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Qualitative Research – Methods, Analysis Types and Guide

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Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

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.

Venturing into Qualitative Research: A Practical Guide to Getting Started

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In this commentary, we offer an introduction to qualitative research. Our goal is to provide guidance so that others can avoid common missteps and benefit from our lessons learned. We explain what qualitative data and research are, the value of qualitative research, and features that make qualitative research excellent, as well as how qualitative data can be collected and used to study undergraduate research. Our advice and recommendations are targeted at researchers who, like us, were first trained in fields with tendencies to overlook or underestimate qualitative research and its contributions. We share examples from our own and others’ research related to undergraduate research settings. We provide a table of resources researchers may find useful as they continue to learn about and conduct qualitative studies.

Introduction

We both started our scholarly journeys as biologists. As we trained, we both grew interested in researching undergraduate education and we transitioned to doing education research. We quickly came to realize that our training in experimental approaches and quantitative methods was woefully insufficient to study the diversity of ways students think, believe, value, feel, behave, and change in a variety of learning environments and educational systems.

For instance, there are established ways to quantify some educational variables, but not others. In addition, there may be phenomena at play that we haven’t thought of or that might be counterintuitive, which could lead us to quantify things that end up being irrelevant or meaningless. Herein lies the power of qualitative research. Qualitative research generates new knowledge by enabling rich, multifaceted descriptions of phenomena of interest, known as constructs (i.e., latent, unobservable variables), and producing possible explanations of how phenomena are occurring (i.e., mechanisms or relationships between constructs in different contexts and situations with different individuals and groups).

In this essay, we aim to offer an approachable explanation of qualitative research, including the types of questions that qualitative research is suited to address, the characteristics of robust qualitative research, and guidance on how to get started. We use examples from our own and others’ research to illustrate our explanations, and we cite references where readers can learn more. We expect Scholarship and Practice of Undergraduate Research (SPUR) readers from disciplines with a tradition of qualitative research might question why we would write this piece and what makes us qualified to do so. There are many scholars with much more qualitative research expertise than we have. Yet, we think we can offer a unique perspective to SPUR readers who are new to qualitative research or coming from disciplines where qualitative research is unfamiliar or undervalued. We have both designed, conducted, and published qualitative research in the context of undergraduate education and research experiences. We draw upon this experience in the recommendations we offer here.

Doing qualitative research involves acknowledging your “positionality,” or how your own background, lived experiences, and philosophical understandings of research influence how you approach and interpret the work (e.g., Hampton, Reeping, and Ozkan 2021; Holmes and Darwin 2020). Our positionalities have influenced our approach to this article and qualitative research generally. I (MAP) first learned about qualitative research from my undergraduate academic adviser. She invited me to help her implement and evaluate a capstone course in which groups of microbiology undergraduates engaged in a semester-long research project to address problems faced by community organizations (Watson, Willford, and Pfeifer 2018). At the time, I wasn’t aware of the long-standing history of qualitative research or its different forms and approaches. I just knew that reading quote data helped me understand human experiences in a way that survey numbers did not. Since my introduction to qualitative research, I’ve been fortunate to receive formal training. I consider my most valuable lessons about qualitative research to be through the practical experience of doing qualitative research and being mentored by qualitative researchers.

When I (ELD) first learned about qualitative research, I thought it meant words – perhaps collected through surveys, focus groups, interviews, or class recordings. I thought qualitative research would be easy – it was just words after all, and I had been using words almost my whole life. I assumed if I collected some words and summarized what I thought they meant (think word cloud), I would be doing qualitative research. As we will elaborate here, this is a limited view of what qualitative research is and what qualitative research can accomplish. When I began presenting qualitative research, I found it helpful to draw analogies to qualitative studies in natural science and medical disciplines. For instance, in the field of biology, the invention of technologies (e.g., lenses, microscopes) allowed for detailed observation and rich descriptions of cells (i.e., qualitative research) that led to the development of cell theory, the establishment of the field of cell biology, and quantitative research on cell structure, function, and dysfunction. In my own field of neuroscience, Henry Moliason, known as HM, was the focus of qualitative case study because he lost the ability to form new long-term memories due to a surgical treatment for severe epilepsy. Rich (i.e., comprehensive and detailed) description of Mr. Moliason’s memory impairment was the basis for hippocampal function being proposed as the main mechanism through which memories are formed. These examples of “non-numbery” research that produce influential descriptions and testable mechanisms helped me recognize the potential value and impact of qualitative research.

Types of Qualitative Research Questions

Qualitative research is useful for addressing two main types of questions: descriptive and mechanistic. Descriptive questions ask what is happening, for whom, and in what circumstances. Mechanistic questions ask how a phenomenon of interest happening. Here we explain each type of question and highlight some example studies conducted in the context of undergraduate research.

Descriptive Questions

Descriptive research seeks to elucidate details that enhance our overall understanding of a particular phenomenon—it answers questions about what a phenomenon is, including its defining features (i.e., dimensions) and what makes it distinct from other phenomena (Loeb et al. 2017). Descriptive research can also reveal who experiences the phenomenon, as well as when and where a phenomenon occurs (Loeb et al. 2017). Details like these serve as a starting point for future research, policy development, and enhanced practice. For instance, Hunter, Laursen, and Seymour (2007) carried out a qualitative study that identified and described the benefits of undergraduate research from the perspectives of both students and faculty. This work prompted calls for expansion of undergraduate research nationally and led to numerous quantitative studies (Gentile, Brenner, and Stephens 2017). Among these were quantitative studies from our group on the influences of research mentors on undergraduate researchers (Aikens et al. 2016, 2017; Joshi, Aikens, and Dolan 2019). Although these studies were framed to identify beneficial outcomes, we observed that undergraduates who had less favorable experiences with mentors were opting not to participate in our studies. Given this observation and the dearth of research on negative experiences in undergraduate research, we carried out a descriptive qualitative study of the dimensions (i.e., the what) of negative mentoring—that is, problematic or ineffective mentoring—in undergraduate life science research (Limeri et al. 2019). This study revealed that negative mentoring in undergraduate research included the absence of support from mentors and actively harmful mentor behaviors. These results served as the basis for practical guidance on how to curtail negative mentoring and its effects and for ongoing quantitative research. We use this study as the basis for the extended examples highlighted in Table 1.

Descriptive research is also suited to investigating the experiences of groups that are marginalized or minoritized in higher education. These studies offer insights into student experiences that may be otherwise overlooked or masked in larger quantitative studies (Vaccaro et al. 2015). For example, descriptive qualitative research shed light on how Black women in undergraduate and graduate STEM programs recognized and responded to structural racism, sexism, and race-gender bias. This research identified how high-achieving Black STEM students experienced racial battle fatigue and offered program-level suggestions for how to better support Black students (McGee and Bentley 2017). Descriptive qualitative research of deaf students involved in undergraduate research revealed that lack of awareness of Deaf culture of research mentors as well as lack of communication hindered students’ research experiences (Majocha et al. 2018). This research led to recommendations for research programs, research mentors, and students themselves. Another descriptive qualitative study showed how Latine students’ science identity changed over time when involved in an undergraduate research program (Vasquez-Salgado et al. 2023). Specifically, Vasguez-Salgado and colleagues identified patterns in students’ science identity through three waves of data collection spanning 18 months. Students’ identities showed consistent or fast achievement of feeling like a scientist, gradual achievement of feeling like a scientist, achievement adjustment of feeling like a scientist at one point and less so later in the program, or never feeling like a scientist. Together, these and other studies have generated knowledge that raises questions for future research and informs our collective efforts to make undergraduate research more accessible and inclusive.

Mechanistic Questions

Mechanistic qualitative research aims to address questions of how or why a phenomenon occurs. In the context of undergraduate research, an investigator may seek to understand how or why a particular practice or program design affects students. Recently, we conducted a mechanistic qualitative study that aimed, in part, to understand how early career researchers (undergraduate, postbaccalaureate, and graduate students) conceptualized their science identity (Pfeifer et al. 2023). Previous research theorized that someone is more likely to identify as a scientist if they are interested in science, believe they are competent in and can perform science, and feel recognized by others for their scientific aptitude or accomplishments (Carlone and Johnson 2007; Hazari et al. 2010; Potvin and Hazari 2013). However, this theory is somewhat limited in that it does not fully explain how context affects science identity or how science identity evolves, especially as researchers advance in their scientific training (Hazari et al. 2020; Kim and Sinatra 2018). To address this, we integrated science identity theory with research on professional identity development to design our study (Pratt, Rockmann, and Kaufmann 2006). We analyzed data from two national samples, including open-ended survey responses from 548 undergraduates engaged in research training and interview data from 30 early career researchers in the natural sciences. We found that they conceptualized science identity as a continuum that encompassed being a science student, being a science researcher, and being a career researcher. How students saw their science identity depended on how they viewed the purpose of their daily research, the level of intellectual responsibility they have for their research, and the extent of their autonomy in their research. We consider these findings to be hypotheses that can be tested quantitatively to better understand science identity dynamics in research training contexts. By asking this mechanistic question about science identity, we sought to add to and refine existing theory.

Key Attributes of Qualitative Research

For any type of research to be meaningful, it must possess some degree of rigor—what qualitative researchers call trustworthiness (Morse et al. 2002; Yilmaz 2013). Qualitative research is more trustworthy if it is characterized by credibility, transferability, dependability, and confirmability (Creswell and Poth 2016; Lincoln and Guba 1985). For instance, like accuracy and precision in quantitative research, do qualitative findings reflect what is being studied and are the interpretations true to the data (credibility)? Similar to reproducibility in quantitative research, how can qualitative research findings be applied to similar contexts (transferability)? Like validity in quantitative research, to what degree are the framing, methods, and findings of qualitative research appropriate given the aims (dependability)? Similar to the idea of replicability in quantitative research, if the same analytic tools were applied to the same data set could similar findings be reached by someone outside the original research team (confirmability)? The exact dimensions of trustworthiness, how trustworthiness manifests in the research process, the best ways to achieve trustworthiness, and how to talk about trustworthiness in research products are the subject of ongoing and often-spirited debate (e.g., Gioia et al. 2022; Mays and Pope 2020; Morse et al. 2002; Ritchie et al. 2013; Tracy 2010; Welch 2018; Yadav 2022). Central to these dialogues is the fact that qualitative research is composed of different philosophical approaches that emerged and evolved from diverse social science fields (Creswell and Poth 2016; Ritchie et al. 2013). Identifying universally agreed-upon criteria and the means to achieve these criteria is complex.

In our own work, we have found Tracy’s (2010) eight criteria for excellent qualitative research particularly useful. These criteria have helped us design studies, make decisions during the course of research, and articulate in our papers how our research seeks to achieve trustworthiness (e.g., Pfeifer, Cordero, and Stanton 2023). The full list of criteria is: worthy topic, rich rigor, sincerity, credibility, resonance, significant contribution, ethical conduct, and meaningful coherence (Tracy 2010). These criteria borrow from and build on the presented concepts of credibility, transferability, dependability, and confirmability. In our view, these criteria are presented and described in a way that makes sense to us and fits our approach to research. Here we highlight two criteria that may be particularly relevant if you are new to qualitative research.

Worthy Topics

As scholars familiar with undergraduate research and scholarly inquiry, SPUR readers are well-positioned to design studies that address research questions that are significant and timely in the context of undergraduate research. The first step in doing qualitative research (or any research) is to figure out what you want to study. You’ll want to select a topic that you find interesting, relevant, or otherwise compelling so you are motivated to spend time and effort investigating it. One way to find a topic is to notice what is happening in your environment and your work. What are you observing about undergraduate research? Something about students who participate (or not)? Something about colleagues who work with undergraduate researchers (or not)? Something about the design, implementation, or outcomes of the research experience? Something about the programmatic or institutional context? For a topic to be worthy of research, it should be interesting to you and to others. Consider sharing your observations with a few critical friends (i.e., trusted colleagues who will give you honest feedback) about whether they find your observations interesting or worth your time and energy to explore.

Like other human research, qualitative studies must adhere to basic ethical principles of respect for persons, beneficence, and justice (National Commission for the Protection of Human Subjects 1978). Respect for persons means treating all people as autonomous and protecting individuals with diminished autonomy (e.g., students whom we teach and assess). Beneficence involves treating people in an ethical manner, including respecting their decisions, protecting them from harm, and securing their well-being. Justice refers to the balance between benefiting from research and bearing its burdens; in other words, people should be able to benefit from research and should not be expected to bear the burden of research if they cannot benefit. Although it is beyond the scope of this essay to provide guidance on how to adhere to these principles, it is important to recognize that qualitative methods like interviewing can be highly personal and sometimes powerful experiences for both participants (and researchers). Investigators should carefully consider how their participants may be affected by data collection. For example, you may interview or survey participants about a personally difficult or painful experience. Do you then bear responsibility for helping them find support to navigate these difficulties? What if a participant reveals to you a serious mental health issue or physical safety concern? These situations occurred during our negative mentoring studies. We provided information to participants about where they could seek counseling or support for specific issues that can occur with mentors, such as harassment and discrimination.

Certainly not all qualitative data collection brings up these issues, but it can and does happen more frequently than you might expect. Your institutional review board (IRB), collaborators, and critical friends can be helpful resources when planning for and navigating tough scenarios like this. If working with an IRB is new to you, we recommend finding colleagues at your institution who have conducted IRB-reviewed research and asking them for guidance and examples. Some IRBs offer training for individuals new to developing human research protocols, and there are likely to be templates for everything from recruitment letters to consent forms to study information. We have found the process of developing IRB protocols helps refine research questions and study plans. Furthermore, IRB review is needed before you collect data that will be used for your study; IRBs rarely if ever allow for retrospective review and approval. In our experience, these studies are likely to be determined as exempt from IRB review because they involve minimal risk and use standard educational research procedures. However, the IRB is still responsible for making this determination and is a valuable partner for helping investigators navigate sensitive or complex situations that occur in human research.

Getting Started with Qualitative Research

Now that you have a sense of the purposes of qualitative research and what features help to ensure its quality, you are probably wondering how to do it. We want to emphasize that there are entire programs of study, whole courses, and lengthy texts that aim to teach qualitative research. We cannot come close to describing what can be learned from these more substantial resources. With this is mind, we share our own process of carrying out qualitative research as an example that others might find helpful to follow. We outline this “how to” as a series of steps, but qualitative research (like all research) is iterative and dynamic (University of California Museum of Paleontology 2022). Feel free to read through the steps in a linear fashion but then move in non-linear ways through the various steps. Extended discussion of each of these steps with examples from our research on negative mentoring is provided in Table 1 along with an abridged list of our go-to references.

Observe, Search, and Read

For a topic to be worthy of qualitative research (or any research), it should also have the potential to address a knowledge gap. After we identify a “worthy topic,” we try to find as much information about that topic as possible (Dolan 2013). We read, then we keep reading, and then we read some more. This may seem obvious, but we find that investing time reading literature can save us a lot of time designing, conducting, and writing up a study on a phenomenon that is already well known or understood by others and just not (yet) by us. To help us in our searching, we will sometimes reach out to colleagues in related fields to describe the phenomenon we are interested in studying and see if they have terms that they use to describe the phenomenon or theories they think are related. Theory informs our research questions, study designs, analytic approaches, and interpretation and reporting of findings, and enables alignment among all of these elements of research (e.g., Grant and Osanloo 2014; Luft et al. 2022; Spangler and Williams 2019). Theory also serves as a touchstone for connecting our findings to larger bodies of knowledge and communicating these connections in a way that promotes collective understanding of whatever we are investigating.

Formulate a Question

Once you have selected a topic and identified a knowledge gap, consider research questions that, if answered, would address the knowledge gap. Recall that qualitative research is suited to questions that require a descriptive (what) or mechanistic (how) answer.

Decide on a Study Design

Just like quantitative research, qualitative research has characteristic approaches, designs, and methodologies, each of which has affordances and constraints (Creswell and Poth 2016; Merriam 2014; Miles, Huberman, and Saldana 2014). Creswell and Poth provide a valuable resource for learning more about different types of qualitative research study designs, including which designs are suited to address which kinds of research questions. Given the labor intensiveness of qualitative data collection and analysis, it is critical to think carefully about how to recruit and select study participants. What this looks like and who might be appropriate study participants will depend on many factors, including the knowledge gap, research question, study design, and methods. Questions that can be helpful to ask are: Who do I need to study to answer my research question? What should the study participants have in common? In what ways should study participants vary to provide rich, complex, and varied insight into what I am studying? To whom do I want to generalize my findings, keeping in mind the qualitative nature of the work?

Based on the answers to these questions, you may opt for purposeful sampling in which you collect data only from participants who meet the characteristics you decide upon given the aims of your study. In this case, you will likely send a screening survey to potential participants to determine what their characteristics of interest are, which will help you decide if you will invite them for further data collection or not. A purposeful sample contrasts with a convenience sample where essentially any person who agrees to participate in the study will be selected for further data collection.

Collect and Analyze Data Systematically

Qualitative data can be collected in a variety of ways, including surveys, interviews, and focus groups, as well as audio and video recordings of learning experiences such as class sessions. To decide which method(s) to use for data collection, it is helpful to consider what you aim to learn from study participants. Surveys tend to be easier to distribute to a larger sample, but may elicit shorter or shallower responses, which are challenging to interpret because there is less information (i.e., words) and no opportunity to clarify with participants. Focus groups can be effective for quickly gathering input from a group of participants. However, social dynamics may result in one or a few people dominating the discussion, or “group think,” when people agree with one another rather than providing their own unique perspectives. Interviews with individuals can be a rich and varied data source because each participant has time and space to offer their own distinct perspective. Interviews also allow for follow-up questions that are difficult through survey methods. Yet, conducting interviews skillfully—avoiding leading questions and ensuring that the line of questioning yields the desired data—takes a lot of thought and practice. Kvale (1996) offers detailed guidance on how to design and carry out research interviews. Observing an expert interviewer and having them observe and give feedback as you interview can help improve your skills. Audio and video recordings of learning experiences like class sessions or group work can provide a plethora of information (e.g., verbal and nonverbal exchanges among students or between students and instructors) in a more natural setting than surveys or interviews. Yet deciding what information will serve as data to answer your research question, or how that large body of data will be systematically analyzed, can be cumbersome.

Regardless of the data collection method, you’ll need to decide how much data to collect. There is no one right sample size. A good rule of thumb is collecting data until you reach “saturation,” which is the notion that the same ideas are coming up repeatedly and that no new ideas are emerging during data collection. This means that your data collection and analysis are likely to overlap in time, with some data collection then some analysis and then more data collection.

Analytic methods in qualitative research vary widely in their interpretive complexity. As natural scientists, we favor sticking close to the data and analyzing using a method called qualitative content analysis. Content analysis involves taking quotes or segments of text and capturing their meaning with short words or phrases called codes. The process of developing codes and systematically applying them to a dataset is called coding. Coding is highly iterative and time-consuming because it typically requires multiple, careful passes through the dataset to ensure all codes have been evenly applied to all data. In a recent study, we spent 10 to 15 person-hours to code a single interview, and about 400 person-hours to complete coding for a 30-participant study. The time involved in coding depends on what is being studied, the type of coding, and who is coding the data. Saldaña (2016) provides excellent guidance on the coding process, including various ways of making sense of codes by grouping them into themes. Content analysis is just one approach to qualitative data analysis. We encourage you to learn more about different forms of qualitative approaches and choose what works best for you, including your skill level, research goals, and data (e.g., Creswell and Poth 2016; Starks and Brown Trinidad 2007).

Interpret and Write Results

There are many ways to effectively write up results, often called findings, from qualitative research. Because qualitative research involves extensive interpretation, it can sometimes be easier to integrate the results and discussion of a qualitative paper. Integration allows the interpretation (discussion) to be directly supported by the evidence in the form of quotations (results). The conclusions of the paper should avoid repeating the results and instead comment on the implications and applications of the findings: why they matter and what to do as a result. Because qualitative data are quotations rather than numbers, qualitative papers tend to be longer than papers presenting quantitative studies. That said, qualitative papers should still aim to be succinct. For instance, depending on the approach and methods, quotations can be lightly edited to remove extra words or filler language (e.g., um, uh) that is a natural part of language but otherwise irrelevant to the findings. Presenting only the most pertinent part of a quotation not only facilitates succinctness, but helps readers attend to the specific evidence that supports the claims being made. Another strategy to shorten qualitative papers is to present some findings in supplemental materials.

Final Recommendations

In closing our article, we offer some advice that we wish we knew when we began conducting qualitative research. We hope that these recommendations will help you think through issues that are likely emerge as you delve deeper into qualitative analysis, both as a producer and a consumer of qualitative research.

Consensus Coding in Qualitative Analysis

In qualitative analysis, we work to ensure that the analysis yields trustworthy findings by coding to consensus, meaning that the analytic team reaches 100 percent agreement on the application of each code to the data. Any disagreement between coders is discussed until a resolution is resolved. In some cases, these discussions may result in a code description being redefined. Redefinition of a code requires that all data previously coded using the original code be reanalyzed to ensure fit with the revised definition. As you might imagine, coding to consensus can be time-consuming. Yet, in our experience, the time invested in coding to consensus is well spent because the analysis yields deeper insights about the data and phenomenon being investigated. We also see coding to consensus as a great way to take advantage of the diverse viewpoints that team members bring to our research. By coding to consensus, we consider multiple interpretations of the data throughout the analysis process. We are well-positioned to develop theory (as appropriate for our study design) as a team because we all have engaged in meaningful conversations about our findings throughout analysis.

Some qualitative research relies on a calculated measure of intercoder reliability (ICR) instead of coding to consensus. ICR values indicate how often a set of coders agree on the application of a code in the dataset. This quantification of coding is tempting because we love numbers, yet it can also be problematic (O’Connor and Joffe 2020). For instance, aiming for high ICR can create situations when coders are pressured to agree with each other rather than bringing their own unique perspective to the coding process (e.g., Belur et al. 2018; Morse 1997). Quantifying qualitative work also can imply a false precision in the analysis. In some research, ICR is calculated partway through the analysis to determine whether an “acceptable” level of agreement has been reached, at which point the remainder of the data are coded by just one researcher. This approach of using ICR as a cut-off runs counter to what many argue is the value of qualitative research: generating new theoretical understandings informed by multiple perspectives.

Using Numbers in Qualitative Analysis

Although numbers certainly have a place in qualitative analysis (Sandelowski 2001), we encourage researchers to move beyond word clouds or frequency counts of codes and themes in their results for two reasons. First, a code or theme that is infrequently observed in the data set can still be important to the phenomenon being studied. As an analogy, consider making qualitative observations of living cells under a typical light microscope. We would most frequently see a relatively stationary cell that is punctuated by a relatively rare cell division or mitosis. If we only reported stationary observations in findings, we would overlook describing mitosis, one of the most dynamic and fundamental processes that cells display. Second, given limited sample sizes, it may be that a unique and important code or theme is reported by only one participant in the data set. In fact, rare observations can serve as “a-ha moments” that lead to a more comprehensive understanding of the phenomenon under investigation. These rare observations also may inspire new studies about topics that were not initially anticipated; this speaks to the value of qualitative research.

Closing Thoughts

We encourage readers to continue to learn about qualitative research as there is much that could not be addressed in a single article. For instance, we did not introduce how philosophical stances, like how someone views the nature of truth or what counts as evidence, influence the research process. (Creswell and Poth 2016). For now, we will close with one final piece of advice. We both became better qualitative researchers by working with mentors and collaborators who have this expertise. We encourage you to find colleagues in your networks or at your institutions who may be interested in being a collaborator, mentor, or critical friend. The complexity of students and their experiences lend themselves to qualitative approaches. We hope this article might serve as an impetus for you to learn more about qualitative research and even start your own investigations.

Data Availability Statement

The data included in this commentary have been published in an open-access journal under a Creative Commons license. Citations are included in the text.

Institutional Review Board Statement

Not applicable.

Conflict of Interest Statement

The authors have no conflicts of interest to report.

Acknowledgments

This material is based upon work supported by the National Science Foundation under award number OCE-2019589. This is the National Science Foundation’s Center for Chemical Currencies of a Microbial Planet (C-Comp) publication #026. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We thank Patricia Mabrouk for inviting us to contribute this commentary. We thank members of the Biology Education Research Group at the University of Georgia and Daniel Dries, Joseph Provost, and Verónica Segarra for their thoughtful feedback on manuscript drafts.

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Mariel A. Pfeifer

University of Georgia, [email protected]

Mariel A. Pfeifer is a postdoctoral researcher at the University of Georgia’s SPREE (Social Psychology of Research Experiences and Education) Lab. Her passion for biology education research was sparked by her experiences as an undergraduate teaching assistant, a pre-service science teacher, and a disability services coordinator. Soon Pfeifer will begin her new role as an assistant professor of biology at the University of Mississippi.

Erin L. Dolan is a professor of biochemistry and molecular biology and Georgia Athletic Association Professor of Innovative Science Education at the University of Georgia As a graduate student, Dolan volunteered in K–12 schools, which inspired her pursuit of a biology education career. She teaches introductory biology and her research group, the SPREE Lab, works to delineate features of undergraduate and graduate research that influence students’ career decisions.

Building Research Skills through an Undergraduate Research Project on Local Community

This research aims to build economic research skills and stimulate students’ interest in the local economy through data-based undergraduate research in entry-level economics courses. The authors developed two assignments and one student survey assessing students’ learning outcomes and implemented them in two introductory-level classes from fall 2019 to fall 2021. The survey responses confirmed that the assignments positively affected students’ primary research skills and increased students’ interest in local economic issues. The study also provides empirical evidence that undergraduate research can be carried out in both face-to-face and online classes. It confirms the positive contribution of exposing students to the research culture early in their academic journey by improving students’ skills in collecting, processing, and interpreting data on the local economy.

Fulfilling the Land-Grant Mission in Undergraduate Research in English Studies

The pandemic provided a natural experiment to test an alternative approach to teaching a traditional classroom-based research methods course; as teachers, we should not wait for such interventions but try out various strategies for effectiveness. Remote mentoring is entirely feasible for successful undergraduate research experiences. This is a particularly crucial finding in the humanities, which relies very much on discussion-based formats rather than lectures for its courses. Faculty have learned new technologies, such as Zoom, to ensure that meaningful interactions would occur.

Making Research Accessible for All through a Fully Online Cancer Genomics CURE

Course-based undergraduate research experiences (CUREs) can engage large numbers of students and provide a structured environment in which to learn valuable research skills. The ability to implement laboratory-based CUREs was hindered by the COVID-19 pandemic, generating a greater need for online options. A pilot study of an adaptation of a fully online cancer genomics CURE is described here. Students utilized freely available databases such as cBioPortal to develop novel scientific questions, generate and analyze data, collaborate with peers, and present their findings in an online environment. This format preserved the defining aspects of CUREs while promoting student ownership over their projects. Although the most common challenge was developing a hypothesis, students valued peer and instructor feedback throughout the process as well as flexible formats for communicating their research findings.

Laboratory-Based Undergraduate Research Experiences (LUREs): Evidence of Effectiveness from the Social Sciences

The impact of undergraduate research experiences (UREs) is supported by evidence from physical and life science fields, especially when student-apprentices work in traditional laboratories. Within social sciences specifically, some excellent student outcomes associated with UREs adhere to non–lab-based modalities like course-based research experiences (CUREs). Here, the authors evaluate the laboratory-based undergraduate research experiences (LUREs) as a potentially valuable approach for incorporating social science undergraduates in research. Using comparative analysis of survey data from students completing three types of social science-based UREs (n = 235), individual research experiences (IREs), CUREs, or LUREs, students perceived gains overall regardless of the type of experience, with some indication that LUREs are the most effective.

SUREbyts: Presenting Early-Year Undergraduate Students with Videos on Research Topics

Undergraduate research initiatives such as mentoring programs, conferences, and journals typically focus on the later stages of undergraduate studies. It is not unusual for a student to reach the final year of their program without developing their awareness of research within their discipline or their institution. SUREbyts is a project that provides first- and second-year undergraduate students with access to research through video recordings of professional researchers and research students discussing their own research, with each video structured around a research question with a set of possible solutions. This article presents the successes and challenges faced by the project’s initial implementation in six higher education institutions in Ireland and offers advice to institutions globally that are considering engaging their students with research in this way.

Systematic Review of Outcomes for Faculty Mentors in Undergraduate Research

Significant research has highlighted the benefits and outcomes of mentored research experiences for undergraduate students. Substantially less empirical research has examined the benefits and outcomes of these experiences for the other member of the mentoring dyad: the mentors themselves. To address this gap, a systematic review of 1,915 articles was conducted. After review, 16 articles were determined relevant. Articles were categorized based on design and theoretical framework. Further analysis revealed three categorizations: faculty mentor outcomes, barriers to mentorship of undergraduate research students, and supporting factors associated with mentorship of students. Results indicate that faculty mentors in undergraduate research contexts face more barriers to mentorship than supporting factors. Three lines of inquiry are proposed for future researchers.

Undergraduate Research: Why and How Do We Mentor?

Our advocacy for undergraduate research, mentorship, and equity is strengthened by being explicit about why they are vital. Research mentors guide undergraduates in learning how to learn, in evaluating evidence, and in discovering talents and career interests. Our mission can be extended by bringing a research perspective to the courses we teach. Effective mentorship involves appreciating individual differences while having shared goals. As mentors, we aim to provide undergraduates with opportunities to participate in research that can lead to discovering new knowledge. Students can build on this experience to become proactive in making a difference. The global future depends on today’s undergraduates, who are tomorrow’s decision-makers, innovators, and leaders.

Step Up for SPUR

This editorial details observations from a departing associate editor of SPUR about the experience of working on the journal. The author contextualizes this work within the academic journal editorial process, focusing on specific challenges in recruiting reviewers for the peer review process. The author stresses the importance of broad participation from the undergraduate research community, including submitting articles to SPUR , accepting requests to review manuscripts, and participating in the editorial board, all to ensure the sustainability and intellectual vitality of the journal.

SUBSCRIPTION

SPUR advances knowledge and understanding of novel and effective approaches to mentored undergraduate research, scholarship, and creative inquiry by publishing high-quality, rigorously peer reviewed studies written by scholars and practitioners of undergraduate research, scholarship, and creative inquiry. The SPUR Journal is a leading CUR member benefit. Gain access to all electronic articles by joining CUR.

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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What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

How does social media shape body image in teenagers?
How do children and adults interpret healthy eating in the UK?
What factors influence employee retention in a large organisation?
How is anxiety experienced around the world?
How can teachers integrate social issues into science curriculums?

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

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

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

Qualitative research approaches
Approach	What does it involve?
Grounded theory	Researchers collect rich data on a topic of interest and develop theories .
	Researchers immerse themselves in groups or organisations to understand their cultures.
	Researchers and participants collaboratively link theory to practice to drive social change.
Phenomenological research	Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences.
Narrative research	Researchers examine how stories are told to understand how participants perceive and make sense of their experiences.

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

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

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

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

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

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

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

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

Qualitative data analysis
Approach	When to use	Example
	To describe and categorise common words, phrases, and ideas in qualitative data.	A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps.
	To identify and interpret patterns and themes in qualitative data.	A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity.
	To examine the content, structure, and design of texts.	A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade.
	To study communication and how language is used to achieve effects in specific contexts.	A political scientist could use discourse analysis to study how politicians generate trust in election campaigns.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

Unreliability

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

Subjectivity

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

Limited generalisability

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

Labour-intensive

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Qualitative Research Questions

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How to write qualitative research questions.

11 min read Here’s how to write effective qualitative research questions for your projects, and why getting it right matters so much.

What is qualitative research?

Qualitative research is a blanket term covering a wide range of research methods and theoretical framing approaches. The unifying factor in all these types of qualitative study is that they deal with data that cannot be counted. Typically this means things like people’s stories, feelings, opinions and emotions , and the meanings they ascribe to their experiences.

Qualitative study is one of two main categories of research, the other being quantitative research. Quantitative research deals with numerical data – that which can be counted and quantified, and which is mostly concerned with trends and patterns in large-scale datasets.

What are research questions?

Research questions are questions you are trying to answer with your research. To put it another way, your research question is the reason for your study, and the beginning point for your research design. There is normally only one research question per study, although if your project is very complex, you may have multiple research questions that are closely linked to one central question.

A good qualitative research question sums up your research objective. It’s a way of expressing the central question of your research, identifying your particular topic and the central issue you are examining.

Research questions are quite different from survey questions, questions used in focus groups or interview questions. A long list of questions is used in these types of study, as opposed to one central question. Additionally, interview or survey questions are asked of participants, whereas research questions are only for the researcher to maintain a clear understanding of the research design.

Research questions are used in both qualitative and quantitative research , although what makes a good research question might vary between the two.

In fact, the type of research questions you are asking can help you decide whether you need to take a quantitative or qualitative approach to your research project.

Discover the fundamentals of qualitative research

Quantitative vs. qualitative research questions

Writing research questions is very important in both qualitative and quantitative research, but the research questions that perform best in the two types of studies are quite different.

Quantitative research questions

Quantitative research questions usually relate to quantities, similarities and differences.

It might reflect the researchers’ interest in determining whether relationships between variables exist, and if so whether they are statistically significant. Or it may focus on establishing differences between things through comparison, and using statistical analysis to determine whether those differences are meaningful or due to chance.

How much? This kind of research question is one of the simplest. It focuses on quantifying something. For example:

How many Yoruba speakers are there in the state of Maine?

What is the connection?

This type of quantitative research question examines how one variable affects another.

For example:

How does a low level of sunlight affect the mood scores (1-10) of Antarctic explorers during winter?

What is the difference? Quantitative research questions in this category identify two categories and measure the difference between them using numerical data.

Do white cats stay cooler than tabby cats in hot weather?

If your research question fits into one of the above categories, you’re probably going to be doing a quantitative study.

Qualitative research questions

Qualitative research questions focus on exploring phenomena, meanings and experiences.

Unlike quantitative research, qualitative research isn’t about finding causal relationships between variables. So although qualitative research questions might touch on topics that involve one variable influencing another, or looking at the difference between things, finding and quantifying those relationships isn’t the primary objective.

In fact, you as a qualitative researcher might end up studying a very similar topic to your colleague who is doing a quantitative study, but your areas of focus will be quite different. Your research methods will also be different – they might include focus groups, ethnography studies, and other kinds of qualitative study.

A few example qualitative research questions:

What is it like being an Antarctic explorer during winter?
What are the experiences of Yoruba speakers in the USA?
How do white cat owners describe their pets?

Qualitative research question types

Marshall and Rossman (1989) identified 4 qualitative research question types, each with its own typical research strategy and methods.

Exploratory questions

Exploratory questions are used when relatively little is known about the research topic. The process researchers follow when pursuing exploratory questions might involve interviewing participants, holding focus groups, or diving deep with a case study.

Explanatory questions

With explanatory questions, the research topic is approached with a view to understanding the causes that lie behind phenomena. However, unlike a quantitative project, the focus of explanatory questions is on qualitative analysis of multiple interconnected factors that have influenced a particular group or area, rather than a provable causal link between dependent and independent variables.

Descriptive questions

As the name suggests, descriptive questions aim to document and record what is happening. In answering descriptive questions , researchers might interact directly with participants with surveys or interviews, as well as using observational studies and ethnography studies that collect data on how participants interact with their wider environment.

Predictive questions

Predictive questions start from the phenomena of interest and investigate what ramifications it might have in the future. Answering predictive questions may involve looking back as well as forward, with content analysis, questionnaires and studies of non-verbal communication (kinesics).

Why are good qualitative research questions important?

We know research questions are very important. But what makes them so essential? (And is that question a qualitative or quantitative one?)

Getting your qualitative research questions right has a number of benefits.

It defines your qualitative research project Qualitative research questions definitively nail down the research population, the thing you’re examining, and what the nature of your answer will be.This means you can explain your research project to other people both inside and outside your business or organization. That could be critical when it comes to securing funding for your project, recruiting participants and members of your research team, and ultimately for publishing your results. It can also help you assess right the ethical considerations for your population of study.
It maintains focus Good qualitative research questions help researchers to stick to the area of focus as they carry out their research. Keeping the research question in mind will help them steer away from tangents during their research or while they are carrying out qualitative research interviews. This holds true whatever the qualitative methods are, whether it’s a focus group, survey, thematic analysis or other type of inquiry.That doesn’t mean the research project can’t morph and change during its execution – sometimes this is acceptable and even welcome – but having a research question helps demarcate the starting point for the research. It can be referred back to if the scope and focus of the project does change.
It helps make sure your outcomes are achievable

Because qualitative research questions help determine the kind of results you’re going to get, it helps make sure those results are achievable. By formulating good qualitative research questions in advance, you can make sure the things you want to know and the way you’re going to investigate them are grounded in practical reality. Otherwise, you may be at risk of taking on a research project that can’t be satisfactorily completed.

Developing good qualitative research questions

All researchers use research questions to define their parameters, keep their study on track and maintain focus on the research topic. This is especially important with qualitative questions, where there may be exploratory or inductive methods in use that introduce researchers to new and interesting areas of inquiry. Here are some tips for writing good qualitative research questions.

1. Keep it specific

Broader research questions are difficult to act on. They may also be open to interpretation, or leave some parameters undefined.

Strong example: How do Baby Boomers in the USA feel about their gender identity?

Weak example: Do people feel different about gender now?

2. Be original

Look for research questions that haven’t been widely addressed by others already.

Strong example: What are the effects of video calling on women’s experiences of work?

Weak example: Are women given less respect than men at work?

3. Make it research-worthy

Don’t ask a question that can be answered with a ‘yes’ or ‘no’, or with a quick Google search.

Strong example: What do people like and dislike about living in a highly multi-lingual country?

Weak example: What languages are spoken in India?

4. Focus your question

Don’t roll multiple topics or questions into one. Qualitative data may involve multiple topics, but your qualitative questions should be focused.

Strong example: What is the experience of disabled children and their families when using social services?

Weak example: How can we improve social services for children affected by poverty and disability?

4. Focus on your own discipline, not someone else’s

Avoid asking questions that are for the politicians, police or others to address.

Strong example: What does it feel like to be the victim of a hate crime?

Weak example: How can hate crimes be prevented?

5. Ask something researchable

Big questions, questions about hypothetical events or questions that would require vastly more resources than you have access to are not useful starting points for qualitative studies. Qualitative words or subjective ideas that lack definition are also not helpful.

Strong example: How do perceptions of physical beauty vary between today’s youth and their parents’ generation?

Weak example: Which country has the most beautiful people in it?

Related resources

Qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, mixed methods research 17 min read, market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, request demo.

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Education and Communications

How to Do Qualitative Research

Last Updated: October 26, 2022 Fact Checked

This article was co-authored by Jeremiah Kaplan . Jeremiah Kaplan is a Research and Training Specialist at the Center for Applied Behavioral Health Policy at Arizona State University. He has extensive knowledge and experience in motivational interviewing. In addition, Jeremiah has worked in the mental health, youth engagement, and trauma-informed care fields. Using his expertise, Jeremiah supervises Arizona State University’s Motivational Interviewing Coding Lab. Jeremiah has also been internationally selected to participate in the Motivational Interviewing International Network of Trainers sponsored Train the Trainer event. Jeremiah holds a BS in Human Services with a concentration in Family and Children from The University of Phoenix. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 752,136 times.

Qualitative research is a broad field of inquiry that uses unstructured data collections methods, such as observations, interviews, surveys and documents, to find themes and meanings to inform our understanding of the world. [1] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source Qualitative research tends to try to cover the reasons for behaviors, attitudes and motivations, instead of just the details of what, where and when. Qualitative research can be done across many disciplines, such as social sciences, healthcare and businesses, and it is a common feature of nearly every single workplace and educational environment.

Preparing Your Research

Step 1 Decide on a question you want to study.

The research questions is one of the most important pieces of your research design. It determines what you want to learn or understand and also helps to focus the study, since you can't investigate everything at once. Your research question will also shape how you conduct your study since different questions require different methods of inquiry.
You should start with a burning question and then narrow it down more to make it manageable enough to be researched effectively. For example, "what is the meaning of teachers' work to teachers" is too broad for a single research endeavor, but if that's what you're interested you could narrow it by limiting the type of teacher or focusing on one level of education. For example, "what is the meaning of teachers' work to second career teachers?" or "what is the meaning of teachers' work to junior high teachers?"

Tip: Find the balance between a burning question and a researchable question. The former is something you really want to know about and is often quite broad. The latter is one that can be directly investigated using available research methods and tools.

For example, if your research question focuses on how second career teachers attribute meaning to their work, you would want to examine the literature on second career teaching - what motivates people to turn to teaching as a second career? How many teachers are in their second career? Where do most second career teachers work? Doing this reading and review of existing literature and research will help you refine your question and give you the base you need for your own research. It will also give you a sense of the variables that might impact your research (e.g., age, gender, class, etc.) and that you will need to take into consideration in your own study.
A literature review will also help you to determine whether you are really interested and committed to the topic and research question and that there is a gap in the existing research that you want to fill by conducting your own investigation.

Step 3 Evaluate whether qualitative research is the right fit for your research question.

For example, if your research question is "what is the meaning of teachers' work to second career teachers?" , that is not a question that can be answered with a 'yes' or 'no'. Nor is there likely to be a single overarching answer. This means that qualitative research is the best route.

Step 4 Consider your ideal sampling size.

Consider the possible outcomes. Because qualitative methodologies are generally quite broad, there is almost always the possibility that some useful data will come out of the research. This is different than in a quantitative experiment, where an unproven hypothesis can mean that a lot of time has been wasted.
Your research budget and available financial resources should also be considered. Qualitative research is often cheaper and easier to plan and execute. For example, it is usually easier and cost-saving to gather a small number of people for interviews than it is to purchase a computer program that can do statistical analysis and hire the appropriate statisticians.

Step 5 Choose a qualitative research methodology.

Action Research – Action research focuses on solving an immediate problem or working with others to solve problem and address particular issues. [7] X Research source
Ethnography – Ethnography is the study of human interaction in communities through direct participation and observation within the community you wish to study. Ethnographic research comes from the discipline of social and cultural anthropology but is now becoming more widely used. [8] X Research source
Phenomenology – Phenomenology is the study of the subjective experiences of others. It researches the world through the eyes of another person by discovering how they interpret their experiences. [9] X Research source
Grounded Theory – The purpose of grounded theory is to develop theory based on the data systematically collected and analyzed. It looks at specific information and derives theories and reasons for the phenomena.
Case Study Research – This method of qualitative study is an in-depth study of a specific individual or phenomena in its existing context. [10] X Research source

Collecting and Analyzing Your Data

Direct observation – Direct observation of a situation or your research subjects can occur through video tape playback or through live observation. In direct observation, you are making specific observations of a situation without influencing or participating in any way. [12] X Research source For example, perhaps you want to see how second career teachers go about their routines in and outside the classrooms and so you decide to observe them for a few days, being sure to get the requisite permission from the school, students and the teacher and taking careful notes along the way.
Participant observation – Participant observation is the immersion of the researcher in the community or situation being studied. This form of data collection tends to be more time consuming, as you need to participate fully in the community in order to know whether your observations are valid. [13] X Research source
Interviews – Qualitative interviewing is basically the process of gathering data by asking people questions. Interviewing can be very flexible - they can be on-on-one, but can also take place over the phone or Internet or in small groups called "focus groups". There are also different types of interviews. Structured interviews use pre-set questions, whereas unstructured interviews are more free-flowing conversations where the interviewer can probe and explore topics as they come up. Interviews are particularly useful if you want to know how people feel or react to something. For example, it would be very useful to sit down with second career teachers in either a structured or unstructured interview to gain information about how they represent and discuss their teaching careers.
Surveys – Written questionnaires and open ended surveys about ideas, perceptions, and thoughts are other ways by which you can collect data for your qualitative research. For example, in your study of second career schoolteachers, perhaps you decide to do an anonymous survey of 100 teachers in the area because you're concerned that they may be less forthright in an interview situation than in a survey where their identity was anonymous.
"Document analysis" – This involves examining written, visual, and audio documents that exist without any involvement of or instigation by the researcher. There are lots of different kinds of documents, including "official" documents produced by institutions and personal documents, like letters, memoirs, diaries and, in the 21st century, social media accounts and online blogs. For example, if studying education, institutions like public schools produce many different kinds of documents, including reports, flyers, handbooks, websites, curricula, etc. Maybe you can also see if any second career teachers have an online meet group or blog. Document analysis can often be useful to use in conjunction with another method, like interviewing.

Coding – In coding, you assign a word, phrase, or number to each category. Start out with a pre-set list of codes that you derived from your prior knowledge of the subject. For example, "financial issues" or "community involvement" might be two codes you think of after having done your literature review of second career teachers. You then go through all of your data in a systematic way and "code" ideas, concepts and themes as they fit categories. You will also develop another set of codes that emerge from reading and analyzing the data. For example, you may see while coding your interviews, that "divorce" comes up frequently. You can add a code for this. Coding helps you organize your data and identify patterns and commonalities. [15] X Research source tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
Descriptive Statistics – You can analyze your data using statistics. Descriptive statistics help describe, show or summarize the data to highlight patterns. For example, if you had 100 principal evaluations of teachers, you might be interested in the overall performance of those students. Descriptive statistics allow you to do that. Keep in mind, however, that descriptive statistics cannot be used to make conclusions and confirm/disprove hypotheses. [16] X Research source
Narrative analysis – Narrative analysis focuses on speech and content, such as grammar, word usage, metaphors, story themes, meanings of situations, the social, cultural and political context of the narrative. [17] X Research source
Hermeneutic Analysis – Hermeneutic analysis focuses on the meaning of a written or oral text. Essentially, you are trying to make sense of the object of study and bring to light some sort of underlying coherence. [18] X Research source
Content analysis / Semiotic analysis – Content or semiotic analysis looks at texts or series of texts and looks for themes and meanings by looking at frequencies of words. Put differently, you try to identify structures and patterned regularities in the verbal or written text and then make inferences on the basis of these regularities. [19] X Research source For example, maybe you find the same words or phrases, like "second chance" or "make a difference," coming up in different interviews with second career teachers and decide to explore what this frequency might signify.

Community Q&A

Qualitative research is often regarded as a precursor to quantitative research, which is a more logical and data-led approach which statistical, mathematical and/or computational techniques. Qualitative research is often used to generate possible leads and formulate a workable hypothesis that is then tested with quantitative methods. [20] X Research source Thanks Helpful 0 Not Helpful 0
Try to remember the difference between qualitative and quantitative as each will give different data. Thanks Helpful 4 Not Helpful 0

↑ https://www.ncbi.nlm.nih.gov/books/NBK470395/
↑ https://owl.purdue.edu/owl/research_and_citation/conducting_research/writing_a_literature_review.html
↑ https://academic.oup.com/humrep/article/31/3/498/2384737?login=false
↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4275140/
↑ http://www.qual.auckland.ac.nz/
↑ http://www.socialresearchmethods.net/kb/qualapp.php
↑ http://www.socialresearchmethods.net/kb/qualdata.php
↑ tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
↑ https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php
↑ https://explorable.com/qualitative-research-design

About This Article

To do qualitative research, start by deciding on a clear, specific question that you want to answer. Then, do a literature review to see what other experts are saying about the topic, and evaluate how you will best be able to answer your question. Choose an appropriate qualitative research method, such as action research, ethnology, phenomenology, grounded theory, or case study research. Collect and analyze data according to your chosen method, determine the answer to your question. For tips on performing a literature review and picking a method for collecting data, read on! Did this summary help you? Yes No

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Qualitative research: methods and examples

Last updated

13 April 2023

Reviewed by

Qualitative research involves gathering and evaluating non-numerical information to comprehend concepts, perspectives, and experiences. It’s also helpful for obtaining in-depth insights into a certain subject or generating new research ideas.

As a result, qualitative research is practical if you want to try anything new or produce new ideas.

There are various ways you can conduct qualitative research. In this article, you'll learn more about qualitative research methodologies, including when you should use them.

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What is qualitative research?

Qualitative research is a broad term describing various research types that rely on asking open-ended questions. Qualitative research investigates “how” or “why” certain phenomena occur. It is about discovering the inherent nature of something.

The primary objective of qualitative research is to understand an individual's ideas, points of view, and feelings. In this way, collecting in-depth knowledge of a specific topic is possible. Knowing your audience's feelings about a particular subject is important for making reasonable research conclusions.

Unlike quantitative research , this approach does not involve collecting numerical, objective data for statistical analysis. Qualitative research is used extensively in education, sociology, health science, history, and anthropology.

Types of qualitative research methodology

Typically, qualitative research aims at uncovering the attitudes and behavior of the target audience concerning a specific topic. For example, “How would you describe your experience as a new Dovetail user?”

Some of the methods for conducting qualitative analysis include:

Focus groups

Hosting a focus group is a popular qualitative research method. It involves obtaining qualitative data from a limited sample of participants. In a moderated version of a focus group, the moderator asks participants a series of predefined questions. They aim to interact and build a group discussion that reveals their preferences, candid thoughts, and experiences.

Unmoderated, online focus groups are increasingly popular because they eliminate the need to interact with people face to face.

Focus groups can be more cost-effective than 1:1 interviews or studying a group in a natural setting and reporting one’s observations.

Focus groups make it possible to gather multiple points of view quickly and efficiently, making them an excellent choice for testing new concepts or conducting market research on a new product.

However, there are some potential drawbacks to this method. It may be unsuitable for sensitive or controversial topics. Participants might be reluctant to disclose their true feelings or respond falsely to conform to what they believe is the socially acceptable answer (known as response bias).

Case study research

A case study is an in-depth evaluation of a specific person, incident, organization, or society. This type of qualitative research has evolved into a broadly applied research method in education, law, business, and the social sciences.

Even though case study research may appear challenging to implement, it is one of the most direct research methods. It requires detailed analysis, broad-ranging data collection methodologies, and a degree of existing knowledge about the subject area under investigation.

Historical model

The historical approach is a distinct research method that deeply examines previous events to better understand the present and forecast future occurrences of the same phenomena. Its primary goal is to evaluate the impacts of history on the present and hence discover comparable patterns in the present to predict future outcomes.

Oral history

This qualitative data collection method involves gathering verbal testimonials from individuals about their personal experiences. It is widely used in historical disciplines to offer counterpoints to established historical facts and narratives. The most common methods of gathering oral history are audio recordings, analysis of auto-biographical text, videos, and interviews.

Qualitative observation

One of the most fundamental, oldest research methods, qualitative observation , is the process through which a researcher collects data using their senses of sight, smell, hearing, etc. It is used to observe the properties of the subject being studied. For example, “What does it look like?” As research methods go, it is subjective and depends on researchers’ first-hand experiences to obtain information, so it is prone to bias. However, it is an excellent way to start a broad line of inquiry like, “What is going on here?”

Record keeping and review

Record keeping uses existing documents and relevant data sources that can be employed for future studies. It is equivalent to visiting the library and going through publications or any other reference material to gather important facts that will likely be used in the research.

Grounded theory approach

The grounded theory approach is a commonly used research method employed across a variety of different studies. It offers a unique way to gather, interpret, and analyze. With this approach, data is gathered and analyzed simultaneously. Existing analysis frames and codes are disregarded, and data is analyzed inductively, with new codes and frames generated from the research.

Ethnographic research

Ethnography is a descriptive form of a qualitative study of people and their cultures. Its primary goal is to study people's behavior in their natural environment. This method necessitates that the researcher adapts to their target audience's setting.

Thereby, you will be able to understand their motivation, lifestyle, ambitions, traditions, and culture in situ. But, the researcher must be prepared to deal with geographical constraints while collecting data i.e., audiences can’t be studied in a laboratory or research facility.

This study can last from a couple of days to several years. Thus, it is time-consuming and complicated, requiring you to have both the time to gather the relevant data as well as the expertise in analyzing, observing, and interpreting data to draw meaningful conclusions.

Narrative framework

A narrative framework is a qualitative research approach that relies on people's written text or visual images. It entails people analyzing these events or narratives to determine certain topics or issues. With this approach, you can understand how people represent themselves and their experiences to a larger audience.

Phenomenological approach

The phenomenological study seeks to investigate the experiences of a particular phenomenon within a group of individuals or communities. It analyzes a certain event through interviews with persons who have witnessed it to determine the connections between their views. Even though this method relies heavily on interviews, other data sources (recorded notes), and observations could be employed to enhance the findings.

Qualitative research methods (tools)

Some of the instruments involved in qualitative research include:

Document research: Also known as document analysis because it involves evaluating written documents. These can include personal and non-personal materials like archives, policy publications, yearly reports, diaries, or letters.

Focus groups: This is where a researcher poses questions and generates conversation among a group of people. The major goal of focus groups is to examine participants' experiences and knowledge, including research into how and why individuals act in various ways.

Secondary study: Involves acquiring existing information from texts, images, audio, or video recordings.

Observations: This requires thorough field notes on everything you see, hear, or experience. Compared to reported conduct or opinion, this study method can assist you in getting insights into a specific situation and observable behaviors.

Structured interviews : In this approach, you will directly engage people one-on-one. Interviews are ideal for learning about a person's subjective beliefs, motivations, and encounters.

Surveys: This is when you distribute questionnaires containing open-ended questions

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Make sense of your research by automatically summarizing key takeaways through our free content analysis tool.

What are common examples of qualitative research?

Everyday examples of qualitative research include:

Conducting a demographic analysis of a business

For instance, suppose you own a business such as a grocery store (or any store) and believe it caters to a broad customer base, but after conducting a demographic analysis, you discover that most of your customers are men.

You could do 1:1 interviews with female customers to learn why they don't shop at your store.

In this case, interviewing potential female customers should clarify why they don't find your shop appealing. It could be because of the products you sell or a need for greater brand awareness, among other possible reasons.

Launching or testing a new product

Suppose you are the product manager at a SaaS company looking to introduce a new product. Focus groups can be an excellent way to determine whether your product is marketable.

In this instance, you could hold a focus group with a sample group drawn from your intended audience. The group will explore the product based on its new features while you ensure adequate data on how users react to the new features. The data you collect will be key to making sales and marketing decisions.

Conducting studies to explain buyers' behaviors

You can also use qualitative research to understand existing buyer behavior better. Marketers analyze historical information linked to their businesses and industries to see when purchasers buy more.

Qualitative research can help you determine when to target new clients and peak seasons to boost sales by investigating the reason behind these behaviors.

Qualitative research: data collection

Data collection is gathering information on predetermined variables to gain appropriate answers, test hypotheses, and analyze results. Researchers will collect non-numerical data for qualitative data collection to obtain detailed explanations and draw conclusions.

To get valid findings and achieve a conclusion in qualitative research, researchers must collect comprehensive and multifaceted data.

Qualitative data is usually gathered through interviews or focus groups with videotapes or handwritten notes. If there are recordings, they are transcribed before the data analysis process. Researchers keep separate folders for the recordings acquired from each focus group when collecting qualitative research data to categorize the data.

Qualitative research: data analysis

Qualitative data analysis is organizing, examining, and interpreting qualitative data. Its main objective is identifying trends and patterns, responding to research questions, and recommending actions based on the findings. Textual analysis is a popular method for analyzing qualitative data.

Textual analysis differs from other qualitative research approaches in that researchers consider the social circumstances of study participants to decode their words, behaviors, and broader meaning.

Learn more about qualitative research data analysis software

When to use qualitative research

Qualitative research is helpful in various situations, particularly when a researcher wants to capture accurate, in-depth insights.

Here are some instances when qualitative research can be valuable:

Examining your product or service to improve your marketing approach

When researching market segments, demographics, and customer service teams

Identifying client language when you want to design a quantitative survey

When attempting to comprehend your or someone else's strengths and weaknesses

Assessing feelings and beliefs about societal and public policy matters

Collecting information about a business or product's perception

Analyzing your target audience's reactions to marketing efforts

When launching a new product or coming up with a new idea

When seeking to evaluate buyers' purchasing patterns

Qualitative research methods vs. quantitative research methods

Qualitative research examines people's ideas and what influences their perception, whereas quantitative research draws conclusions based on numbers and measurements.

Qualitative research is descriptive, and its primary goal is to comprehensively understand people's attitudes, behaviors, and ideas.

In contrast, quantitative research is more restrictive because it relies on numerical data and analyzes statistical data to make decisions. This research method assists researchers in gaining an initial grasp of the subject, which deals with numbers. For instance, the number of customers likely to purchase your products or use your services.

What is the most important feature of qualitative research?

A distinguishing feature of qualitative research is that it’s conducted in a real-world setting instead of a simulated environment. The researcher is examining actual phenomena instead of experimenting with different variables to see what outcomes (data) might result.

Can I use qualitative and quantitative approaches together in a study?

Yes, combining qualitative and quantitative research approaches happens all the time and is known as mixed methods research. For example, you could study individuals’ perceived risk in a certain scenario, such as how people rate the safety or riskiness of a given neighborhood. Simultaneously, you could analyze historical data objectively, indicating how safe or dangerous that area has been in the last year. To get the most out of mixed-method research, it’s important to understand the pros and cons of each methodology, so you can create a thoughtfully designed study that will yield compelling results.

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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.

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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.

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What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on September 5, 2024 by Pritha Bhandari.

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

Your overall research objectives and approach
Whether you’ll rely on primary research or secondary research
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

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

You might have to write up a research design as a standalone assignment, or it might be part of a larger research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.

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

Introduction

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

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

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

Qualitative approach	Quantitative approach
	and describe frequencies, averages, and correlations about relationships between variables

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and quasi-experimental designs allow you to test cause-and-effect relationships
Descriptive and correlational designs allow you to measure variables and describe relationships between them.

Type of design	Purpose and characteristics
Experimental	relationships effect on a
Quasi-experimental	)
Correlational
Descriptive

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

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

Types of qualitative research designs

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

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

Type of design	Purpose and characteristics


Grounded theory
Phenomenology

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

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

Defining the population

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

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

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

Sampling methods

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

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

Probability sampling	Non-probability sampling

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Questionnaires	Interviews
	)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

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

Quantitative observation

Other methods of data collection

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

Field	Examples of data collection methods
Media & communication	Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology	Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education	Using tests or assignments to collect data on knowledge and skills
Physical sciences	Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

Reliability	Validity
	) )

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

Approach	Characteristics
Thematic analysis
Discourse analysis

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

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

Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

Your research questions and/or hypotheses
Your overall approach (e.g., qualitative or quantitative )
The type of design you’re using (e.g., a survey , experiment , or case study )
Your data collection methods (e.g., questionnaires , observations)
Your data collection procedures (e.g., operationalization , timing and data management)
Your data analysis methods (e.g., statistical tests or thematic analysis )

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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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9 Data Collection Methods in Qualitative Research

Explore top methods for collecting qualitative data, from interviews to social media monitoring, to gain deeper customer insights for your strategy.

In the world of customer insights, having access to the right data is crucial. Numbers and metrics can provide valuable direction, but they often fail to capture the full picture of how your customers truly feel, what they need, or why they behave in certain ways.

That’s where qualitative research shines. Using multiple qualitative data collection methods is like casting a wider net for insights — the more varied your approach, the better your chances of capturing nuanced feedback that standard surveys might miss.

Whether it’s through in-depth interviews or mining customer chat logs, the diversity of data sources can help build a robust understanding of your customers’ experiences.

In this article, we’ll cover the top methods you can use to collect qualitative data to inform your customer experience strategy .

Qualitative vs quantitative methods, 9 essential qualitative data collection methods.

In-depth Interviews

Focus Groups

Observational Research

Case Studies

Surveys with Open-ended Questions

Ethnographic Research

Customer Support Center Chat History

Social Media Conversation Monitoring

Review Sites

Pitfalls to Avoid in Qualitative Data Collection

Analyzing qualitative data.

When it comes to gathering customer insights, there are two main avenues: qualitative and quantitative research. Both are crucial, but they serve different purposes.

Quantitative methods rely on numerical data. Think of it as your go-to for answering “how many?” and “how much?” questions. It’s all about measurable facts, trends, and patterns. For example, you might run a large-scale survey asking customers to rate their satisfaction on a 1-10 scale, and you’ll get hard numbers to analyze. This kind of data is easy to visualize in graphs and charts, which helps you track customer satisfaction metrics like NPS or CSAT scores over time.

But qualitative methods ? This is where you dig deeper. These methods focus on the “why” and “how,” uncovering insights into the emotions, motivations, and thought processes behind customer behaviors. Instead of numerical data, qualitative research gives you rich, detailed feedback in the form of words. The qualitative data collected through these methods provides detailed and nuanced insights into individuals' or groups' experiences, perspectives, and behaviors. It’s an excellent way to get to the heart of customer experiences and understand their pain points on a human level.

Why Qualitative Research Is Critical for Customer Experience Strategy

Quantitative data can tell you what’s happening, but qualitative data tells you why it’s happening. The qualitative data collected through various methods can explain the underlying reasons behind customer satisfaction scores. If your quantitative research shows a drop in customer satisfaction scores, qualitative research can explain why. By diving into customer stories, open-ended survey responses, or even analyzing chat logs, you gain invaluable insights into where things might be going wrong (or right!).

Let’s dive into the most impactful methods you can use to gather valuable customer insights. Each of these methods offers a unique lens into the customer experience, helping you build a comprehensive understanding of your audience. Understanding both qualitative and quantitative data is essential for building a comprehensive understanding of your audience.

Data Collection Methods in Qualitative Research In-Depth Interviews

1. In-Depth Interviews

In-depth interviews are one-on-one conversations where the researcher asks open-ended questions , allowing the customer to share their thoughts and experiences in detail. These interviews are incredibly useful when you want to understand the “why” behind customer behavior or preferences. The qualitative data collected through in-depth interviews provides rich, detailed insights into customer behavior and preferences.

Maximizing the method: To get the most out of in-depth interviews, focus on creating a comfortable environment where participants feel free to express their honest opinions. Listen actively, ask follow-up questions, and don’t shy away from allowing the conversation to go off-script if it leads to richer insights.

Example: Imagine you’re an insights manager at a retail brand conducting an in-depth interview with a frequent shopper. By asking about their shopping habits, you can uncover that the customer values sustainability and chooses brands with eco-friendly packaging. This insight could inform future product packaging decisions.

Data Collection Methods in Qualitative Research Focus Groups

2. Focus Groups

A focus group is a facilitated discussion with a small group of customers – usually around 6-10 people. The goal is to encourage interaction between participants, sparking conversations that reveal insights through group dynamics. The collective experience of a focus group can surface opinions that may not emerge in individual interviews. The qualitative data collected through focus groups can reveal collective opinions and insights that may not emerge in individual interviews.

Maximizing the method: Ensure that the focus group facilitator is skilled at guiding discussions without leading them. It’s important to let the conversation flow naturally, but the facilitator should know when to probe deeper or refocus the group when necessary.

Example: Let’s say a tech company runs a focus group with power users of their app. During the session, one participant mentions a feature they find confusing, which prompts others to agree. This shared feedback provides the company with a clear signal to revisit that feature for usability improvements.

3. Observational Research

Observational research (sometimes called field research) involves observing customers in their natural environment, whether it’s a store, website, or another setting. Instead of asking questions, researchers watch how customers interact with products, services, or environments in real-time. The qualitative data collected through observational research provides real-time insights into customer interactions and behaviors.

Maximizing the method: The key to observational research is to remain unobtrusive. Customers should behave naturally without being influenced by the researcher’s presence. It’s also crucial to take detailed notes on both the behaviors you expected, and any surprising actions that arise.

Example: A coffee shop chain might use observational research to see how customers navigate their in-store experience. Do they head straight to the counter or linger at the menu? Are they confused about the ordering process? These observations could highlight ways to improve the store layout or ordering flow.

Data Collection Methods in Qualitative Research Case Studies

4. Case Studies

Case studies are in-depth analyses of individual customer experiences, often focusing on how a product or service has solved a specific problem for them. By following a single customer’s journey from problem to solution, case studies offer detailed narratives that can illustrate the broader impact of your offerings. The qualitative data collected through case studies offers detailed narratives that illustrate the broader impact of your offerings.

Maximizing the method: Choose case study subjects that reflect common challenges or experiences within your customer base. The more relatable the story, the more likely other customers will see themselves in the narrative.

Example: A B2B SaaS company could create a case study around a client that successfully used their software to reduce employee churn. By detailing the challenges, implementation, and results, the case study could serve as a powerful testimonial for potential clients.

Data Collection Methods in Qualitative Research Open Ended Survey Questions

5. Surveys with Open-Ended Questions

While many surveys are typically quantitative, surveys with open-ended questions provide a qualitative element by allowing customers to write out their responses in their own words. This method bridges the gap between structured data and personal insights, making it easier to spot recurring themes or unique perspectives. The qualitative data collected through open-ended survey questions bridges the gap between structured data and personal insights.

Maximizing the method: Be strategic with the placement of open-ended questions. Too many can overwhelm respondents, but including one or two at key points in your survey allows for deeper insights without causing survey fatigue.

Example: A travel company might send out a post-trip survey asking, “What was the most memorable part of your experience?” The open-ended responses could reveal customer preferences that the company wasn’t previously aware of, informing future offerings or services.

Data Collection Methods in Qualitative Research Ethnographic Research

6. Ethnographic Research

Ethnographic research takes immersion to a new level. In this method, researchers embed themselves in the customer’s environment for extended periods to observe and experience their behaviors firsthand. It’s about gaining a deep understanding of customer culture, motivations, and interactions. The qualitative data collected through ethnographic research provides a deep understanding of customer culture and interactions.

Maximizing the method: This method works best when researchers fully integrate into the customer’s world, whether that’s living among a target community or spending time on-site with customers in their daily routines. It’s a time-intensive process, but the insights can be incredibly rich.

Example: A researcher for a clothing brand might spend several weeks with a group of customers, observing how they shop for and wear clothes in their daily lives. This immersive research could uncover nuanced preferences about fabric types, fit, and style that surveys alone wouldn’t reveal.

Data Collection Methods in Qualitative Research Customer Support Chat History

7. Customer Support Center Chat History

Your customer support center chat history can be a treasure trove of qualitative data. By analyzing conversations between customers and support agents, you can identify recurring issues, concerns, and sentiments that might not surface in formal surveys or interviews. This method provides an authentic view of how customers feel in real-time as they interact with your brand for problem-solving. The qualitative data collected from chat histories provides an authentic view of customer sentiments in real-time.

Maximizing the method: Use text analysis tools to sift through large volumes of chat data, identifying common themes and patterns. Pay special attention to moments of frustration or satisfaction, as these often hold the key to customer experience improvements.

Example: A software company analyzes its chat history and notices that many customers express confusion about a particular feature. This insight leads the product team to create clearer in-app tutorials, ultimately reducing the number of support requests related to that feature.

Data Collection Methods in Qualitative Research Social Media Conversation Monitoring

8. Social Media Conversation Monitoring

Social media platforms are filled with candid, unsolicited customer feedback. Social media conversation monitoring involves tracking brand mentions, hashtags, and keywords to gauge customer sentiment and uncover insights about your audience. This method gives you access to a wide range of voices, including those who may never participate in formal research. The qualitative data collected from social media conversations offers a wide range of customer insights.

Maximizing the method: Leverage social listening tools to automate the process of monitoring and analyzing conversations across platforms like Instagram, Meta, or X. Be sure to track both direct mentions of your brand and broader industry-related conversations that could reveal trends or shifting customer preferences.

Example: A beauty brand might notice that customers are frequently discussing a competitor’s eco-friendly packaging on social media. By monitoring this trend, the brand could introduce more sustainable packaging solutions to align with emerging customer values.

9. Review Sites

Review sites such as Yelp, Google Reviews, and Trustpilot are another goldmine for qualitative data. Customers who leave reviews are often highly motivated to share their experiences, whether positive or negative. By mining these reviews, you can gather insights into customer satisfaction, product issues, and potential areas for improvement. The qualitative data collected from review sites provides insights into customer satisfaction and areas for improvement.

Maximizing the method: Don’t just focus on star ratings—read through the text of each review to extract the underlying emotions and motivations. Look for patterns in the language used and the specific aspects of your product or service that are frequently mentioned.

Example: A restaurant chain may notice through online reviews that customers often comment on the long wait times during dinner hours. This feedback prompts management to reassess staffing levels during peak times, improving both operational efficiency and customer satisfaction.

As with any research process, there are a few key pitfalls to watch out for when collecting qualitative data. Avoiding these three common mistakes will ensure that your insights are both accurate and actionable.

1. Bias in Data Collection

Bias can creep into qualitative research in many forms, from how questions are phrased in interviews or surveys to how data is interpreted. For example, leading questions might push respondents toward a specific answer. Similarly, during observational research or focus groups, the presence or behavior of the researcher could unintentionally influence participants.

How to avoid it: Ensure your research methods are designed to be neutral and that questions are open-ended. It’s also important to train researchers to minimize their influence during interviews or observations. Using standardized protocols can help maintain consistency across different data collection methods.

Data Collection Methods in Qualitative Research Pitfalls

2. Over-reliance on a Single Method

While one method may seem like the easiest or most convenient to implement, relying solely on one form of data collection can lead to incomplete or skewed insights. For example, in-depth interviews might provide detailed information, but they won’t capture broad patterns across your entire customer base.

How to avoid it: Combine multiple data collection methods, like surveys, focus groups, and social media monitoring, to get a fuller picture. Each method will reveal different aspects of customer experience, and when analyzed together, they provide more comprehensive insights.

3. Failing to Document the Research Process

One of the easiest ways to undermine the quality of your qualitative data is by failing to document the research process adequately. Without a clear record of how data was collected, analyzed, and interpreted, it becomes difficult to validate findings or replicate the study in the future.

How to avoid it: Keep detailed notes, records, and transcriptions of every stage of the research process. Having a clear audit trail ensures that your findings are credible and can be trusted by decision-makers.

With these qualitative data collection methods at your disposal, you’ll find yourself with a wealth of unstructured qualitative data. While an abundance of data is valuable, it also presents a significant challenge: how to make sense of it all efficiently.

This is where advanced tools and technology come into play.

The Challenge of Unstructured Data

Qualitative research methods produce, by their nature, unstructured data. Whether you’re working with transcripts from focus groups, feedback from review sites, or social media conversations, the data doesn’t neatly fit into rows and columns like quantitative data does. Instead, you’re dealing with text—rich, narrative-driven, and full of context. This makes it incredibly insightful but also hard to analyze manually.

Manually categorizing themes, identifying patterns, and summarizing key takeaways from large datasets is time-consuming and prone to human error. It’s easy to miss out on emerging trends or nuances that could offer strategic value, especially if you're dealing with diverse data sources.

How Kapiche’s AI-Powered Auto-Theming Can Help

Kapiche’s automatic theming feature is designed to solve this problem. By leveraging AI-powered technology, Kapiche cleans, categorizes, and analyzes your text data quickly and accurately. The platform automatically identifies themes, clusters related data points, and even provides summaries that help you interpret what your customers are saying.

Kapiche qualitative research auto-theming

For example, Kapiche can scan through customer support chat histories or social media mentions and instantly group similar pieces of feedback together—whether customers are talking about product performance, customer service, or price sensitivity. With these insights readily available, you can take faster action to improve your customer experience.

Benefits of Auto-Theming for Insights Managers

Here's how an auto-theming can transform your qualitative data analysis:

Speed and Efficiency: Automating the process saves you countless hours of manual work.

Comprehensive Analysis: By aggregating data from multiple sources, you get a fuller picture of customer sentiment across various touchpoints.

Uncover Hidden Insights: The AI detects patterns that you might not notice through manual analysis, offering deeper insights into customer behavior.

Actionable Summaries: Instead of wading through raw text, Kapiche provides concise summaries of key themes and trends, enabling you to act on insights faster.

With tools like this at your disposal, the overwhelming task of analyzing qualitative data becomes manageable, empowering your insights team to make data-driven decisions more effectively.

Let Us Help You

Navigating the complexities of qualitative data collection and analysis can be challenging, but you don’t have to do it alone. At Kapiche, we’re committed to helping insights teams like yours make the most of your qualitative customer data.

Our AI-powered auto-theming capabilities simplify the process by automatically categorizing, analyzing, and summarizing your data. This means you can quickly uncover key insights and trends without getting bogged down by the sheer volume of unstructured information.

Ready to see how Kapiche can transform your research process? Click the link below to watch an on-demand demo and discover how our platform can enhance your customer insights strategy.

Book a Demo with Kapiche

How to Conduct Qualitative Data Analysis? (+The Best Tool to Use)

12 min read

Let’s face it: qualitative data analysis is vital to understanding why users act in a particular way and how they feel about your product in a way that quantitative product analytics can’t.

This article will teach you how to analyze qualitative data to inform product development and improve the product experience.

You will discover:

Five qualitative data analysis methods.
A six-step analysis process and how to streamline it with Userpilot.
How to act on your research findings.

What is qualitative data analysis?

Qualitative data analysis (QDA) involves organizing, examining, and interpreting non-numerical data collected from customers . For example, their survey responses or session recordings.

The purpose?

To understand their interactions with the product and gain in-depth insights into user pain points , unmet needs, motivations, and expectations.

Quantitative data analysis vs. qualitative data analysis

Quantitative and qualitative data analyses have different objectives and use different data types, methods, and tools to achieve them.

The aim of quantitative research is to illustrate objectively what happens inside the product, while qualitative research focuses on the why . For example, quantitative data can reveal the percentage of users who drop off at a particular touchpoint, whereas qualitative data explains the reasons for that.
Quantitative data analysis uses numerical and measurable data, such as event occurrences. In contrast, qualitative data analysis relies on non-numerical and descriptive data, like open-ended survey responses .
To collect quantitative data, you use closed-ended survey questions and analytics tools to track user actions . Qualitative data, on the other hand, comes from open-ended survey questions , interviews, focus groups, phone calls and chats, and session recordings.
Analyzing quantitative data involves visualizing it in charts and graphs and conducting statistical operations. Qualitative analysis is about finding themes and patterns in data, often manually.

Benefits of qualitative data analysis

Here are the key advantages of qualitative data analysis that underscore its importance in customer and market research :

Deep insights : Qualitative analysis helps understand complex patterns by looking at reasons and perspectives behind the data.
Flexibility : It allows researchers to adjust their approach as they continuously discover new information or themes.
Contextual understanding : It explores contextual factors, which adds depth to number-based findings and reveals hidden connections.
Participant voice : It highlights what participants actually say, experience, and feel and uses their input to shape the analysis and the results.

Challenges of qualitative data analysis

Let’s get it straight: qualitative data analysis comes with its challenges. The key ones include:

Data overload and management : Qualitative data analysis involves tons of text or multimedia, which is challenging to organize, manage, and analyze.
Reliability and validity : Ensuring the reliability and validity of qualitative findings is difficult because the process isn’t as standardized as quantitative analysis. Personal biases can skew the results.
Time-intensive nature : Qualitative data analysis is resource-intensive and time-consuming. It involves iterative processes of coding, categorizing, and synthesizing data.

What are the 5 qualitative data analysis methods?

There are 5 main methods of qualitative research studies:

Content analysis.
Narrative analysis.
Discourse analysis.
Thematic analysis.
Grounded theory analysis.

Mind you, the differences between them aren’t always clear-cut, and there is always some overlap.

1. Content analysis

Content analysis is a qualitative data analysis method that systematically evaluates a text to identify specific features or patterns. This could be anything from a customer interview transcript to survey responses, social media posts, or customer success calls.

The first step in the content analysis research method is data coding, or labeling and categorizing.

For example, if you are looking at customer feedback , you might code all mentions of “price” as “P,” all mentions of “quality” as “Q,” and so on.

Once manual coding is done, start looking for patterns and trends in the codes.

While it’s a qualitative approach, it often aims to quantify the content, for example, by counting how many times a word or theme appears.

One of the main advantages of content analysis is its relative simplicity – anyone with a good understanding of the data can do it.

The advantages of the content analysis process

Content analysis can provide rich insights into how customers feel about your product, what their unmet needs are, and their motives.
Once you have developed a coding system, content analysis is relatively quick and easy because it’s a systematic process.
Content analysis requires very little investment since all you need is a good understanding of the data, and it doesn’t require any specialist qualitative research data analysis software .

The disadvantages of the content analysis process

Coding data takes time, particularly if you have large amounts to analyze.
Content analysis can ignore the context in which the data was collected. This may lead to misinterpretations.
Some people argue that content analysis is a reductive approach to qualitative data because it involves breaking the data down into smaller pieces and quantifying, which can lead to oversimplifications.

2. Narrative analysis

Narrative analysis involves identifying, analyzing, and interpreting customer stories, for example, in the form of customer interviews or testimonials.

This kind of analysis helps product managers understand customers’ feelings toward the product, identify trends in customer behavior, and personalize their in-app experiences .

The advantages of narrative analysis

The stories people tell give a deep understanding of customers’ needs and pain points.
It collects unique, in-depth data based on customer stories.

The disadvantages of narrative analysis

Hard to implement in large-scale studies.
Transcribing customer interviews or testimonials is labor-intensive.
Impossible to replicate as it relies on unique customer stories and your ability to interpret them.

3. Discourse analysis

Discourse analysis is about understanding how people communicate with each other. You can use it to analyze written or spoken language.

For instance, product teams can use discourse analysis to understand how customers talk about their products on the web.

The advantages of discourse analysis

Uncovers emotions behind customers’ words.
Gives insights into customer data.

The disadvantages of disclosure analysis

Takes a lot of time and effort as the process is highly specialized and requires training and practice. There’s no “right” way to do it.
Focuses solely on language.

4. Thematic analysis

Thematic analysis is similar to content analysis.

It also looks for patterns and themes in qualitative data and involves labeling and categorizing the data.

The difference?

It focuses on the meaning of the data and how the themes relate to the research questions .

You can pair it with sentiment analysis to determine whether a piece of writing is positive, negative, or neutral. This is done using a lexicon (i.e., a list of words and their associated sentiment scores).

SaaS companies use thematic analysis to analyze qualitative NPS survey responses to identify patterns among their customer base.

The advantages of thematic analysis

Anyone with little training on how to label the data can perform thematic analysis.
Survey or customer interview raw data can be easily converted into insights and quantitative data with the help of labeling.
If done automatically, it’s an effective way to process large amounts of data (you need AI tools for this).

The disadvantages of thematic analysis

If the data isn’t coded correctly, it can be difficult to identify themes since it’s a phrase-based method.
It’s difficult to implement from scratch because balancing the themes and categories is tricky. They shouldn’t be too generic or too large.

5. Grounded theory analysis

Grounded theory analysis is a method used by qualitative researchers when there is little existing theory on the topic. Instead of starting with hypotheses and testing them through research, it involves generating them from the data on the fly as it emerges.

The grounded theory approach is useful for product managers who want to understand how customers interact with their products. You can also use it to generate hypotheses about how customers will behave in the future.

Suppose product teams want to understand the reasons behind the high churn rate . They can use customer surveys and grounded theory to analyze responses and develop hypotheses about why users churn and how to reengage inactive ones .

The advantages of grounded theory analysis

It reduces bias because it doesn’t rely on assumptions.
It’s great for analyzing poorly researched topics.

The disadvantages of grounded theory analysis

It can come across as overly theoretical.
It requires a lot of objectivity, creativity, and critical thinking.

Which qualitative data analysis method should you choose?

The choice of the appropriate qualitative data analysis method depends on various factors, including:

Research question : Different qualitative methods are suitable for different research questions. For example, you can use content analysis to categorize and quantify customer feedback from support tickets to prioritize areas for improvement. Contrastingly, discourse analysis will show you how users feel about your product.
Nature of data : Choose qualitative data analysis techniques that align with the data’s characteristics. For instance, content analysis is versatile and can be applied to various types of qualitative data, while narrative analysis focuses specifically on stories and narratives.
Researcher expertise : Consider your own skills and expertise in qualitative analysis techniques. Some methods may require specialized training or familiarity with specific software tools . Choose a method that you feel comfortable with and confident in applying effectively.
Research goals and resources : Evaluate your research goals, timeline, and resources available for analysis. Consider the balance between the depth of analysis and practical constraints. For example, narrative analysis is more time-consuming or resource-intensive than others.

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How to perform qualitative data analysis? A step-by-step process

With all that qualitative research methods covered, it’s time for our step-by-step guide to qualitative data analysis.

Step 1: Define your qualitative research questions

It’s important to be as specific as possible with the questions, as this will guide how you collect qualitative research data and the rest of your analysis.

Examples are:

What are the primary reasons for customer dissatisfaction with our product?
How does X group of users feel about our new feature?
What are our customers’ needs, and how do they vary by segment?
How do our products fit into our customers’ lives?
What factors influence the low usage rate of the new feature ?

Step 2: Gather your qualitative customer data

Now, you decide what type of data collection to use based on previously defined goals.

Here are 5 methods to collect qualitative data for product companies:

In-app surveys : triggered at a specific time or contextually, when the user completes an action, they allow you to reach product users (not account managers) and to collect qualitative data at scale. They should include both closed-ended and open-ended follow-up questions.
Email surveys : delivered to users’ mailboxes, these surveys tend to have lower response rates than in-app surveys, but they’re sometimes the only way to reach inactive users .

Review sites : Customer reviews on sites like G2, Capterra, or Clutch are often unfiltered and give you honest insights into customer sentiment and how to improve the product .

Customer reviews are an excellent source of qualitative data

User interviews : one-to-one conversations with customers give you the flexibility that no survey offers. A skilled interviewer can follow up on responses to help users get to the root cause of an issue, even if they’re not very good at articulating their thoughts or reflecting on their behaviors.

Focus groups : Just like interviews, these moderated group discussions can offer valuable insights about your product and customer preferences . They aren’t very scalable, though.

Pro tip : Use a mix of in-app surveys and in-person interviews to collect qualitative data. For example, send regular NPS surveys to customers to track their sentiment over time. Segment your detractors and invite them to interviews to better understand why they’re dissatisfied with the product.

Step 3: Organize and categorize collected data

Before analyzing customer feedback and assigning any value, organize the unstructured feedback data in a single place. This is to easily detect patterns and similar themes.

One way to do this is to create a spreadsheet with all the data organized by research questions. Then, arrange the data by theme or category within each research question.

Or use Userpilot’s NPS response tagging to group similar responses. For example, you can tag all feedback about technical issues with “bug.”

NPS qualitative response tagging in Userpilot

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Step 4: use qualitative data coding to identify themes and patterns.

Themes in data analysis help you organize and make sense of your information.

In SaaS products, common themes from customer feedback might include:

Product issues ( bugs or defects).
Pricing concerns.
Customer service experiences.
Usability problems.
User interface (UI) design.
User experience (UX) issues.
Missing features.

Identifying specific themes is just a start. You also need to identify their patterns: how often they occur, when, and who is affected. For example, if users complain about a missing feature, find out how many and what their JTBDs are.

You can detect those patterns using survey analytics .

Step 5: Act on the insights to improve product metrics

Once you understand the nature of the problem, look for ways to resolve it.

How you act on feedback depends on the issues.

Imagine your users request a feature . But it turns out you have a similar feature that allows them to complete the same task – they just haven’t found it. So, the solution is redesigning your onboarding process to improve feature discovery .

And if you have to build the feature, don’t just blindly follow customer requests or what competitors are doing – look for innovative solutions to get the edge.

Step 6: Continuously analyze qualitative data to stay ahead of changing customer needs

Qualitative data analysis isn’t a one-off exercise. As customer needs constantly evolve, you must always keep your hand on the pulse.

Collect customer feedback in-app at regular intervals and make a habit of interviewing customers all the time, even if you’re not planning to launch new features or products.

Qualitative data analysis example: How Unolo reduced customer churn with Userpilot NPS survey

One of our customers, Unolo, faced a challenge: a high month-on-month churn rate of around 3%.

They implemented in-app NPS surveys to analyze qualitative and quantitative responses about why their customers were leaving.

In addition to collecting the feedback in-app, their customer success team also reached out to the dissatisfied customers directly.

What was the outcome?

UX and product experience improvements that reduced the churn by up to 1%!

How to perform qualitative data analysis with Userpilot?

Userpilot is a product growth platform with advanced feedback, analytics, and engagement features. So you can use it to collect qualitative and quantitative data and act on the insights to improve user experience .

Leyre Iniquez of Cuvama about Userpilot’s versatility. It isn’t just for collecting qualitative data

Collect qualitative feedback from users with in-app surveys

You can collect qualitative user data in Userpilot through in-app surveys .

Thanks to the template library and visual editor, creating customized surveys takes minutes.

Once ready, there are two ways to trigger them.

One is by setting the date, time, and page where the user should see it. Great for regular surveys, like CSAT or NPS .

The other is through event-based triggering. That’s when you link the survey to another user action in-app, for example, using a feature. That’s how you can measure their satisfaction with specific product aspects and collect ideas on how to improve them.

In both instances, you can choose to send the survey to specific user segments .

Finally, Userpilot offers localization functionality, so you can automatically translate the survey into multiple languages for users around the globe.

Analyze data no-code using survey analytics

Once you collect the data, you can easily analyze it without any coding.

Userpilot offers you survey analytics where you can quickly view the results and analyze user engagement with the surveys.

And let’s not forget about the NPS dashboard , which provides a comprehensive overview of your NPS scores over time, allowing you to track changes and trends in user loyalty and advocacy.

And don’t worry, Userpilot does all the NPS analysis for you: calculates the overall score and segments users into detractors, passives , and promoters.

You can also tag the NPS responses to identify common themes.

Gather customer insights via chatbots

With Userpilot, you can embed a chatbot in the resource center .

This serves two purposes:

It allows you to support your users in-app and help them overcome issues that could lower their satisfaction and potentially lead to churn .
You can also use it to collect qualitative customer feedback .

Collect qualitative user data through a chatbot

Build different in-app experiences based on the insights from qualitative data analysis

By analyzing qualitative feedback collected through in-app surveys , you can segment users based on these insights and create targeted in-app experiences designed to address specific user concerns or enhance key workflows.

For example, you can personalize the onboarding experiences for different user personas so that they discover the most relevant features as quickly as possible. Or use interactive walkthroughs to guide them through challenging processes.

Creating in-app experience with Userpilot requires no coding

The qualitative data analysis process is iterative and should be revisited as new data is collected. The goal is to constantly refine your understanding of your customer base and how they interact with your product.

Want to get started with qualitative analysis? Get a Userpilot demo and automate the qualitative data collection process. Save time on mundane work and understand your customers better!

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Open access
Published: 19 September 2024

Strengthening rural healthcare outcomes through digital health: qualitative multi-site case study

Leanna Woods 1 , 2 , 3 ,
Rebekah Eden 2 , 4 ,
Sophie Macklin 2 ,
Jenna Krivit 2 ,
Rhona Duncan 5 ,
Helen Murray 6 ,
Raelene Donovan 7 &
Clair Sullivan 1 , 2 , 8

BMC Health Services Research volume 24 , Article number: 1096 ( 2024 ) Cite this article

97 Accesses

Metrics details

Rural populations experience ongoing health inequities with disproportionately high morbidity and mortality rates, but digital health in rural settings is poorly studied. Our research question was: How does digital health influence healthcare outcomes in rural settings? The objective was to identify how digital health capability enables the delivery of outcomes in rural settings according to the quadruple aims of healthcare: population health, patient experience, healthcare costs and provider experience.

A multi-site qualitative case study was conducted with interviews and focus groups performed with healthcare staff ( n = 93) employed in rural healthcare systems ( n = 10) in the state of Queensland, Australia. An evidence-based digital health capability framework and the quadruple aims of healthcare served as classification frameworks for deductive analysis. Theoretical analysis identified the interrelationships among the capability dimensions, and relationships between the capability dimensions and healthcare outcomes.

Seven highly interrelated digital health capability dimensions were identified from the interviews: governance and management; information technology capability; people, skills, and behaviours; interoperability; strategy; data analytics; consumer centred care. Outcomes were directly influenced by all dimensions except strategy. The interrelationship analysis demonstrated the influence of strategy on all digital health capability dimensions apart from data analytics, where the outcomes of data analytics shaped ongoing strategic efforts.

Conclusions

The study indicates the need to coordinate improvement efforts targeted across the dimensions of digital capability, optimise data analytics in rural settings to further support strategic decision making, and consider how consumer-centred care could influence digital health capability in rural healthcare services. Digital transformation in rural healthcare settings is likely to contribute to the achievement of the quadruple aims of healthcare if transformation efforts are supported by a clear, resourced digital strategy that is fit-for-purpose to the nuances of rural healthcare delivery.

Peer Review reports

Continuing significant health inequalities exist within and across countries [ 1 ]. In Australia, people in rural and remote areas have a 40% increased disease risk and a 2.3 times higher preventable mortality rate than those in major cities [ 2 ]. First Nations Australians are particularly disadvantaged: those living in remote areas have a life expectancy 6–7 years lower than those in major cities [ 3 ]. The population is unevenly dispersed across vast distances and harsh environments, contributing to healthcare access issues [ 4 ].

In these rural settings, geographic, resourcing and health equity challenges provide great opportunity for digital models of care [ 5 ]. Precision medicine and virtual care hold the promise of more integrated and value-based health systems with improved outcomes and care closer to home [ 6 ]. Outcomes are increasingly measured and mapped using the quadruple aim of healthcare: population health, patient experience, healthcare costs and provider experience [ 7 ]. The quadruple aim enables a balanced view of healthcare outcomes beyond traditionally measured productivity measures to benefits that are meaningful to staff, clinicians and consumers [ 8 ].

Literature on digital health transformation acknowledges the disparities faced by rural communities and their difficulty in delivering on the quadruple aim of healthcare given geographic constraints [ 9 , 10 , 11 ]. Although digital adoption across healthcare settings varies [ 12 ], the need for digital health is highest in rural and resource-constrained settings [ 13 ]. Rural areas need more reliable digital connectivity to compensate for the geographical remoteness, yet rural communities are generally less and worse connected by technologies [ 10 ]. Described as a digital vulnerability [ 10 ], rurality is contributing to widening digital health inequities [ 9 ]. Careful consideration of transformation efforts is required to ensure the unanticipated consequences [ 8 ] such as exacerbating the rural digital divide, are adequately managed [ 10 , 14 ]. ‘TechQuity’ has become an increasingly prioritised commitment to use technology to eliminate structural inequities among diverse social, economic, demographic or geographic groups [ 14 ]. Although enormous efforts are currently underway to bring digital services to rural and remote areas, challenges remain in supporting an ageing and understaffed rural workforce [ 15 ] to adopt digitally-enabled models of care with immature infrastructure, network fragility, public policy constraints, adoption barriers, lack of digital devices, constrained local technical knowledge, and variable digital inclusion of citizens [ 9 , 10 , 11 ].

Digital health capability refers to the enabling environment required for executing digital health [ 16 ]. Digital health capability (or ‘maturity’) assessments examine the enabling environment across groups of variables often referred to as dimensions [ 16 , 17 , 18 ]. Despite the interest of digital transformation leaders to define clear targets for success, and the many frameworks available to assess digital health capability (e.g., Electronic Medical Record Adoption Model, Picture Archiving and Communication Systems Maturity Model, Clinical Digital Maturity Index) [ 17 ], international consensus on how digital health capability should be measured remains elusive [ 19 ]. Digital capability assessments have traditionally focused on technical implementation alone [ 19 ]. To consolidate the diverse dimensions, we conducted a systematic literature review and developed a synthesised digital health capability framework [ 17 ], and refined it through a consultative process with various healthcare stakeholders [ 18 ] (Fig. 1 ). This framework provides health organisations with a more comprehensive understanding of the current state of digital health capability and guidelines to develop roadmaps for digital health to result in meaningful improvement in patient care, health outcomes and health equity. While building digital health capability can positively contribute to the health equity gap [ 20 ], the digital health capability of rural and remote health services is poorly studied.

Digital health capability framework

Previous research suggests the need for healthcare organisations to improve digital health capability [ 19 ] with calls for research to investigate: (1) digital health capability in resource-constrained rural health services [ 12 , 13 , 21 ], (2) the interrelationships among the different dimensions of digital health capability [ 17 ], and (3) the outcomes that meaningfully benefit patients and populations [ 17 , 21 , 22 ]. To address this gap, we sought to answer the research question: How does digital health capability influence healthcare outcomes in rural settings according to the quadruple aims of healthcare?

Our objectives were to:

Identify digital health capability dimensions in rural settings.

Identify the relationships among the digital health capability dimensions and healthcare outcomes.

Identify the interrelationships among the digital health capability dimensions.

A multi-site qualitative case study was conducted, using semi-structured interviews supplemented with focus groups.

Setting and participants

In the geographically large Australian state of Queensland, approximately 38% of the total population and 66% of First Nations people live in non-metropolitan areas [ 4 ]. Universal healthcare is delivered by Queensland Health across 16 geographically defined healthcare systems, providing a range of public healthcare services in small rural clinics to large quaternary academic hospitals [ 23 ]. Of the 16 healthcare systems, six are considered regional as they have a mix of regional, rural and remote healthcare services and four are considered remote as they serve only rural and remote communities [ 23 , 24 ] (Fig. 2 ). Queensland Health’s ten-year digital strategic plan includes the vision to improve access to care and support better health outcomes for rural and remote Queenslanders [ 2 ]. Regional ( n = 6) and remote ( n = 4) healthcare systems were included in this study and are collectively referred to as rural healthcare systems ( n = 10).

Regional and remote healthcare systems in Queensland, Australia

Data collection

As digital transformation is an interdisciplinary endeavour, a purposive sample [ 25 ] of healthcare staff working in diverse roles (e.g., clinicians, executives, informatics team members) within rural healthcare systems were eligible participants. Site contact persons and the state-wide health executive forum helped identify participants. Participants were invited to attend an interview or focus group via videoconferencing and a semi-structured interview guide was administered by two interviewers. The interview guide contained questions pertaining to strategic vision, experiences of implementations, and evaluations of digital transformations, tailored to each professional group (Appendix 1 ). Participation was voluntary. Interviews and focus groups were audio recorded, transcribed, and anonymised.

Data analysis

The interview data was deductively analysed using the digital health capability framework [ 18 ] and quadruple aims of healthcare [ 7 ] as classification frameworks. The analysis involved two researchers coding the interview data and classifying the relevant perceptions of interview participants to the respective subdimensions of digital health capability and healthcare outcomes. Through an initial qualitative analysis, there were consistency amongst the themes described by regional and remote healthcare systems and therefore the analysis was performed on the combined sample. When the analysis revealed a theme not included in the existing framework [ 18 ] it was inductively analysed [ 26 ]. Three new sub-dimensions resulted: resources (governance and management dimension); fit-for-purpose (IT capability dimension); and attitudes (people, skills and behaviour dimension) (Fig. 1 , Appendix 2 ).

The quadruple aims of healthcare was used to identify the outcomes related to population health (e.g., equity, access, disparities), patient experience (e.g., preferences, communication, access, engagement), provider experience (e.g., workload, preferences), and healthcare costs (i.e., resourcing, efficiency) [ 7 , 27 ].

To address objective 1, the analysis of the quotes regarding the sub-dimensions and dimensions of the digital health capability framework were synthesised in tables and reported narratively. We did not identify divergent perspectives in this analysis.

To address objective 2 and 3, theoretical coding was performed to identify relationships between the dimensions of the digital health capability framework and outcomes (e.g., between interoperability and population health) and interrelationships among the dimensions (e.g., between interoperability and strategy), respectively. The analyses for objectives 2 and 3 were supported by the matrix querying functionality in NVivo (version 12, QSR International). The outputs of the matrix queries were manually analysed by four researchers to confirm the relationships, and to identify the direction of the relationship.

In all analyses, findings were refined and finalised through consensus in researcher workshops [ 25 ].

In total, 93 participants attended an interview or focus group across regional ( n = 6) and remote ( n = 4) healthcare systems (Table 1 ).

Perceptions of rural digital health capability

The perceptions of digital health capability dimensions in rural healthcare systems are described below. Three new sub-dimensions were identified; resources (governance and management dimension); fit-for-purpose (IT capability dimension); and attitudes (people, skills and behaviour dimension). Appendix 3 provides additional evidence of the capability subdimensions.

Consumer-centred care , in terms of supporting consumers to manage their own health through technology-enhanced care and improved consumer health literacy, was valued by participants: “ Communication with patients , so the ability to send text messages or have an app that allows them to track their own health , in terms of appointments , results , medications …would be …great .” (G6). Limited resourcing, time availability and dispersed location of consumers were described as barriers to the access and use of technological infrastructure needed to enable consumer-centred care.

Governance and management were considered vital as rural sites transitioned from paper to digital. Adequate resourcing and support for staff through this transition was important: “ In …the last five years , we’ve introduced them (staff) to [the electronic medical record] and that has been a huge challenge with a lot of change initiatives and management to …bring them along on the journey.” ( E2). Participants identified that governance and management efforts should be targeted at providing effective data governance, protocols for sharing data with external providers, and reducing unsafe workarounds.

In the IT capability dimension, digital infrastructure was limited by internet connectivity: “ The other barrier …is our connectivity within our sites , with being rural and remote. We don’t always have the greatest internet. …Our bandwidth and our speed [is poor] ” (F8). During transition phases, participants reported the ‘hybrid’ paper-digital model resulted in duplicated information and poor data accuracy. The use of dashboards was valuable for visualising data.

In the people , skills and behaviour dimension, the digital literacy of the healthcare workforce mediates their acceptance of digital health: “ The hardest thing was they’re extremely experienced and knowledgeable clinicians , and they’ve had to now go into something where they feel inadequate and feel that they can’t do their job ” (E1). Participants indicated that the continued use of telehealth and investment in education to enhance individual competence and clinician confidence in digital technologies can minimise unnecessary workarounds and contribute to providing equitable care to rural populations.

Interoperable systems were perceived to facilitate efficient and accurate exchange of clinical information. Continuity of care is difficult between primary care providers, state-funded health services, and external providers in rural settings: “ I can see a benefit in patient care delivery for when the entire health service is on the same system , because it will help with the transfer of the patient care through the different journeys ” (B4). Participants emphasised that poor information visibility with external providers limited external interoperability, and the numerous systems utilised within a healthcare system limited internal interoperability.

The strategic focus of rural healthcare systems is interoperability, digital competency and investment in education and training. Strategic adaptability and alignment to organisational strategies is reported as challenging in rural contexts as digital health solutions were originally tailored for other healthcare systems in Queensland: “ The state-wide solutions don’t really cater for the [rural health] , because of how isolated and remote it is ” (J10).

Rural healthcare providers use data analytics for healthcare performance tracking. Accurate data input by clinicians was critical for data analytics: “ [The digital system] is our source of truth and it needs to be correct and up to date because that’s where all of our information from a funding perspective comes from ” (B3). The continued use of paper is required for manual auditing. Participants saw value in extending data analytics to provide insight into trends and identify clinical risks, particularly for chronic disease management.

Relationships among digital health capability dimensions and outcomes

All four healthcare outcomes (population health, patient experience, provider experience, and healthcare costs) were described to be influenced by the digital health capability dimensions (Fig. 3 ; Table 2 , Appendix 4 ). Strategy did not directly impact any outcome.

Dimensions of digital health capability and their relationships to the healthcare outcomes

Relationships among the digital health capability dimensions

Interrelationships were present among all digital health capability dimensions (Fig. 4 , Appendix 5 ).

Interrelationships among the digital health capability dimensions

Participants described the strategy dimension influenced the governance and management, people skills and behaviour, IT capability, interoperability and consumer-centred care dimensions. To incorporate digital transformation in the strategy and facilitate “ care closer to the home ” (J4), monetary and human resources are required. Enacting the digital strategy requires balancing priorities to develop the digital capability of the health system and “ invest in the education and training of staff ” (F7) to improve people, skills, and behaviour. Investment in technology infrastructure is required: “ to invest in any further digital transformation , [it] must come with updated hardware infrastructure ” (F6). Interoperability was core to the strategy, with the vision to “ have one system , …so …all of our users …are only seeing one system , and if that’s not possible , then get interoperability front and centre on everyone’s roadmap ” (G2).

Participants reported the challenge of governance and management and described its influence on all dimensions. Resource constraints and a lack of digital health governance within individual healthcare systems have hampered the digital health strategy from being realised. Implementation of technologies (e.g., telehealth) and structures (“ consumer and community engagement team ” (I6)) to facilitate consumer-centred care have been introduced in rural settings. Due to the segmentation of healthcare systems and primary and secondary care, participants expressed the importance of interoperability standards: “ blanket rules that all digital platforms …talk(ed) to each other ” (B6). In some instances, efforts to improve interoperability have been introduced by management, ensuring that the same system is used across sites: “ Patient flow manager for us is our communication tool and we made sure that every site has [it] ” (D12). Despite “ government documents that dictate how it should be used , …they’re not well enforced ” (F8) and individual variations exist in technology use. Establishing a “ culture …[that] supports very open and transparent reporting of incidents ” (A7) and structures such as a “ business analysis and decision support team …[and] a nurse informatics [area] ” (B10) facilitated data analytics.

IT capability influenced the governance and management, data analytics, consumer-centred care, people, skills, and behaviour, and interoperability dimensions. Governance and management were facilitated by providing “ a full audit system …we can audit , we can review , we can look at gaps ” (F8). IT also enabled analytics: “ we use [software] to collate and to present data back to clinical teams , …for decision making , …to understand trends , …[and] for reporting …to the board and executive ” (I6). Leveraging the capability of web-based portals, community healthcare directories, and telehealth have “ enabled deeper engagement with the community ” (F9) facilitating consumer-centred care. In some instances, IT improved interoperability: “ we can see every admission to a public hospital in Queensland , and clinical notes , and discharge summaries ” (B2). However, other IT did not meet interoperability standards and, the inclusion of additional IT was sometimes met with fear and frustration.

Interoperability influenced the governance and management, data analytics, people skills and behaviour, and IT capability dimensions. Participants expressed frustration with the limited interoperability: “ instead of having one single channel which is commonly shared by everybody , we now have five different variants at both ends ” (A3). Suboptimal interoperability impedes IT capability and data analytics: “ if everything is linked to all databases , …you [would] have these wonderful capabilities to put your parameters in and run a report ” (D9). Participants noted that a lack of interoperability between state-wide systems is “ fraught with risk ” (C9) that need to be managed.

Participants described how data analytics influenced the strategy, governance and management, and people, skills, and behaviour dimensions. Reporting and visualisation of data facilitated “ operational and strategic planning ” (J8). Data analytics can be used to actively monitor whether national standards are met: “ We have a national standard called Emergency Length of Stay – …making sure patients leave within four hours. …I use the [digital] system …to keep tracking ‘where are we at? What’s our timing , what are we doing’” (E1). However, individuals had a “ sense that the data will be used against people ” (D6) and expressed concerns “ over being a micro-manager ” (D4).

The consumer-centred care dimension did not influence any digital health capability dimensions.

People skills and behaviour appeared to influence the data analytics and consumer-centred care dimensions. Healthcare professionals perform workarounds to overcome system limitations, often resulting in poor data quality hampering data analytics: “g arbage in , garbage out. …Because people didn’t enter information correctly , it … threw out the whole dataset .” (E9). Digital health literacy of healthcare professionals and consumers influenced the delivery of consumer-centred care: “ To access healthcare moving forward you will have to have a level of digital acuity and we need to be responsible for building competency and checking for competency. …It’s not just for the patients , …it’s a skill that we need to embed with our staff .” [B7].

Despite the potentially transformative role of digital health to address access and equity [ 16 ], healthcare systems can struggle with implementing digital health in a way that addresses the health needs of marginalised communities. The key challenges across the seven capability dimensions involved enhancing workforce education and support, increasing IT capability, enabling interoperability, and catering healthcare delivery to rural settings. The existing digital health capability framework was improved by adding the sub-dimensions of resourcing, fit-for-purpose, and attitude, which are essential for rural digital transformation. The extensive use of telehealth and clinical dashboards are exemplar technological implementations enhancing rural digital health capability.

Our study in rural and remote Australia adds critical insight into the interrelationships among dimensions of digital health capability, confirming the need for rigorous strategy, governance, optimised digital technologies and a capable workforce to drive digital transformation. Second, it links the capability dimensions with outcomes reporting how healthcare outcomes in the rural setting are directly influenced by six of seven digital health capability dimensions. Third, it extends knowledge on the critical role of digital health in healthcare settings where need is high but appropriate, sustainable adoption is challenging.

Implications for practice

Healthcare leaders need to coordinate improvement efforts targeted across the dimensions of digital health. First, rural healthcare systems require a robust digital health strategy. The strategy dimension did not appear to influence outcomes directly, rather, the strategy dimension influenced other dimensions which then influenced outcomes. To deliver on the digital strategy, governance and management frameworks need to be established to support the provisioning of technical elements and development of workforce capability and consumer digital literacy. To ensure local needs are met, digital capability building initiatives require a clear local digital strategy, critical for coordinating activities [ 19 ], setting clear targets towards the desired future state [ 28 ], and motivating stakeholders towards pursuit of the quadruple aims of healthcare [ 19 ]. A concurrent state-wide study of digital health capability in Queensland identified the need for strategic initiatives to address variations in capability in rural healthcare systems [ 29 , 30 ]. A metropolitan-originated digital health strategy is unlikely to address rural healthcare delivery challenges.

Data analytics capability can be optimised in rural settings to support decision-making and strategic development. We evidenced that data analytics can influence strategy and has a bidirectional relationship with the governance and management and people, skills and behaviour dimensions. Foundationally, data analytics capability is influenced by the interoperability and IT capability dimensions. This indicates that improving interoperability and IT capability to enhance information exchange and the usability of data can strengthen the data analytics capability, resulting in better informed and executed strategy supported by governance and management. In settings challenged with health inequities and coverage of essential healthcare [ 1 ], investing in data analytics capability will build a foundation for predictive and prescriptive analytics to benefit individual and population health [ 31 , 32 ]. Clinical insights gained from data analytics can direct strategic priorities of rural healthcare delivery. We found that rural healthcare workers are seeing these early benefits through identification of clinical risk, particularly in chronic disease management and for early intervention.

Our findings demonstrate that participants view consumer-centred care as a capability that is shaped by other dimensions rather than informing other dimensions. Existing literature has struggled to articulate the intersection between consumers and digital health in system wide transformation. A systematic review of digital health capability revealed less than 10% of studies examined consumer-centred care [ 17 ]. The importance of the consumer-centred care dimension was evident in participants perceptions that it improved population health and patient experience. Emerging insights from broader literature indicate the need to consider additional consumer digital health themes (e.g., ethical implications, choice, transparency) [ 33 , 34 , 35 ]. Defining best practice processes (e.g., consumer panels, committees) and people (e.g., diversity, inclusion) to integrate consumer perspectives into digital initiatives remains necessary to rural healthcare improvement to address access and equity challenges.

Limitations

The qualitative methodology is limited to inferring association among the capability dimensions and outcomes, rather than attributing causality. Perceptions of digital health capability were captured from the purposive sample of interviewed participants with diverse roles in the health service. Differences in perspectives across different participant cohorts were not examined. We are unable to account for the confounders to perceived digital capability and outcomes which is likely to be influenced by the experience of participants in their work setting, at the time of data collection.

Digital health holds the promise of overcoming the “tyranny of distance”. Improvement of essential health services in rural settings is critical to health and wellbeing of populations experiencing health inequities. Using a multi-site case study analysis of rural healthcare systems, this study evidenced the complex interplay among the dimensions of digital health capability and the association between the dimensions and healthcare outcomes. The drivers of digitally enabled healthcare improvement in rural settings transcend single dimensions of digital health capability. A focus on digital strategy is foundational to enabling improvements across technical and human capabilities. Better leveraging data analytics and embedding consumer-centred care are likely to enable digital health improvements that meet local needs, enhancing the patient experience, improving population health, reducing the cost of care, and improving the provider experience. Improving care for our rural populations is critical to entire system improvement.

Data availability

The data that support the findings of this study are not openly available in accordance with the approved Human Research Ethics Application and institutional requirements. For enquiries about the availability of data and materials contact the corresponding author at [email protected].

Abbreviations

Information technology

United Nations Department of Economic and Social Affairs Sustainable Development. Ensure healthy lives and promote well-being for all at all ages: United Nations; 2023 [cited 2023 29 September 2023]. https://sdgs.un.org/goals/goal3

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Acknowledgements

The authors would like to extend their appreciation to the many Queensland Health staff members who participated in this research.

This research was supported by the Digital Health CRC Limited (“DHCRC”) and Queensland Department of Health. DHCRC is funded under the Australian Commonwealth’s Cooperative Research Centres (CRC) Program.

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LW, RE and CS devised the project with strategic direction from HM and RaD. LW, RE and RhD contributed to data collection under the supervision of CS. SM and JK contributed to data analysis and reporting of results with the assistance of LW, RE and CS. LW and RE wrote the manuscript with assistance from SM, JK, ReD, HM, RhD and CS. All authors contributed to the article and approved the submitted version.

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The study received a multisite ethics approval from the Royal Brisbane and Women’s Hospital [ID: HREC/2020/QRBW/66895], followed by site-specific research governance approvals. Informed, written content was provided by participants prior to data collection.

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Woods, L., Eden, R., Macklin, S. et al. Strengthening rural healthcare outcomes through digital health: qualitative multi-site case study. BMC Health Serv Res 24 , 1096 (2024). https://doi.org/10.1186/s12913-024-11402-4

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How to do qualitative research?

Elaine denny.

1 Faculty of Health, Education and Life Sciences, Birmingham City University, Birmingham UK

Annalise Weckesser

Associated data.

Not applicable

Learning points

The main methods used in qualitative research are interview, focus group and observation.
Recruitment is purposive, or strategic, in that the aim is to achieve a sample that is relevant to the research question.
Data are collected until saturation of themes and insights is reached.

Qualitative research begins with one or more relatively broad research questions that may be revised iteratively as the research is carried out to narrow the research aim or purpose. This is different from quantitative research, where a narrow research question is set at the start and remains fixed. For example, the aim of a study may be to explore the experiences of women who are pregnant while living with epilepsy. The initial research question may be ‘How do women with epilepsy experience pregnancy?’ However, from preliminary findings this may change to ‘How do women manage their epilepsy during pregnancy?’

There are three main methods used in qualitative research.

The first and most commonly used is interviewing. Semi‐structured interviews contain pre‐set, open‐ended questions, with further questions emerging from the discussion. Unstructured interviews cover a few issues in great depth, for example they can be used for life history narratives.

Focus groups are group discussions facilitated by a researcher, who will have guidelines to focus the group. Data collection consists of group interaction as well as discussion content. They can be stand alone, but more commonly are used to clarify or extend data collected by other methods.

Both interviews and focus groups tend to be flexible and non‐standardised, with greater interest in the participants' perspectives and experience than for quantitative research. However, it is important that flexibility does not result in asking leading questions.

Another method is observation, which is the act of watching social phenomena in real‐world settings, recording what people do, rather than what they profess to do. The observer may be part of the scene being observed (participant observation) or stand outside it (non‐participant).

Sampling for qualitative research tends to be purposive (that is recruitment on the basis of a shared experience that is relevant to the research question), convenience (based for example on accessibility or cost) or snowballing (where a few individuals from the target population will connect the researcher with their network). In the example above, women were purposefully recruited as pregnant, living with epilepsy and willing to be interviewed about how this impacted upon their lives (Weckesser & Denny. Soc Sci Med 2017:185;210–17).

The amount of data collected in qualitative research is not fixed or calculable, but continues until saturation is reached. That is, data are collected until emerging concepts have been explored and additional data are not producing fresh insights (Bryman. Social Research Methods , 3rd edn. Oxford: Oxford University Press; 2008). Generally speaking, the study sample size tends to be much smaller when compared with quantitative research.

Interviews and focus group discussions are usually audio‐recorded, with the consent of participants, and then transcribed verbatim. Written notes may also be made by the researcher for use in analysis. For the method of observation, extensive field notes are recorded during and after the event. Copious data are usually generated, which need to be organised for analysis, which is the focus of the next article in this series.

CONFLICT OF INTEREST

None declared. Completed disclosure of interests form available to view online as supporting information.

AUTHOR CONTRIBUTION

Elaine Denny and Annalise Weckesser contributed equally to the paper.

USEFUL RESOURCES

Britten N. Qualitative research: qualitative interviews in medical research. BMJ 1995;311:251–3.

Kitzinger J. Qualitative research: introducing focus groups. BMJ 1995;311:229.

Savage J. Participative observation: standing in the shoes of others? Qual Health Res 2000;3:324–39.

Supporting information

Appendix S1

Appendix S2

Denny E, Weckesser A. How to do qualitative research? . BJOG . 2022; 129 :1166–1167. 10.1111/1471-0528.17150 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

DATA AVAILABILITY STATEMENT

Open access
Published: 19 September 2024

Should my child be given antibiotics? A systematic review of parental decision making in rural and remote locations

Stephanie A. Marsh 1 ,
Sara Parsafar 2 &
Mitchell K. Byrne 1

Antimicrobial Resistance & Infection Control volume 13 , Article number: 105 ( 2024 ) Cite this article

122 Accesses

Metrics details

The emergence and growth in antibiotic resistant bacteria is a critical public health problem exacerbated by the misuse of antibiotics. Children frequently succumb to illness and are often treated with antibiotic medicines which may be used improperly by the parent. There is limited evidence of the factors influencing parental decision-making about the use of antibiotics in low-resource contexts. The aim of this systematic review was to understand and describe how parents living in rural and remote locations make choices about their children’s antibiotic use.

The CINAHL, Web of Science, Medline, Scopus and Academic Search Premier databases were systematically searched from 31 January until 28 June in 2023. No date restrictions were applied and additional search methods were utilised to identify further studies that met inclusion criteria. Eligibility criteria included studies which reported on factors contributing to parental decisions about their children’s use of antibiotics in rural and remote settings. The Joanna Briggs Institute Critical Appraisal Checklists were employed to evaluate studies. Characteristics and findings were extracted from studies, and data was synthesised descriptively and presented in summary tables.

A total of 3827 articles were screened and 25 worldwide studies comprising of quantitative, qualitative and prospective designs were included in the review. Studies that reported the number of rural caregivers consisted of 12 143 participants. Data analysis produced six broad themes representing the mechanisms that influenced parents in their access and use of antibiotics: the child’s symptoms; external advice and influences; parent-related determinants; barriers to healthcare; access to antibiotics; and socio-demographic characteristics.

Conclusions

A number of factors that influence parents’ prudent use of antibiotics in rural contexts were identified. In seeking to enhance appropriate use of antibiotics by parents in rural and remote settings, these determinants can serve to inform interventions. However, the identified studies all relied upon parental self-reports and not all studies reviewed reported survey validation. Further research incorporating validated measures and intervention strategies is required.

Registration details

Should my child be given antibiotics? A systematic review of parental decision making in rural and remote locations ; CRD42023382169; 29 January 2023 (date of registration). Available from PROSPERO.

Antibiotics are critical in the treatment of infections caused by bacteria and can be lifesaving medicines in early life [ 1 ]. However, widespread and indiscriminate use of antibiotics is a significant contributor to the development of drug resistant pathogens, known as antimicrobial resistance (AMR) [ 2 , 3 ]. AMR is increasing on a global scale, and currently accounts for approximately 700 000 deaths each year worldwide. This rate is predicted to increase exponentially to 10 million deaths by 2050, deepening the impact on health systems as infections become harder to treat [ 4 ]. Accordingly, the World Health Organization (WHO) has ranked AMR in the top ten global health threats [ 5 ]. Children are vulnerable to frequent bouts of illness [ 6 ] and are among the highest consumers of antibiotics [ 1 , 7 ]. The United Nations Children’s Fund (UNICEF) has described AMR as “…perhaps the greatest threat to child survival and health of this generation” [ 8 p.2]. However, parental use of antibiotics with their children can be a key contributor to AMR. For example, despite the common experience of respiratory illness in childhood and the frequent use of antibiotics, only a small proportion of upper respiratory infections are of bacterial origin requiring antibiotic treatment [ 2 ].

The drivers of antibiotic overuse and misuse in children are multi-factorial and relate to both over-prescribing by health professionals [ 9 ] and the way that parents use antibiotics, such as autonomous practices [ 10 ] and failure to follow antibiotic treatment instructions [ 4 ]. Given that parents are the end-users and decide on behalf of the child how medication is obtained and used [ 11 ], understanding parent choices in their use of antibiotics is crucial to the determinants of inappropriate antibiotic use [ 12 ]. Parental decisions about antibiotic use with their children are influenced by a range of person and context variables. Previous systematic reviews have examined and quantified non-prescription antibiotic use in children [ 3 , 13 ], describing parental knowledge about the use of antibiotics [ 2 ] and attitudes of parents about antibiotic prescribing in children as key drivers in antibiotic use [ 4 ]. Furthermore, systematic review findings indicate that residing in a rural location, and distance to healthcare, are associated with parents using antibiotics without consulting a doctor [ 13 ], a practice linked to the emergence of drug resistance [ 14 ]. This systematic review builds upon prior research and reviews by examining patterns in the decisional processes of parents living exclusively in rural communities around the world towards their use of antibiotics. This research also draws upon theoretical models explaining health-related behaviours to interpret and understand the review findings.

The mechanisms by which rurality and healthcare access influence parental decisions about antibiotic use are integral to understanding how parents in such locations can be supported to make more judicious decisions regarding the use of antibiotics with their children. Recent Australian research examining parents living in rural locations found that parental decisions about their children’s antibiotic use were influenced by fear of serious illness, and exacerbated by limited access to healthcare [ 15 ]. The influence of contextual factors is pertinent, as high rates of antibiotic resistant bacteria have been detected in children living in rural communities [ 16 ]. Drug resistant bacteria in children is especially concerning because of the contraindications of some antibiotics there are fewer options available to safely treat children [ 17 ].

Recent Australian research involving parents living in remote areas [ 15 ] provides some insight into how the context of rurality and healthcare access influence parental decisions. However, there are no systematic reviews which have examined the decision-making processes and influences of parents towards their children’s antibiotic use in rural, resource-limited settings. Understanding the drivers of parental behaviour in rural contexts can help to guide policy and target interventions to slow the growth of AMR. Thus, the objective of this review was to systematically describe the decision-making process of rural parents regarding their use of antibiotics. We sought to address the following research question: What factors influence the decisions of parents with children living in rural and remote locations in their use of antibiotics? Using the insights drawn from the recent Australian study [ 15 ] to facilitate search terms, we reviewed the international literature to identify factors influencing parents to initiate antibiotic therapy and the motivators of their antibiotic use behaviours.

A pre-defined research protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) prior to the commencement of the review. This research was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines to ensure transparency and accurate reporting [ 18 ]. No amendments were made to the protocol during the review process. (Further details of the completed PRISMA 2020 checklist and adherence to the statement can be found in Additional file 1 ).

Eligibility criteria

Inclusion and exclusion criteria were pre-specified in the study protocol and applied during screening. Worldwide studies available in English were included, and no restrictions were placed on the publication period. Both peer-reviewed and grey literature were accepted. Studies were eligible if they provided data on the decision-making process of parents living in rural and remote locations in their use of antibiotics with their children (aged 0–18 years). This included an examination of all factors influencing parent decisions to use antibiotics, and factors contributing to how antibiotics were acquired and used. Parents were defined in accordance with the National Library of Medicine as: ‘persons functioning as natural, adoptive, or substitute parents’, such as caregivers [ 19 ]. Factors were described as circumstances, facts or influences contributing to parental decisions. We considered quantitative and qualitative designs, mixed methods, observational, prospective, systematic reviews, cross-sectional and longitudinal studies. Studies incorporating both rural and urban parents met inclusion criteria if there was clear delineation between urban and rural parent responses. If multiple medications were examined, or other population sub-groups were in the sample, these studies were included if the exclusive results were provided for antibiotics and parents. We excluded studies without data (i.e., editorials, protocol designs and letters) and intervention studies relating to antibiotic stewardship and treatment compliance. Studies that did not provide data on the decision-making process of parents towards their children’s antibiotic use, non-parent/caregiver samples and studies specifically examining other antimicrobial agents (antiviral, anti-fungal, anti-parasitic), or other medicines, were excluded. Studies based in urban and semi-urban settings were also excluded from the review.

Search strategy

Five electronic databases were systematically searched: Web of Science; Scopus; EBSCOhost databases (Academic Search Premier, CINAHL and Medline). The search was performed from 31 January 2023 and monitored weekly through database alerts until 28 June 2023. A final update of each database was performed on 16 June 2023. Our search strategy included a combination of key words and Medical Subject Heading (MeSH) terms. Search terms were developed using a variation of the Population, Intervention, Comparison, Outcomes, Study design/Setting (PICOS) elements and were guided by prior study findings [ 15 ]. The search strategy was reviewed and refined by an academic librarian. Terms were separated into their synonym groups when combined in searches to build a multi-line search strategy, connected with Boolean connectors (AND/OR). A preliminary search was conducted using the search string to test for the identification of records. Search terms were adapted for use to account for changes in database symbols, or other search syntax, particular to a database. Database-specific filters were applied where available. (Details of the full search strategies for all databases are outlined in Additional file 2 ). To identify additional articles, the reference lists of studies eligible for full-text review and grey literature websites/search engines, were searched between 3 and 22 March 2023. Grey literature covered both accessible and inaccessible articles and included searching: Trove; Open Grey; OpenDOAR; NZresearch.org.nz; MedNar; Western Pacific Region Index Medicus; Clinical Trials Search Portal; Theses Canada; PsycINFO; and Google Scholar. A mix of key words and simplified search strings were used to account for differences in search interfaces after consultation with a research librarian. (Further details of the supplementary searches are provided in Additional file 2 ).

Selection of studies

Records identified from electronic databases and other sources described were downloaded and stored in EndNote software version X9. All records were screened for duplication by the principal reviewer (SM) using EndNote tools and through a manual process of checking, identifying and removing duplicate records. The titles and abstracts of records were screened by SM using a pre-defined decision tree to guide judgements in the screening and selection of studies for inclusion in the review. The decision tree was a methodological approach used to assist the reviewers to stay organised and transparent in their approach and to ensure that the inclusion and exclusion of studies was based on predefined criteria to minimise the risk of bias in the review process. Fidelity checks were conducted by the other reviewers (MB and SP) on a random selection (10%) of included and excluded records. Discrepancies were resolved through team discussion, or referred to the third reviewer to determine study inclusion. Articles included in the next stage, underwent full-text evaluation by SM using a full-text decision tree tool to assess study eligibility. (Further details of both decision trees are available in Additional file 3 ). A random sample (10%) of full-text articles were reviewed by (MB and SP) to check decisions against the decision tree criteria. Inconsistencies in opinion were discussed until a consensus was reached, or when there was disagreement, the third reviewer made the final decision regarding study inclusion. During screening, one study investigator was emailed to confirm participant characteristics, to no avail. Subsequently, study inclusion was discussed amongst the researchers until agreement was attained.

Data extraction

A standardised format was used to collect and enter study data into a Microsoft Excel spreadsheet using labelled columns. The initial extraction was performed by one reviewer (SM) and re-checked for accuracy. Subsequently, two other members of the research team (MB and SP) crossed-checked the extracted data. Findings were discussed and agreement was reached on the corresponding information. In one study, participants were described as ‘parents/carers of young children’ and the age range of the children was not specified [ 20 ]. However, we included this study based on the assumption that ‘young children’ was the same as pre-adolescents (primary school age and younger), given that parents were recruited through early childhood education services and playgroups. The following pre-specified data was collected from each eligible study, similar to the information extracted in prior reviews [ 4 ]: author name; year of publication; country; study aim; study setting/context; participant information (caregiver and child characteristics); study design and methodology; key findings (principal outcomes and results relevant to the current research); and study limitations. The primary outcome of interest was: the drivers/determinants of parents’ decisions towards the use of antibiotics with their children. Variables investigated included factors predisposing parents to use antibiotics (factors associated with use), and determinants of how antibiotics were accessed and used by parents (i.e., prescription/non-prescription use, asking prescribers for antibiotics, and antibiotic adherence behaviour). We considered any measure of parental decision-making, which was predominantly self-report.

Critical appraisal

We assessed the possibility of bias and the methodological quality of studies included in the next phase of the review, using the Joanna Briggs Institute (JBI) critical appraisal checklists. JBI has shifted towards using the term ‘risk of bias’, notably for quantitative analytical designs, which does not include assessing quality constructs. However, different terminology remains, and is suitable when assessing qualitative or other types of research evidence [ 21 ]. The JBI tools were selected according to the design of each study assessed, and all items in the corresponding checklists were utilised. Subsequently, we employed the following four JBI checklists: studies reporting prevalence data; qualitative research; cohort studies; and analytical cross-sectional studies. Mixed-methods designs were appraised using the checklist reporting prevalence data, as well as the checklist for qualitative research. Two reviewers (SM and SP) independently assessed full-text studies (i.e., appraisals were performed by the principal reviewer and checked by SP). A proportion (10%) of randomly selected studies were assessed by the third reviewer (MB) using the JBI tools. Responses were recorded using the JBI forms and comments were added to support judgements, where necessary. Discrepancies in appraisals were managed through discussion between the reviewers to reach agreement.

A pre-determined JBI score of 60% or above was decided by the review team, which is indicative of moderate to high-quality studies [ 22 ] and has been applied in past reviews [ 3 ]. Inclusion of studies scoring low on JBI may jeopardise the validity of the review findings [ 23 ]. Thus, only studies reaching 60% or higher ‘yes’ results after discrepancies between reviewers had been resolved were included in the review. For mixed-methods designs, if the study met only partial inclusion, a reasoned and collaborative approach was employed by the research team [ 24 ] to consider the component that passed critical appraisal. We used the following score ranges to rate the potential risk of bias of individual studies: low risk of bias for studies achieving 70% or more ‘yes’ scores; moderate risk of bias for studies obtaining between 50 and 69% ‘yes’ scores (noting studies below 60% were excluded); and high risk of bias if the number of ‘yes’ scores was below 50% [ 25 ].

Data synthesis

Data from individual studies included in the review was summarised by tabulating extracted information in a Microsoft excel spreadsheet for transparent reporting and to present a comparison of the findings. Studies were ordered within the spreadsheet by the year of publication to present the most recent research in the field to the earliest. A narrative synthesis of the findings was provided following the guidance of Popay and colleagues (2006) [ 26 ]. There was considerable heterogeneity in the study methods and approaches used, subsequently, a meta-analysis was not performed. However, the purpose of the review was to describe, rather than quantify, a process of decision-making. Key findings data was analysed to identify common patterns and differences across studies. This information was then synthesised and summarised descriptively in ‘Primary findings’ tables to address the research question. Comparisons were made between Australian and international research, where applicable. Primary findings were categorised into groups for the synthesis: (1) the process of decision-making to use or not use antibiotics i.e., influences on antibiotic use (2) factors contributing to specific behaviours with antibiotics identified i.e., non-prescription use of antibiotics, non-adherence to antibiotic treatment, requests for antibiotic prescriptions. Broader themes of the mechanisms underlying antibiotic use decisions were identified and categorised in the tables of primary findings. Data synthesis wasn’t categorised according to the age of the child as nearly all studies sampled children < 12yrs. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was not used to assess the certainty of evidence based on the limitation that all studies which are non-randomised control trials are rated as low evidence by GRADE [ 27 ].

Study selection

Electronic database searches and registers identified 4324 records and an additional 22 records were found via citation and grey literature search. No unpublished articles were identified. However, some grey literature sources searched both accessible and inaccessible articles. This assisted to uncover records that were not identified through the original electronic database search. After duplicate records were removed, 3805 titles and abstracts were screened. Most records excluded at this stage were unrelated to antibiotic use. Following this process and the identification of records from reference lists and other methods, 88 eligible full-text articles were sought. Only 1 report was unable to be retrieved in a full-text English language version. A total of 87 full-text articles were reviewed for eligibility. Reports were excluded for the following three reasons using the full-text decision tree tool: (1) participant characteristics were not suitable (2) the study did not provide exclusive results for parents/caregivers, or antibiotics, or parents living in rural or remote areas (3) the study did not provide data on the decision-making process of rural parents towards their children’s antibiotic use. Three studies appeared to meet inclusion criteria but were excluded because of difficulty delineating specific antibiotic use decisions for rural parents [ 28 , 29 ], or we were unable to determine if both urban and rural sub-districts were amongst the findings [ 30 ]. During the final phase of screening, 32 studies were evaluated using the JBI checklists. Two mixed-methods studies that met partial inclusion were excluded from the review after team discussion. These were excluded because either the quantitative study was developed based on the qualitative research, which did not reach JBI score requirements, or the qualitative component met inclusion, but did not provide data relevant to the research question. After all appraisals were completed, 25 studies remained, and were included in the review. Figure 1 details the study selection process using the PRISMA 2020 flow diagram.

PRISMA 2020 flow diagram outlining the study selection process

*Reasons for full-text exclusion: Reason 1 : participant characteristics were not suitable; Reason 2 : the study did not provide exclusive results for parents/caregivers, or antibiotics, or parents living in rural or remote areas; Reason 3 : the study did not provide data on the decision-making process of rural parents towards their children’s antibiotic use

Characteristics of studies

The 25 studies included in the review represented 14 countries across 6 continents: Asia (14); Africa (7); Central America (1); South America (1); Europe (1) and Australia (1). The highest number of studies (6) were conducted in Vietnam [ 31 , 32 , 33 , 34 , 35 , 36 ]. Publication periods spanned between 1996 and 2022. The design of 18 studies was quantitative [ 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], 6 were qualitative [ 20 , 31 , 35 , 52 , 53 , 54 ] and 1 study was prospective [ 32 ]. Sample sizes varied from 13 to 3117 caregivers [ 48 , 52 ] and up 4087 children [ 33 ] and depended on the study design. Cross-sectional studies had the largest samples [ 33 , 37 , 48 ] whereas qualitative designs with only a proportion of rural caregivers had the smallest [ 52 ]. Studies recruited either caregivers (parents, grandparents, other relatives, or carers), or specified parents or mothers. However, across all studies, most respondents were mothers. Only 2 studies included a small proportion of children > 12yrs [ 41 , 51 ] and the majority sampled were children < 6yrs. One study stated that 2% of children were > 10yrs [ 45 ], and another specified ‘young children’ [ 20 ]. All other studies sampled children < 7yrs. Of the included studies: 18 studies examined the knowledge, attitudes or perceptions and behaviours of parents towards the use of antibiotics for their children [ 31 , 32 , 34 , 37 , 38 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 49 , 51 , 52 , 53 , 54 ]; 4 studies investigated parent opinions and practices towards childhood illnesses and health-seeking behaviours [ 20 , 40 , 48 , 50 ]; and 3 studies examined patterns of drug use, including antibiotic use, amongst parents [ 33 , 35 , 36 ]. (Additional file 4 describes the characteristics and key findings of individual studies included in the review and includes all data used for analysis).

Risk of bias quality assessment

The risk of bias for studies included in the review was classified as low for 16 studies [ 20 , 31 , 32 , 33 , 34 , 35 , 36 , 38 , 41 , 42 , 44 , 46 , 51 , 52 , 53 , 54 ] and moderate for 9 studies [ 37 , 39 , 40 , 43 , 45 , 47 , 48 , 49 , 50 ]. (A summary of the risk of bias assessments, including each domain/item assessed for all studies appraised is provided in Additional file 5 ). There is a possibility of recall and response bias in all of the studies, as each depended on parent self-reports of their experiences, perceptions and practices [ 4 ]. For most quantitative studies, it was unclear if the survey instrument had undergone full measurement validation. In some studies, pre-testing and pilot studies had been conducted (44%), indicating relative validation of the measurement tool [ 38 , 40 , 42 , 43 , 44 , 45 , 46 , 48 ]. Other studies didn’t discuss instrument validation (33%) [ 33 , 36 , 37 , 39 , 41 , 50 ]. Only a minority of studies (22%) reported the survey had been validated [ 34 , 47 , 49 , 51 ]. All qualitative and prospective studies were considered low risk of bias [ 20 , 31 , 32 , 35 , 52 , 53 , 54 ]. However, some reporting elements were missing from the qualitative research: most did not include information about the researcher’s cultural or theoretical orientation (67%); and the majority of qualitative studies did not report the researcher’s philosophical perspective (67%).

Tables 1 , 2 , 3 and 4 synthesise the primary findings of the review and represent the finding of themes generated from data analysis. The themes and sub-themes describe the factors found to underlie parent decisions about antibiotic use. We established six themes that impacted the decisions of parents residing in rural locations in their use of antibiotics: the child’s symptoms; external advice and influences; parent-related determinants; barriers to healthcare; access to antibiotics; and socio-demographic characteristics. Mechanisms covered by these themes contributed to overuse and misuse behaviours related to the access and use of antibiotics in rural contexts and were tabulated according to the four core areas of investigation (i.e., influences on antibiotic use, non-prescription use of antibiotics, non-adherence to antibiotic treatment, requests for antibiotic prescriptions). The tables present data on when, why and how parents engaged with antibiotics in rural setting. (Further details of data coding and thematic development can be found in Additional file 6 ).

Table 1 describes the factors that motivated parents to use or not use antibiotics with their children.

Table 2 details the predictors of parental use of antibiotics without medical guidance.

Table 3 outlines the factors that influenced parents to cease their child’s antibiotic therapy early and not complete a full treatment course.

Lastly, Table 4 indicates the reasons parents placed demand on doctors to prescribe antibiotics for their child.

The findings of this review suggest that six themes underpinned the decisions of parents living in rural and remote settings towards their use of antibiotic medicines. The first theme was the ‘child’s symptoms’ , which represented findings concerning the nature and severity of illness. Our results highlight that parents perceived antibiotics as appropriate in the treatment of a variety of symptoms exhibited by their children including cough, fever, diarrhea, respiratory illness, breathing impairment, and ear infections. The nature of the child’s symptoms both motivated parental expectations for the provision of antibiotics and influenced antibiotic use, including autonomous practices (without reference to medical advice), which has been substantiated in prior reviews [ 13 ]. In more than half of the studies, parents used antibiotics autonomously to treat their children’s cough, diarrhea, fever, colds/respiratory infection and breathing difficulties. Cough and fever were the symptoms associated most with parental expectations to use antibiotics, and antibiotic use in general, including both prescribed and unprescribed use. However, when only non-prescription antibiotic use was examined, children with fever received unprescribed antibiotics in fewer studies than those with cough and diarrhea symptoms. This finding might be explained by research in China which reported that while parents were most concerned about fever and cough symptoms, they were less likely to use antibiotics to treat fever without consulting a doctor [ 41 ]. The severity of the child’s illness, duration and number of symptoms influenced antibiotic use and expectations for the child to receive antibiotics [ 31 , 32 , 33 , 37 , 39 , 48 ], which is supported by previous research findings [ 4 , 15 ]. It is likely that parental use of prescribed antibiotics was influenced by the prescriber’s decisions and advice [ 31 , 32 , 39 ], highlighting the importance of the opinions of others in decision-making. However, in relation to unprescribed use, parents treated their children with antibiotics for perceived minor illness in four studies [ 32 , 35 , 43 , 51 ] and in two studies when the illness was more severe or multiple symptoms were reported [ 33 , 37 ]. This finding may reflect that parents might try to seek medical advice for their child when the illness is believed to be serious, but appear to use antibiotics autonomously for a range of symptoms and severity levels when medical advice is less available.

The second theme that emerged from our analysis was ‘external advice and influences’. This theme relates to findings that parents were influenced by the opinions and advice of others when making decisions about the use of antibiotics with their children. As expected, prescriber advice informed parental understanding of when antibiotics were required in a number of studies [ 31 , 32 , 39 , 40 , 41 , 42 , 43 , 49 , 51 ]. In two studies, even when parents perceived antibiotics were not necessary, they adhered to medical recommendations to treat their child with antibiotics [ 31 , 32 ], highlighting the value placed on guidance from prescribers. Information from doctors about antibiotics has been identified as an important source of knowledge for parents in past research [ 13 ]. However, parents also considered the advice of friends and family, such as those with more experience, or ‘decision makers’ regarding their child’s treatment needs and the use of antibiotics [ 40 , 43 , 49 , 52 , 54 ]. We found that seeking advice from others contributed to parental decisions to use non-prescription antibiotics with their children. In nine studies where unprescribed antibiotics were used, parents obtained treatment advice from drug suppliers, health professionals living in their community, friends and relatives [ 32 , 34 , 35 , 44 , 47 , 48 , 49 , 52 , 54 ]. Additionally, social and cultural norms influenced autonomous antibiotic use and non-adherence behaviours. For example, parents acted in accordance with the views of others [ 53 , 54 ], were influenced by pharmaceutical promotions [ 36 ] and obtained antibiotics from people in their network [ 46 ]. Findings in respect to non-adherence behaviours highlight that external factors, such as palatability and lack of refrigeration influenced early discontinuation of the child’s antibiotic course in an Australian study [ 20 ]. This highlights that some reasons for non-adherence were child-related or influenced by socio-economic factors.

The third theme identified from this review was ‘parent-related determinants’ . This theme reflects findings that knowledge, attitudes/beliefs and cognitive factors contributed to parental behaviours with antibiotics across the four core areas of investigation. A lack of parental knowledge and confusion regarding antibiotic use [ 20 , 34 , 35 , 40 , 42 , 53 ], and beliefs about symptom improvement [ 35 , 41 , 42 , 51 , 53 , 54 ] mostly accounted for non-adherence decisions. One study identified that parents pressured prescribers for prescriptions because they perceived it was appropriate to ask for antibiotics [ 51 ]. This finding underscores the influence of attitudinal (a right to receive a prescription) or knowledge (lack of understanding of the need for antibiotics) factors on behaviour. In relation to unprescribed antibiotic use, limited knowledge about the indications for use or the risks of use was identified in numerous studies where parents used antibiotics autonomously [ 34 , 35 , 37 , 38 , 40 , 41 , 42 , 45 , 51 ]. There was some inconsistency with this finding, with other studies reporting no association between parental knowledge and unprescribed antibiotic use [ 47 ] or observing that having adequate knowledge of prescription requirements was associated with increased likelihood of retaining leftovers [ 42 ]. Nonetheless, research did indicate that knowledge played an important role in the appropriate use of antibiotics, which has been supported by past reviews [ 2 , 13 ]. However, possessing knowledge does not necessarily ensure responsible behaviours with antibiotics [ 2 ], as other factors appear to also drive autonomous practices [ 47 ]. For example, strong beliefs regarding the efficacy of antibiotics were discussed in a number of studies where non-prescription antibiotic use was prevalent [ 33 , 34 , 35 , 36 , 49 , 51 , 52 ]. Of these, four studies discussed that antibiotics were perceived as a cure-all [ 33 , 35 , 36 , 52 ], highlighting firm beliefs about antibiotics as curative drugs used to treat a range of illnesses, which likely influenced antibiotic use decisions. These results might explain why parents used antibiotics to treat many symptoms of childhood illness irrespective of the microbial source. Parental attitudes that the child’s condition is too minor to see a doctor and prior experience using prescription antibiotics to treat a similar illness, may explain parents self-treating symptoms perceived to be minor.

The results of this review identified ‘barriers to healthcare’ as a further theme. This theme includes findings of the difficulties experienced by parents in accessing health facilities in rural settings. Poor access to formal healthcare was a barrier to appropriate antibiotic use and predominantly impacted parents administering antibiotics to their children without consulting a doctor, consistent with previous research [ 13 ]. In the current review, ten studies reported on the specific mechanisms of limited access to healthcare believed to contribute to the non-prescription use of antibiotics. Insufficient time, money or service availability [ 35 , 43 , 47 , 49 , 52 , 54 ], greater distance to travel [ 36 , 37 , 46 , 53 , 54 ], lack of transportation [ 36 , 37 , 53 ] and poor road conditions [ 36 , 37 ] were challenges which influenced autonomous practices with antibiotics. Living greater than 5 miles [ 37 ] or 5 km [ 46 ] from the nearest hospital or health facility was associated with parents using unprescribed antibiotics with their children in two studies. Living far from healthcare facilities also provided the impetus to store leftover antibiotics as a strategy to manage resource limitations [ 53 ]. In seven studies citing poor access to healthcare, parents reported to obtain treatment advice from drug suppliers, family, friends from a health background [ 35 , 47 , 49 , 52 , 54 ] or parents shared antibiotics between their personal network [ 46 , 53 ]. Analysing the information in the current review does not determine whether lack of access to healthcare caused parents to seek advice from others. However, separate to a predilection to listen to non-medical advice, when there are impediments to accessing healthcare, parents may utilise other available resources to assist with decision-making, or adopt strategies to gain access to antibiotics.

The fifth theme established from data analysis was ‘access to antibiotics’. This theme relates to findings that availability of unprescribed antibiotics (i.e., over-the-counter sales, storing or sharing antibiotics) enabled autonomous use by parents. The availability of over-the-counter antibiotics at drug stores was discussed in almost half the studies as a contributor to non-prescription use [ 33 , 34 , 35 , 36 , 37 , 47 , 48 , 49 , 50 , 51 , 52 , 54 ]. In five studies, purchasing antibiotics without a prescription and storing antibiotics and leftovers in the home were associated with parents using antibiotics autonomously [ 36 , 41 , 42 , 47 , 51 ]. Antibiotic sharing also contributed to parents using antibiotics without consulting a doctor [ 46 , 52 , 53 ]. In a number of studies, drug stores were described as easily accessible in terms of distance, affordability or extended operating hours and may be negotiable in price, quantity or brand [ 35 , 36 , 37 , 48 , 49 , 50 ]. It is important to note that in some settings it was reported that unregulated drug stores were the only feasible avenue for the community to access medicines and healthcare [ 54 ]. In nine studies reporting barriers to the formal health system, parents accessed antibiotics via non-prescription sale or by sharing or storing antibiotics [ 35 , 36 , 37 , 46 , 47 , 49 , 52 , 53 , 54 ]. The findings indicate that access to unprescribed antibiotics is a strong predictor of antibiotic use. Healthcare challenges appear to exacerbate autonomous behaviours resulting in parents deciding to seek more available treatment pathways.

The final theme of this review was ‘socio-demographic characteristics’ , which includes variables associated with antibiotic use and misuse, such as the child and parents’ age, parent education, and household composition. There were no clear patterns that emerged from socio-demographic factors, which is consistent with previous research [ 13 ]. In two studies it was observed that children over 2-3yrs of age had a higher likelihood of using antibiotics [ 32 , 42 ], and increasing age of the child was associated with non-prescription antibiotic use in one study [ 51 ]. Studies in Australia and Vietnam indicate that parents may try to safeguard their young children from antibiotics [ 15 , 32 ]. However, in contrast, other research found that younger children had the highest rates of antibiotic use [ 39 , 48 ] and were more likely to be treated with unprescribed antibiotics [ 37 ]. In a Nigerian study it was opined that younger children may have received more antibiotics because of parental perceptions that illness is more serious in early life [ 48 ]. In the case of prescribed antibiotic use, we are unable to determine from our analysis of some studies if the age of the child impacted the parents’ decision to seek antibiotics, or if healthcare providers were inclined to prescribe more antibiotics based on the child’s age [ 32 , 39 ].

With respect to non-prescription use of antibiotics, the parent’s age and level of education also produced conflicting findings. For example, in Nigeria, mothers with higher levels of education were more aware of the risks of misuse and were less inclined to self-treat with antibiotics [ 38 ]. On the contrary, studies in Vietnam observed more highly educated mothers were prone to autonomous antibiotic use [ 32 , 36 ] and reported confidence in when to initiate antibiotics [ 36 ]. The mixed results produced for parent/child features and antibiotic use may reflect the number of different countries, cultures and their values. Cultural awareness and sensitivity should be considered when planning interventions to meet the needs of the target population. Nonetheless, having multiple children/household members was found to be associated with parents using unprescribed antibiotics in two Chinese studies [ 41 , 51 ]. This finding has been supported in past reviews [ 13 ] and suggests that increased experience with using antibiotics may enhance confidence to self-treat.

Theoretical models

The various themes that emerged from the analysis can be interpreted with reference to theories explaining health-related behaviours. Based on the results of this review, the most comprehensive explanation of parental behaviours with antibiotics is provided by the Theory of Planned Behaviour (TPB) and Self-Determination Theory (SDT). The TPB posits that three key constructs influence intention to perform a behaviour: attitude about the behaviour; subjective norms that others approve of the behaviour; and perceptions of control that the behaviour can be facilitated [ 55 ]. One or a combination of these variables facilitates an understanding of parental choices towards their children’s antibiotic use in rural settings. For example, the data suggests strong beliefs about the effectiveness of antibiotics in the treatment of numerous symptoms and favourable attitudes towards autonomous antibiotic use are likely predictors of antibiotic use behaviours. Associated with attitudes, deficits in antimicrobial knowledge likely contributed to the formation of parental attitudes about antibiotic use, although knowledge did not always motivate autonomous behaviour. Attitudes and beliefs were also found to contribute to non-adherence decisions and requests for antibiotic prescriptions. Our findings also indicate that advice and assent from others, subjective norms, including both medical and non-medical connections, contributed to antibiotic use and autonomous use. Relatedly, social pressure and cultural factors were also found to play a role in the non-adherence decisions of Malawian parents [ 53 ]. Perceived behavioural control to act appropriately and obtain medical guidance from a prescriber was impeded by cost, time, distance to services and limited healthcare options. While ease of availability to unregulated antibiotics facilitated antibiotic use and increased behavioural control over autonomous use decisions as a strategy to manage healthcare barriers.

Alternatively, the SDT model provides an alternative framework to understand why parents might initiate antibiotic therapy without consulting a doctor. SDT proposes that people have three core psychological needs: autonomy (i.e., the need for choice in their behaviour); competence (i.e., the need to feel capable and effective in influencing favourable outcomes); and relatedness (i.e., the feeling of connectedness, being understood and belonging with others). When the health context supports people in meeting these needs, people are increasingly motivated to be autonomous in their actions and maintain their health behaviours [ 56 ]. The data from this study suggests that unregulated access to antibiotics and support received from others in the social environment in the decision-making process towards autonomous antibiotic use, facilitates and maintains behavioural autonomy with antibiotics. Parental beliefs about the efficacy and potency of antibiotics in treating multiple symptoms likely influences feelings of competency in aiding their child’s recovery from illness by using antibiotics. Feelings of competency may be maintained by knowledge deficiencies about the risks and indications for antibiotic use. Furthermore, widespread use of over-the-counter antibiotics and acceptance of storing and sharing practices among households, likely supports feelings of relatedness and normalises behaviours utilised to access antibiotics and manage healthcare difficulties.

Summary and limitations

This review uncovered six themes representing factors which contributed to when, why and how parents in rural and remote locations accessed and used antibiotics. These insights may assist in the development of programs to facilitate the appropriate use of antibiotics in rural and remote settings. By identifying the bespoke drivers of antibiotic use by parents within context of place and culture, interventions focussing on knowledge, attitudes, social norms, and behavioural competency in the management of children’s health needs can be developed. Such bespoke interventions necessitate measurement of the drivers of target parent behaviours using reliable and valid strategies.

A number of limitations are acknowledged in this research. This systematic review was limited to studies in English. However, grey literature and reference lists were searched without date restrictions, and a range of study designs were included in order to identify as much research as possible, which was a strength of this study. Care was taken to systematically categorise studies into themes and sub-themes. Nevertheless, we acknowledge this might introduce some degree of subjectivity to data analysis. Although the review was worldwide, limited Australian studies were identified that met the inclusion criteria. This presents a need for further research in rural and remote settings of Australia to investigate the appropriate use of antibiotics on larger samples of parents. Future reviews may also consider a comparison of urban and rural parent samples to ascertain any key differences/similarities of note in how decisions are formed. Lastly, survey validation procedures of studies included in this review were underreported. This highlights an opportunity for future research to report these processes more transparently to enhance the robustness of the findings.

This study identified a number of determinants motivating parents in rural and remote areas to use antibiotics and factors which influenced their practices. Nearly all studies identified parents treating their children with non-prescription antibiotics, which was the most common misuse practice noted. Overuse and misuse of antibiotics is a critical driver in selecting and facilitating the development of drug resistant bacteria in communities. Accordingly, the results of this review give emphasis to several mechanisms that enabled antibiotic use as well as obstacles that prevented parents from making more optimal decisions about the use of antibiotics. The way in which these mechanisms inter-relate to influence the decisions and behaviour of parents should be considered when developing bespoke interventions to contain the impact of AMR in rural, low-resource contexts.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

Antimicrobial resistance

World Health Organization

United Nations Children’s Fund

International Prospective Register of Systematic Reviews

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

Medical Subject Headings

Population, Intervention, Comparison, Outcomes, Study design/Setting

Joanna Briggs Institute

Grading of Recommendations Assessment, Development and Evaluation

Acute Respiratory Tract Infection

Patent and Proprietary Medicine Vendors

Theory of Planned Behaviour

Self-Determination Theory

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Acknowledgements

SM would also like to acknowledge the support of the Commonwealth through an Australian Government Research Training Program Scholarship.

This study was part of a PhD study, funded by the Australian Government Research Training Program Scholarship. The funding source had no input into the design of the study, the collection, analysis or interpretation of the data or the writing of the manuscript.

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Stephanie A. Marsh & Mitchell K. Byrne

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This study was designed in collaboration between SM, MB and SP. SM completed the conceptual development, study design, data collection and analysis under the guidance and visualisation of MB and SP. MB and SP checked SM’s decisions at each stage of the review, participated in critically appraising studies, and oversaw data analysis. Discrepancies were resolved through regular team discussion and consultation. SM drafted the manuscript and MB and SP reviewed, modified and approved the final version.

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Correspondence to Sara Parsafar .

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MB is co-founder of the Wollongong Antimicrobial Resistance Research Alliance (WARRA) [ 57 ] and has authored several publications traversing issues in AMR. MB is Adjunct Associate Professor with the School of Psychology at the University of Wollongong. SP is a Psychology Lecturer and the Course Coordinator of Psychology Honours, and has authored several systematic reviews. SM is a PhD Student and a Registered Psychologist with experience in Health Psychology.

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Additional file 2: search strategy for all databases and supplementary searches (.docx), additional file 3: decision tree tools used for data screening (.docx), additional file 4: summary of study characteristics of included studies and all data used for analysis (.xls), additional file 5: jbi risk of bias quality assessments (.docx), additional file 6: details of data coding/analysis and thematic development (.docx), rights and permissions.

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Marsh, S.A., Parsafar, S. & Byrne, M.K. Should my child be given antibiotics? A systematic review of parental decision making in rural and remote locations. Antimicrob Resist Infect Control 13 , 105 (2024). https://doi.org/10.1186/s13756-024-01409-1

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What is qualitative research?

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Quantitative vs. qualitative research questions

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Qualitative research questions

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Why are good qualitative research questions important?

Developing good qualitative research questions

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