with few resources.

Positivists critique the critical paradigm on multiple fronts. First and foremost, the focus on oppression and values as part of the research process is seen as likely to bias the research process, most problematically, towards confirmation bias. If you start out with the assumption that oppression exists and must be dealt with, then you are likely to find that regardless of whether it is truly there or not. Similarly, positivists may fault critical researchers for focusing on how the world should be, rather than how it truly is . In this, they may focus too much on theoretical and abstract inquiry and less on traditional experimentation and empirical inquiry. Finally, the goal of social transformation is seen as inherently unscientific, as science is not a political practice.

Interpretivists often find common cause with critical researchers. Feminist studies, for example, may explore the perspectives of women while centering gender-based oppression as part of the research process. In interpretivist research, the focus is less on radical change as part of the research process and more on small, incremental changes based on the results and conclusions drawn from the research project. Additionally, some critical researchers’ focus on individuality of experience is in stark contrast to the consensus-orientation of interpretivists. Interpretivists seek to understand people’s true selves. Some critical theorists argue that people have multiple selves or no self at all.

From your literature search, identify an article relevant to your working question or broad research topic that uses a critical perspective. You should look for articles where the authors are clear that they are applying a critical approach to research like feminism, anti-racism, Marxism and critical theory, decolonization, anti-oppressive practice, or other social justice-focused theoretical perspectives. To target your search further, include keywords in your queries to research methods commonly used in the critical paradigm like participatory action research and community-based participatory research. If you have trouble identifying an article for this exercise, consult your professor for some help. These articles may be more challenging to find, but reviewing one is necessary to get a feel for what research in this paradigm is like.
Review the assumptions of the critical research paradigm.
Discuss in a few sentences how the author’s conclusions are based on some of these paradigmatic assumptions. How might a researcher operating from different assumptions (like values-neutrality or researcher as neutral and unbiased) critique the conclusions of this study?

Pragmatism: Researcher as “strategist”

“Essentially, all models are wrong but some are useful.” (Box, 1976) [14]

Pragmatism is a research paradigm that suspends questions of philosophical ‘truth’ and focuses more on how different philosophies, theories, and methods can be used strategically to provide a multidimensional view of a topic. Researchers employing pragmatism will mix elements of positivist, interpretivist, and critical research depending on the purpose of a particular project and the practical constraints faced by the researcher and their research context. We favor this approach for student projects because it avoids getting bogged down in choosing the “right” paradigm and instead focuses on the assumptions that help you answer your question, given the limitations of your research context. Student research projects are completed quickly and moving in the direction of pragmatism can be a route to successfully completing a project. Your project is a representation of what you think is feasible, ethical, and important enough for you to study.

The crucial consideration for the pragmatist is whether the outcomes of research have any real-world application, rather than whether they are “true.” The methods, theories, and philosophies chosen by pragmatic researchers are guided by their working question. There are no distinctively pragmatic research methods since this approach is about making judicious use whichever methods fit best with the problem under investigation. Pragmatic approaches may be less likely to prioritize ontological, epistemological or axiological consistency when combining different research methods. Instead, the emphasis is on solving a pressing problem and adapting to the limitations and opportunities in the researchers’ context.

Adopt a multi-paradigmatic perspective

Believe it or not, there is a long literature of acrimonious conflict between scientists from positivist, interpretivist, and critical camps (see Heineman-Pieper et al., 2002 [15] for a longer discussion). Pragmatism is an old idea, but it is appealing precisely because it attempts to resolve the problem of multiple incompatible philosophical assumptions in social science. To a pragmatist, there is no “correct” paradigm. All paradigms rely on assumptions about the social world that are the subject of philosophical debate. Each paradigm is an incomplete understanding of the world, and it requires a scientific community using all of them to gain a comprehensive view of the social world. This multi-paradigmatic perspective is a unique gift of social work research, as our emphasis on empathy and social change makes us more critical of positivism, the dominant paradigm in social science.

We offered the metaphors of expert, empathizer, activist, and strategist for each paradigm. It’s important not to take these labels too seriously. For example, some may view that scientists should be experts or that activists are biased and unscientific. Nevertheless, we hope that these metaphors give you a sense of what it feels like to conduct research within each paradigm.

One of the unique aspects of paradigmatic thinking is that often where you think you are most at home may actually be the opposite of where your research project is. For example, in my graduate and doctoral education, I thought I was a critical researcher. In fact, I thought I was a radical researcher focused on social change and transformation. Yet, often times when I sit down to conceptualize and start a research project, I find myself squarely in the positivist paradigm, thinking through neat cause-and-effect relationships that can be mathematically measured. There is nothing wrong with that! Your task for your research project is to find the paradigm that best matches your research question. Think through what you really want to study and how you think about the topic, then use assumptions of that paradigm to guide your inquiry.

Another important lesson is that no research project fits perfectly in one paradigm or another. Instead, there is a spectrum along which studies are, to varying degrees, interpretivist, positivist, and critical. For example, all social work research is a bit activist in that our research projects are designed to inform action for change on behalf of clients and systems. However, some projects will focus on the conclusions and implications of projects informing social change (i.e., positivist and interpretivist projects) while others will partner with community members and design research projects collaboratively in a way that leads to social change (i.e. critical projects). In section 7.5, we will describe a pragmatic approach to research design guided by your paradigmatic and theoretical framework.

Key Takeaways

Social work research falls, to some degree, in each of the four paradigms: positivism, interpretivism, critical, and pragmatist.
Adopting a pragmatic, multi-paradigmatic approach to research makes sense for student researchers, as it directs students to use the philosophical assumptions and methodological approaches that best match their research question and research context.
Research in all paradigms is necessary to come to a comprehensive understanding of a topic, and social workers must be able to understand and apply knowledge from each research paradigm.
Describe which paradigm best fits your perspective on the world and which best fits with your project.
Identify any similarities and differences in your personal assumptions and the assumption your research project relies upon. For example, are you a more critical and radical thinker but have chosen a more “expert” role for yourself in your research project?

Learners will be able to…

Apply the assumptions of each paradigm to your project
Summarize what aspects of your project stem from positivist, interpretivist, or critical assumptions

In the previous sections, we reviewed the major paradigms and theories in social work research. In this section, we will provide an example of how to apply theory and paradigm in research. This process is depicted in Figure 7.2 below with some quick summary questions for each stage. Some questions in the figure below have example answers like designs (i.e., experimental, survey) and data analysis approaches (i.e., discourse analysis). These examples are arbitrary. There are a lot of options that are not listed. So, don’t feel like you have to memorize them or use them in your study.

This diagram (taken from an archived Open University (UK) course entitled E89 - Educational Inquiry ) shows one way to visualize the research design process. While research is far from linear, in general, this is how research projects progress sequentially. Researchers begin with a working question, and through engaging with the literature, develop and refine those questions into research questions (a process we will finalize in Chapter 9 ). But in order to get to the part where you gather your sample, measure your participants, and analyze your data, you need to start with paradigm. Based on your work in section 7.3, you should have a sense of which paradigm or paradigms are best suited to answering your question. The approach taken will often reflect the nature of the research question; the kind of data it is possible to collect; and work previously done in the area under consideration. When evaluating paradigm and theory, it is important to look at what other authors have done previously and the framework used by studies that are similar to the one you are thinking of conducting.

Once you situate your project in a research paradigm, it becomes possible to start making concrete choices about methods. Depending on the project, this will involve choices about things like:

What is my final research question?
What are the key variables and concepts under investigation, and how will I measure them?
How do I find a representative sample of people who experience the topic I’m studying?
What design is most appropriate for my research question?
How will I collect and analyze data?
How do I determine whether my results describe real patterns in the world or are the result of bias or error?

The data collection phase can begin once these decisions are made. It can be very tempting to start collecting data as soon as possible in the research process as this gives a sense of progress. However, it is usually worth getting things exactly right before collecting data as an error found in your approach further down the line can be harder to correct or recalibrate around.

Designing a study using paradigm and theory: An example

Paradigm and theory have the potential to turn some people off since there is a lot of abstract terminology and thinking about real-world social work practice contexts. In this section, I’ll use an example from my own research, and I hope it will illustrate a few things. First, it will show that paradigms are really just philosophical statements about things you already understand and think about normally. It will also show that no project neatly sits in one paradigm and that a social work researcher should use whichever paradigm or combination of paradigms suit their question the best. Finally, I hope it is one example of how to be a pragmatist and strategically use the strengths of different theories and paradigms to answering a research question. We will pick up the discussion of mixed methods in the next chapter.

Thinking as an expert: Positivism

In my undergraduate research methods class, I used an open textbook much like this one and wanted to study whether it improved student learning. You can read a copy of the article we wrote on based on our study . We’ll learn more about the specifics of experiments and evaluation research in Chapter 13 , but you know enough to understand what evaluating an intervention might look like. My first thought was to conduct an experiment, which placed me firmly within the positivist or “expert” paradigm.

Experiments focus on isolating the relationship between cause and effect. For my study, this meant studying an open textbook (the cause, or intervention) and final grades (the effect, or outcome). Notice that my position as “expert” lets me assume many things in this process. First, it assumes that I can distill the many dimensions of student learning into one number—the final grade. Second, as the “expert,” I’ve determined what the intervention is: indeed, I created the book I was studying, and applied a theory from experts in the field that explains how and why it should impact student learning.

Theory is part of applying all paradigms, but I’ll discuss its impact within positivism first. Theories grounded in positivism help explain why one thing causes another. More specifically, these theories isolate a causal relationship between two (or more) concepts while holding constant the effects of other variables that might confound the relationship between the key variables. That is why experimental design is so common in positivist research. The researcher isolates the environment from anything that might impact or bias the cause and effect relationship they want to investigate.

But in order for one thing to lead to change in something else, there must be some logical, rational reason why it would do so. In open education, there are a few hypotheses (though no full-fledged theories) on why students might perform better using open textbooks. The most common is the access hypothesis , which states that students who cannot afford expensive textbooks or wouldn’t buy them anyway can access open textbooks because they are free, which will improve their grades. It’s important to note that I held this theory prior to starting the experiment, as in positivist research you spell out your hypotheses in advance and design an experiment to support or refute that hypothesis.

Notice that the hypothesis here applies not only to the people in my experiment, but to any student in higher education. Positivism seeks generalizable truth, or what is true for everyone. The results of my study should provide evidence that anyone who uses an open textbook would achieve similar outcomes. Of course, there were a number of limitations as it was difficult to tightly control the study. I could not randomly assign students or prevent them from sharing resources with one another, for example. So, while this study had many positivist elements, it was far from a perfect positivist study because I was forced to adapt to the pragmatic limitations of my research context (e.g., I cannot randomly assign students to classes) that made it difficult to establish an objective, generalizable truth.

Thinking like an empathizer: Interpretivism

One of the things that did not sit right with me about the study was the reliance on final grades to signify everything that was going on with students. I added another quantitative measure that measured research knowledge, but this was still too simplistic. I wanted to understand how students used the book and what they thought about it. I could create survey questions that ask about these things, but to get at the subjective truths here, I thought it best to use focus groups in which students would talk to one another with a researcher moderating the discussion and guiding it using predetermined questions. You will learn more about focus groups in Chapter 18 .

Researchers spoke with small groups of students during the last class of the semester. They prompted people to talk about aspects of the textbook they liked and didn’t like, compare it to textbooks from other classes, describe how they used it, and so forth. It was this focus on understanding and subjective experience that brought us into the interpretivist paradigm. Alongside other researchers, I created the focus group questions but encouraged researchers who moderated the focus groups to allow the conversation to flow organically.

We originally started out with the assumption, for which there is support in the literature, that students would be angry with the high-cost textbook that we used prior to the free one, and this cost shock might play a role in students’ negative attitudes about research. But unlike the hypotheses in positivism, these are merely a place to start and are open to revision throughout the research process. This is because the researchers are not the experts, the participants are! Just like your clients are the experts on their lives, so were the students in my study. Our job as researchers was to create a group in which they would reveal their informed thoughts about the issue, coming to consensus around a few key themes.

When we initially analyzed the focus groups, we uncovered themes that seemed to fit the data. But the overall picture was murky. How were themes related to each other? And how could we distill these themes and relationships into something meaningful? We went back to the data again. We could do this because there isn’t one truth, as in positivism, but multiple truths and multiple ways of interpreting the data. When we looked again, we focused on some of the effects of having a textbook customized to the course. It was that customization process that helped make the language more approachable, engaging, and relevant to social work practice.

Ultimately, our data revealed differences in how students perceived a free textbook versus a free textbook that is customized to the class. When we went to interpret this finding, the remix hypothesis of open textbook was helpful in understanding that relationship. It states that the more faculty incorporate editing and creating into the course, the better student learning will be. Our study helped flesh out that theory by discussing the customization process and how students made sense of a customized resource.

In this way, theoretical analysis operates differently in interpretivist research. While positivist research tests existing theories, interpretivist research creates theories based on the stories of research participants. However, it is difficult to say if this theory was totally emergent in the dataset or if my prior knowledge of the remix hypothesis influenced my thinking about the data. Interpretivist researchers are encouraged to put a box around their prior experiences and beliefs, acknowledging them, but trying to approach the data with fresh eyes. Interpretivists know that this is never perfectly possible, though, as we are always influenced by our previous experiences when interpreting data and conducting scientific research projects.

Thinking like an activist: Critical

Although adding focus groups helped ease my concern about reducing student learning down to just final grades by providing a more rich set of conversations to analyze. However, my role as researcher and “expert” was still an important part of the analysis. As someone who has been out of school for a while, and indeed has taught this course for years, I have lost touch with what it is like to be a student taking research methods for the first time. How could I accurately interpret or understand what students were saying? Perhaps I would overlook things that reflected poorly on my teaching or my book. I brought other faculty researchers on board to help me analyze the data, but this still didn’t feel like enough.

By luck, an undergraduate student approached me about wanting to work together on a research project. I asked her if she would like to collaborate on evaluating the textbook with me. Over the next year, she assisted me with conceptualizing the project, creating research questions, as well as conducting and analyzing the focus groups. Not only would she provide an “insider” perspective on coding the data, steeped in her lived experience as a student, but she would serve as a check on my power through the process.

Including people from the group you are measuring as part of your research team is a common component of critical research. Ultimately, critical theorists would find my study to be inadequate in many ways. I still developed the research question, created the intervention, and wrote up the results for publication, which privileges my voice and role as “expert.” Instead, critical theorists would emphasize the role of students (community members) in identifying research questions, choosing the best intervention to used, and so forth. But collaborating with students as part of a research team did address some of the power imbalances in the research process.

Critical research projects also aim to have an impact on the people and systems involved in research. No students or researchers had profound personal realizations as a result of my study, nor did it lessen the impact of oppressive structures in society. I can claim some small victory that my department switched to using my textbook after the study was complete (changing a system), though this was likely the result of factors other than the study (my advocacy for open textbooks).

Social work research is almost always designed to create change for people or systems. To that end, every social work project is at least somewhat critical. However, the additional steps of conducting research with people rather than on people reveal a depth to the critical paradigm. By bringing students on board the research team, study had student perspectives represented in conceptualization, data collection, and analysis. That said, there was much to critique about this study from a critical perspective. I retained a lot of the power in the research process, and students did not have the ability to determine the research question or purpose of the project. For example, students might likely have said that textbook costs and the quality of their research methods textbook were less important than student debt, racism, or other potential issues experienced by students in my class. Instead of a ground-up research process based in community engagement, my research included some important participation by students on project created and led by faculty.

Conceptualization is an iterative process

I hope this conversation was useful in applying paradigms to a research project. While my example discusses education research, the same would apply for social work research about social welfare programs, clinical interventions, or other topics. Paradigm and theory are covered at the beginning of the conceptualization of your project because these assumptions will structure the rest of your project. Each of the research steps that occur after this chapter (e.g., forming a question, choosing a design) rely upon philosophical and theoretical assumptions. As you continue conceptualizing your project over the next few weeks, you may find yourself shifting between paradigms. That is normal, as conceptualization is not a linear process. As you move through the next steps of conceptualizing and designing a project, you’ll find philosophies and theories that best match how you want to study your topic.

Viewing theoretical and empirical arguments through this lens is one of the true gifts of the social work approach to research. The multi-paradigmatic perspective is a hallmark of social work research and one that helps us contribute something unique on research teams and in practice.

Multi-paradigmatic research is a distinguishing hallmark of social work research. Understanding the limitations and strengths of each paradigm will help you justify your research approach and strategically choose elements from one or more paradigms to answer your question.
Paradigmatic assumptions help you understand the “blind spots” in your research project and how to adjust and address these areas. Keep in mind, it is not necessary to address all of your blind spots, as all projects have limitations.
Sketch out which paradigm applies best to your project. Second, building on your answer to the exercise in section 7.3, identify how the theory you chose and the paradigm in which you find yourself are consistent or are in conflict with one another. For example, if you are using systems theory in a positivist framework, you might talk about how they both rely on a deterministic approach to human behavior with a focus on the status-quo and social order.
Define and provide an example of an idiographic causal explanation
Differentiate between idiographic and nomothetic causal relationships
Link idiographic and nomothetic causal relationships with the process of theory building and theory testing
Describe how idiographic and nomothetic causal explanations can be complementary

As we transition away from positivism, it is important to highlight the assumptions it makes about the scientific process–the hypothetico-deductive method, sometimes referred to as the research circle.

The hypothetico-deductive method

The primary way that researchers in the positivist paradigm use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers choose an existing theory. Then, they make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary.

This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 8.8 shows, this approach meshes nicely with the process of conducting a research project—creating a more detailed model of “theoretically motivated” or “theory-driven” research. Together, they form a model of theoretically motivated research.

Keep in mind the hypothetico-deductive method is only one way of using social theory to inform social science research. It starts with describing one or more existing theories, deriving a hypothesis from one of those theories, testing your hypothesis in a new study, and finally reevaluating the theory based on the results data analyses. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

But what if your research question is more interpretive? What if it is less about theory-testing and more about theory-building? This is what our next chapter covers: the process of inductively deriving theory from people’s stories and experiences. This process looks different than that depicted in Figure 8.8. It still starts with your research question and answering that question by conducting a research study. But instead of testing a hypothesis you created based on a theory, you will create a theory of your own that explain the data you collected. This format works well for qualitative research questions and for research questions that existing theories do not address.

Inductive reasoning is most commonly found in studies using qualitative methods, such as focus groups and interviews. Because inductive reasoning involves the creation of a new theory, researchers need very nuanced data on how the key concepts in their working question operate in the real world. Qualitative data is often drawn from lengthy interactions and observations with the individuals and phenomena under examination. For this reason, inductive reasoning is most often associated with qualitative methods, though it is used in both quantitative and qualitative research.

Whose truth does science establish?

Social work is concerned with the “isms” of oppression (ableism, ageism, cissexism, classism, heterosexism, racism, sexism, etc.), and so our approach to science must reconcile its history as both a tool of oppression and its exclusion of oppressed groups. Science grew out of the Enlightenment, a philosophical movement which applied reason and empirical analysis to understanding the world. While the Enlightenment brought forth tremendous achievements, the critiques of Marxian, feminist, and other critical theorists complicated the Enlightenment understanding of science. For this section, I will focus on feminist critiques of science, building upon an entry in the Stanford Encyclopedia of Philosophy (Crasnow, 2020). [16]

In its original formulation, science was an individualistic endeavor. As we learned in Chapter 1 , a basic statement of the scientific method is that a researcher studies existing theories on a topic, formulates a hypothesis about what might be true, and either confirms or disconfirms their hypothesis through experiment and rigorous observation. Over time, our theories become more accurate in their predictions and more comprehensive in their conclusions. Scientists put aside their preconceptions, look at the data, and build their theories based on objective rationality.

Yet, this cannot be perfectly true. Scientists are human, after all. As a profession historically dominated by white men, scientists have dismissed women and other minorities as being psychologically unfit for the scientific profession. While attitudes have improved, science, technology, engineering, mathematics (STEM) and related fields remain dominated by white men (Grogan, 2019). [17] Biases can persist in social work theory and research when social scientists do not have similar experiences to the populations they study.

Gender bias can influence the research questions scientists choose to answer. Feminist critiques of medical science drew attention to women’s health issues, spurring research and changing standards of care. The focus on domestic violence in the empirical literature can also be seen as a result of feminist critique. Thus, critical theory helps us critique what is on the agenda for science. If science is to answer important questions, it must speak to the concerns of all people. Through the democratization in access to scientific knowledge and the means to produce it, science becomes a sister process of social development and social justice.

The goal of a diverse and participatory scientific community lies in contrast to much of what we understand to be “proper” scientific knowledge. Many of the older, classic social science theories were developed based on research which observed males or from university students in the United States or other Western nations. How these observations were made, what questions were asked, and how the data were interpreted were shaped by the same oppressive forces that existed in broader society, a process that continues into the present. In psychology, the concept of hysteria or hysterical women was believed to be caused by a wandering womb (Tasca et al., 2012). [18] Even today, there are gender biases in diagnoses of histrionic personality disorder and racial biases in psychotic disorders (Klonsky et al., 2002) [19] because the theories underlying them were created in a sexist and racist culture. In these ways, science can reinforce the truth of the white Western male perspective.

Finally, it is important to note that social science research is often conducted on populations rather than with populations. Historically, this has often meant Western men traveling to other countries and seeking to understand other cultures through a Western lens. Lacking cultural humility and failing to engage stakeholders, ethnocentric research of this sort has led to the view of non-Western cultures as inferior. Moreover, the use of these populations as research subjects rather than co-equal participants in the research process privileges the researcher’s knowledge over that from other groups or cultures. Researchers working with indigenous cultures, in particular, had a destructive habit of conducting research for a short time and then leaving, without regard for the impact their study had on the population. These critiques of Western science aim to decolonize social science and dismantle the racist ideas the oppress indigenous and non-Western peoples through research (Smith, 2013). [20]

The central concept in feminist, anti-racist, and decolonization critiques (among other critical frames) is epistemic injustice. Epistemic injustice happens when someone is treated unfairly in their capacity to know something or describe their experience of the world. As described by Fricker (2011), [21] the injustice emerges from the dismissal of knowledge from oppressed groups, discrimination against oppressed groups in scientific communities, and the resulting gap between what scientists can make sense of from their experience and the experiences of people with less power who have lived experience of the topic. We recommend this video from Edinburgh Law School which applies epistemic injustice to studying public health emergencies, disabilities, and refugee services .

The letters IV on the left side with an arrow pointing to the letters DV on the right

Positivism relies on nomothetic causality, or the idea that “one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief.” Then, we described one kind of causality: a simple cause-and-effect relationship supported by existing theory and research on the topic, also known as a nomothetic causal relationship. But what if there is not a lot of literature on your topic? What if your question is more exploratory than explanatory? Then, you need a different kind of causal explanation, one that accounts for the complexity of human interactions.

How can we build causal relationships if we are just describing or exploring a topic? Recall the definitions of exploratory research , descriptive research , and explanatory research from Chapter 2. Wouldn’t we need to do explanatory research to build any kind of causal explanation? Explanatory research attempts to establish nomothetic causal relationships: an independent variable is demonstrated to cause change in a dependent variable. Exploratory and descriptive qualitative research contains some causal relationships, but they are actually descriptions of the causal relationships established by the study participants.

What do idiographic causal explanations look like?

An idiographic causal relationship tries to identify the many, interrelated causes that account for the phenomenon the researcher is investigating. So, if idiographic causal explanations do not look like Figure 8.5, 8.6, or 8.7 what do they look like? Instead of saying “x causes y,” your participants will describe their experiences with “x,” which they will tell you was caused and influenced by a variety of other factors, as interpreted through their unique perspective, time, and environment. As we stated before, idiographic causal explanations are messy. Your job as a social science researcher is to accurately describe the patterns in what your participants tell you.

Let’s think about this using an example. If I asked you why you decided to become a social worker, what might you say? For me, I would say that I wanted to be a mental health clinician since I was in high school. I was interested in how people thought, and I was privileged enough to have psychology courses at my local high school. I thought I wanted to be a psychologist, but at my second internship in my undergraduate program, my supervisors advised me to become a social worker because the license provided greater authority for independent practice and flexibility for career change. Once I found out social workers were like psychologists who also raised trouble about social justice, I was hooked.

That’s not a simple explanation at all! But it’s definitely a causal explanation. It is my individual, subjective truth of a complex process. If we were to ask multiple social workers the same question, we might find out that many social workers begin their careers based on factors like personal experience with a disability or social injustice, positive experiences with social workers, or a desire to help others. No one factor is the “most important factor,” like with nomothetic causal relationships. Instead, a complex web of factors, contingent on context, emerge when you interpret what people tell you about their lives.

Understanding “why?”

In creating an idiographic explanation, you are still asking “why?” But the answer is going to be more complex. Those complexities are described in Table 8.1 as well as this short video comparing nomothetic and idiographic relationships .

Table 8.1: Comparing nomothetic and idiographic causal relationships
	Nomothetic causal relationships	Idiographic causal relationships
Paradigm	Positivist	Interpretivist
Purpose of research	Prediction & generalization	Understanding & particularity
Reasoning	Deductive	Inductive
Purpose of research	Explanatory	Exploratory or descriptive
Research methods	Quantitative	Qualitative
Causality	Simple: cause and effect	Complex: context-dependent, sometimes circular or contradictory
Role of theory	Theory testing	Theory building

Remember our question from the last section, “Are you trying to generalize or nah?” If you answered nah (or no, like a normal person), you are trying to establish an idiographic causal explanation. The purpose of that explanation isn’t to predict the future or generalize to larger populations, but to describe the here-and-now as it is experienced by individuals within small groups and communities. Idiographic explanations are focused less on what is generally experienced by all people but more on the particularities of what specific individuals in a unique time and place experience.

Researchers seeking idiographic causal relationships are not trying to generalize or predict, so they have no need to reduce phenomena to mathematics. In fact, only examining things that can be counted can rob a causal relationship of its meaning and context. Instead, the goal of idiographic causal relationships is understanding, rather than prediction. Idiographic causal relationships are formed by interpreting people’s stories and experiences. Usually, these are expressed through words. Not all qualitative studies use word data, as some can use interpretations of visual or performance art. However, the vast majority of qualitative studies do use word data, like the transcripts from interviews and focus groups or documents like journal entries or meeting notes. Your participants are the experts on their lives—much like in social work practice—and as in practice, people’s experiences are embedded in their cultural, historical, and environmental context.

Idiographic causal explanations are powerful because they can describe the complicated and interconnected nature of human life. Nomothetic causal explanations, by comparison, are simplistic. Think about if someone asked you why you wanted to be a social worker. Your story might include a couple of vignettes from your education and early employment. It might include personal experience with the social welfare system or family traditions. Maybe you decided on a whim to enroll in a social work course during your graduate program. The impact of each of these events on your career is unique to you.

Idiographic causal explanations are concerned with individual stories, their idiosyncrasies, and the patterns that emerge when you collect and analyze multiple people’s stories. This is the inductive reasoning we discussed at the beginning of this chapter. Often, idiographic causal explanations begin by collecting a lot of qualitative data, whether though interviews, focus groups, or looking at available documents or cultural artifacts. Next, the researcher looks for patterns in the data and arrives at a tentative theory for how the key ideas in people’s stories are causally related.

Unlike nomothetic causal relationships, there are no formal criteria (e.g., covariation) for establishing causality in idiographic causal relationships. In fact, some criteria like temporality and nonspuriousness may be violated. For example, if an adolescent client says, “It’s hard for me to tell whether my depression began before my drinking, but both got worse when I was expelled from my first high school,” they are recognizing that it may not so simple that one thing causes another. Sometimes, there is a reciprocal relationship where one variable (depression) impacts another (alcohol abuse), which then feeds back into the first variable (depression) and into other variables as well (school). Other criteria, such as covariation and plausibility, still make sense, as the relationships you highlight as part of your idiographic causal explanation should still be plausible and its elements should vary together.

Theory building and theory testing

As we learned in the previous section, nomothetic causal explanations are created by researchers applying deductive reasoning to their topic and creating hypotheses using social science theories. Much of what we think of as social science is based on this hypothetico-deductive method, but this leaves out the other half of the equation. Where do theories come from? Are they all just revisions of one another? How do any new ideas enter social science?

Through inductive reasoning and idiographic causal explanations!

Let’s consider a social work example. If you plan to study domestic and sexual violence, you will likely encounter the Power and Control Wheel, also known as the Duluth Model (Figure 8.9). The wheel is a model designed to depict the process of domestic violence. The wheel was developed based on qualitative focus groups conducted by sexual and domestic violence advocates in Duluth, MN. This video explains more about the Duluth Model of domestic abuse.

Power and control wheel indicating the factors like

The Power and Control Wheel is an example of what an idiographic causal relationship looks like. By contrast, look back at the previous section’s Figure 8.5, 8.6, and 8.7 on nomothetic causal relationships between independent and dependent variables. See how much more complex idiographic causal explanations are?! They are complex, but not difficult to understand. At the center of domestic abuse is power and control, and while not every abuser would say that is what they were doing, that is the understanding of the survivors who informed this theoretical model. Their power and control is maintained through a variety of abusive tactics from social isolation to use of privilege to avoid consequences.

What about the role of hypotheses in idiographic causal explanations? In nomothetic causal explanations, researchers create hypotheses using existing theory and then test them for accuracy. Hypotheses in idiographic causality are much more tentative, and are probably best considered as “hunches” about what they think might be true. Importantly, they might indicate the researcher’s prior knowledge and biases before the project begins, but the goal of idiographic research is to let your participants guide you rather than existing social work knowledge. Continuing with our Duluth Model example, advocates likely had some tentative hypotheses about what was important in a relationship with domestic violence. After all, they worked with this population for years prior to the creation of the model. However, it was the stories of the participants in these focus groups that led the Power and Control Wheel explanation for domestic abuse.

As qualitative inquiry unfolds, hypotheses and hunches are likely to emerge and shift as researchers learn from what their participants share. Because the participants are the experts in idiographic causal relationships, a researcher should be open to emerging topics and shift their research questions and hypotheses accordingly. This is in contrast to hypotheses in quantitative research, which remain constant throughout the study and are shown to be true or false.

Over time, as more qualitative studies are done and patterns emerge across different studies and locations, more sophisticated theories emerge that explain phenomena across multiple contexts. Once a theory is developed from qualitative studies, a quantitative researcher can seek to test that theory. For example, a quantitative researcher may hypothesize that men who hold traditional gender roles are more likely to engage in domestic violence. That would make sense based on the Power and Control Wheel model, as the category of “using male privilege” speaks to this relationship. In this way, qualitatively-derived theory can inspire a hypothesis for a quantitative research project, as we will explore in the next section.

Complementary approaches

If idiographic and nomothetic still seem like obscure philosophy terms, let’s consider another example. Imagine you are working for a community-based non-profit agency serving people with disabilities. You are putting together a report to lobby the state government for additional funding for community support programs. As part of that lobbying, you are likely to rely on both nomothetic and idiographic causal relationships.

If you looked at nomothetic causal relationships, you might learn how previous studies have shown that, in general, community-based programs like yours are linked with better health and employment outcomes for people with disabilities. Nomothetic causal explanations seek to establish that community-based programs are better for everyone with disabilities, including people in your community.

If you looked at idiographic causal explanations, you would use stories and experiences of people in community-based programs. These individual stories are full of detail about the lived experience of being in a community-based program. You might use one story from a client in your lobbying campaign, so policymakers can understand the lived experience of what it’s like to be a person with a disability in this program. For example, a client who said “I feel at home when I’m at this agency because they treat me like a family member,” or “this is the agency that helped me get my first paycheck,” can communicate richer, more complex causal relationships.

Neither kind of causal explanation is better than the other. A decision to seek idiographic causal explanations means that you will attempt to explain or describe your phenomenon exhaustively, attending to cultural context and subjective interpretations. A decision to seek nomothetic causal explanations, on the other hand, means that you will try to explain what is true for everyone and predict what will be true in the future. In short, idiographic explanations have greater depth, and nomothetic explanations have greater breadth.

Most importantly, social workers understand the value of both approaches to understanding the social world. A social worker helping a client with substance abuse issues seeks idiographic explanations when they ask about that client’s life story, investigate their unique physical environment, or probe how their family relationships. At the same time, a social worker also uses nomothetic explanations to guide their interventions. Nomothetic explanations may help guide them to minimize risk factors and maximize protective factors or use an evidence-based therapy, relying on knowledge about what in general helps people with substance abuse issues.

So, which approach speaks to you? Are you interested in learning about (a) a few people’s experiences in a great deal of depth, or (b) a lot of people’s experiences more superficially, while also hoping your findings can be generalized to a greater number of people? The answer to this question will drive your research question and project. These approaches provide different types of information and both types are valuable.

Idiographic causal explanations focus on subjectivity, context, and meaning.
Idiographic causal explanations are best suited to exploratory research questions and qualitative methods.
Idiographic causal explanations are used to create new theories in social science.
Explore the literature on the theory you identified in section 8.1.
Read about the origins of your theory. Who developed it and from what data?
See if you can find a figure like Figure 8.9 in an article or book chapter that depicts the key concepts in your theory and how those concepts are related to one another causally. Write out a short statement on the causal relationships contained in the figure.
List the key terms associated with qualitative research questions
Distinguish between qualitative and quantitative research questions

Qualitative research questions differ from quantitative research questions. Because qualitative research questions seek to explore or describe phenomena, not provide a neat nomothetic explanation, they are often more general and openly worded. They may include only one concept, though many include more than one. Instead of asking how one variable causes changes in another, we are instead trying to understand the experiences , understandings , and meanings that people have about the concepts in our research question. These keywords often make an appearance in qualitative research questions.

Let’s work through an example from our last section. In Table 9.1, a student asked, “What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care?” In this question, it is pretty clear that the student believes that adolescents in foster care who identify as LGBTQ+ may be at greater risk for homelessness. This is a nomothetic causal relationship—LGBTQ+ status causes changes in homelessness.

However, what if the student were less interested in predicting homelessness based on LGBTQ+ status and more interested in understanding the stories of foster care youth who identify as LGBTQ+ and may be at risk for homelessness? In that case, the researcher would be building an idiographic causal explanation . The youths whom the researcher interviews may share stories of how their foster families, caseworkers, and others treated them. They may share stories about how they thought of their own sexuality or gender identity and how it changed over time. They may have different ideas about what it means to transition out of foster care.

Because qualitative questions usually center on idiographic causal relationships, they look different than quantitative questions. Table 9.3 below takes the final research questions from Table 9.1 and adapts them for qualitative research. The guidelines for research questions previously described in this chapter still apply, but there are some new elements to qualitative research questions that are not present in quantitative questions.

Qualitative research questions often ask about lived experience, personal experience, understanding, meaning, and stories.
Qualitative research questions may be more general and less specific.
Qualitative research questions may also contain only one variable, rather than asking about relationships between multiple variables.

Table 9.3 Quantitative vs. qualitative research questions

How does witnessing domestic violence impact a child’s romantic relationships in adulthood?	How do people who witness domestic violence understand its effects on their current relationships?
What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care?	What is the experience of identifying as LGBTQ+ in the foster care system?
How does income inequality affect ambivalence in high-density urban areas?	What does racial ambivalence mean to residents of an urban neighborhood with high income inequality?
How does race impact rates of mental health diagnosis for children in foster care?	How do African-Americans experience seeking help for mental health concerns?

Qualitative research questions have one final feature that distinguishes them from quantitative research questions: they can change over the course of a study. Qualitative research is a reflexive process, one in which the researcher adapts their approach based on what participants say and do. The researcher must constantly evaluate whether their question is important and relevant to the participants. As the researcher gains information from participants, it is normal for the focus of the inquiry to shift.

For example, a qualitative researcher may want to study how a new truancy rule impacts youth at risk of expulsion. However, after interviewing some of the youth in their community, a researcher might find that the rule is actually irrelevant to their behavior and thoughts. Instead, their participants will direct the discussion to their frustration with the school administrators or the lack of job opportunities in the area. This is a natural part of qualitative research, and it is normal for research questions and hypothesis to evolve based on information gleaned from participants.

However, this reflexivity and openness unacceptable in quantitative research for good reasons. Researchers using quantitative methods are testing a hypothesis, and if they could revise that hypothesis to match what they found, they could never be wrong! Indeed, an important component of open science and reproducability is the preregistration of a researcher’s hypotheses and data analysis plan in a central repository that can be verified and replicated by reviewers and other researchers. This interactive graphic from 538 shows how an unscrupulous research could come up with a hypothesis and theoretical explanation after collecting data by hunting for a combination of factors that results in a statistically significant relationship. This is an excellent example of how the positivist assumptions behind quantitative research and intepretivist assumptions behind qualitative research result in different approaches to social science.

Qualitative research questions often contain words or phrases like “lived experience,” “personal experience,” “understanding,” “meaning,” and “stories.”
Qualitative research questions can change and evolve over the course of the study.
Using the guidance in this chapter, write a qualitative research question. You may want to use some of the keywords mentioned above.
Kivuna, C. & Kuyini, A. B. (2017). Understanding and applying research paradigms in educational contexts. International Journal of Higher Education, 6 (5), 26-41. https://eric.ed.gov/?id=EJ1154775 ↵
Kuhn, T. (1962). The structure of scientific revolutions . Chicago: University of Chicago Press. ↵
Fleuridas, C., & Krafcik, D. (2019). Beyond four forces: The evolution of psychotherapy. Sage Open , 9 (1), 2158244018824492. ↵
Shneider, A. M. (2009). Four stages of a scientific discipline; four types of scientist. Trends in Biochemical Sciences 34 (5), 217-233. https://doi.org/10.1016/j.tibs.2009.02.00 ↵
Burrell, G. & Morgan, G. (1979). Sociological paradigms and organizational analysis . Routledge. Guba, E. (ed.) (1990). The paradigm dialog . SAGE. ↵
Routledge. Guba, E. (ed.) (1990). The paradigm dialog . SAGE. ↵
Burrell, G. & Morgan, G. (1979). Sociological paradigms and organizational analysis . Here is a summary of Burrell & Morgan from Babson College , and our classification collapses radical humanism and radical structuralism into the critical paradigm, following Guba and Lincoln's three-paradigm framework. We feel this approach is more parsimonious and easier for students to understand on an introductory level. ↵
For more about how the meanings of hand gestures vary by region, you might read the following blog entry: Wong, W. (2007). The top 10 hand gestures you’d better get right . Retrieved from: http://www.languagetrainers.co.uk/blog/2007/09/24/top-10-hand-gestures ↵
Rosario, M., Schrimshaw, E. W., Hunter, J., & Levy-Warren, A. (2009). The coming-out process of young lesbian and bisexual women: Are there butch/femme differences in sexual identity development?. Archives of sexual behavior , 38 (1), 34-49. ↵
Calhoun, C., Gerteis, J., Moody, J., Pfaff, S., & Virk, I. (Eds.). (2007). Classical sociological theory (2nd ed.). Malden, MA: Blackwell. ↵
Fraser, N. (1989). Unruly practices: Power, discourse, and gender in contemporary social theory . Minneapolis, MN: University of Minnesota Press. ↵
Here are links to two HBSE open textbooks, if you are unfamiliar with social work theories and would like more background. https://uark.pressbooks.pub/hbse1/ and https://uark.pressbooks.pub/humanbehaviorandthesocialenvironment2/ ↵
Box, G. E. P.. (1976). Science and statistics. Journal of the American Statistical Association, 71 (356), 791. ↵
Heineman-Pieper, J., Tyson, K., & Pieper, M. H. (2002). Doing good science without sacrificing good values: Why the heuristic paradigm is the best choice for social work. Families in Society , 83 (1), 15-28. ↵
Crasnow, S. (2020). Feminist perspectives on science. In E. N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (Winter 2020 Edition). Retrieved from: https://plato.stanford.edu/entries/feminist-science/ ↵
Grogan, K.E. (2019) How the entire scientific community can confront gender bias in the workplace. Nature Ecology & Evolution, 3 , 3–6. doi:10.1038/s41559-018-0747-4 ↵
Tasca, C., Rapetti, M., Carta, M. G., & Fadda, B. (2012). Women and hysteria in the history of mental health. Clinical practice and epidemiology in mental health: Clinical practice & epidemiology in mental health , 8 , 110-119. ↵
Klonsky, E. D., Jane, J. S., Turkheimer, E., & Oltmanns, T. F. (2002). Gender role and personality disorders. Journal of personality disorders , 16 (5), 464-476. ↵
Smith, L. T. (2013). Decolonizing methodologies: Research and indigenous peoples . Zed Books Ltd. ↵
Fricker, M. (2011). Epistemic injustice: Power and the ethics of knowing . Oxford University Press. ↵

The highest level of measurement. Denoted by mutually exclusive categories, a hierarchy (order), values can be added, subtracted, multiplied, and divided, and the presence of an absolute zero.

a paradigm based on the idea that social context and interaction frame our realities

a paradigm in social science research focused on power, inequality, and social change

a research paradigm that suspends questions of philosophical ‘truth’ and focuses more on how different philosophies, theories, and methods can be used strategically to resolve a problem or question within the researcher's unique context

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

when someone is treated unfairly in their capacity to know something or describe their experience of the world

conducted during the early stages of a project, usually when a researcher wants to test the feasibility of conducting a more extensive study or if the topic has not been studied in the past

research that describes or defines a particular phenomenon

explains why particular phenomena work in the way that they do; answers “why” questions

attempts to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants

"Assuming that the null hypothesis is true and the study is repeated an infinite number times by drawing random samples from the same populations(s), less than 5% of these results will be more extreme than the current result" (Cassidy et al., 2019, p. 233).

Scientific Inquiry in Social Work (2nd Edition) Copyright © 2020 by Matthew DeCarlo, Cory Cummings, and Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The word qualitative implies an emphasis on the qualities of entities and on processes and meanings that are not experimentally examined or measured [if measured at all] in terms of quantity, amount, intensity, or frequency. Qualitative researchers stress the socially constructed nature of reality, the intimate relationship between the researcher and what is studied, and the situational constraints that shape inquiry. Such researchers emphasize the value-laden nature of inquiry. They seek answers to questions that stress how social experience is created and given meaning. In contrast, quantitative studies emphasize the measurement and analysis of causal relationships between variables, not processes. Qualitative forms of inquiry are considered by many social and behavioral scientists to be as much a perspective on how to approach investigating a research problem as it is a method.

Denzin, Norman. K. and Yvonna S. Lincoln. “Introduction: The Discipline and Practice of Qualitative Research.” In The Sage Handbook of Qualitative Research . Norman. K. Denzin and Yvonna S. Lincoln, eds. 3 rd edition. (Thousand Oaks, CA: Sage, 2005), p. 10.

Characteristics of Qualitative Research

Below are the three key elements that define a qualitative research study and the applied forms each take in the investigation of a research problem.

Naturalistic -- refers to studying real-world situations as they unfold naturally; non-manipulative and non-controlling; the researcher is open to whatever emerges [i.e., there is a lack of predetermined constraints on findings].
Emergent -- acceptance of adapting inquiry as understanding deepens and/or situations change; the researcher avoids rigid designs that eliminate responding to opportunities to pursue new paths of discovery as they emerge.
Purposeful -- cases for study [e.g., people, organizations, communities, cultures, events, critical incidences] are selected because they are “information rich” and illuminative. That is, they offer useful manifestations of the phenomenon of interest; sampling is aimed at insight about the phenomenon, not empirical generalization derived from a sample and applied to a population.

The Collection of Data

Data -- observations yield a detailed, "thick description" [in-depth understanding]; interviews capture direct quotations about people’s personal perspectives and lived experiences; often derived from carefully conducted case studies and review of material culture.
Personal experience and engagement -- researcher has direct contact with and gets close to the people, situation, and phenomenon under investigation; the researcher’s personal experiences and insights are an important part of the inquiry and critical to understanding the phenomenon.
Empathic neutrality -- an empathic stance in working with study respondents seeks vicarious understanding without judgment [neutrality] by showing openness, sensitivity, respect, awareness, and responsiveness; in observation, it means being fully present [mindfulness].
Dynamic systems -- there is attention to process; assumes change is ongoing, whether the focus is on an individual, an organization, a community, or an entire culture, therefore, the researcher is mindful of and attentive to system and situational dynamics.

The Analysis

Unique case orientation -- assumes that each case is special and unique; the first level of analysis is being true to, respecting, and capturing the details of the individual cases being studied; cross-case analysis follows from and depends upon the quality of individual case studies.
Inductive analysis -- immersion in the details and specifics of the data to discover important patterns, themes, and inter-relationships; begins by exploring, then confirming findings, guided by analytical principles rather than rules.
Holistic perspective -- the whole phenomenon under study is understood as a complex system that is more than the sum of its parts; the focus is on complex interdependencies and system dynamics that cannot be reduced in any meaningful way to linear, cause and effect relationships and/or a few discrete variables.
Context sensitive -- places findings in a social, historical, and temporal context; researcher is careful about [even dubious of] the possibility or meaningfulness of generalizations across time and space; emphasizes careful comparative case study analysis and extrapolating patterns for possible transferability and adaptation in new settings.
Voice, perspective, and reflexivity -- the qualitative methodologist owns and is reflective about her or his own voice and perspective; a credible voice conveys authenticity and trustworthiness; complete objectivity being impossible and pure subjectivity undermining credibility, the researcher's focus reflects a balance between understanding and depicting the world authentically in all its complexity and of being self-analytical, politically aware, and reflexive in consciousness.

Berg, Bruce Lawrence. Qualitative Research Methods for the Social Sciences . 8th edition. Boston, MA: Allyn and Bacon, 2012; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 2nd ed. Thousand Oaks, CA: Sage Publications, 1995; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Basic Research Design for Qualitative Studies

Unlike positivist or experimental research that utilizes a linear and one-directional sequence of design steps, there is considerable variation in how a qualitative research study is organized. In general, qualitative researchers attempt to describe and interpret human behavior based primarily on the words of selected individuals [a.k.a., “informants” or “respondents”] and/or through the interpretation of their material culture or occupied space. There is a reflexive process underpinning every stage of a qualitative study to ensure that researcher biases, presuppositions, and interpretations are clearly evident, thus ensuring that the reader is better able to interpret the overall validity of the research. According to Maxwell (2009), there are five, not necessarily ordered or sequential, components in qualitative research designs. How they are presented depends upon the research philosophy and theoretical framework of the study, the methods chosen, and the general assumptions underpinning the study. Goals Describe the central research problem being addressed but avoid describing any anticipated outcomes. Questions to ask yourself are: Why is your study worth doing? What issues do you want to clarify, and what practices and policies do you want it to influence? Why do you want to conduct this study, and why should the reader care about the results? Conceptual Framework Questions to ask yourself are: What do you think is going on with the issues, settings, or people you plan to study? What theories, beliefs, and prior research findings will guide or inform your research, and what literature, preliminary studies, and personal experiences will you draw upon for understanding the people or issues you are studying? Note to not only report the results of other studies in your review of the literature, but note the methods used as well. If appropriate, describe why earlier studies using quantitative methods were inadequate in addressing the research problem. Research Questions Usually there is a research problem that frames your qualitative study and that influences your decision about what methods to use, but qualitative designs generally lack an accompanying hypothesis or set of assumptions because the findings are emergent and unpredictable. In this context, more specific research questions are generally the result of an interactive design process rather than the starting point for that process. Questions to ask yourself are: What do you specifically want to learn or understand by conducting this study? What do you not know about the things you are studying that you want to learn? What questions will your research attempt to answer, and how are these questions related to one another? Methods Structured approaches to applying a method or methods to your study help to ensure that there is comparability of data across sources and researchers and, thus, they can be useful in answering questions that deal with differences between phenomena and the explanation for these differences [variance questions]. An unstructured approach allows the researcher to focus on the particular phenomena studied. This facilitates an understanding of the processes that led to specific outcomes, trading generalizability and comparability for internal validity and contextual and evaluative understanding. Questions to ask yourself are: What will you actually do in conducting this study? What approaches and techniques will you use to collect and analyze your data, and how do these constitute an integrated strategy? Validity In contrast to quantitative studies where the goal is to design, in advance, “controls” such as formal comparisons, sampling strategies, or statistical manipulations to address anticipated and unanticipated threats to validity, qualitative researchers must attempt to rule out most threats to validity after the research has begun by relying on evidence collected during the research process itself in order to effectively argue that any alternative explanations for a phenomenon are implausible. Questions to ask yourself are: How might your results and conclusions be wrong? What are the plausible alternative interpretations and validity threats to these, and how will you deal with these? How can the data that you have, or that you could potentially collect, support or challenge your ideas about what’s going on? Why should we believe your results? Conclusion Although Maxwell does not mention a conclusion as one of the components of a qualitative research design, you should formally conclude your study. Briefly reiterate the goals of your study and the ways in which your research addressed them. Discuss the benefits of your study and how stakeholders can use your results. Also, note the limitations of your study and, if appropriate, place them in the context of areas in need of further research.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Heath, A. W. The Proposal in Qualitative Research. The Qualitative Report 3 (March 1997); Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 3rd edition. Thousand Oaks, CA: Sage, 1999; Maxwell, Joseph A. "Designing a Qualitative Study." In The SAGE Handbook of Applied Social Research Methods . Leonard Bickman and Debra J. Rog, eds. 2nd ed. (Thousand Oaks, CA: Sage, 2009), p. 214-253; Qualitative Research Methods. Writing@CSU. Colorado State University; Yin, Robert K. Qualitative Research from Start to Finish . 2nd edition. New York: Guilford, 2015.

Strengths of Using Qualitative Methods

The advantage of using qualitative methods is that they generate rich, detailed data that leave the participants' perspectives intact and provide multiple contexts for understanding the phenomenon under study. In this way, qualitative research can be used to vividly demonstrate phenomena or to conduct cross-case comparisons and analysis of individuals or groups.

Among the specific strengths of using qualitative methods to study social science research problems is the ability to:

Obtain a more realistic view of the lived world that cannot be understood or experienced in numerical data and statistical analysis;
Provide the researcher with the perspective of the participants of the study through immersion in a culture or situation and as a result of direct interaction with them;
Allow the researcher to describe existing phenomena and current situations;
Develop flexible ways to perform data collection, subsequent analysis, and interpretation of collected information;
Yield results that can be helpful in pioneering new ways of understanding;
Respond to changes that occur while conducting the study ]e.g., extended fieldwork or observation] and offer the flexibility to shift the focus of the research as a result;
Provide a holistic view of the phenomena under investigation;
Respond to local situations, conditions, and needs of participants;
Interact with the research subjects in their own language and on their own terms; and,
Create a descriptive capability based on primary and unstructured data.

Anderson, Claire. “Presenting and Evaluating Qualitative Research.” American Journal of Pharmaceutical Education 74 (2010): 1-7; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Limitations of Using Qualitative Methods

It is very much true that most of the limitations you find in using qualitative research techniques also reflect their inherent strengths . For example, small sample sizes help you investigate research problems in a comprehensive and in-depth manner. However, small sample sizes undermine opportunities to draw useful generalizations from, or to make broad policy recommendations based upon, the findings. Additionally, as the primary instrument of investigation, qualitative researchers are often embedded in the cultures and experiences of others. However, cultural embeddedness increases the opportunity for bias generated from conscious or unconscious assumptions about the study setting to enter into how data is gathered, interpreted, and reported.

Some specific limitations associated with using qualitative methods to study research problems in the social sciences include the following:

Drifting away from the original objectives of the study in response to the changing nature of the context under which the research is conducted;
Arriving at different conclusions based on the same information depending on the personal characteristics of the researcher;
Replication of a study is very difficult;
Research using human subjects increases the chance of ethical dilemmas that undermine the overall validity of the study;
An inability to investigate causality between different research phenomena;
Difficulty in explaining differences in the quality and quantity of information obtained from different respondents and arriving at different, non-consistent conclusions;
Data gathering and analysis is often time consuming and/or expensive;
Requires a high level of experience from the researcher to obtain the targeted information from the respondent;
May lack consistency and reliability because the researcher can employ different probing techniques and the respondent can choose to tell some particular stories and ignore others; and,
Generation of a significant amount of data that cannot be randomized into manageable parts for analysis.

Research Tip

Human Subject Research and Institutional Review Board Approval

Almost every socio-behavioral study requires you to submit your proposed research plan to an Institutional Review Board. The role of the Board is to evaluate your research proposal and determine whether it will be conducted ethically and under the regulations, institutional polices, and Code of Ethics set forth by the university. The purpose of the review is to protect the rights and welfare of individuals participating in your study. The review is intended to ensure equitable selection of respondents, that you have met the requirements for obtaining informed consent , that there is clear assessment and minimization of risks to participants and to the university [read: no lawsuits!], and that privacy and confidentiality are maintained throughout the research process and beyond. Go to the USC IRB website for detailed information and templates of forms you need to submit before you can proceed. If you are unsure whether your study is subject to IRB review, consult with your professor or academic advisor.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Labaree, Robert V. "Working Successfully with Your Institutional Review Board: Practical Advice for Academic Librarians." College and Research Libraries News 71 (April 2010): 190-193.

Another Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

NOTE : For a list of online communities, research centers, indispensable learning resources, and personal websites of leading qualitative researchers, GO HERE .

For a list of scholarly journals devoted to the study and application of qualitative research methods, GO HERE .

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1 University of Nebraska Medical Center
2 GDB Research and Statistical Consulting
3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
PMID: 29262162
Bookshelf ID: NBK470395

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 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." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. 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. 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.

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Neag School of Education

Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative. QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

Categorical variables are groups…such as gender or type of degree sought. Quantitative variables are numbers that have a range…like weight in pounds or baskets made during a ball game. When we analyze data we do turn the categorical variables into numbers but only for identification purposes…e.g. 1 = male and 2 = female. Just because 2 = female does not mean that females are better than males who are only 1. With quantitative data having a higher number means you have more of something. So higher values have meaning.

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language: Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable: Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis: There is no relationship between height and weight. Alternative Hypothesis: There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better. Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis: Boys do not like reading more than girls. Alternative Hypothesis: Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis: There is no difference between boys’ and girls’ attitude towards reading. –or– Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis: There is a difference between boys’ and girls’ attitude towards reading. –or– Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

Basics of Qualitative Data Collection

First Online: 10 June 2024

Cite this chapter

Rashina Hoda ORCID: orcid.org/0000-0001-5147-8096 2

Qualitative data is the main currency of the trade in a socio-technical grounded theory (STGT) study and in qualitative research in general. The quality of a STGT study depends first and foremost on the quality of the data collected. In this chapter, we will learn about popular sampling techniques such as convenience, snowball, random, representative, and theoretical sampling. Then we will learn some tips for data collection , including tips for effective sampling, recruitment, and engagement. Finally, we will learn about several aspects of dealing with data such as data ethics, data sharing, data quality using data quality evaluation guidelines , data usage, and data quantity including theoretical saturation. Researchers will benefit from reading this chapter in conjunction with the Techniques of Qualitative Data Collection , Qualitative Data Preparation and Filtering , and Socio-technical Grounded Theory for Qualitative Data Analysis chapters. Collectively, they cover socio-technical grounded theory’s basic stage.

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Aldiabat, K. M., & Le Navenec, C.-L. (2018). Data saturation: The mysterious step in grounded theory methodology. The Qualitative Report, 23 (1), 245–261.

Google Scholar

Breckenridge, J., & Jones, D. (2009). Demystifying theoretical sampling in grounded theory research. Grounded Theory Review, 8(2), 113–126.

Carver, J. C., & Prikladnicki, R. (2018). Industry–academia collaboration in software engineering. IEEE Software, 35 (5), 120–124.

Article Google Scholar

Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis . Sage.

Coyne, I. T. (1997). Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries?. Journal of Advanced Nursing, 26 (3), 623–630.

Dittrich, D., Kenneally, E. et al. (2012). The menlo report: Ethical principles guiding information and communication technology research. Technical Report , US Department of Homeland Security.

El-Emam, K. (2001). Ethics and open source. Empirical Software Engineering, 6 (4), 291.

Glaser, B. G., & Strauss, A. L. (2017). Discovery of grounded theory: Strategies for qualitative research . Routledge (First Published 1967).

Gold, N. E., & Krinke, J. (2020). Ethical mining: A case study on MSR mining challenges. In Proceedings of the 17th International Conference on Mining Software Repositories (pp. 265–276).

Gold, N. E., & Krinke, J. (2022). Ethics in the mining of software repositories. Empirical Software Engineering, 27 (1), 17.

Gonzalez-Barahona, J. (2020). Mining software repositories while respecting privacy . Mining Software Repositories Tutorial. https://2020.msrconf.org/details/msr-2020-Education/1/Mining-Software-Repositories-While-Respecting-Privacy

Hoda, R. (2022). Socio-technical grounded theory for software engineering. IEEE Transactions on Software Engineering, 48 (10), 3808–3832.

Hoda, R., Noble, J., & Marshall, S. (2010). Organizing self-organizing teams. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1 (pp. 285–294).

Hoda, R., Noble, J., & Marshall, S. (2012). Self-organizing roles on agile software development teams. IEEE Transactions on Software Engineering, 39 (3), 422–444.

Kvale, S., & Brinkmann, S. (2009). Interviews: Learning the craft of qualitative research interviewing . Sage.

Markham, A., Buchanan, E., with Feedback from the AOIR Ethics Working Committee et al. (2012). Ethical decision-making and internet research: Recommendations from the AOIR ethics working committee (version 2.0) . http://aoir.org/reports/ethics2.pdf

Morse, J. M. (2010). Sampling in grounded theory. In The SAGE handbook of grounded theory (pp. 229–244).

Patton, M. Q. (1990). Qualitative evaluation and research methods . SAGE Publications.

Salleh, N., Hoda, R., Su, M. T., Kanij, T., & Grundy, J. (2018). Recruitment, engagement and feedback in empirical software engineering studies in industrial contexts. Information and Software Technology, 98 , 161–172.

Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing & Health, 18 (2), 179–183.

Singer, J., & Vinson, N. G. (2001). Why and how research ethics matters to you, yes you! Empirical Software Engineering, 6 (4), 287–290.

Singer, J., & Vinson, N. G. (2002). Ethical issues in empirical studies of software engineering. IEEE Transactions on Software Engineering, 28(12), 1171–1180.

Thomson, S. B. (2010). Sample size and grounded theory. Thomson, SB (2010). Grounded Theory-Sample Size. Journal of Administration and Governance, 5 (1), 45–52.

Townsend, L., & Wallace, C. (2016). Social media research: A guide to ethics. University of Aberdeen, 1 (16), 1–16. https://www.gla.ac.uk/media/Media_487729_smxx.pdf

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Indian Dermatol Online J
v.10(1); Jan-Feb 2019

Types of Variables, Descriptive Statistics, and Sample Size

Feroze kaliyadan.

Department of Dermatology, King Faisal University, Al Hofuf, Saudi Arabia

Vinay Kulkarni

1 Department of Dermatology, Prayas Amrita Clinic, Pune, Maharashtra, India

This short “snippet” covers three important aspects related to statistics – the concept of variables , the importance, and practical aspects related to descriptive statistics and issues related to sampling – types of sampling and sample size estimation.

What is a variable?[ 1 , 2 ] To put it in very simple terms, a variable is an entity whose value varies. A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same. The importance of variables is that they help in operationalization of concepts for data collection. For example, if you want to do an experiment based on the severity of urticaria, one option would be to measure the severity using a scale to grade severity of itching. This becomes an operational variable. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance. Variables can be classified into various ways as discussed below.

Quantitative vs qualitative

A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type)

A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks).

Quantitative variables can be either discrete or continuous

Discrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria).

Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test. If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. The mid-way value would be 11.5 min which makes sense. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense.

Under the umbrella of qualitative variables, you can have nominal/categorical variables and ordinal variables

Nominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis).

Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching).

Dependent and independent variables

In the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study. For example, in a clinical trial on psoriasis, the PASI (psoriasis area severity index) would possibly be one dependent variable. The independent variable (sometime also called explanatory variable) is something which is not affected by the experiment itself but which can be manipulated to affect the dependent variable. Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment.

Descriptive Statistics

Statistics can be broadly divided into descriptive statistics and inferential statistics.[ 3 , 4 ] Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary. When we describe the population using tools such as frequency distribution tables, percentages, and other measures of central tendency like the mean, for example, we are talking about descriptive statistics. When we use a specific statistical test (e.g., Mann–Whitney U-test) to compare the mean scores and express it in terms of statistical significance, we are talking about inferential statistics. Descriptive statistics can help in summarizing data in the form of simple quantitative measures such as percentages or means or in the form of visual summaries such as histograms and box plots.

Descriptive statistics can be used to describe a single variable (univariate analysis) or more than one variable (bivariate/multivariate analysis). In the case of more than one variable, descriptive statistics can help summarize relationships between variables using tools such as scatter plots.

Descriptive statistics can be broadly put under two categories:

Sorting/grouping and illustration/visual displays
Summary statistics.

Sorting and grouping

Sorting and grouping is most commonly done using frequency distribution tables. For continuous variables, it is generally better to use groups in the frequency table. Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”).

Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description.

Suppose the weight in kilograms of a group of 10 patients is as follows:

56, 34, 48, 43, 87, 78, 54, 62, 61, 59

The “stem” records the value of the “ten's” place (or higher) and the “leaf” records the value in the “one's” place [ Table 1 ].

Stem and leaf plot


0	-
1	-
2	-
3	4
4	3 8
5	4 6 9
6	1 2
7	8
8	7
9	-

Illustration/visual display of data

The most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). Composite bar charts can be used to compare variables. For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1 .

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g001.jpg

Composite bar chart

A pie chart helps show how a total quantity is divided among its constituent variables. Scatter diagrams can be used to illustrate the relationship between two variables. For example, global scores given for improvement in a condition like acne by the patient and the doctor [ Figure 2 ].

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g002.jpg

Scatter diagram

Summary statistics

The main tools used for summary statistics are broadly grouped into measures of central tendency (such as mean, median, and mode) and measures of dispersion or variation (such as range, standard deviation, and variance).

Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order:

30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86

Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data.

The mean is basically the sum of all the values divided by the total number. In this case, we get a value of 45.

The problem is that some extreme values (outliers), like “'86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50 th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8 th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median.

The mode is the most common data point. In our example, this would be 38. The mode as in our case may not necessarily be in the center of the distribution.

The median is the best measure of central tendency from among the mean, median, and mode. In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3 , a right skewed distribution is seen (direction of skew is based on the tail); data values' distribution is longer on the right-hand (positive) side than on the left-hand side. The mean is typically greater than the median in such cases.

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g003.jpg

Location of mode, median, and mean

Measures of dispersion

The range gives the spread between the lowest and highest values. In our previous example, this will be 86-30 = 56.

A more valuable measure is the interquartile range. A quartile is one of the values which break the distribution into four equal parts. The 25 th percentile is the data point which divides the group between the first one-fourth and the last three-fourth of the data. The first one-fourth will form the first quartile. The 75 th percentile is the data point which divides the distribution into a first three-fourth and last one-fourth (the last one-fourth being the fourth quartile). The range between the 25 th percentile and 75 th percentile is called the interquartile range.

Variance is also a measure of dispersion. The larger the variance, the further the individual units are from the mean. Let us consider the same example we used for calculating the mean. The mean was 45.

For the first value (30), the deviation from the mean will be 15; for the last value (86), the deviation will be 41. Similarly we can calculate the deviations for all values in a sample. Adding these deviations and averaging will give a clue to the total dispersion, but the problem is that since the deviations are a mix of negative and positive values, the final total becomes zero. To calculate the variance, this problem is overcome by adding squares of the deviations. So variance would be the sum of squares of the variation divided by the total number in the population (for a sample we use “n − 1”). To get a more realistic value of the average dispersion, we take the square root of the variance, which is called the “standard deviation.”

The box plot

The box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ].

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g004.jpg

The concept of skewness and kurtosis

Skewness is a measure of the symmetry of distribution. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. When this is not the case, it is said to be skewed. Kurtosis is a representation of outliers. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. There are formulas to calculate both skewness and kurtosis [Figures [Figures5 5 – 8 ].

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g005.jpg

Positive skew

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g008.jpg

High kurtosis (positive kurtosis – also called leptokurtic)

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g006.jpg

Negative skew

An external file that holds a picture, illustration, etc.
Object name is IDOJ-10-82-g007.jpg

Low kurtosis (negative kurtosis – also called “Platykurtic”)

Sample Size

In an ideal study, we should be able to include all units of a particular population under study, something that is referred to as a census.[ 5 , 6 ] This would remove the chances of sampling error (difference between the outcome characteristics in a random sample when compared with the true population values – something that is virtually unavoidable when you take a random sample). However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error.

We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. Examples of probability sampling include methods such as simple random sampling (each member in a population has an equal chance of being selected), stratified random sampling (in nonhomogeneous populations, the population is divided into subgroups – followed be random sampling in each subgroup), systematic (sampling is based on a systematic technique – e.g., every third person is selected for a survey), and cluster sampling (similar to stratified sampling except that the clusters here are preexisting clusters unlike stratified sampling where the researcher decides on the stratification criteria), whereas nonprobability sampling, where every unit in the population does not have an equal chance of inclusion into the sample, includes methods such as convenience sampling (e.g., sample selected based on ease of access) and purposive sampling (where only people who meet specific criteria are included in the sample).

An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis. A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources.

We will first go through the sample size calculation for a hypothesis-based design (like a randomized control trial).

The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance (standard deviation for quantitative data), and expected proportions in the case of qualitative data. This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size). It is also important to know whether the data follow a normal distribution or not.

Two essential aspects we must understand are the concept of Type I and Type II errors. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error. When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test. While there are no absolute rules, the minimal levels accepted are 0.05 for α (corresponding to a significance level of 5%) and 0.20 for β (corresponding to a minimum recommended power of “1 − 0.20,” or 80%).

Effect size and minimal clinically relevant difference

For a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant (for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.). While we will not go into details of the formula for sample size calculation, some important points are as follows:

In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size. What this means in effect is that reducing the effect size will lead to an increase in the required sample size.

Reducing the level of significance (alpha) or increasing power (1-β) will lead to an increase in the calculated sample size.

An increase in variance of the outcome leads to an increase in the calculated sample size.

A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. This includes an idea about total population size (this generally does not make a major difference when population size is above 20,000, so in situations where population size is not known we can assume a population of 20,000 or more). The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates.

Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size. In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this.

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Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

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

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

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

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

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

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

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

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analyzing quantitative data

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

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

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

Analyzing qualitative data

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

Some common approaches to analyzing qualitative data include:

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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Qualitative research designs tend to be more flexible and inductive, allowing you to adjust your approach based on what you find throughout the research process.. Qualitative research example If you want to generate new ideas for online teaching strategies, a qualitative approach would make the most sense. You can use this type of research to explore exactly what teachers and students struggle ...
What is Qualitative in Qualitative Research
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.
Basics of Qualitative Data Collection
Qualitative data is the main currency of the trade in a socio-technical grounded theory (STGT) study and in qualitative research in general. The quality of a STGT study depends first and foremost on the quality of the data collected.
Qualitative Research: Data Collection, Analysis, and Management
THE PARTICIPANT'S VIEWPOINT. What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients' reasons for nonadherence with medication therapy or to explore ...
Independent vs. Dependent Variables
Independent vs. Dependent Variables | Definition & Examples. Published on February 3, 2022 by Pritha Bhandari.Revised on June 22, 2023. In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores. Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
Types of Variables, Descriptive Statistics, and Sample Size
A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type) A simple test which can be used to differentiate between qualitative ...
Qualitative vs. Quantitative Research
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.