data gathering procedure example in qualitative research

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4 Gathering and Analyzing Qualitative Data

Gathering and analyzing qualitative data.

As the role of clinician researchers expands beyond the bedside, it is important to consider the possibilities of inquiry beyond the quantitative approach. In contrast to the quantitative approach, qualitative methodology is highly inductive and relies on the background and interpretation of the researcher to derive meaning from the gathering and analytic processes central to qualitative inquiry.

Chapter 4: Learning Objectives

As you explore the research opportunities central to your interests to consider whether qualitative component would enrich your work, you’ll be able to:

• Define what qualitative research is
• Compare qualitative and quantitative approaches
• Describe the process of creating themes from recurring ideas gleaned from narrative interviews

What Is Qualitative Research?

Quantitative researchers typically start with a focused research question or hypothesis, collect a small amount of numerical data from a large number of individuals, describe the resulting data using statistical techniques, and draw general conclusions about some large population. Although this method is by far the most common approach to conducting empirical research in fields such as respiratory care and other clinical fields, there is an important alternative called qualitative research. Qualitative research originated in the disciplines of anthropology and sociology but is now used to study psychological topics as well. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques, such as grounded theory, thematic analysis, critical discourse analysis, or interpretative phenomenological analysis. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants.

Consider, for example, a study by researcher Per Lindqvist and his colleagues, who wanted to learn how the families of teenage suicide victims cope with their loss (Lindqvist, Johansson, & Karlsson, 2008). They did not have a specific research question or hypothesis, such as, What percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To address this question, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected.

The Purpose of Qualitative Research

The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior. This method is how we know that people have a strong tendency to obey authority figures, for example, and that female undergraduate students are not substantially more talkative than male undergraduate students. But while quantitative research is good at providing precise answers to specific research questions, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And quantitative research is not very good at communicating what it is actually like to be a member of a particular group in a particular situation.

But the relative weaknesses of quantitative research are the relative strengths of qualitative research. Qualitative research can help researchers to generate new and interesting research questions and hypotheses. The research of Lindqvist and colleagues, for example, suggests that there may be a general relationship between how unexpected a suicide is and how consumed the family is with trying to understand why the teen committed suicide. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Among qualitative researchers, this depth is often referred to as “thick description” (Geertz, 1973) .

Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the “lived experience” of the research participants. Lindqvist and colleagues, for example, describe how all the families spontaneously offered to show the interviewer the victim’s bedroom or the place where the suicide occurred—revealing the importance of these physical locations to the families. It seems unlikely that a quantitative study would have discovered this detail. The table below lists some contrasts between qualitative and quantitative research

Data Collection and Analysis in Qualitative Research

Data collection approaches in qualitative research are quite varied and can involve naturalistic observation, participant observation, archival data, artwork, and many other things. But one of the most common approaches, especially for psychological research, is to conduct interviews. Interviews in qualitative research can be unstructured—consisting of a small number of general questions or prompts that allow participants to talk about what is of interest to them—or structured, where there is a strict script that the interviewer does not deviate from. Most interviews are in between the two and are called semi-structured interviews, where the researcher has a few consistent questions and can follow up by asking more detailed questions about the topics that come up. Such interviews can be lengthy and detailed, but they are usually conducted with a relatively small sample. The unstructured interview was the approach used by Lindqvist and colleagues in their research on the families of suicide victims because the researchers were aware that how much was disclosed about such a sensitive topic should be led by the families, not by the researchers.

Another approach used in qualitative research involves small groups of people who participate together in interviews focused on a particular topic or issue, known as focus groups. The interaction among participants in a focus group can sometimes bring out more information than can be learned in a one- on-one interview. The use of focus groups has become a standard technique in business and industry among those who want to understand consumer tastes and preferences. The content of all focus group interviews is usually recorded and transcribed to facilitate later analyses. However, we know from social psychology that group dynamics are often at play in any group, including focus groups, and it is useful to be aware of those possibilities. For example, the desire to be liked by others can lead participants to provide inaccurate answers that they believe will be perceived favorably by the other participants. The same may be said for personality characteristics. For example, highly extraverted participants can sometimes dominate discussions within focus groups.

Data Analysis in Qualitative Research

Although quantitative and qualitative research generally differ along several important dimensions (e.g., the specificity of the research question, the type of data collected), it is the method of data analysis that distinguishes them more clearly than anything else. To illustrate this idea, imagine a team of researchers that conducts a series of unstructured interviews with people recovering from alcohol use disorder to learn about the role of their religious faith in their recovery. Although this project sounds like qualitative research, imagine further that once they collect the data, they code the data in terms of how often each participant mentions God (or a “higher power”), and they then use descriptive and inferential statistics to find out whether those who mention God more often are more successful in abstaining from alcohol. Now it sounds like quantitative research. In other words, the quantitative-qualitative distinction depends more on what researchers do with the data they have collected than with why or how they collected the data.

But what does qualitative data analysis look like? Just as there are many ways to collect data in qualitative research, there are many ways to analyze data. Here we focus on one general approach called grounded theory (Glaser & Strauss, 1967) . This approach was developed within the field of sociology in the 1960s and has gradually gained popularity in psychology. Remember that in quantitative research, it is typical for the researcher to start with a theory, derive a hypothesis from that theory, and then collect data to test that specific hypothesis. In qualitative research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this analysis in stages. First, they identify ideas that are repeated throughout the data. Then they organize these ideas into a smaller number of broader themes. Finally, they write a theoretical narrative—an interpretation of the data in terms of the themes that they have identified. This theoretical narrative focuses on the subjective experience of the participants and is usually supported by many direct quotations from the participants themselves.

As an example, consider a study by researchers Laura Abrams and Laura Curran, who used the grounded theory approach to study the experience of postpartum depression symptoms among low-income mothers (Abrams & Curran, 2009) . Their data were the result of unstructured interviews with 19 participants. The table below hows the five broad themes the researchers identified and the more specific repeating ideas that made up each of those themes. In their research report, they provide numerous quotations from their participants, such as this one from “Destiny:”

“Well, just recently my apartment was broken into and the fact that his Medicaid for some reason was cancelled so a lot of things was happening within the last two weeks all at one time. So that in itself I don’t want to say almost drove me mad but it put me in a funk….Like I really was depressed. (p. 357)”

Their theoretical narrative focused on the participants’ experience of their symptoms, not as an abstract “affective disorder” but as closely tied to the daily struggle of raising children alone under often difficult circumstances. The table below illustrates the process of creating themes from repeating ideas in the qualitative research gathering and analysis process.

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize qualitative methods on the grounds that they lack objectivity, are difficult to evaluate in terms of reliability and validity, and do not allow generalization to people or situations other than those actually studied. At the same time, some qualitative researchers criticize quantitative methods on the grounds that they overlook the richness of human behavior and experience and instead answer simple questions about easily quantifiable variables.

In general, however, qualitative researchers are well aware of the issues of objectivity, reliability, validity, and generalizability. In fact, they have developed a number of frameworks for addressing these issues (which are beyond the scope of our discussion). And in general, quantitative researchers are well aware of the issue of oversimplification. They do not believe that all human behavior and experience can be adequately described in terms of a small number of variables and the statistical relationships among them. Instead, they use simplification as a strategy for uncovering general principles of human behavior.

Many researchers from both the quantitative and qualitative camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). In fact, the studies by Lindqvist and colleagues and by Abrams and Curran both combined quantitative and qualitative approaches. One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. Again, while a qualitative study might suggest that families who experience an unexpected suicide have more difficulty resolving the question of why, a well-designed quantitative study could test a hypothesis by measuring these specific variables in a large sample. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge and how can they be reconciled?

Using qualitative research can often help clarify quantitative results via triangulation. Trenor, Yu, Waight, Zerda, and Sha (2008) investigated the experience of female engineering students at a university. In the first phase, female engineering students were asked to complete a survey, where they rated a number of their perceptions, including their sense of belonging. Their results were compared across the student ethnicities, and statistically, the various ethnic groups showed no differences in their ratings of their sense of belonging.

One might look at that result and conclude that ethnicity does not have anything to do with one’s sense of belonging. However, in the second phase, the authors also conducted interviews with the students, and in those interviews, many minority students reported how the diversity of cultures at the university enhanced their sense of belonging. Without the qualitative component, we might have drawn the wrong conclusion about the quantitative results.

This example shows how qualitative and quantitative research work together to help us understand human behavior. Some researchers have characterized qualitative research as best for identifying behaviors or the phenomenon whereas quantitative research is best for understanding meaning or identifying the mechanism. However, Bryman (2012) argues for breaking down the divide between these arbitrarily different ways of investigating the same questions.

Key Takeaways

• The qualitative approach is centered on an inductive method of reasoning
• The qualitative approach focuses on understanding phenomenon through the perspective of those experiencing it
• Researchers search for recurring topics and group themes to build upon theory to explain findings
• A mixed methods approach uses both quantitative and qualitative methods to explain different aspects of a phenomenon, processes, or practice
• This chapter can be attributed to Research Methods in Psychology by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. This adaptation constitutes the fourth edition of this textbook, and builds upon the second Canadian edition by Rajiv S. Jhangiani (Kwantlen Polytechnic University) and I-Chant A. Chiang (Quest University Canada), the second American edition by Dana C. Leighton (Texas A&M University-Texarkana), and the third American edition by Carrie Cuttler (Washington State University) and feedback from several peer reviewers coordinated by the Rebus Community. This edition is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ↵

Gathering and Analyzing Qualitative Data Copyright © by megankoster is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 10. Introduction to Data Collection Techniques

Introduction.

Now that we have discussed various aspects of qualitative research, we can begin to collect data. This chapter serves as a bridge between the first half and second half of this textbook (and perhaps your course) by introducing techniques of data collection. You’ve already been introduced to some of this because 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. Thus, some of this chapter will operate as a review of material already covered, but we will be approaching it from the data-collection side rather than the tradition-of-inquiry side we explored in chapters 2 and 4.

Revisiting Approaches

There are four primary techniques of data collection used in qualitative research: interviews, focus groups, observations, and document review. [1] There are other available techniques, such as visual analysis (e.g., photo elicitation) and biography (e.g., autoethnography) that are sometimes used independently or supplementarily to one of the main forms. Not to confuse you unduly, but these various data collection techniques are employed differently by different qualitative research traditions so that sometimes the technique and the tradition become inextricably entwined. This is largely the case with observations and ethnography. The ethnographic tradition is fundamentally based on observational techniques. At the same time, traditions other than ethnography also employ observational techniques, so it is worthwhile thinking of “tradition” and “technique” separately (see figure 10.1).

Figure 10.1. Data Collection Techniques

Each of these data collection techniques will be the subject of its own chapter in the second half of this textbook. This chapter serves as an orienting overview and as the bridge between the conceptual/design portion of qualitative research and the actual practice of conducting qualitative research.

Overview of the Four Primary Approaches

Interviews are at the heart of qualitative research. Returning to epistemological foundations, it is during the interview that the researcher truly opens herself to hearing what others have to say, encouraging her interview subjects to reflect deeply on the meanings and values they hold. Interviews are used in almost every qualitative tradition but are particularly salient in phenomenological studies, studies seeking to understand the meaning of people’s lived experiences.

Focus groups can be seen as a type of interview, one in which a group of persons (ideally between five and twelve) is asked a series of questions focused on a particular topic or subject. They are sometimes used as the primary form of data collection, especially outside academic research. For example, businesses often employ focus groups to determine if a particular product is likely to sell. Among qualitative researchers, it is often used in conjunction with any other primary data collection technique as a form of “triangulation,” or a way of increasing the reliability of the study by getting at the object of study from multiple directions. [2] Some traditions, such as feminist approaches, also see the focus group as an important “consciousness-raising” tool.

If interviews are at the heart of qualitative research, observations are its lifeblood. Researchers who are more interested in the practices and behaviors of people than what they think or who are trying to understand the parameters of an organizational culture rely on observations as their primary form of data collection. The notes they make “in the field” (either during observations or afterward) form the “data” that will be analyzed. Ethnographers, those seeking to describe a particular ethnos, or culture, believe that observations are more reliable guides to that culture than what people have to say about it. Observations are thus the primary form of data collection for ethnographers, albeit often supplemented with in-depth interviews.

Some would say that these three—interviews, focus groups, and observations—are really the foundational techniques of data collection. They are far and away the three techniques most frequently used separately, in conjunction with one another, and even sometimes in mixed methods qualitative/quantitative studies. Document review, either as a form of content analysis or separately, however, is an important addition to the qualitative researcher’s toolkit and should not be overlooked (figure 10.1). Although it is rare for a qualitative researcher to make document review their primary or sole form of data collection, including documents in the research design can help expand the reach and the reliability of a study. Document review can take many forms, from historical and archival research, in which the researcher pieces together a narrative of the past by finding and analyzing a variety of “documents” and records (including photographs and physical artifacts), to analyses of contemporary media content, as in the case of compiling and coding blog posts or other online commentaries, and content analysis that identifies and describes communicative aspects of media or documents.

In addition to these four major techniques, there are a host of emerging and incidental data collection techniques, from photo elicitation or photo voice, in which respondents are asked to comment upon a photograph or image (particularly useful as a supplement to interviews when the respondents are hesitant or unable to answer direct questions), to autoethnographies, in which the researcher uses his own position and life to increase our understanding about a phenomenon and its historical and social context.

Taken together, these techniques provide a wide range of practices and tools with which to discover the world. They are particularly suited to addressing the questions that qualitative researchers ask—questions about how things happen and why people act the way they do, given particular social contexts and shared meanings about the world (chapter 4).

Triangulation and Mixed Methods

Because the researcher plays such a large and nonneutral role in qualitative research, one that requires constant reflectivity and awareness (chapter 6), there is a constant need to reassure her audience that the results she finds are reliable. Quantitative researchers can point to any number of measures of statistical significance to reassure their audiences, but qualitative researchers do not have math to hide behind. And she will also want to reassure herself that what she is hearing in her interviews or observing in the field is a true reflection of what is going on (or as “true” as possible, given the problem that the world is as large and varied as the elephant; see chapter 3). For those reasons, it is common for researchers to employ more than one data collection technique or to include multiple and comparative populations, settings, and samples in the research design (chapter 2). A single set of interviews or initial comparison of focus groups might be conceived as a “pilot study” from which to launch the actual study. Undergraduate students working on a research project might be advised to think about their projects in this way as well. You are simply not going to have enough time or resources as an undergraduate to construct and complete a successful qualitative research project, but you may be able to tackle a pilot study. Graduate students also need to think about the amount of time and resources they have for completing a full study. Masters-level students, or students who have one year or less in which to complete a program, should probably consider their study as an initial exploratory pilot. PhD candidates might have the time and resources to devote to the type of triangulated, multifaceted research design called for by the research question.

We call the use of multiple qualitative methods of data collection and the inclusion of multiple and comparative populations and settings “triangulation.” Using different data collection methods allows us to check the consistency of our findings. For example, a study of the vaccine hesitant might include a set of interviews with vaccine-hesitant people and a focus group of the same and a content analysis of online comments about a vaccine mandate. By employing all three methods, we can be more confident of our interpretations from the interviews alone (especially if we are hearing the same thing throughout; if we are not, then this is a good sign that we need to push a little further to find out what is really going on). [3] Methodological triangulation is an important tool for increasing the reliability of our findings and the overall success of our research.

Methodological triangulation should not be confused with mixed methods techniques, which refer instead to the combining of qualitative and quantitative research methods. Mixed methods studies can increase reliability, but that is not their primary purpose. Mixed methods address multiple research questions, both the “how many” and “why” kind, or the causal and explanatory kind. Mixed methods will be discussed in more detail in chapter 15.

Let us return to the three examples of qualitative research described in chapter 1: Cory Abramson’s study of aging ( The End Game) , Jennifer Pierce’s study of lawyers and discrimination ( Racing for Innocence ), and my own study of liberal arts college students ( Amplified Advantage ). Each of these studies uses triangulation.

Abramson’s book is primarily based on three years of observations in four distinct neighborhoods. He chose the neighborhoods in such a way to maximize his ability to make comparisons: two were primarily middle class and two were primarily poor; further, within each set, one was predominantly White, while the other was either racially diverse or primarily African American. In each neighborhood, he was present in senior centers, doctors’ offices, public transportation, and other public spots where the elderly congregated. [4] The observations are the core of the book, and they are richly written and described in very moving passages. But it wasn’t enough for him to watch the seniors. He also engaged with them in casual conversation. That, too, is part of fieldwork. He sometimes even helped them make it to the doctor’s office or get around town. Going beyond these interactions, he also interviewed sixty seniors, an equal amount from each of the four neighborhoods. It was in the interviews that he could ask more detailed questions about their lives, what they thought about aging, what it meant to them to be considered old, and what their hopes and frustrations were. He could see that those living in the poor neighborhoods had a more difficult time accessing care and resources than those living in the more affluent neighborhoods, but he couldn’t know how the seniors understood these difficulties without interviewing them. Both forms of data collection supported each other and helped make the study richer and more insightful. Interviews alone would have failed to demonstrate the very real differences he observed (and that some seniors would not even have known about). This is the value of methodological triangulation.

Pierce’s book relies on two separate forms of data collection—interviews with lawyers at a firm that has experienced a history of racial discrimination and content analyses of news stories and popular films that screened during the same years of the alleged racial discrimination. I’ve used this book when teaching methods and have often found students struggle with understanding why these two forms of data collection were used. I think this is because we don’t teach students to appreciate or recognize “popular films” as a legitimate form of data. But what Pierce does is interesting and insightful in the best tradition of qualitative research. Here is a description of the content analyses from a review of her book:

In the chapter on the news media, Professor Pierce uses content analysis to argue that the media not only helped shape the meaning of affirmative action, but also helped create white males as a class of victims. The overall narrative that emerged from these media accounts was one of white male innocence and victimization. She also maintains that this narrative was used to support “neoconservative and neoliberal political agendas” (p. 21). The focus of these articles tended to be that affirmative action hurt white working-class and middle-class men particularly during the recession in the 1980s (despite statistical evidence that people of color were hurt far more than white males by the recession). In these stories fairness and innocence were seen in purely individual terms. Although there were stories that supported affirmative action and developed a broader understanding of fairness, the total number of stories slanted against affirmative action from 1990 to 1999. During that time period negative stories always outnumbered those supporting the policy, usually by a ratio of 3:1 or 3:2. Headlines, the presentation of polling data, and an emphasis in stories on racial division, Pierce argues, reinforced the story of white male victimization. Interestingly, the news media did very few stories on gender and affirmative action. The chapter on the film industry from 1989 to 1999 reinforces Pierce’s argument and adds another layer to her interpretation of affirmative action during this time period. She sampled almost 60 Hollywood films with receipts ranging from four million to 184 million dollars. In this chapter she argues that the dominant theme of these films was racial progress and the redemption of white Americans from past racism. These movies usually portrayed white, elite, and male experiences. People of color were background figures who supported the protagonist and “anointed” him as a savior (p. 45). Over the course of the film the protagonists move from “innocence to consciousness” concerning racism. The antagonists in these films most often were racist working-class white men. A Time to Kill , Mississippi Burning , Amistad , Ghosts of Mississippi , The Long Walk Home , To Kill a Mockingbird , and Dances with Wolves receive particular analysis in this chapter, and her examination of them leads Pierce to conclude that they infused a myth of racial progress into America’s cultural memory. White experiences of race are the focus and contemporary forms of racism are underplayed or omitted. Further, these films stereotype both working-class and elite white males, and underscore the neoliberal emphasis on individualism. ( Hrezo 2012 )

With that context in place, Pierce then turned to interviews with attorneys. She finds that White male attorneys often misremembered facts about the period in which the law firm was accused of racial discrimination and that they often portrayed their firms as having made substantial racial progress. This was in contrast to many of the lawyers of color and female lawyers who remembered the history differently and who saw continuing examples of racial (and gender) discrimination at the law firm. In most of the interviews, people talked about individuals, not structure (and these are attorneys, who really should know better!). By including both content analyses and interviews in her study, Pierce is better able to situate the attorney narratives and explain the larger context for the shared meanings of individual innocence and racial progress. Had this been a study only of films during this period, we would not know how actual people who lived during this period understood the decisions they made; had we had only the interviews, we would have missed the historical context and seen a lot of these interviewees as, well, not very nice people at all. Together, we have a study that is original, inventive, and insightful.

My own study of how class background affects the experiences and outcomes of students at small liberal arts colleges relies on mixed methods and triangulation. At the core of the book is an original survey of college students across the US. From analyses of this survey, I can present findings on “how many” questions and descriptive statistics comparing students of different social class backgrounds. For example, I know and can demonstrate that working-class college students are less likely to go to graduate school after college than upper-class college students are. I can even give you some estimates of the class gap. But what I can’t tell you from the survey is exactly why this is so or how it came to be so . For that, I employ interviews, focus groups, document reviews, and observations. Basically, I threw the kitchen sink at the “problem” of class reproduction and higher education (i.e., Does college reduce class inequalities or make them worse?). A review of historical documents provides a picture of the place of the small liberal arts college in the broader social and historical context. Who had access to these colleges and for what purpose have always been in contest, with some groups attempting to exclude others from opportunities for advancement. What it means to choose a small liberal arts college in the early twenty-first century is thus different for those whose parents are college professors, for those whose parents have a great deal of money, and for those who are the first in their family to attend college. I was able to get at these different understandings through interviews and focus groups and to further delineate the culture of these colleges by careful observation (and my own participation in them, as both former student and current professor). Putting together individual meanings, student dispositions, organizational culture, and historical context allowed me to present a story of how exactly colleges can both help advance first-generation, low-income, working-class college students and simultaneously amplify the preexisting advantages of their peers. Mixed methods addressed multiple research questions, while triangulation allowed for this deeper, more complex story to emerge.

In the next few chapters, we will explore each of the primary data collection techniques in much more detail. As we do so, think about how these techniques may be productively joined for more reliable and deeper studies of the social world.

Advanced Reading: Triangulation

Denzin ( 1978 ) identified four basic types of triangulation: data, investigator, theory, and methodological. Properly speaking, if we use the Denzin typology, the use of multiple methods of data collection and analysis to strengthen one’s study is really a form of methodological triangulation. It may be helpful to understand how this differs from the other types.

Data triangulation occurs when the researcher uses a variety of sources in a single study. Perhaps they are interviewing multiple samples of college students. Obviously, this overlaps with sample selection (see chapter 5). It is helpful for the researcher to understand that these multiple data sources add strength and reliability to the study. After all, it is not just “these students here” but also “those students over there” that are experiencing this phenomenon in a particular way.

Investigator triangulation occurs when different researchers or evaluators are part of the research team. Intercoding reliability is a form of investigator triangulation (or at least a way of leveraging the power of multiple researchers to raise the reliability of the study).

Theory triangulation is the use of multiple perspectives to interpret a single set of data, as in the case of competing theoretical paradigms (e.g., a human capital approach vs. a Bourdieusian multiple capital approach).

Methodological triangulation , as explained in this chapter, is the use of multiple methods to study a single phenomenon, issue, or problem.

Further Readings

Carter, Nancy, Denise Bryant-Lukosius, Alba DiCenso, Jennifer Blythe, Alan J. Neville. 2014. “The Use of Triangulation in Qualitative Research.” Oncology Nursing Forum 41(5):545–547. Discusses the four types of triangulation identified by Denzin with an example of the use of focus groups and in-depth individuals.

Mathison, Sandra. 1988. “Why Triangulate?” Educational Researcher 17(2):13–17. Presents three particular ways of assessing validity through the use of triangulated data collection: convergence, inconsistency, and contradiction.

Tracy, Sarah J. 2010. “Qualitative Quality: Eight ‘Big-Tent’ Criteria for Excellent Qualitative Research.” Qualitative Inquiry 16(10):837–851. Focuses on triangulation as a criterion for conducting valid qualitative research.

• Marshall and Rossman ( 2016 ) state this slightly differently. They list four primary methods for gathering information: (1) participating in the setting, (2) observing directly, (3) interviewing in depth, and (4) analyzing documents and material culture (141). An astute reader will note that I have collapsed participation into observation and that I have distinguished focus groups from interviews. I suspect that this distinction marks me as more of an interview-based researcher, while Marshall and Rossman prioritize ethnographic approaches. The main point of this footnote is to show you, the reader, that there is no single agreed-upon number of approaches to collecting qualitative data. ↵
• See “ Advanced Reading: Triangulation ” at end of this chapter. ↵
• We can also think about triangulating the sources, as when we include comparison groups in our sample (e.g., if we include those receiving vaccines, we might find out a bit more about where the real differences lie between them and the vaccine hesitant); triangulating the analysts (building a research team so that your interpretations can be checked against those of others on the team); and even triangulating the theoretical perspective (as when we “try on,” say, different conceptualizations of social capital in our analyses). ↵

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

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

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

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

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

• 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 organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, 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 emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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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 analyzing 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 organize 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 categorize 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 analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

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 analyzing 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 analyzing 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 generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

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 organization 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 organizations.

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

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

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

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• Published: 22 March 2008

Methods of data collection in qualitative research: interviews and focus groups

• P. Gill 1 ,
• K. Stewart 2 ,
• E. Treasure 3 &
• B. Chadwick 4  

British Dental Journal volume  204 ,  pages 291–295 ( 2008 ) Cite this article

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Interviews and focus groups are the most common methods of data collection used in qualitative healthcare research

Interviews can be used to explore the views, experiences, beliefs and motivations of individual participants

Focus group use group dynamics to generate qualitative data

Qualitative research in dentistry

Conducting qualitative interviews with school children in dental research

Analysing and presenting qualitative data

This paper explores the most common methods of data collection used in qualitative research: interviews and focus groups. The paper examines each method in detail, focusing on how they work in practice, when their use is appropriate and what they can offer dentistry. Examples of empirical studies that have used interviews or focus groups are also provided.

You have full access to this article via your institution.

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Interviews in the social sciences

A review of technical and quality assessment considerations of audio-visual and web-conferencing focus groups in qualitative health research

Professionalism in dentistry: deconstructing common terminology, introduction.

Having explored the nature and purpose of qualitative research in the previous paper, this paper explores methods of data collection used in qualitative research. There are a variety of methods of data collection in qualitative research, including observations, textual or visual analysis (eg from books or videos) and interviews (individual or group). 1 However, the most common methods used, particularly in healthcare research, are interviews and focus groups. 2 , 3

The purpose of this paper is to explore these two methods in more detail, in particular how they work in practice, the purpose of each, when their use is appropriate and what they can offer dental research.

Qualitative research interviews

There are three fundamental types of research interviews: structured, semi-structured and unstructured. Structured interviews are, essentially, verbally administered questionnaires, in which a list of predetermined questions are asked, with little or no variation and with no scope for follow-up questions to responses that warrant further elaboration. Consequently, they are relatively quick and easy to administer and may be of particular use if clarification of certain questions are required or if there are likely to be literacy or numeracy problems with the respondents. However, by their very nature, they only allow for limited participant responses and are, therefore, of little use if 'depth' is required.

Conversely, unstructured interviews do not reflect any preconceived theories or ideas and are performed with little or no organisation. 4 Such an interview may simply start with an opening question such as 'Can you tell me about your experience of visiting the dentist?' and will then progress based, primarily, upon the initial response. Unstructured interviews are usually very time-consuming (often lasting several hours) and can be difficult to manage, and to participate in, as the lack of predetermined interview questions provides little guidance on what to talk about (which many participants find confusing and unhelpful). Their use is, therefore, generally only considered where significant 'depth' is required, or where virtually nothing is known about the subject area (or a different perspective of a known subject area is required).

Semi-structured interviews consist of several key questions that help to define the areas to be explored, but also allows the interviewer or interviewee to diverge in order to pursue an idea or response in more detail. 2 This interview format is used most frequently in healthcare, as it provides participants with some guidance on what to talk about, which many find helpful. The flexibility of this approach, particularly compared to structured interviews, also allows for the discovery or elaboration of information that is important to participants but may not have previously been thought of as pertinent by the research team.

For example, in a recent dental public heath study, 5 school children in Cardiff, UK were interviewed about their food choices and preferences. A key finding that emerged from semi-structured interviews, which was not previously thought to be as highly influential as the data subsequently confirmed, was the significance of peer-pressure in influencing children's food choices and preferences. This finding was also established primarily through follow-up questioning (eg probing interesting responses with follow-up questions, such as 'Can you tell me a bit more about that?') and, therefore, may not have emerged in the same way, if at all, if asked as a predetermined question.

The purpose of research interviews

The purpose of the research interview is to explore the views, experiences, beliefs and/or motivations of individuals on specific matters (eg factors that influence their attendance at the dentist). Qualitative methods, such as interviews, are believed to provide a 'deeper' understanding of social phenomena than would be obtained from purely quantitative methods, such as questionnaires. 1 Interviews are, therefore, most appropriate where little is already known about the study phenomenon or where detailed insights are required from individual participants. They are also particularly appropriate for exploring sensitive topics, where participants may not want to talk about such issues in a group environment.

Examples of dental studies that have collected data using interviews are 'Examining the psychosocial process involved in regular dental attendance' 6 and 'Exploring factors governing dentists' treatment philosophies'. 7 Gibson et al . 6 provided an improved understanding of factors that influenced people's regular attendance with their dentist. The study by Kay and Blinkhorn 7 provided a detailed insight into factors that influenced GDPs' decision making in relation to treatment choices. The study found that dentists' clinical decisions about treatments were not necessarily related to pathology or treatment options, as was perhaps initially thought, but also involved discussions with patients, patients' values and dentists' feelings of self esteem and conscience.

There are many similarities between clinical encounters and research interviews, in that both employ similar interpersonal skills, such as questioning, conversing and listening. However, there are also some fundamental differences between the two, such as the purpose of the encounter, reasons for participating, roles of the people involved and how the interview is conducted and recorded. 8

The primary purpose of clinical encounters is for the dentist to ask the patient questions in order to acquire sufficient information to inform decision making and treatment options. However, the constraints of most consultations are such that any open-ended questioning needs to be brought to a conclusion within a fairly short time. 2 In contrast, the fundamental purpose of the research interview is to listen attentively to what respondents have to say, in order to acquire more knowledge about the study topic. 9 Unlike the clinical encounter, it is not to intentionally offer any form of help or advice, which many researchers have neither the training nor the time for. Research interviewing therefore requires a different approach and a different range of skills.

The interview

When designing an interview schedule it is imperative to ask questions that are likely to yield as much information about the study phenomenon as possible and also be able to address the aims and objectives of the research. In a qualitative interview, good questions should be open-ended (ie, require more than a yes/no answer), neutral, sensitive and understandable. 2 It is usually best to start with questions that participants can answer easily and then proceed to more difficult or sensitive topics. 2 This can help put respondents at ease, build up confidence and rapport and often generates rich data that subsequently develops the interview further.

As in any research, it is often wise to first pilot the interview schedule on several respondents prior to data collection proper. 8 This allows the research team to establish if the schedule is clear, understandable and capable of answering the research questions, and if, therefore, any changes to the interview schedule are required.

The length of interviews varies depending on the topic, researcher and participant. However, on average, healthcare interviews last 20-60 minutes. Interviews can be performed on a one-off or, if change over time is of interest, repeated basis, 4 for example exploring the psychosocial impact of oral trauma on participants and their subsequent experiences of cosmetic dental surgery.

Developing the interview

Before an interview takes place, respondents should be informed about the study details and given assurance about ethical principles, such as anonymity and confidentiality. 2 This gives respondents some idea of what to expect from the interview, increases the likelihood of honesty and is also a fundamental aspect of the informed consent process.

Wherever possible, interviews should be conducted in areas free from distractions and at times and locations that are most suitable for participants. For many this may be at their own home in the evenings. Whilst researchers may have less control over the home environment, familiarity may help the respondent to relax and result in a more productive interview. 9 Establishing rapport with participants prior to the interview is also important as this can also have a positive effect on the subsequent development of the interview.

When conducting the actual interview it is prudent for the interviewer to familiarise themselves with the interview schedule, so that the process appears more natural and less rehearsed. However, to ensure that the interview is as productive as possible, researchers must possess a repertoire of skills and techniques to ensure that comprehensive and representative data are collected during the interview. 10 One of the most important skills is the ability to listen attentively to what is being said, so that participants are able to recount their experiences as fully as possible, without unnecessary interruptions.

Other important skills include adopting open and emotionally neutral body language, nodding, smiling, looking interested and making encouraging noises (eg, 'Mmmm') during the interview. 2 The strategic use of silence, if used appropriately, can also be highly effective at getting respondents to contemplate their responses, talk more, elaborate or clarify particular issues. Other techniques that can be used to develop the interview further include reflecting on remarks made by participants (eg, 'Pain?') and probing remarks ('When you said you were afraid of going to the dentist what did you mean?'). 9 Where appropriate, it is also wise to seek clarification from respondents if it is unclear what they mean. The use of 'leading' or 'loaded' questions that may unduly influence responses should always be avoided (eg, 'So you think dental surgery waiting rooms are frightening?' rather than 'How do you find the waiting room at the dentists?').

At the end of the interview it is important to thank participants for their time and ask them if there is anything they would like to add. This gives respondents an opportunity to deal with issues that they have thought about, or think are important but have not been dealt with by the interviewer. 9 This can often lead to the discovery of new, unanticipated information. Respondents should also be debriefed about the study after the interview has finished.

All interviews should be tape recorded and transcribed verbatim afterwards, as this protects against bias and provides a permanent record of what was and was not said. 8 It is often also helpful to make 'field notes' during and immediately after each interview about observations, thoughts and ideas about the interview, as this can help in data analysis process. 4 , 8

Focus groups

Focus groups share many common features with less structured interviews, but there is more to them than merely collecting similar data from many participants at once. A focus group is a group discussion on a particular topic organised for research purposes. This discussion is guided, monitored and recorded by a researcher (sometimes called a moderator or facilitator). 11 , 12

Focus groups were first used as a research method in market research, originating in the 1940s in the work of the Bureau of Applied Social Research at Columbia University. Eventually the success of focus groups as a marketing tool in the private sector resulted in its use in public sector marketing, such as the assessment of the impact of health education campaigns. 13 However, focus group techniques, as used in public and private sectors, have diverged over time. Therefore, in this paper, we seek to describe focus groups as they are used in academic research.

When focus groups are used

Focus groups are used for generating information on collective views, and the meanings that lie behind those views. They are also useful in generating a rich understanding of participants' experiences and beliefs. 12 Suggested criteria for using focus groups include: 13

As a standalone method, for research relating to group norms, meanings and processes

In a multi-method design, to explore a topic or collect group language or narratives to be used in later stages

To clarify, extend, qualify or challenge data collected through other methods

To feedback results to research participants.

Morgan 12 suggests that focus groups should be avoided according to the following criteria:

If listening to participants' views generates expectations for the outcome of the research that can not be fulfilled

If participants are uneasy with each other, and will therefore not discuss their feelings and opinions openly

If the topic of interest to the researcher is not a topic the participants can or wish to discuss

If statistical data is required. Focus groups give depth and insight, but cannot produce useful numerical results.

Conducting focus groups: group composition and size

The composition of a focus group needs great care to get the best quality of discussion. There is no 'best' solution to group composition, and group mix will always impact on the data, according to things such as the mix of ages, sexes and social professional statuses of the participants. What is important is that the researcher gives due consideration to the impact of group mix (eg, how the group may interact with each other) before the focus group proceeds. 14

Interaction is key to a successful focus group. Sometimes this means a pre-existing group interacts best for research purposes, and sometimes stranger groups. Pre-existing groups may be easier to recruit, have shared experiences and enjoy a comfort and familiarity which facilitates discussion or the ability to challenge each other comfortably. In health settings, pre-existing groups can overcome issues relating to disclosure of potentially stigmatising status which people may find uncomfortable in stranger groups (conversely there may be situations where disclosure is more comfortable in stranger groups). In other research projects it may be decided that stranger groups will be able to speak more freely without fear of repercussion, and challenges to other participants may be more challenging and probing, leading to richer data. 13

Group size is an important consideration in focus group research. Stewart and Shamdasani 14 suggest that it is better to slightly over-recruit for a focus group and potentially manage a slightly larger group, than under-recruit and risk having to cancel the session or having an unsatisfactory discussion. They advise that each group will probably have two non-attenders. The optimum size for a focus group is six to eight participants (excluding researchers), but focus groups can work successfully with as few as three and as many as 14 participants. Small groups risk limited discussion occurring, while large groups can be chaotic, hard to manage for the moderator and frustrating for participants who feel they get insufficient opportunities to speak. 13

Preparing an interview schedule

Like research interviews, the interview schedule for focus groups is often no more structured than a loose schedule of topics to be discussed. However, in preparing an interview schedule for focus groups, Stewart and Shamdasani 14 suggest two general principles:

Questions should move from general to more specific questions

Question order should be relative to importance of issues in the research agenda.

There can, however, be some conflict between these two principles, and trade offs are often needed, although often discussions will take on a life of their own, which will influence or determine the order in which issues are covered. Usually, less than a dozen predetermined questions are needed and, as with research interviews, the researcher will also probe and expand on issues according to the discussion.

Moderating a focus group looks easy when done well, but requires a complex set of skills, which are related to the following principles: 15

Participants have valuable views and the ability to respond actively, positively and respectfully. Such an approach is not simply a courtesy, but will encourage fruitful discussions

Moderating without participating: a moderator must guide a discussion rather than join in with it. Expressing one's own views tends to give participants cues as to what to say (introducing bias), rather than the confidence to be open and honest about their own views

Be prepared for views that may be unpalatably critical of a topic which may be important to you

It is important to recognise that researchers' individual characteristics mean that no one person will always be suitable to moderate any kind of group. Sometimes the characteristics that suit a moderator for one group will inhibit discussion in another

Be yourself. If the moderator is comfortable and natural, participants will feel relaxed.

The moderator should facilitate group discussion, keeping it focussed without leading it. They should also be able to prevent the discussion being dominated by one member (for example, by emphasising at the outset the importance of hearing a range of views), ensure that all participants have ample opportunity to contribute, allow differences of opinions to be discussed fairly and, if required, encourage reticent participants. 13

Other relevant factors

The venue for a focus group is important and should, ideally, be accessible, comfortable, private, quiet and free from distractions. 13 However, while a central location, such as the participants' workplace or school, may encourage attendance, the venue may affect participants' behaviour. For example, in a school setting, pupils may behave like pupils, and in clinical settings, participants may be affected by any anxieties that affect them when they attend in a patient role.

Focus groups are usually recorded, often observed (by a researcher other than the moderator, whose role is to observe the interaction of the group to enhance analysis) and sometimes videotaped. At the start of a focus group, a moderator should acknowledge the presence of the audio recording equipment, assure participants of confidentiality and give people the opportunity to withdraw if they are uncomfortable with being taped. 14

A good quality multi-directional external microphone is recommended for the recording of focus groups, as internal microphones are rarely good enough to cope with the variation in volume of different speakers. 13 If observers are present, they should be introduced to participants as someone who is just there to observe, and sit away from the discussion. 14 Videotaping will require more than one camera to capture the whole group, as well as additional operational personnel in the room. This is, therefore, very obtrusive, which can affect the spontaneity of the group and in a focus group does not usually yield enough additional information that could not be captured by an observer to make videotaping worthwhile. 15

The systematic analysis of focus group transcripts is crucial. However, the transcription of focus groups is more complex and time consuming than in one-to-one interviews, and each hour of audio can take up to eight hours to transcribe and generate approximately 100 pages of text. Recordings should be transcribed verbatim and also speakers should be identified in a way that makes it possible to follow the contributions of each individual. Sometimes observational notes also need to be described in the transcripts in order for them to make sense.

The analysis of qualitative data is explored in the final paper of this series. However, it is important to note that the analysis of focus group data is different from other qualitative data because of their interactive nature, and this needs to be taken into consideration during analysis. The importance of the context of other speakers is essential to the understanding of individual contributions. 13 For example, in a group situation, participants will often challenge each other and justify their remarks because of the group setting, in a way that perhaps they would not in a one-to-one interview. The analysis of focus group data must therefore take account of the group dynamics that have generated remarks.

Focus groups in dental research

Focus groups are used increasingly in dental research, on a diverse range of topics, 16 illuminating a number of areas relating to patients, dental services and the dental profession. Addressing a special needs population difficult to access and sample through quantitative measures, Robinson et al . 17 used focus groups to investigate the oral health-related attitudes of drug users, exploring the priorities, understandings and barriers to care they encounter. Newton et al . 18 used focus groups to explore barriers to services among minority ethnic groups, highlighting for the first time differences between minority ethnic groups. Demonstrating the use of the method with professional groups as subjects in dental research, Gussy et al . 19 explored the barriers to and possible strategies for developing a shared approach in prevention of caries among pre-schoolers. This mixed method study was very important as the qualitative element was able to explain why the clinical trial failed, and this understanding may help researchers improve on the quantitative aspect of future studies, as well as making a valuable academic contribution in its own right.

Interviews and focus groups remain the most common methods of data collection in qualitative research, and are now being used with increasing frequency in dental research, particularly to access areas not amendable to quantitative methods and/or where depth, insight and understanding of particular phenomena are required. The examples of dental studies that have employed these methods also help to demonstrate the range of research contexts to which interview and focus group research can make a useful contribution. The continued employment of these methods can further strengthen many areas of dentally related work.

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Gill, P., Stewart, K., Treasure, E. et al. Methods of data collection in qualitative research: interviews and focus groups. Br Dent J 204 , 291–295 (2008). https://doi.org/10.1038/bdj.2008.192

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Qualitative research methods allow us to better understand the experiences of patients and carers; they allow us to explore how decisions are made and provide us with a detailed insight into how interventions may alter care. To develop such insights, qualitative research requires data which are holistic, rich and nuanced, allowing themes and findings to emerge through careful analysis. This article provides an overview of the core approaches to data collection in qualitative research, exploring their strengths, weaknesses and challenges.

Collecting data through interviews with participants is a characteristic of many qualitative studies. Interviews give the most direct and straightforward approach to gathering detailed and rich data regarding a particular phenomenon. The type of interview used to collect data can be tailored to the research question, the characteristics of participants and the preferred approach of the researcher. Interviews are most often carried out face-to-face, though the use of telephone interviews to overcome geographical barriers to participant recruitment is becoming more prevalent. 1

A common approach in qualitative research is the semistructured interview, where core elements of the phenomenon being studied are explicitly asked about by the interviewer. A well-designed semistructured interview should ensure data are captured in key areas while still allowing flexibility for participants to bring their own personality and perspective to the discussion. Finally, interviews can be much more rigidly structured to provide greater control for the researcher, essentially becoming questionnaires where responses are verbal rather than written.

Deciding where to place an interview design on this ‘structural spectrum’ will depend on the question to be answered and the skills of the researcher. A very structured approach is easy to administer and analyse but may not allow the participant to express themselves fully. At the other end of the spectrum, an open approach allows for freedom and flexibility, but requires the researcher to walk an investigative tightrope that maintains the focus of an interview without forcing participants into particular areas of discussion.

Example of an interview schedule 3

What do you think is the most effective way of assessing a child’s pain?

Have you come across any issues that make it difficult to assess a child’s pain?

What pain-relieving interventions do you find most useful and why?

When managing pain in children what is your overall aim?

Whose responsibility is pain management?

What involvement do you think parents should have in their child’s pain management?

What involvement do children have in their pain management?

Is there anything that currently stops you managing pain as well as you would like?

What would help you manage pain better?

Interviews present several challenges to researchers. Most interviews are recorded and will need transcribing before analysing. This can be extremely time-consuming, with 1 hour of interview requiring 5–6 hours to transcribe. 4 The analysis itself is also time-consuming, requiring transcriptions to be pored over word-for-word and line-by-line. Interviews also present the problem of bias the researcher needs to take care to avoid leading questions or providing non-verbal signals that might influence the responses of participants.

Focus groups

The focus group is a method of data collection in which a moderator/facilitator (usually a coresearcher) speaks with a group of 6–12 participants about issues related to the research question. As an approach, the focus group offers qualitative researchers an efficient method of gathering the views of many participants at one time. Also, the fact that many people are discussing the same issue together can result in an enhanced level of debate, with the moderator often able to step back and let the focus group enter into a free-flowing discussion. 5 This provides an opportunity to gather rich data from a specific population about a particular area of interest, such as barriers perceived by student nurses when trying to communicate with patients with cancer. 6

From a participant perspective, the focus group may provide a more relaxing environment than a one-to-one interview; they will not need to be involved with every part of the discussion and may feel more comfortable expressing views when they are shared by others in the group. Focus groups also allow participants to ‘bounce’ ideas off each other which sometimes results in different perspectives emerging from the discussion. However, focus groups are not without their difficulties. As with interviews, focus groups provide a vast amount of data to be transcribed and analysed, with discussions often lasting 1–2 hours. Moderators also need to be highly skilled to ensure that the discussion can flow while remaining focused and that all participants are encouraged to speak, while ensuring that no individuals dominate the discussion. 7

Observation

Participant and non-participant observation are powerful tools for collecting qualitative data, as they give nurse researchers an opportunity to capture a wide array of information—such as verbal and non-verbal communication, actions (eg, techniques of providing care) and environmental factors—within a care setting. Another advantage of observation is that the researcher gains a first-hand picture of what actually happens in clinical practice. 8 If the researcher is adopting a qualitative approach to observation they will normally record field notes . Field notes can take many forms, such as a chronological log of what is happening in the setting, a description of what has been observed, a record of conversations with participants or an expanded account of impressions from the fieldwork. 9 10

As with other qualitative data collection techniques, observation provides an enormous amount of data to be captured and analysed—one approach to helping with collection and analysis is to digitally record observations to allow for repeated viewing. 11 Observation also provides the researcher with some unique methodological and ethical challenges. Methodologically, the act of being observed may change the behaviour of the participant (often referred to as the ‘Hawthorne effect’), impacting on the value of findings. However, most researchers report a process of habitation taking place where, after a relatively short period of time, those being observed revert to their normal behaviour. Ethically, the researcher will need to consider when and how they should intervene if they view poor practice that could put patients at risk.

The three core approaches to data collection in qualitative research—interviews, focus groups and observation—provide researchers with rich and deep insights. All methods require skill on the part of the researcher, and all produce a large amount of raw data. However, with careful and systematic analysis 12 the data yielded with these methods will allow researchers to develop a detailed understanding of patient experiences and the work of nurses.

• Twycross AM ,
• Williams AM ,
• Huang MC , et al
• Onwuegbuzie AJ ,
• Dickinson WB ,
• Leech NL , et al
• Twycross A ,
• Emerson RM ,
• Meriläinen M ,
• Ala-Kokko T

Competing interests None declared.

Patient consent Not required.

Provenance and peer review Commissioned; internally peer reviewed.

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

87 Comments

This has been very helpful. Thank you.

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Great to hear that. Good luck with your qualitative data analysis, Pramod!

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Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

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Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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Clear explanation on qualitative and how about Case study

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so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

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Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

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Thank you very much for such a wonderful content

do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

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This is an eye opener for me and very informative, I have used some of your guidance notes on my Thesis, I wonder if you can assist with your 1. name of your book, year of publication, topic etc., this is for citing in my Bibliography,

I certainly hope to hear from you

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Home Market Research

Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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• Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
• Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

• If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
• You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

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.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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Home » Qualitative Data – Types, Methods and Examples

Qualitative Data – Types, Methods and Examples

Table of Contents

Qualitative Data

Definition:

Qualitative data is a type of data that is collected and analyzed in a non-numerical form, such as words, images, or observations. It is generally used to gain an in-depth understanding of complex phenomena, such as human behavior, attitudes, and beliefs.

Types of Qualitative Data

There are various types of qualitative data that can be collected and analyzed, including:

• Interviews : These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic.
• Focus Groups: These are group discussions where a facilitator leads a discussion on a specific topic, allowing participants to share their views and experiences.
• Observations : These involve observing and recording the behavior and interactions of individuals or groups in a particular setting.
• Case Studies: These involve in-depth analysis of a particular individual, group, or organization, usually over an extended period.
• Document Analysis : This involves examining written or recorded materials, such as newspaper articles, diaries, or public records, to gain insight into a particular topic.
• Visual Data : This involves analyzing images or videos to understand people’s experiences or perspectives on a particular topic.
• Online Data: This involves analyzing data collected from social media platforms, forums, or online communities to understand people’s views and opinions on a particular topic.

Qualitative Data Formats

Qualitative data can be collected and presented in various formats. Some common formats include:

• Textual data: This includes written or transcribed data from interviews, focus groups, or observations. It can be analyzed using various techniques such as thematic analysis or content analysis.
• Audio data: This includes recordings of interviews or focus groups, which can be transcribed and analyzed using software such as NVivo.
• Visual data: This includes photographs, videos, or drawings, which can be analyzed using techniques such as visual analysis or semiotics.
• Mixed media data : This includes data collected in different formats, such as audio and text. This can be analyzed using mixed methods research, which combines both qualitative and quantitative research methods.
• Field notes: These are notes taken by researchers during observations, which can include descriptions of the setting, behaviors, and interactions of participants.

Qualitative Data Analysis Methods

Qualitative data analysis refers to the process of systematically analyzing and interpreting qualitative data to identify patterns, themes, and relationships. Here are some common methods of analyzing qualitative data:

• Thematic analysis: This involves identifying and analyzing patterns or themes within the data. It involves coding the data into themes and subthemes and organizing them into a coherent narrative.
• Content analysis: This involves analyzing the content of the data, such as the words, phrases, or images used. It involves identifying patterns and themes in the data and examining the relationships between them.
• Discourse analysis: This involves analyzing the language and communication used in the data, such as the meaning behind certain words or phrases. It involves examining how the language constructs and shapes social reality.
• Grounded theory: This involves developing a theory or framework based on the data. It involves identifying patterns and themes in the data and using them to develop a theory that explains the phenomenon being studied.
• Narrative analysis : This involves analyzing the stories and narratives present in the data. It involves examining how the stories are constructed and how they contribute to the overall understanding of the phenomenon being studied.
• Ethnographic analysis : This involves analyzing the culture and social practices present in the data. It involves examining how the cultural and social practices contribute to the phenomenon being studied.

Qualitative Data Collection Guide

Here are some steps to guide the collection of qualitative data:

• Define the research question : Start by clearly defining the research question that you want to answer. This will guide the selection of data collection methods and help to ensure that the data collected is relevant to the research question.
• Choose data collection methods : Select the most appropriate data collection methods based on the research question, the research design, and the resources available. Common methods include interviews, focus groups, observations, document analysis, and participatory research.
• Develop a data collection plan : Develop a plan for data collection that outlines the specific procedures, timelines, and resources needed for each data collection method. This plan should include details such as how to recruit participants, how to conduct interviews or focus groups, and how to record and store data.
• Obtain ethical approval : Obtain ethical approval from an institutional review board or ethics committee before beginning data collection. This is particularly important when working with human participants to ensure that their rights and interests are protected.
• Recruit participants: Recruit participants based on the research question and the data collection methods chosen. This may involve purposive sampling, snowball sampling, or random sampling.
• Collect data: Collect data using the chosen data collection methods. This may involve conducting interviews, facilitating focus groups, observing participants, or analyzing documents.
• Transcribe and store data : Transcribe and store the data in a secure location. This may involve transcribing audio or video recordings, organizing field notes, or scanning documents.
• Analyze data: Analyze the data using appropriate qualitative data analysis methods, such as thematic analysis or content analysis.
• I nterpret findings : Interpret the findings of the data analysis in the context of the research question and the relevant literature. This may involve developing new theories or frameworks, or validating existing ones.
• Communicate results: Communicate the results of the research in a clear and concise manner, using appropriate language and visual aids where necessary. This may involve writing a report, presenting at a conference, or publishing in a peer-reviewed journal.

Qualitative Data Examples

Some examples of qualitative data in different fields are as follows:

• Sociology : In sociology, qualitative data is used to study social phenomena such as culture, norms, and social relationships. For example, a researcher might conduct interviews with members of a community to understand their beliefs and practices.
• Psychology : In psychology, qualitative data is used to study human behavior, emotions, and attitudes. For example, a researcher might conduct a focus group to explore how individuals with anxiety cope with their symptoms.
• Education : In education, qualitative data is used to study learning processes and educational outcomes. For example, a researcher might conduct observations in a classroom to understand how students interact with each other and with their teacher.
• Marketing : In marketing, qualitative data is used to understand consumer behavior and preferences. For example, a researcher might conduct in-depth interviews with customers to understand their purchasing decisions.
• Anthropology : In anthropology, qualitative data is used to study human cultures and societies. For example, a researcher might conduct participant observation in a remote community to understand their customs and traditions.
• Health Sciences: In health sciences, qualitative data is used to study patient experiences, beliefs, and preferences. For example, a researcher might conduct interviews with cancer patients to understand how they cope with their illness.

Application of Qualitative Data

Qualitative data is used in a variety of fields and has numerous applications. Here are some common applications of qualitative data:

• Exploratory research: Qualitative data is often used in exploratory research to understand a new or unfamiliar topic. Researchers use qualitative data to generate hypotheses and develop a deeper understanding of the research question.
• Evaluation: Qualitative data is often used to evaluate programs or interventions. Researchers use qualitative data to understand the impact of a program or intervention on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population. Researchers use qualitative data to identify the most pressing needs of the population and develop strategies to address those needs.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail. Researchers use qualitative data to understand the context, experiences, and perspectives of the people involved in the case.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences. Researchers use qualitative data to gain insights into consumer attitudes, opinions, and motivations.
• Social and cultural research : Qualitative data is often used in social and cultural research to understand social phenomena such as culture, norms, and social relationships. Researchers use qualitative data to understand the experiences, beliefs, and practices of individuals and communities.

Purpose of Qualitative Data

The purpose of qualitative data is to gain a deeper understanding of social phenomena that cannot be captured by numerical or quantitative data. Qualitative data is collected through methods such as observation, interviews, and focus groups, and it provides descriptive information that can shed light on people’s experiences, beliefs, attitudes, and behaviors.

Qualitative data serves several purposes, including:

• Generating hypotheses: Qualitative data can be used to generate hypotheses about social phenomena that can be further tested with quantitative data.
• Providing context : Qualitative data provides a rich and detailed context for understanding social phenomena that cannot be captured by numerical data alone.
• Exploring complex phenomena : Qualitative data can be used to explore complex phenomena such as culture, social relationships, and the experiences of marginalized groups.
• Evaluating programs and intervention s: Qualitative data can be used to evaluate the impact of programs and interventions on the people who participate in them.
• Enhancing understanding: Qualitative data can be used to enhance understanding of the experiences, beliefs, and attitudes of individuals and communities, which can inform policy and practice.

When to use Qualitative Data

Qualitative data is appropriate when the research question requires an in-depth understanding of complex social phenomena that cannot be captured by numerical or quantitative data.

Here are some situations when qualitative data is appropriate:

• Exploratory research : Qualitative data is often used in exploratory research to generate hypotheses and develop a deeper understanding of a research question.
• Understanding social phenomena : Qualitative data is appropriate when the research question requires an in-depth understanding of social phenomena such as culture, social relationships, and experiences of marginalized groups.
• Program evaluation: Qualitative data is often used in program evaluation to understand the impact of a program on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail.

Characteristics of Qualitative Data

Here are some characteristics of qualitative data:

• Descriptive : Qualitative data provides a rich and detailed description of the social phenomena under investigation.
• Contextual : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena.
• Subjective : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation.
• Flexible : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question.
• Emergent : Qualitative data analysis is often an iterative process, where new themes and patterns emerge as the data is analyzed.
• Interpretive : Qualitative data analysis involves interpretation of the data, which requires the researcher to be reflexive and aware of their own biases and assumptions.
• Non-standardized: Qualitative data collection methods are often non-standardized, which means that the data is not collected in a standardized or uniform way.

Advantages of Qualitative Data

Some advantages of qualitative data are as follows:

• Richness : Qualitative data provides a rich and detailed description of the social phenomena under investigation, allowing for a deeper understanding of the phenomena.
• Flexibility : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question, allowing for a more nuanced exploration of social phenomena.
• Contextualization : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena and their cultural and social context.
• Subjectivity : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation, allowing for a more holistic understanding of the phenomena.
• New insights : Qualitative data can generate new insights and hypotheses that can be further tested with quantitative data.
• Participant voice : Qualitative data collection methods often involve direct participation by the individuals and communities under investigation, allowing for their voices to be heard.
• Ethical considerations: Qualitative data collection methods often prioritize ethical considerations such as informed consent, confidentiality, and respect for the autonomy of the participants.

Limitations of Qualitative Data

Here are some limitations of qualitative data:

• Subjectivity : Qualitative data is subjective, and the interpretation of the data depends on the researcher’s own biases, assumptions, and perspectives.
• Small sample size: Qualitative data collection methods often involve a small sample size, which limits the generalizability of the findings.
• Time-consuming: Qualitative data collection and analysis can be time-consuming, as it requires in-depth engagement with the data and often involves iterative processes.
• Limited statistical analysis: Qualitative data is often not suitable for statistical analysis, which limits the ability to draw quantitative conclusions from the data.
• Limited comparability: Qualitative data collection methods are often non-standardized, which makes it difficult to compare findings across different studies or contexts.
• Social desirability bias : Qualitative data collection methods often rely on self-reporting by the participants, which can be influenced by social desirability bias.
• Researcher bias: The researcher’s own biases, assumptions, and perspectives can influence the data collection and analysis, which can limit the objectivity of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Short on time? Get an AI generated summary of this article instead

In the late 16th century, Francis Bacon coined the phrase "knowledge is power," which implies that knowledge is a powerful force, like physical strength. In the 21st century, knowledge in the form of data is unquestionably powerful.

But data isn't something you just have - you need to collect it. This means utilizing a data collection process and turning the collected data into knowledge that you can leverage into a successful strategy for your business or organization.

Believe it or not, there's more to data collection than just conducting a Google search. In this complete guide, we shine a spotlight on data collection, outlining what it is, types of data collection methods, common challenges in data collection, data collection techniques, and the steps involved in data collection.

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Uncover hidden nuggets in all types of qualitative data when you analyze it in Dovetail

• What is data collection?

There are two specific data collection techniques: primary and secondary data collection. Primary data collection is the process of gathering data directly from sources. It's often considered the most reliable data collection method, as researchers can collect information directly from respondents.

Secondary data collection is data that has already been collected by someone else and is readily available. This data is usually less expensive and quicker to obtain than primary data.

• What are the different methods of data collection?

There are several data collection methods, which can be either manual or automated. Manual data collection involves collecting data manually, typically with pen and paper, while computerized data collection involves using software to collect data from online sources, such as social media, website data, transaction data, etc. 

Here are the five most popular methods of data collection:

Surveys are a very popular method of data collection that organizations can use to gather information from many people. Researchers can conduct multi-mode surveys that reach respondents in different ways, including in person, by mail, over the phone, or online.

As a method of data collection, surveys have several advantages. For instance, they are relatively quick and easy to administer, you can be flexible in what you ask, and they can be tailored to collect data on various topics or from certain demographics.

However, surveys also have several disadvantages. For instance, they can be expensive to administer, and the results may not represent the population as a whole. Additionally, survey data can be challenging to interpret. It may also be subject to bias if the questions are not well-designed or if the sample of people surveyed is not representative of the population of interest.

Interviews are a common method of collecting data in social science research. You can conduct interviews in person, over the phone, or even via email or online chat.

Interviews are a great way to collect qualitative and quantitative data . Qualitative interviews are likely your best option if you need to collect detailed information about your subjects' experiences or opinions. If you need to collect more generalized data about your subjects' demographics or attitudes, then quantitative interviews may be a better option.

Interviews are relatively quick and very flexible, allowing you to ask follow-up questions and explore topics in more depth. The downside is that interviews can be time-consuming and expensive due to the amount of information to be analyzed. They are also prone to bias, as both the interviewer and the respondent may have certain expectations or preconceptions that may influence the data.

Direct observation

Observation is a direct way of collecting data. It can be structured (with a specific protocol to follow) or unstructured (simply observing without a particular plan).

Organizations and businesses use observation as a data collection method to gather information about their target market, customers, or competition. Businesses can learn about consumer behavior, preferences, and trends by observing people using their products or service.

There are two types of observation: participatory and non-participatory. In participatory observation, the researcher is actively involved in the observed activities. This type of observation is used in ethnographic research , where the researcher wants to understand a group's culture and social norms. Non-participatory observation is when researchers observe from a distance and do not interact with the people or environment they are studying.

There are several advantages to using observation as a data collection method. It can provide insights that may not be apparent through other methods, such as surveys or interviews. Researchers can also observe behavior in a natural setting, which can provide a more accurate picture of what people do and how and why they behave in a certain context.

There are some disadvantages to using observation as a method of data collection. It can be time-consuming, intrusive, and expensive to observe people for extended periods. Observations can also be tainted if the researcher is not careful to avoid personal biases or preconceptions.

Automated data collection

Business applications and websites are increasingly collecting data electronically to improve the user experience or for marketing purposes.

There are a few different ways that organizations can collect data automatically. One way is through cookies, which are small pieces of data stored on a user's computer. They track a user's browsing history and activity on a site, measuring levels of engagement with a business’s products or services, for example.

Another way organizations can collect data automatically is through web beacons. Web beacons are small images embedded on a web page to track a user's activity.

Finally, organizations can also collect data through mobile apps, which can track user location, device information, and app usage. This data can be used to improve the user experience and for marketing purposes.

Automated data collection is a valuable tool for businesses, helping improve the user experience or target marketing efforts. Businesses should aim to be transparent about how they collect and use this data.

Sourcing data through information service providers

Organizations need to be able to collect data from a variety of sources, including social media, weblogs, and sensors. The process to do this and then use the data for action needs to be efficient, targeted, and meaningful.

In the era of big data, organizations are increasingly turning to information service providers (ISPs) and other external data sources to help them collect data to make crucial decisions. 

Information service providers help organizations collect data by offering personalized services that suit the specific needs of the organizations. These services can include data collection, analysis, management, and reporting. By partnering with an ISP, organizations can gain access to the newest technology and tools to help them to gather and manage data more effectively.

There are also several tools and techniques that organizations can use to collect data from external sources, such as web scraping, which collects data from websites, and data mining, which involves using algorithms to extract data from large data sets. 

Organizations can also use APIs (application programming interface) to collect data from external sources. APIs allow organizations to access data stored in another system and share and integrate it into their own systems.

Finally, organizations can also use manual methods to collect data from external sources. This can involve contacting companies or individuals directly to request data, by using the right tools and methods to get the insights they need.

• What are common challenges in data collection?

There are many challenges that researchers face when collecting data. Here are five common examples:

Big data environments

Data collection can be a challenge in big data environments for several reasons. It can be located in different places, such as archives, libraries, or online. The sheer volume of data can also make it difficult to identify the most relevant data sets.

Second, the complexity of data sets can make it challenging to extract the desired information. Third, the distributed nature of big data environments can make it difficult to collect data promptly and efficiently.

Therefore it is important to have a well-designed data collection strategy to consider the specific needs of the organization and what data sets are the most relevant. Alongside this, consideration should be made regarding the tools and resources available to support data collection and protect it from unintended use.

Data bias is a common challenge in data collection. It occurs when data is collected from a sample that is not representative of the population of interest. 

There are different types of data bias, but some common ones include selection bias, self-selection bias, and response bias. Selection bias can occur when the collected data does not represent the population being studied. For example, if a study only includes data from people who volunteer to participate, that data may not represent the general population.

Self-selection bias can also occur when people self-select into a study, such as by taking part only if they think they will benefit from it. Response bias happens when people respond in a way that is not honest or accurate, such as by only answering questions that make them look good. 

These types of data bias present a challenge because they can lead to inaccurate results and conclusions about behaviors, perceptions, and trends. Data bias can be avoided by identifying potential sources or themes of bias and setting guidelines for eliminating them.

Lack of quality assurance processes

One of the biggest challenges in data collection is the lack of quality assurance processes. This can lead to several problems, including incorrect data, missing data, and inconsistencies between data sets.

Quality assurance is important because there are many data sources, and each source may have different levels of quality or corruption. There are also different ways of collecting data, and data quality may vary depending on the method used. 

There are several ways to improve quality assurance in data collection. These include developing clear and consistent goals and guidelines for data collection, implementing quality control measures, using standardized procedures, and employing data validation techniques. By taking these steps, you can ensure that your data is of adequate quality to inform decision-making.

Limited access to data

Another challenge in data collection is limited access to data. This can be due to several reasons, including privacy concerns, the sensitive nature of the data, security concerns, or simply the fact that data is not readily available.

Legal and compliance regulations

Most countries have regulations governing how data can be collected, used, and stored. In some cases, data collected in one country may not be used in another. This means gaining a global perspective can be a challenge. 

For example, if a company is required to comply with the EU General Data Protection Regulation (GDPR), it may not be able to collect data from individuals in the EU without their explicit consent. This can make it difficult to collect data from a target audience.

Legal and compliance regulations can be complex, and it's important to ensure that all data collected is done so in a way that complies with the relevant regulations.

• What are the key steps in the data collection process?

There are five steps involved in the data collection process. They are:

1. Decide what data you want to gather

Have a clear understanding of the questions you are asking, and then consider where the answers might lie and how you might obtain them. This saves time and resources by avoiding the collection of irrelevant data, and helps maintain the quality of your datasets. 

2. Establish a deadline for data collection

Establishing a deadline for data collection helps you avoid collecting too much data, which can be costly and time-consuming to analyze. It also allows you to plan for data analysis and prompt interpretation. Finally, it helps you meet your research goals and objectives and allows you to move forward.

3. Select a data collection approach

The data collection approach you choose will depend on different factors, including the type of data you need, available resources, and the project timeline. For instance, if you need qualitative data, you might choose a focus group or interview methodology. If you need quantitative data , then a survey or observational study may be the most appropriate form of collection.

4. Gather information

When collecting data for your business, identify your business goals first. Once you know what you want to achieve, you can start collecting data to reach those goals. The most important thing is to ensure that the data you collect is reliable and valid. Otherwise, any decisions you make using the data could result in a negative outcome for your business.

5. Examine the information and apply your findings

As a researcher, it's important to examine the data you're collecting and analyzing before you apply your findings. This is because data can be misleading, leading to inaccurate conclusions. Ask yourself whether it is what you are expecting? Is it similar to other datasets you have looked at? 

There are many scientific ways to examine data, but some common methods include:

looking at the distribution of data points

examining the relationships between variables

looking for outliers

By taking the time to examine your data and noticing any patterns, strange or otherwise, you can avoid making mistakes that could invalidate your research.

• How qualitative analysis software streamlines the data collection process

Knowledge derived from data does indeed carry power. However, if you don't convert the knowledge into action, it will remain a resource of unexploited energy and wasted potential.

Luckily, data collection tools enable organizations to streamline their data collection and analysis processes and leverage the derived knowledge to grow their businesses. For instance, qualitative analysis software can be highly advantageous in data collection by streamlining the process, making it more efficient and less time-consuming.

Secondly, qualitative analysis software provides a structure for data collection and analysis, ensuring that data is of high quality. It can also help to uncover patterns and relationships that would otherwise be difficult to discern. Moreover, you can use it to replace more expensive data collection methods, such as focus groups or surveys.

Overall, qualitative analysis software can be valuable for any researcher looking to collect and analyze data. By increasing efficiency, improving data quality, and providing greater insights, qualitative software can help to make the research process much more efficient and effective.

Learn more about qualitative research data analysis software

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• Chapter Five: Qualitative Data (Part 2)

Qualitative Data Gathering Research Designs

In their search for understanding communication phenomena, researchers have multiple qualitative methods from which to choose. Depending on a variety of factors (such as the nature of the research question, access to participants, time and resource commitments, etc.), researchers may select one or more of the following methods:

• ethnography
• in-depth field interviews
• focus group interviews
• the collection of narratives. 

Selecting the appropriate method for data collection is a vital component of the research process. Regardless of the method selected, researchers must reconcile the established traditions of the methodology with the specific requirements of the group or individuals participating in the research. We now discuss how to plan and implement your qualitative study. This section begins with topic selection and research focus and then proceeds to a discussion of each of the different qualitative methods.

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 1)
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)
• Chapter Seven: Presenting Your Results

Identifying the Research Setting, Research Group, and Research Focus

Researchers have several choices when deciding how to proceed with a qualitative study. Some studies may begin with a specific communication concept, such as family communication. Researchers then begin to identify potential study participants. On other occasions, a researcher might be interested in a specific setting, such as a tattoo parlor. Gaining access to that setting to see what interesting communication concepts emerge would be very helpful. In both cases, research proceeds inductively, and conclusions emerge from the carefully gathered data. Regardless of how the initial inspiration strikes, the subsequent steps in the procedure follow a similar pattern. The following chart demonstrates the typical qualitative data gathering process:

Selecting a topic and narrowing the research focus.  During the earliest phases of the qualitative research process, researchers are tasked with identifying a focus for their study. Like all research, the individuals conducting the study are often drawn to those communication phenomena that are of the most interest to them. Perhaps a researcher has a friend or a family member who recently met his or her spouse through an on-line dating service and the researcher becomes interested in understanding how on-line dating develops. An initial research question might be, “What are the normative behaviors regarding on-line courtship?” From this point, it is important for the researcher to develop a rationale for the research. Sometimes, as in the case of on-line dating, the rationale is self-evident. As the number of people who participate in on-line dating continues to grow, it becomes an important and useful social activity to investigate. Regardless of whether or not the utility of the study seems self-evident, the researcher has an obligation to demonstrate the relevance of his or her study rationale through a review of the existing literature.

The literature review is an important component of any carefully designed research study. Although existing theory typically guides the more deductive approach of quantitative research, there are several differences regarding the more inductive, qualitative literature review. In qualitative research, the literature review is not completely finalized before data collection begins. In fact, in many cases, the literature review proceeds alongside the interviews or observations in which the researcher may be engaged. In the previous example regarding on-line courtship, the researcher would likely construct a literature review based on articles that examine on-line dating behaviors, as well as articles that examine off-line dating behaviors. However, if during the process of interviewing participants, several interviewees discuss the importance of having friends who accepted and encouraged their on-line dating attempts, the importance of a concept like  social support  might emerge. The qualitative researcher, well into the process of interviewing, might gather existing literature on social support and then ask questions regarding social support in future interviews. In some cases, the literature review continues to grow and develop as the data is being collected.

Another difference, though to a lesser degree, is that qualitative researchers often conduct a broad, rather than a deep, literature review—at least initially. The broad approach familiarizes the researcher with multiple topics that seem related to his or her research purpose. However, given that the specific data, rather than general theory, guides the entire qualitative process, the researcher can choose to analyze more deeply those topics that begin to emerge from the interaction with, or observation of, the participants. It is imperative that researchers are responsive to data collected over the course of the qualitative project so that they can augment or de-emphasize segments of the literature review as needed.

Choosing the appropriate methodology and accessing the setting or participants.  Although you can choose from many acceptable qualitative methods, including case study analysis, autoethnography, or qualitative content analysis, this chapter will focus on four common methods of collecting qualitative data: ethnography, interviewing, focus group interviewing, and narrative inquiry. Each of these methods, including their strengths and limitations, will be discussed in more detail later in this section. Choosing the appropriate method is based on a variety of factors including the nature and scope of your research question or questions, access to study participants, and researcher training and familiarity with potential methods, to name a few. As with any type of scholarly research, the law of the hammer need not apply. The law of the hammer states that if the only tool available is a hammer, then every problem will resemble a nail. If a researcher is trained and comfortable with conducting focus groups, but the best method of data collection for answering a specific research question is ethnographic research, then the researcher needs to take the necessary amount of time familiarizing himself or herself with the steps of an ethnography rather than forcing the question to conform to a focus group format. As always, the research purpose should guide the methodology, rather than the methodology guiding the purpose of the research.  

Access and trust are fundamental elements of successful data collection in qualitative research. A researcher must be able to access the research setting or interview participants in order to gather data. If the researcher is unable to gain access, it is possible that the study will have to be abandoned or significantly altered. In the case of Tom Hall’s research into government secrecy and its effects on democracy several years ago, he attempted to interview the members of the U.S. Senate and the House of Representatives who served on the Senate and House intelligence committees, respectively. Of the nearly thirty members serving at the time (2003-2004), he was not able to gain access to a single congressperson. In fact, only a handful responded to his numerous attempts at contact. Due to this lack of access, he had to shift his project entirely to a textual analysis of public documents, such as Executive Orders, Congressional Research Reports, and Department of Energy documents, in order to proceed with the research. This lack of interview access changed the general purpose of the research project and significantly altered the research questions he sought to answer. Needless to say, flexibility is an important attribute for a qualitative researcher.

In many cases, whether or not a group or organization provides access stems from whether or not the members of that organization trust the researcher. Imagine your suspicion if an individual appeared at your place of employment one day asking questions and writing information in a notebook. Just because a supervisor or gatekeeper has granted access to a researcher does not mean that all of the members of the organization or group are ready to trust the motivations of the researcher. Trust is person specific. The key to collecting solid data is for those individuals who are being observed or interviewed to understand why you are there and how you plan to use the data. If an interviewee does not trust your motivations, it is highly unlikely that s/he will be forthcoming in her/his responses—if s/he chooses to participate at all. While access to a group often relies on gaining the permission of a gatekeeper or other organizational leader, trust develops over time.

Examples of the early stages of qualitative research.  Sally is interested in studying a successful student organization on her campus. She realizes that PRSSA is an award-winning public relations organization on campus, so she contacts the faculty advisor and the current student leaders in the organization. Sally must gain the consent of the faculty advisor and other student leaders in order to begin her research on PRSSA. Sally decides to immerse herself as fully as possible with PRSSA—attending meetings, interviewing members, and observing committees, among other activities. Over the course of her research, all of Sally’s data collection efforts will be focused on this one organization. Although the initial approval of the faculty advisor and the student leaders of the organization are vital to gain access, it is important to remember that Sally will still need to gain consent from any other organization members who participate in her study.

Another example involves Bob who is also interested in studying reasons for participation in student organizations on his campus. Rather than focusing on a single organization, he decides to interview multiple participants in multiple organizations. Over the course of several weeks or months, Bob interviews numerous individuals and learns why they chose to involve themselves in student organizations. Bob uses a snowball or volunteer sample in order to recruit participants for his study. While there is no single faculty advisor or organization leader needed to gain access to these groups, Bob will still have to acquire individual consent from all of the participants in his study.

The early stages of a qualitative project are crucial for providing the foundations for a credible study. Developing the research purpose, examining the existing literature, selecting an appropriate method, and gaining access to and the trust of participants are all necessary steps.  

Qualitative Data Collection

In this section the authors discuss each of the more common qualitative methods, including the purpose, the steps involved in the particular method, and the strengths and limitations of the method. An extended example of each method is provided before moving on to the next method. The methods discussed include: ethnography, interviewing, focus group interviewing, and narrative interviewing.

Ethnography . This method is the total immersion of the researcher into the research setting. The roots of ethnography lie in cultural anthropology, which is when researchers attempt to fully understand the culture of a group by integrating themselves into the culture under investigation. Some communication scholars, such as Lawrence Wieder (1999), consider ethnography to be the main qualitative method. Although observation is the central practice of ethnographic research, a researcher may employ multiple other qualitative methodologies during the course of the project. Ethnography is the method of choice when a researcher decides to study the participants within their natural environment. Therefore, a study examining the communication patterns of college wrestlers would involve extensive interaction with a college wrestling team.

Traditional ethnography concerns a researcher or group of researchers studying the activities of a specific group or culture Over the last twenty years, an additional type of ethnographic research has emerged, where a person tells the story of some experience within their own life, with a scholarly purpose. This practice of autoethnography has become more popular in the past few years, and its validation as a legitimate form of knowledge development has also increased. Autoethnography places the researcher at the center of the investigation by directing the analysis towards the researcher’s role in the natural setting. By definition, an autoethnographer is a complete participant in his or her research, and is engaged in a full scale, in-depth, critical analysis on his or her life as it is being lived. A college athlete might detail his or her experiences as a member of a team—critically exploring his or her assimilation and identification with the group or chronicling the personal difficulties of competing at the highest level athletically. Whatever the specific focus of research, the data is drawn from detailed accounts of a person’s own experiences.  

The purpose of ethnographic research . Given though the individual goals vary from one ethnographic project to another, the overall purpose of the research remains the same: to accurately capture the social activities of the group or organization being studied. Frequently, the researcher does not have clearly defined research questions, preferring instead to capture, through observation and interviews, the social practices of the organization members. The purpose is to experience the participants in their natural setting and interpret those experiences accurately, in order to develop a better understanding of the interaction processes of the social group. Remember that one of the overarching goals of qualitative research is developing an understanding of an event, phenomenon, communicative practice, or cultural group. Ethnography is one of the ways that a researcher might accomplish this goal.

The steps in the ethnographic process . You should follow four distinct steps when conducting ethnographic research.

• Identify the research site. In ethnographic research, the principle investigator is often attracted to a particular group or organization. The person conducting the study may already be a member of the group (emic) he or she desires to research. An example of this would be when an individual who is a member of a book club decides to research the group in order to understand the group better. An etic approach might involve a graduate student who is interested in high school coaching selecting an athletic team to observe. Observation is a necessary component of ethnographic research. The full range of observation techniques are available to the researcher—from complete participant to complete observer.
• Gain access to the organization. Gaining access to an organization is vital to ethnography. Simply put, without access, ethnography is impossible. In order for the research to proceed, investigators must gain access to the organization or group. Gaining access relies on building trust with gatekeepers, informants, and other key organizational personnel. For example, if a researcher wanted to study the cultural climate of a Communication Studies Department, he or she might first begin by approaching the head of the department for approval. In this example, the department head might act as a gatekeeper—permitting or denying the research. However, even with the consent and trust of the department head, successful data collection and a successful research project are not assured. The researcher would likely need to approach each of the professors and staff in order to gain their consent to be observed during meetings and classes and office activities. While the consent of the department head may be enough to gain access to the data site, the success of the data collection portion of the project relies on earning the trust of as many of the organizational members as possible. 
• Collect and analyze data. During this stage, the real work begins. Researchers immerse themselves in the natural environment of the participants, collecting copious field notes and analyzing the data frequently. This is by far the most time consuming portion of the research process. At a minimum, most ethnographers spend six months engaged in this portion of the research and spending one or two years immersed in a group is not uncommon.
• Leave the field of investigation. When subsequent observations are no longer producing new data, the researcher is ready to wrap up the project and begin writing the final report. There are considerations when leaving the field. For one, the researcher may want to schedule additional meetings—focus groups or interviews—after the conclusion of the observation-based data gathering, in order to have the participants validate the findings or in order to follow up on interest areas not revealed through observation. The researcher should maintain the same high levels of trust that were so important during the initiation of the research project.

Recording observations using field notes . Field notes are a vital tool of the ethnographer. They are a written record of the researcher’s observations during his or her time in the field. Researchers would not be capable of remembering everything that they see during their observations, therefore keeping a notebook detailing the various activities of the community in which they are immersed is critical. Field notes are comprised of detailed records of observations as they occur. Over the course of compiling field notes, probative analysis also develops. Observers write down tentative and initial questions or conceptual possibilities alongside the field notes as they are collected. These are often referred to as “theoretical asides.” As observations and field notes grow in number, conceptual and theoretical elements may be combined and reflected upon in greater detail. More in-depth writing, focusing on explicit connections among and between the asides, are referred to as “observer commentaries” and represent a more sustained analytic treatment of the evidence collected. When the writer develops, in paragraph form, a rough draft explicating tentative themes, these writings are known as “in-process memos.”   

Strengths and limitations of ethnographic research . Ethnographic research has several strengths. First, ethnographic research observes the participants in their natural setting. In fact, to borrow terminology popular in quantitative social science, one would say that ethnographic research is ecologically valid. Rather than relying on a self-reported survey where someone reveals how they would act in a conflict situation in the workplace or relying on an experimental design where participants might role-play a workplace conflict in an artificial setting, ethnographers observe the participants in their natural environment. Second, ethnographic researcher stems from the thick, rich descriptions of the social actions of the group under investigation. Imagine the details collected by a researcher who is able to capture and describe group behavior as it unfolds and then follows up with informal interviews in order to gain the perspectives of the participants. This high level of detail is not something that survey data can replicate. Third, due to the extended observation periods in the natural setting and the cultural immersion that occurs with this method, ethnographers are able to gain deep understanding of the social activities of the group or cultural being observed.

Ethnographic research is not without its limitations. First, perhaps its most significant limitation stems from the fact that although one is able to develop an in-depth understanding of a group or culture, that understanding comes at the cost of generalizability. Just because a researcher can understand the social actions of one group or culture at a particular point in time does not mean that the knowledge derived from this understanding is relatable to other groups or cultures, which could then limit the applicability of the research. Second, sheer time and resource commitment required of a researcher to immerse herself and fully understand a culture is a limit. As noted previously, two years is not an exception but is, in many cases, normative. Finally, there exists the possibility that the researcher begins to over-identify with his or her research subjects. One example of this might involve a researcher deliberately leaving out accurate, but negative information about research subjects because the researcher does not want any aspect of the group to be seen in a negative light. Conversely, a researcher may deliberately exaggerate some characteristics to paint the group in a more desirable light. This over-identification is often referred to as  going native , and it seriously jeopardizes the credibility of a study.  

Sample ethnography . If one wants to fully understand the process of ethnographic research, one should identify a research environment, initiate an ethnographic study, and begin data collection and analysis. To facilitate understanding of the ethnographic process, we describe Susan Weinstein’s (2007) study to clarify the process of ethnographic research. Weinstein (2007) spent three years gathering field observations, collecting written artifacts, and conducting informal interviews while studying how “nine low-income, African-American and Latino urban youths” wrote about gender and sexuality through “poetry, prose, and rap lyrics” (p. 28). In her own words, Weinstein states,

In this single paragraph, Weinstein details many of the aspects vital to her study—the locations and settings where data was collected, the length of time spent conducting the research, information about the participants, consent gained, and efforts towards respondent validation. Respondent validation involves the researcher reviewing her tentative conclusions with the study participants in order to elicit their feedback regarding her interpretation of events. Weinstein’s research was guided by her belief that imaginative writing served as an outlet for the identity construction of urban youths. She found that gender and sexuality materialized in often contradictory ways in the writings of the youths and concluded those contradictory writings to be indicative of the complexity of the methods regarding these topics that youths receive from their social environment, such as family and friends and popular culture.

Ethnographic research is a challenging, yet rewarding, scholarly endeavor and a method to be used when one wants to develop as comprehensive and in-depth an understanding of a social environment as possible.

In-depth interviewing . In-depth interviewing is a qualitative method that fully situates the interviewee in the role of providing information to the interviewer. According to Lindlof and Taylor (2002), an interview is “an event in which one person (the interviewer) encourages others to freely articulate their interests and experiences” (p. 170). Ethnography may be a technique that is unfamiliar to many people, but interviewing is a process that most people have encountered at one time or another. A high degree of flexibility and variation characterizes the interview process. Qualitative interviewing can range from very structured formal interviewing to the loosely structured field interviewing that accompanies ethnographic research. This section begins with a discussion of the basic characteristics and goals of interviewing, followed by the steps for conducting an interview and the strengths and limitations of this method. We conclude with an exploration of a study relying on interviewing as the primary method.

Interview goals and characteristics . Qualitative research is inherently subjective. Qualitative research relies on its participants sharing their subjective understanding of certain experiences. Interviewing allows research participants to share their unique perspectives. Therefore, a primary goal of interviewing is for the research participant to answer the questions of the researcher. In essence, interviewing is the process of asking questions and receiving answers. Researchers want their interviewees to provide information about events or experiences that occur separate from the interview setting. Answers may take the form of stories or explanations. Interviews enable the researcher to understand the context and language forms of the social actors. Additionally, interviews allow the researcher to investigate events that he or she would otherwise not be able to access, such as closed meetings, past events, etc. The goal is to get the best possible data that will enable the researcher to successfully answer his or her research question or to provide an in-depth understanding of the social processes under investigation.

Regardless of the formality of the interview structure, an important characteristic of a sound interview is establishing a conversational tone during the interview process. Because trust is such a vital component of an interview, the interviewer should adopt a style that puts the interviewee at ease, and a conversational interview tone often goes a long way towards making the interviewee relaxed. According to Denscombe (2010), other important characteristics of the interview process include being cognizant of the feelings of the interviewee and the ability of the interviewer to tolerate silences. In day-to-day conversations silence is usually not tolerated very well, but when one is asking another to reflect on an issue, time is sometimes essential to allow the interviewee to think through an issue and to feel compelled to dig deeper in this analysis.

Time is another important element of the interview process. Interviews should last a reasonable amount of time. A researcher should not expect a participant to be willing to devote more than an hour of time for an interview, except in the most extreme of cases. Thirty minutes to one hour is considered a reasonable expectation for an interview. However, for some research questions, an even shorter interview may be enough.

Several ethical considerations confront a researcher during the interview process. As with all research involving human participants, gaining the consent of the participants is an obligation of the researcher. The researcher will also want to make sure that the interviewee understands how the researcher plans to use the information and how confidentiality will be ensured. It is common for the researcher to use pseudonyms for the interview subjects in order to mask their identities. A final ethical consideration is for the researcher to be aware of the potential for certain questions to lead the interviewee in a specific direction—questions posed in such a way that they elicit a particular answer from the participant. It is advantageous for the interviewer if the interviewee consents to an audio or video recording of the interview. Recording the interview can ensure the accuracy of the participant’s statements. With or without a recording, it is suggested that interviews be conducted in pairs—this way, one person can conduct and moderate the interview, while the other takes detailed notes regarding the interaction. Of course, the decision to use a second interviewer depends on the comfort level and consent of the participant.

Appropriate types of interview questions . Close-ended, yes or no, type questions are necessary from time to time during a qualitative interview, such as when gathering demographic information about your participants to write your methods section, but open-ended questions are recommended because they provide the interviewee with greater freedom in responding and offer the interviewer more data for analysis. Open-ended questions follow two common formats—non-directive questions and directive questions.

Directive questions  are specific questions designed to discover specific responses. Examples of directive questions would be, “Tell me what kind of professor Tom Hall is” or “How does Tom Hall’s teaching style differ from April Chatham-Carpenter’s teaching style.” In both cases, the interviewer is asking the participant to address a specific point. Compare and contrast questions, as well as Devil’s Advocate questions, are examples of directive questions.

Consider the following  non-directive question  examples. “Tell me about a time when you experienced conflict in the workplace” or “Tell me a little about yourself.” In these examples, the interviewee is free to take the focus of the question in the direction of his or her choosing, and while that response will clearly contain specific elements, it begins with a more general approach than the directive questions.

Also, it is important to remember that not every question must specifically address the research purpose. For example, in order to increase comfort and gain the trust of a participant in the early stages of the interview, the researcher might consider asking a general question like, “Would you please tell me about yourself?” This allows the participant to grow comfortable sharing information with the researcher.

Finally, during the interview process, it is essential that the researcher is skilled at asking probing questions when more specific information is desired, asking clarifying questions when the information provided is unclear, or asking validating questions when the researcher wants to ensure that he or she interpreted the response of the participant correctly.

Steps of conducting an interview .   Five steps will help you through the process.

• Identify the purpose of your study and design the interview guide. The researcher needs to make sure that interviews are the appropriate methodology to employ in answering his or her research focus. If interviews are the appropriate data collection format, then the researcher needs to design a guide for how the interview will proceed. (The focus here is on research where interviews will serve as the primary data collection method and therefore are more formally structured than they might be during the impromptu interviewing that occurs during ethnographic research). A guide is just that, a guide to conducting the interview. It is a means for the researcher to organize his or her thoughts in order to ensure consistent approaches are taken across all interviews. However, it is just a guide and the researcher can add to it, take from it, or restructure questions as the need arises during an individual interview or over the course of multiple interviews.
• Identify the participants of the study and arrange times to conduct the interviews. If the researcher has selected a particular place or location to conduct his or her study, then the selection of the participants is likely a straightforward process. If, however, the researcher seeks to interview people who share common characteristics, rather than a physical location, selection of participants will likely rely on volunteer, snowball, or purposive sampling techniques. For example, suppose a researcher is interested in studying people who read comic books. In the process of identifying the participants, the researcher could gain access to a local comic book store and ask patrons if they are willing to participate in interviews, or the researcher could find one or two people who read comic books, interview them, and then ask them to help identify other potential participants (i.e., snowball sampling).
• Introductions . Ask broad questions to increase the comfort level of the participants. In some cases a broad, introductory question might take the form of “Tell me about some of your communication strengths.” Questions of this type are known as biography questions and are designed to establish a conversational and comfortable tone for the remainder of the interview.
• Research focus questions . As the interview is underway, the researcher turns to those questions to which he or she specifically seeks answers. This is the main portion of the interview, and is where the researcher’s skills at asking probing and follow-up questions help him or her collect the desired information. As the interview moves from one topic to the next, it is helpful for the researcher to summarize the previous topic and solicit feedback from the interviewee before shifting to the next topic.
• Concluding the interview . It is common during this portion of the interview for the researcher to summarize main points in order to seek validation from the interviewee regarding the researcher’s interpretation of the responses. It is also common for the interviewer to ask the participant if he or she has any questions for the researcher. Also, always thank the participant for his or her time.
• Transcribe the interview. In order to accurately collect the data from the interviews, it is often necessary to transcribe the interviews. The transcription process can take as long, if not longer, than the interview process itself. It is essential that the researcher accurately portray the statements of the participants. Transcriptions also convert the data into a format that is often easier to analyze and interpret than the rough notes taken during the interview process.
• Analyze and interpret the data. The final step of the interview process involves analyzing and interpreting the data. Once the interviews have been transcribed, the researcher sorts back through the data in order to identify themes and common elements among the responses. Obviously, the researcher needs to keep the original research purpose firmly in mind when interpreting the data. The practice of analysis and interpretation is similar across the various qualitative methods, so a more lengthy discussion of analysis and interpretation will be presented in the final segment of this chapter.

Strengths and limitations of interviewing . Like so many other qualitative methods, one of the strengths of interview data is, quite simply, the depth of the information gathered by the researcher. Over the course of the interview, the researcher is able to probe, refocus, and follow-up on the various responses from the participant. Because of these characteristics, rich, detailed information is the product of qualitative interviewing. Another advantage stems from the flexibility of the interview process, and this flexibility also contributes to the depth of the information. Although you will likely follow an interview guide, the process itself is not as concrete as survey research questions. The researcher can adjust to the responses during the interview and guide the interview in the direction necessary to speak to the overall research agenda. It is also possible to augment and excise questions following interviews. For example, if the first three interviewees all talk about a specific event, it alerts the researcher to the importance of this event. In preparation for the subsequent interviews, the researcher can include a question to make sure that the previously mentioned event is addressed in future interviews. Interviews also allow for the acquisition of the participants’ subjective interpretation of the events being discussed. This is data in the actual words of the participants. The researcher does not rely on previously established categories but rather on the words and experiences of the interviewees. Finally, in many cases, interviews are the only means of discovering information about events that have already taken place. Interviews allow the researcher to uncover information that otherwise would not be available to him or her.

As far as the limitations of qualitative interviewing go, there are several that are worth mentioning. The sheer amount of time involved in setting up, conducting, transcribing, and analyzing interviews is daunting for many researchers. A one-hour interview may take three times that long to transcribe, particularly without the aid of electronic transcribing devices. In many cases, the interviewee may wander off-course during the interview process. The researcher has to balance the comfort level of the participant with the researcher’s need to gather relevant data. In some cases, brief meandering may be necessary in order to maintain a comfortable conversational flow between the interviewer and interviewee. There are, of course, other concerns. It is one thing to focus the interviewee on answering a specific question, it is quite another to deliberately lead the interviewee to a specific response desired by the researcher. Researchers must be fully aware of the impact and influence that they have on the entire interview process. As is always the case with qualitative data, the data collection instrument is the researcher; therefore, the limits of the researcher will affect the entire process.

Interview study example.

Cohen, M., & Avanzino, S. (2010). We are people first: Framing organizational assimilation  experiences of the physically disabled using co-cultural theory.  Communication Studies ,  61 (3), 272-303.

Cohen and Avanzino examined “how organizational members with disabilities experience and manage organizational assimilation in the workplace” (p. 272). They conducted interviews with 24 individuals with physical disabilities. The researchers employed snowball and purposive sampling to identify participants and “sixteen interviews were conducted face-to-face and eight took place over the telephone due to distance and time constraints” (pp. 280-281). From the twenty-four interviews, 140 pages of transcriptions were produced. From the data, Cohen and Avanzino uncovered eight concepts and two themes, and identified aspects of the difficult process of workplace assimilation, as well as various techniques employed by the study participants to successfully negotiate workplace assimilation.

All of the elements of qualitative interviewing are present here: a general research question focused on understanding rather than prediction and control; a small, purposely selected group of participants; and an in-depth analysis of the participants’ responses to develop themes and facilitate understanding of the assimilation process.

Research methods are not mutually exclusive, and although this study primarily used interviewing, the authors note that they also engaged in observer-participant activities. In fact, researchers often triangulate their methods, combining the next two methods to be discussed—focus groups and narrative interviewing—in conjunction with ethnographic research or qualitative interviewing to strengthen the quality of the study.

Focus group interviewing . The fundamental difference between interviewing and focus group interviewing is that focus group interviewing is designed to allow multiple participants to interact with one another. Regular interviewing often occurs one-on-one; focus groups often bring together 6-12 participants in order to gather data as they interact with one another. Many people are familiar with marketing or political focus groups designed to uncover people’s attitudes towards a particular product, political figure, or idea, but fewer people are familiar with scholarly focus groups. Research focus groups may be similar in number of participants and duration of the interaction (90-120 minutes) to these others types of focus groups, but their purpose is not to gauge attitudes about a brand or political figure. Much like a regular interview, a focus group interview is designed to elicit information from the participants but is arranged in such a way that the participants are able to openly engage in discussion with the other participants. This experience, while often difficult to moderate, can provide a wealth of data. This section includes a discussion of focus group characteristics, moderator concerns, steps in focus group interviewing, and strengths and limitations. It concludes with a look at research employing focus group methodology.

Characteristics of focus groups . In addition to the number of participants and the length of time required to conduct a focus group, other important characteristics distinguish focus groups. Focus groups allow the researcher to interview several people at once in a format that resembles a purposeful discussion. Focus groups allow researchers to gather information from a group of people in a single setting. Some of the characteristics shared with regular interviews include the designing of an interview guide and involving two researchers for the process (one to moderate and another to take notes). In the case of the interview guide, it should be clearly developed but will likely not be as lengthy as the guide for a one-on-one interview. Because focus groups allow the participants to interact with one another, a few questions by the moderator may be all the prompting needed to elicit discussion. Due to the collaborative nature of focus groups, the moderator may only need to initiate the discussion and then can spend the majority of his or her time managing and focusing the ensuing discussion rather than constantly interjecting new questions. In some cases, the interactions among the group may emphasize points of agreement, as several of the participants add on to a topic, idea, or event that has been introduced. On other occasions, the group interaction may result in points of contention, enabling the researcher to see where participants have very differing perspectives on the research questions and topic.  

Moderator concerns . The moderator’s job is much more difficult in a focus group than it is in a one-on-one interview. A focus group moderator has to manage multiple personalities rather than a single personality. It is still important to establish a conversational tone among the participants, but it is also necessary to pay close attention to the various personalities of the group. Is one person dominating the discussion? Are two people ganging up on another member? Are tensions running high among the group? Is one person overly shy and unwilling to open up? These are just a few of the concerns, characteristics, and mannerisms that a focus group moderator may need to address over the course of the focus group interview. Practice is the best tool for learning when to allow disagreements and when to cool discussion down before arguments develop. Remember, the express disagreement that stems from constructive conflict is very different from the hostility associated with destructive group conflict. The focus group moderator must also insure that the group remains focused and does not wander too far from the original intent of the moderator’s topic or question. The moderator should also refrain from displaying any bias over the course of the focus group.

Steps of conducting a focus group . Some of the standard steps of designing and conducting a focus group are as follows:

• Identify the purpose of your study and confirm that focus groups will be the most useful method for your study. Researchers will want to consider their overall research focus and then construct a list of questions that will provide the best opportunity to elicit relevant responses from the participants. You should have a clear purpose to the focus group questions, but you also need to have the flexibility to adapt as the situation merits.
• Recruit participants. Once the researcher has decided which participant attributes are essential to the study, he or she needs to initiate the process of recruiting participants. Once again, snowball sampling and purposive sampling are often the most useful methods of recruitment. Even though a desired number for a focus group is between 6-12, the researcher should select a number that he or she feels capable of moderating. Also, just because people say that they will show up does not mean that they will. It is better to have too many people show up for your focus group session, and have to turn a few people away, than to have too few people show up. It is acceptable to over-recruit by a person or two.
• Introduce purpose of the group, participants, and explain the expectations and ground rules for the discussion . The first segment of the focus group should begin with the researcher/moderator explaining the goals and purpose of the focus group, followed by a presentation of the ground rules and discussion expectations. This is a good time to express the desire for respectful conversation and equal sharing of information. It is also a good time to allow the participants to introduce each other if they are not familiar with one another.
• Ask questions and moderate the ensuing discussion . Questions should be open-ended and may be either directive or non-directive depending on the needs of the researcher. It is during this segment that most of the moderator’s skills will be put to the test as he or she strives to keep the group on task, sharing equally, and clarifying points of contention or agreement.
• Conclude the focus group . Similar to one-on-one interviewing, the moderator should allow time for the participants to clarify or elaborate on any of their previous statements. Participants should also have the opportunity to ask questions of the moderator. Finally, make sure to thank the participants for taking the time to be a part of the focus group.
• Transcribe the focus group data. One of the challenges of focus group research is clearly differentiating the various participants, who in some cases will talk over or interrupt one another. This is one of the reasons that it is good to have a moderator, a note taker, and multiple recording devices (to catch all the voices) throughout the focus group. Transcribing data of this sort can be a time consuming process. When it comes time to analyze and interpret the data, detailed and accurate transcriptions are a necessity.
• Analyze and interpret the data. Once the notes have been transcribed, the researcher sorts through the data in order to identify themes and common elements among the responses. Obviously, the researcher needs to keep the original research purpose firmly in mind when interpreting the data. The practice of analysis and interpretation is similar across the various qualitative methods, so a lengthy discussion of analysis and interpretation will be presented in the final segment of this chapter.

Strengths and limitations of research focus groups . Obviously, the greatest strength of focus group methodology is the interplay among the various focus group participants. The healthy give and take among the participants serves as a fruitful generator of data. In fact, the primary reason for selecting focus groups over one-on-one interviews is so that the researcher can record several people interacting regarding the same topic. Provided that all of the subjects of the focus group participate, the researcher can gather a multitude of opinions and ideas on similar topics. Another advantage is that you can collect a relatively large amount of data in a brief period of time. In the time that it might take to conduct two individual interviews, the researcher can conduct a focus group with 10-12 participants. Although the depth of the data as compared to individual interviews may suffer, the breadth of the data and the discussion of common topics are extremely beneficial.

There are limitations to focus groups, several of which have already been highlighted. A skilled moderator is a necessity or the group can quickly get off task and produce information that does not relate or address the original purpose of the research. There also is the potential that overly dominant group members will lead the discussion in ways that might not represent the feelings of the remainder of the group. Finally, group members might either go along to get along or deliberately disagree (playing Devil’s Advocate)—neither of which will lead to the most accurate data. According to Morgan (1997), there is always the possibility that the moderator has overly influenced the groups. Focus groups do not take place in the natural environment; they are artificially constructed. This, in turn, impacts the ecological validity of the research.

Focus group example .

Hundley, H. L., & Shyles, L. (2010). US teenagers’ perceptions and awareness of digital  technology: A focus group approach.  New Media & Society ,  12 (3), 417-433.

Hundley and Shyles conducted focus groups with 80 middle and high school teenagers. “The chief objective of this research was to further our understanding of what young people think about digital devices and the functions they serve in their lives” (p. 417). A total of eleven focus groups were conducted with five to nine students in each group. Hundley and Shyles utilized both formally structured and semi-structured focus group interview protocols. The formally structured segment consisted of specifically prepared questions, while the semi-structured portions “allowed students to speak freely, elaborate, ask questions and join in group discussions” (p. 419). According to the authors:

From the focus groups, Hundley and Shyles were able to identify several common themes among the 80 participants. Themes included high level of awareness regarding the various types of technology, a lack of awareness regarding amount of time actually spent using the technology (nearly all underestimated time spent), awareness that digital devices help them socialize, and the risk of having personal information available online. Hundley and Shyles found that their research was consistent with the existing literature regarding teens and technology.

Autoethnography  is a relatively new qualitative research method that is generating a great deal of interest. It is based in ethnography, meaning that it, too, is an attempt to understand and describe the insiders’ cultural perspectives—i.e., how insiders construct their world view/culture. Like ethnography, it is also holistic and naturalistic, rather than trying to isolate what is studied and control it. Finally, like ethnography, it requires some degree of participant observation, but in this case, the observer may the reader, not just the researcher.

While there is no one definition of autoethnography, it is the study of some aspect of culture from the author’s personal experience and perspective. Examples of topics where authors have shared their personal experience through this method include surviving breast cancer, an eating disorder, depression, being of multi-ethnic identities,  the process of transitioning from woman to man as a transgender, and much more. The researcher is the subject of study, the key informant. The method is similar to narrative data collection. The researcher/author tells h/his story on a topic or issue the person feels warrants others to learn about from an insider view. The author role is reversed from being a researcher first and then an author to being a story teller first. If the story is not compelling, the research effort has failed.

Unlike other qualitative research that focuses on description, the goal in autoethnography is not just to describe but to evoke feeling and deeper understanding. This takes the view of knowledge as being an embodied experience, not just observation. To do so, the researcher must make h/himself vulnerable, sharing a great deal of self-disclosure and demonstrating a great deal of self-reflexivity.

Nevertheless, autoethnographers still value systematic methods used in other research methods:

If you imagine research methods as on a continuum, quantitative laboratory research would be on one end of the spectrum, and autoethnography would be at the other end of the spectrum, followed by performance studies and other more artistic ways of knowing. Because this method requires a unique set of writing skills, we do not cover it in full here, but offer websites for those who might like to learn more.

For an overview:  http://www.qualitative-research.net/index.php/fqs/article/view/1589/3095

For a focus on analytic, rather than evocative autoethnography:  http://web.media.mit.edu/~kbrennan/mas790/02/Anderson,%20Analytic%20autoethnography.pdf   For an example of autoethnographic research as researcher ethics:  http://jrp.icaap.org/index.php/jrp/article/view/213/183

To perform autoethnography:  http://eppl604.wmwikis.net/file/view/spry.pdf

Media portrayal of what autoethnography is:  http://www.youtube.com/watch?v=pb50nPHgI04

Data Analysis: Interpreting Results

Collecting information is only the first of two parts in the research process. In qualitative research  how  one interprets the results is particularly salient. Recall that the world-view or epistemological approach of qualitative research is the assumption that multiple meanings are always possible and present and that meaning is created, it is perceptual, and influenced by context. It is not just observed, nor is it considered universal or objective. Thus, in the second part of the qualitative research process the researcher purposefully and explicitly  makes meaning  of the study by applying methods to reveal patterns in the data. As discussed in chapter one, making meaning is what people do when they interact every day. They negotiate and construct perspectives through the exchange of verbal and nonverbal cues. In research, the author makes meaning through a negotiation with the verbal and nonverbal messages or data collected. The researcher’s charge is to make every effort to make fair and insightful sense of what might currently be a large pile of data.

As this description suggests, because there is room for multiple interpretations, the researcher has a responsibility to analyze the data or information gathered in a highly  systematic  fashion, drawing from previous methodologists’ recommendations and guidelines. To be systematic means the researcher cannot simply focus on data that is the most convenient, or anecdotal and examine it in a half-hazard fashion. Ideally, the researcher must make every attempt to consider all the information even though it would be impossible and not useful for the researcher to include all the information in the final report of the study.

Common Steps in Qualitative Data Analysis

Analysis methods for qualitative research have several commonalities. Recall that in all qualitative research, the data is words and behaviors, not numbers. It may be in the form of artifacts such as newspaper clippings or diaries from the field of study, field notes you have taken, transcriptions of one-to-one interviews, focus groups, and/or participant’s more naturally occurring conversations. In the analysis phase, the researcher’s job is to select analysis tools  that seem to be a good fit for the type of data being examined and the objectives of the study and then to review all the data in a consistent fashion. From this, the researcher seeks to synthesize the material by organizing it into categories and then identifying connections among the many categories that will make visible a more narrow, manageable focus to make sense of the information. 

There is a variety of methods to conduct qualitative data analysis. Some are more complex, others more accessible, but it is helpful to remember that at their essence, the meaning making process for all of them is to look for patterns -- themes and patterns among the themes by “comparing and contrasting parts of the data” in a series of steps (Keyton, 2011, p. 62). Thus, before reviewing specific types of qualitative methods, we are able to identify basic steps common to all of the methods.

According to qualitative communication scholars, Thomas Lindlof and Bryan Taylor (2002), the overall analysis process involves three basic tasks: “data management, data reduction and conceptual development” (p. 211). In data management, tools to code and categorize data are used to help the researcher gain some control or order over what could be an endless volume of information. From there it becomes easier for the researcher to see that some data may be more central to understanding the results than other data. Data reduction is then when the researcher begins to assign prioritized value to the data, reducing the size of the focus of analysis for a closer look. This does not mean any information is to be thrown away. Remember qualitative researchers believe meaning is created in context. Although it is necessary practically to try to distinguish what is most salient to focus on, the other information may in time provide a rich context for more complex, subtle interpretations. The researcher will want to later return to this secondary data for such considerations. Conceptual development should emerge from accomplishing the prior two tasks. This is where and how the concepts and themes -- or meanings in the study emerge. They are grounded in the data from the specific social context of study as well as influenced by the researcher’s review of previous research and qualitative theory. Together the tasks of data collection, data management, data reduction and conceptual development emulate the  inductive  nature of qualitative research. The researcher studies communication in a smaller, more specific context, gathers extensive data, organizes it and reduces it even further to smaller kernels of knowledge, and then returns the kernels to the larger social and theoretical contexts for further thought about the significance of these interactions in everyday life.    

In applying Lindlof & Taylor’s three steps for qualitative analysis, we break down the process further, adding two additional steps to make the process more explicit. We add an introductory step called data immersion and a concluding step called evaluating results. While we present them as if they are discrete steps, the reader should know researchers begin to think about these steps before all the data is collected and may return to the data collection phase after conducting preliminary analyses. The reader should also be forewarned if you read other descriptions of this process the steps may be divided a bit differently or labeled differently. We tried to do what was most streamlined. In the end, the various descriptions are really about the same basic process.

Member checks add to the credibility of a study. They better assure the participants’ perspectives are respected and that the interpretations are grounded in their perspectives (Lincoln & Guba, 1985). Useful results from member checks are not just results that say, “yes, this is good,” or “you made a mistake in the spelling of my name,” but rather feedback that helps the researcher see the findings through the participants’ eyes, thus extending the depth and breadth of interpretations and resulting theory proposed. Another value added from this method is that it gives some ownership of the results back to the participants. They are empowered to edit and add their input (Lincoln & Guba, 1985).

For example, in a study of African-American women’s self-esteem, DeFrancisco and Chatham-Carpenter (2000) conducted member checks in recognition of their position as White women interpreting the stories of women of color. The meetings caused the researchers to reframe one of the primary themes taken from the study to focusing on the effects of racism on the women’s self-esteem to focusing on the demand for respect. The shift in words from the researcher’s (racism) to the participants’ (respect) may have seemed small, but the former framed the participants as powerless victims of a racist society, the latter framed them as strong women demanding fair treatment.

• Data Immersion: Get to know your entire data set closely. This includes the participants’ contributions as well as your field notes during data collection. A  close-read , includes reading and rereading the data set multiple times, line-by-line, perhaps in different orders (Lindlof & Taylor, 2002). There is nothing that can be substituted for building a close awareness of what you have gathered. You will likely gain a new insight every time you review the material. You need a view of the whole before you can begin to sort the data. As you do so, look for holes – is there anything you need to go back to the field of study to gather? Use a concept we discuss in more depth later called  reflexivity  (Ellis, 2004). Critically monitor and write down your observations, reactions and feelings as you process the information collected, they may influence the directions you take next. The instructions for taking good field notes described under data collection above will be helpful here, as well. It is a good idea to make a complete copy of your data and archive it in case the copy you work from becomes damaged or lost.

The chunk is called a  unit of analysis or unit of data.  Identifying one’s unit of analysis is necessary in qualitative as well as quantitative data. It helps to define what level or size of data the researcher is focusing on – to identify the boundaries of individual units of data being considered. The unit of analysis is the smallest level of the data from which the researcher sorts the data and thus making meaning. The goal is to have the same or similar size for each chunk of data. The size depends on the goals of the study. For example, if a researcher is studying how people use conversation to create and maintain their identities, the researcher’s unit of analysis is typically a person’s turn taken in a conversation. But in most theme/categorization analysis of qualitative data, the unit of analysis can be more flexible. It can be a word, a phrase, a complete sentence, a conceptual theme, a nonverbal expression, a communication episode or event such as episodes from reality television, full texts, such as speeches, and more. Since the size may vary, you may find recommendations from researchers Yvonna Lincoln and Egon Guba (1985) helpful for identifying your unit of analysis. They suggest the unit have two criteria: the first is noted above – that it be the smallest piece of recognizable information, meaning that it does not require other contextual information to be understood. Second, the piece of information should be heuristic, meaning it should help with understanding. It should help address the research question or objective of study.

For example, imagine you are studying how young adults perform their identity by what they choose to post on their Facebook or another social media site. Your unit of analysis will likely be each verbal or nonverbal indicator about the participants’ identity. So, if a person posted, “I’m a 21 year old woman who loves to laugh, but I am dead serious about the type of man I want in my life.” The following is how the sentence might be broken into individual units, or pieces of information about her identity: “I’m a 21 year old/ woman/ who loves to laugh,/ but I am dead serious/ about the type of man I want in my life/.” Thus, from just one sentence, the research can glean 5 units of data representing 5 different categories or themes: age, gender, personality, goal (looking for a mate), and sexual orientation (heterosexual).  

Once the unit of analysis is determined, the researcher can chunk up all the data into units and begin  coding  the data – organizing it into groups under labels. A  coding scheme , is a sort of shorthand device to label and separate the data (Landlof & Taylor, 2002). When finished, the codes will help to reveal categories of concepts or themes and codes make it possible to retrieve the data more easily for further analysis.

So, for the example of social identity in Facebook above, as you review the data collected from other participants’ sites and your interviews with them, imagine you notice a tendency in the descriptors that seem to refer to the participant’s demographics: gender/sex, sexual orientation, age, etc. These demographics may each become a category where you will compile the individual instances that demonstrate that category. A post from a person who identifies as male describing how he lifts weights and strives to attain muscle mass might be categorized under gender, and specifically masculinity. A person who posts a message about being a women who competes in a race for wheel-chair users might be categorized under physical abilities, etc.

The code or coding scheme is to look for demographics, the resulting categories or themes are gender/sex, sexual orientation, age, etc. In the study of coming-out narratives, the code is degree of agency, the categories are the specific degrees noted in the data. In the study of relational conversation, questions are the code, the specific functions of questions are the categories. Coding helps to identify whole families of categories and sub-categories. They help the researcher not only sort, but display the data in visual ways to make the meaning more apparent.

We offer two cautions regarding oversimplifying this process. First, while you may see preliminary codes and categories emerge from the data, the final ones may not be the same. It is important to keep one’s mind open to considering alternative or extended coding systems otherwise one may only see what s/he wants to see. While there are multiple ways to organize data and endless sizes or levels of latter categorization possible, there will likely be a way that makes sense to you. There is also no set number of categories required. You should have as many categories as it takes to account for all the data possible. In the end, you should feel the categories do a good job of representing the data as a whole, both in terms of its similarities and in terms of its differences (Lincoln & Guba, 1985).

Second, a warning about labels. Qualitative researcher use the terms coding schemes and categorizing to refer to the process of organizing data in qualitative analysis, but they do not always use the two terms in the same way. Some researchers use the terms interchangeably. Others claim there are important distinctions between the two terms. We follow Lindlof and Taylor (2002) who argue researchers first  code  the data into groups and then look for categories (how the groups connect in terms of concepts, themes, constructs). Codes are descriptive of the data groupings, whereas categories and themes are more interpretive – they tell what overall meaning the researcher makes of the groupings. Themes and category systems are the result of coding (Saldana, 2009). For example, a code may be chunking the data by participant demographics, which results in a categorization system of data that emerged according to race/ethnicity, social class, age, and gender/sex. A theme for the category of race/ethnicity might be “participants identify with homogeneous others” or “participants celebrate multicultural identities.” Each are conclusions or themes drawn from data within the category of race/ethnicity.

To further assist you in identifying codes and categories, two other researchers proposed a list of questions you might find useful (Lofland & Lofland, 1995).

Tips for coding and categorizing data:

• What is this? What does it represent?
• What is this an example of?
• What do I see going on here? What are people doing? What is happening? What kind of events are at issue here?
• If something exists, what are its types?
• How often does something occur?
• How big, strong, or intense is something?
• Is there a process, a cycle, or phases to the topic of study?
• Does one thing influence another?
• Do people use the interaction in specific ways?

(See also Sociologist Graham Gibbs’ lecture, “What is coding for?”  http://www.youtube.com/watch?v=5xM-9yuBhMc )

Data Reduction: If the data set is relatively small or the initial categories seem particularly salient, the researcher may not need to proceed to this step of refining the focus of analysis. However when conducting extensive ethnographic research it is particularly necessary to reduce the focus of analysis to a size and framework the researcher can better manage. Framing or  frame analysis  is selecting a focus or lens from which to analyze and present the data. When doing so, the researcher acknowledges that multiple frames are possible and that one’s unique perspective may influence how one positions and interprets data. One has to make choices and this fact should be documented and rational provided for the choices made. For example, one’s roles in society may influence the frame taken in analysis: a student, a psychology major, a sociology major, a communication scholar, a parent, a single person, etc. (Grbich, 2007). The goal is to develop in-depth quality in the final analysis, not quantity. Thus the researcher may begin to identify primary categories or themes and secondary ones that may or may not end up in the final report.

If we return to the example of studying participants’ identity construction on social media, the researcher may decide reporting people tend to describe themselves according to demographics is not very insightful or useful. Perhaps the comments suggest a particularly interesting connection among the participants’ comments about their gender, sex, and sexual orientation, and that given the researcher’s review of previous research and/or training, the researcher decides this focus would make a more useful contribution to the state of knowledge. The researcher is then framing the study around themes or categories tied to gender, sex and sexual orientation. Other demographic information such as race, ethnicity and age may be related in the participant comments and serve as a secondary level of themes or categories to be explored.

Throughout the analysis process, but certainly while conducting data reduction, the researcher should be looking for  exemplars  – examples such as quotes from the data that vividly illustrate the themes and categories proposed. The examples should emulate the boundaries or characteristics the researcher has used to distinguish the themes or categories. They should make the themes or categories come alive for the reader (Ryles, as summarized by Geertz, 1973). These are what will be presented in writing the report to represent the qualitative data reviewed.

Conceptual Development: The analysis process would not be very meaningful if the researcher stopped after creating a list of categories. The researcher now needs to determine what meaning to make of the list. In this step the researcher attempts to integrate the categories to consider what they mean when examined together. The researcher looks at how they might relate to or influence each other (Lindlof & Taylor, 2002). This phase is a  meta-analysis  – an examination of the examination of data – the creation of codes, categories and themes created in the previous steps. In grounded theory, described later, this is called  axial coding , but the process and goal is the same across methods – attempting to connect the codes, categories and/or themes.

The process used is basically to repeat the same steps but on a higher level of analysis: review all the codes, categories or themes (instead of the individual instances of each) in comparison to each other and attempt to understand how they relate to each other. It is as if the researcher is constructing a framework or umbrella in which to locate the individual codes, categories and themes. There are many ways researchers may attempt to do so. The manual methods suggested previously for color coding, creating grids or other visual depictions of the categories and themes can help make the connections visible. Researchers may see that the categories or themes fit well with an existing theory and so place them within that theoretical context, they may see that together the parts suggest a new theory or conceptualization of the issue being studied, they may see a metaphor emerge that helps to explain how the parts fit together, etc.

• Triangulation : As defined in the first part of this chapter, triangulation is approaching the study from multiple perspectives to enhance the rigor or integrity of the results of the study. It can include using multiple methods of data collection, gathering multiple sources of data, and for analysis it can include having multiple researchers or coders for the data, and/or drawing from multiple academic disciplines to frame the study. The idea is that if shared meanings emerge from multiple directions of data collection and analysis, those meanings are likely more sound. Sociologist Norman Denzin (2006) proposed this method to directly refute the criticism that because multiple interpretations are possible in qualitative research, the findings proposed from a study are unreliable or invalid. 
• Representativeness:  The researcher should be sure there are multiple exemplars – specific examples of quotes or behaviors that support each theme or conclusion formed. If the researcher cannot come up with enough strong examples, the claim is likely not strong enough to be warranted.
• Member check:  After preliminary or final analyses, the researcher shares the interpretations with participant members of the study to solicit feedback. The sharing can be in the form of face-to-face interviews, focus groups, or written data summaries. The goal is for the researcher to find out if the interpretations rendered ring true to the participants. Do they feel the results reflect their lived experiences? The researcher may want to use a detailed standard list of feedback questions or ask for a more holistic reaction to the research summary. After obtaining input, the researcher implements the information gathered to rethink the findings and/or cite as support for the claims in the study.
• Transparency:  Transparency in research is the expectation that researchers will make the entire process of their work explicit, openly sharing the process of meaning construction with the readers (Hiles, 2008; Seale, Gobo, Gurbrium & Silverman, 2004). This criterion emerged out of a need, as previous qualitative and quantitative research was often not transparent, in part due to page limits and related costs for academic journal publications. Thus research conclusions and what academia comes to call knowledge appeared as if from a vacuum, free of individual decision-making and perceptual influences that are always present in human endeavor, and prevented others from testing out the results further. Transparency is an ethical consideration. When researchers clearly document the steps taken in data collection and analysis and share these with the reader, s/he should be able to better understand and visualize how the conclusions were formed. If this clarity is not present, the value of the study and the researcher’s integrity may come into question. 
• Reflexivity:  An effort to examine how one’s own thoughts, feelings, and behaviors might intermingle with phases of the research process (Bochner & Ellis, 1992). Reflexivity is the recognition that the researcher is a part of what is being studied. The researcher’s unique cultural lens will necessarily affect the research process. Taking the time to place oneself and one’s values and possible biases under examination better assure this inevitable influence is not abused and that not only the researcher, but the participants know the nature of the researcher’s likely influence on the study. Careful field notes and keeping a diary or log during the analysis process will help the researcher examine this criterion.
• Grounded Theory –  Grounded Theory is actually one of the individual analysis methods described below. Its mention here speaks to the pervasive influence of grounded theory on the general process of all qualitative research. The key point regarding using grounded theory as a criterion to assess rigor and integrity is that the researcher should refrain from using her/his words to label themes/categories, and rely wherever possible on the words and images conveyed by the participants. The example above on studying self-esteem from African American women’s perspective illustrates why this is so important. The two White women researchers had initially inadvertently assigned their own label for a theme as racism rather than “respect” which the participants used.

These five steps represent the general process of conducting qualitative analyses. What follows is information about the specific analysis methods most commonly used: thematic analysis, grounded theory, and content analysis. We will also briefly introduce the reader to two methods that focus more specifically on  how  the verbal and nonverbal messages under study were constructed – discourse analysis and conversation analysis.

Thematic Analysis .  This is the most general, easily accessible method of data coding or categorizing. The reason this method is more accessible is that the rules for the general process of analyzing data as described above are more relaxed. This does not mean thematic analysis is unsystematic however. The researcher still defines the unit of analysis and data is coded first to organize it. From the categories the researcher looks for a theme within each category and then an umbrella theme or themes that might connect the individual themes. The concluding themes should best reflect the data as a whole.

In a comparison of grounded theory and thematic analysis, Mohammed Ibrahim Alhojailan (2012) concluded thematic analysis is a comprehensive method, just as is grounded theory, however “It provides flexibility for approaching research patterns in two ways, i.e. inductive and deductive” (p. 39). In its purist sense, grounded theory requires data to be inductive only. In thematic analysis, the themes may be based on information gathered from interviews and previous research and theory. And, the data does not have to be collected at one time. “This makes the process of thematic analysis more appropriate for analyzing the data when the researcher’s aim is to extract information to determine the relationship between variables and to compare different sets of evidence that pertain to different situations in the same study” (p. 39). 

Characteristics of Thematic Analysis:  Theme analysis is often used to study texts, both written and transcribed oral texts such as interviews or focus group discussions. The themes can come from the research questions and objectives guiding the study, from previous research or theory presented in the literature review, from the researcher’s standpoint as a certain gender and sex or ethnicity, social role, etc., from the data itself gathered in the study, or what is most common is from a combination of pre-existing influences and meanings that emerge from the data.  “This makes the process of thematic analysis more appropriate for analyzing the data when the research’s aim is to extract information to determine the relationship between variables and to compare different sets of evidence that pertain to different situations in [the] same study”   Alhojailan (2012, p. 39).  

Steps of Conducting a Thematic Analysis:  While the overall steps are as described above for qualitative data analysis, in thematic analysis steps 3, 4 and 5 take on a particular process. The researcher must answer the question: When do comments or behaviors become a theme? Literally anything could be claimed as a theme, so what makes a given researcher’s claims acceptable? While the comment or behavior needs to occur repeatedly in the data, counting repetition alone is not enough. Interpersonal Communication scholar William Owen (1984) suggests the researcher will know s/he has a theme when it meets the following criteria: recurrence, repetition, and forcefulness.

• Recurrence means that at least two parts of the data have the same meaning, although the meaning may be expressed in different words. The researcher looks for a pattern in the relational or underlying meaning of a message.
• Repetition is about frequency – that the same key words, phrases or sentences are mentioned again.
• Forcefulness refers to the degree of emphasis conveyed in the message. Does the way an idea is said or written suggest it is important to the speaker? This can be through vocal inflection, timing, volume, and/or emphasis placed on words or phrases in written or oral form.

While Owen would require that the data meet all three of these criteria, it may not always be possible to do so. What is important is that the researcher show evidence in the text to support her/his claims, and that they speak to the underlying meanings being expressed. There will always be other meanings in a message; the researcher’s job is to best assure the themes selected seem primary, rather than secondary. 

Owen offers an example from an analysis of a daily log a female college student was asked to make about her relationships. This one is about a high school friend. The short passage reveals all three criteria simultaneously:

Day One: She is an ideal friend. I haven’t really known her for very long, but it seems like we jumped into the middle of a relationship. I feel like I’ve known her forever.  Day Three: That night we burnt chocolate-chip cookies and drank white wine, and for the  first  time since Bud died (her brother), I had someone I could relate to. Day Five:  Special  is the word-of-the-day.

The thematic concept Owen claims is relational uniqueness. The references to “ideal friend,” jumped into the middle of a relationship,” “feel I’ve known her forever,” I had someone I could relate to,” “special is the word-of-the-day,” “It’s like Debbie and I share a secret,” “she is so special, we are so special!” “We have the perfect relationship,” all suggest the theme of uniqueness. Recurrence is apparent as is repetitious, with the word “special” being used three times in one entry. Forcefulness is also apparent with the italicizes used as in “for the  first  time,” and “special.” Comparing the current relationship to the comfort of her relationship with her brother who has now died is quite powerful.  (For an online lecture on the steps of theme analysis, see sociologist Graham Gibbs, University of Huddersfield, UK ).

Strengths and Limitations of Thematic Analysis . Because this method is a general and relatively simple (but not fast) process, it is the most widely used method for analyzing qualitative data. It is a good choice for novice qualitative researchers because the process is easy to understand. It mirrors the social cognition perceptual process we humans engage in every day: organizing countless types and sizes of data into categories to make sense of the world around us. Thematic analysis can be less time consuming than other methods for qualitative data analysis, although to be done well, it still requires repeated reviews of all the data and a willingness to sort and resort data according to appropriate themes. And, it can be applied to analyzing all sorts of data from looking for themes in artifacts, such as news stories about an event, to looking for themes across interviewees’ comments about a particular topic, to looking for themes in  how  individuals who share a cultural identity tend to exhibit similar communicative behaviors or categories. It also works when there is a large volume of data, which is not as easy to do with other qualitative methods. These strengths of the method also become limitations. Because it is so general, it is sometimes criticized for not being systematic enough. While not a failure of the method, some researchers using thematic analysis fail to fully account for their thinking that went into creating the themes claimed as results of the study. A limitation of the method itself is that the meanings of human behavior are interdependent, not independent of other being or the physical and social context. Thematic analysis calls for sorting data into discrete categories, whereby the same comment cannot be placed under two themes. Yet, as the example below will likely suggest to the reader, comments or behaviors often seem connected and could be placed in multiple categories. The themes are almost always interdependent, but the researcher may not acknowledge this. And so, research using thematic analysis is more easily open to criticism regarding the relevance of the themes identified and the bias or hidden agenda of the researcher’s choice in framing the data within the selected themes.       

Example of Thematic Analysis:

Pohl, Gayle, & DeFrancisco, Victoria. (2006). Teaching through crisis.  The International Journal of Diversity in Organisations, [sic]  Communities & Nations

In a study of why some college instructors addressed in their classrooms the events of the airplane bombings in the U.S. on 9/11 of 2001, their comments suggested the following themes: they felt they had no choice but to address the event; they needed to make sense of the events both for themselves and their students; and they felt the events fit their course content (Pohl & DeFrancisco, 2006). Below are sample comments that when sorted suggested the three themes.

• Because I felt I had to both for myself and for my students. The event was too large and had too many ramifications to ignore. I knew that it was going to be an issue that we would be dealing with for a long time.
• I really wrestled with the decision. It seemed that it would be impossible, if not completely inhuman, to try to ignore.
• Both the students and I seemed to need to talk about it and explore the events surrounding 9/11.
• It was a historic, painful, and socially critical event.
• A lot of people, including students, were experiencing a variety of strong emotions. I think it’s important to open discussion for those who want to talk and those who might benefit from listening to others’ ideas. Our students look to us for guidance – we should provide it.
• I teach in the state of New York. During the days and weeks that followed 9/11, a few members of my classes were mobilized as part of the National Guard. Other members were “absent” mentally or physically because they had found out, or hadn’t found out, about whether their family members and dear friends were alive. These issues were so in our faces” that I believed I had an obligation to incorporate the events in my teaching. Furthermore, when I suggested to my Comm. Theory class that we carry on with the projected daily schedule, they decided they WANTED to learn about theory in order to make sense of what was happening.
• I taught on the day of the attacks, and students needed some help in giving meaning to what happened.
• My students seemed paralyzed by the events of 9/11. We incorporated these events because no one else seemed to be talking about them. There was a tremendous need to address what was happening so students could begin to see beyond the events of the day.
• The events occurred shortly before my first class met, and as a relational scholar, I don’t feel we should ignore the things that affect our everyday lives in our teaching. I feel it is most powerful to use our lives in applying communication principles. As it was, we were about to discuss confirmation and disconfirmation and it seemed to me that these events could exemplify those concepts on a larger than interpersonal scale.
• Our college is just 70 miles from New York City. Some had family or friends in the World Trade Center area. My class met on September 12. We were all stunned. A Communication Ethics class will always address such issues as news media decisions to transmit graphic images. These questions were poignantly present, and the arguments on either side very forcefully available to us.
• I teach Epidemiology, human diseases, and environmental health. Terrorism and biological, nuclear, and chemical weapons issues are all topics that have to be dealt with in these courses. Unfortunately, now I realize that I have to make sure I teach these topics because a well-trained responder might save lives in the future. It’s sad I even have to deal with these issue.
• After teaching a course on “Minority Images in American Media” for several years, and regularly emphasizing the manner in which the media--- especially the news media – rely on stereotypes of Arabs and others from the Middle East as terrorists, it was immediately evident to me that I needed to spend more time addressing this issue. Also, I teach a course on Visual Communication and, for several years, had difficult convincing my students that visual images communicate much more powerfully and immediately and effectively than words do. September 11 made that argument much easier to convey with the four-day bombardment of images over and over again, so including issues from Sept. 11 in my lesson plan was an obvious choice.

Together, the four themes suggested a pattern or over-arching theme among the responses – the instructors felt a need to use the course experience to work through the crisis with their students. Some did this in a more formal way by applying relevant course concepts to help critically analyze the contexts surrounding the events, others more basically sought to create a safe space for them and their students to sort through their mixed emotions.  

Grounded Theory Method. The word “theory” in this method of data analysis might be a bit confusing initially. People tend to think of theory and methods as separate, but in this groundbreaking methodology first proposed by sociologists Barney Glaser and Anselm Strauss (1967) the relationship between theory and method are more overtly celebrated. The approach has been refined and is widely practiced today in qualitative research. It is not simply a method for categorizing data, it is a method for using data categorization to reveal underlying theory (and the word  theory  can be used here in its most general sense, as an attempt to explain some phenomena). The resulting theory can be in the form of themes, or other attempts to explain deeper levels of meaning for what is going on in the interactions of study, referred to as  substantive theory , or the result can be  formal theory  that will be used to advance the larger conceptual field of sociological inquiry (Glaser & Strauss, 1967). The idea is that theory can be and is most useful when it is developed from observation, rather than the other way around as done in quantitative research where the theory dictates the direction of a study. Thus Grounded Theory calls for an inductive approach to building theory from the ground up – based on research observation and analysis.

Unique characteristics.  As noted above, the most unique characteristic about grounded theory is that the meanings should emerge from the data itself – from the words and behaviors of the participants. Thus, it is an excellent extension of the ethnographic approach where researchers work to honor the words and perceptions of the participants. The researcher must work to resist letting h/his perspective overly influence the meanings derived from the data. Because of this characteristic, grounded theory has become a criterion for assessing a researcher’s ethics in qualitative research and for assessing the value of the results of the study.  Such criteria demand  a highly systematic and rigorous process of data coding and meaning development.

Steps in conducting.  Grounded Theory is also referred to as  open coding  or the  constant comparative process  of letting the meanings open-up -- emerge from the data, rather than imposing predetermined codes, from previous research, theory, and/or the research question(s). The terms open coding and constant comparative are actually what researchers do in steps 2-5 of the qualitative research process. They will be described further below, as they are the heart of what makes grounded theory a unique qualitative approach.

Step one (data immersion):  The researcher begins with an exhaustive review of the data, trying to capture as much of it as possible.

Step two (data management):    Open coding  is the initial, unrestrictive coding of data” before the researcher knows what the final categories or themes will be (Lindlof & Taylor, 2002, p. 219).  This step still requires defining the smallest unit of analysis and tracking each one closely. The researcher tries to ignore previous theory and research and let the meanings emerge through a process of  constantly comparing  or putting each piece of the data next to the previously coded data to look for similarities and/or differences, letting the meanings emerge piece by piece.

Open coding is a creative process. You can use a pencil, highlighter, post-it notes, or the computer to block quotes and other data and move them around as you begin to see patterns emerge. Each piece of data or instance is compared to the previous ones already categorized to see where the new piece of data belongs. Does it belong in a current category, or does it suggest a new category is warranted? You are looking for what makes sense regarding ways to organize the data. Through repeated reviews of the data the distinguishing characteristics of each category and the coding scheme will emerge.

In vivo coding.  This type of coding is done at the same time as open coding, it is a more specific type of open or grounded coding where the actual names of the codes come directly from what the participants say. The idea is to avoid the researcher imposing her/his work view as reflected in her/his labels for codes and themes. Instead the labels reflect the insider participant perspectives – it is using their words. Thus it is a preferred, more carefully grounded coding system, but it is not always possible to attain. For example, while the researcher may be quite sure the participants are engaging in a great deal of face-saving strategies, as in the case of inappropriate behaviors and creates a coding scheme for these, the participants may not realize or want to verbally recognize those behaviors.

Step three (data reduction):  Eventually the number of new categories will diminish and you, as the researcher can begin to consider whether you have reached the point of what is called  theoretical saturation  (Glaser & Strauss, 1967) .  When no new categories are emerging and the categories you have created seem stable, the researcher can assume no new data is needed for now, and the analyses may be sufficient. However, if the results do not seem very insightful, rendering for example new, unique interpretations, the researcher may still decide to return to the field to collect more information.

Step four (conceptual development):  When the open coding is completed (at least for now), the researcher moves to conduct what is called  axial coding , basically the same general step four described previously. Think of an axis – as identifying a common framework on which the categories or themes can be located. In this step the emergent or grounded theory becomes more overt. The researcher examines how the categories created might relate to each other by conducting a metacoding – codes that attempt to connect the categories, remembering these codes must also be grounded in the data.

A useful tool to test one’s metacoding is called  negative case analysis  (Lincoln & Guba, 1985) .  The basic idea behind negative case analysis is that if the researcher’s emergent theory from the axial coding can account for a specific case, incident, person, or comment that does not seem to fit with the other data codes or categories, then the resulting theory is stronger. Sometimes researchers will return to the field to find a negative case to test their emergent theory conclusions. In this view, the negative case is not to be feared as something that will hurt the researcher’s study, but rather a piece of information that may make the resulting theory more nuanced and reflective of the complexity of participants’ lived experiences. In many cases, the information from the negative case analysis may send the researcher back to the field or back to a previous step in data analysis. The researcher must find a way to account for the negative case, which may require at an extreme completely rejecting the prior analysis, and at a minimum, more carefully describing the categories, themes, and emergent theory.        

Step five (evaluating results):  As noted in the previous step, the process of assessing results has already begun. However, even after the researcher has conducted a negative case analysis and axial coding, the results are subjected to at least one more assessment. At this point the researcher may feel h/his interpretative abilities are exhausted and it is time to get a new perspective form other outside researchers, members of the community of study, or insider perspectives from some of the participants.  Member checking  (Lincoln & Guba, 1985) comes into play here. The researcher may choose to share the results with members of the study to see if the categories, themes, and resulting theory make sense to them. Do they represent their lived experiences? Member checking can be done very informally as in conversations with individuals, or the researcher may choose to circulate a summary of the findings with survey responses, or conduct a focus group discussion with members. It is not necessary, nor usually possible, to solicit feedback from everyone in the study.  The goal is to determine if the researcher’s conclusions ring true to the participants’ actual experiences and perceptions. It is an ethical tool to add value and credibility to the final report of the study.

(For a lecture on this topic, see sociologist Graham Gibbs, University of Huddersfield, UK, “Grounded Theory: Core Elements,”  http://www.youtube.com/watch?v=4SZDTp3_New )

Discourse Analysis and Conversation Analysis

There are two other interdisciplinary methods for studying spoken and written texts or discourse, including the multiple paralinguistic ways in which the speaker delivers the words. Discourse Analysis focuses on speech (or texts) as communicative acts and examines how people use language to construct meaning. The focus of analysis is on the content of what is said and the metamessages the content conveys, perhaps about identities, relationships, positions on a social issue, etc. Conversation Analysis focuses on how the speakers communicate – the patterns of using conversational turn-taking tools such as questions, pauses, volume, and more, to negotiate identities, relationships, etc. Both methods examine the text or discourse in a line-by-line fashion with detailed transcriptions. Conversation Analysis requires the added transcription devices to indicate the amount of time between turns at talk, the paralinguistic qualities of the voice, etc. (see Gail Jefferson’s transcription system, in Sacks, Schegloff & Jefferson, 1974). As you might guess, the meanings derived from both methods are deeply embedded in the unique cultural understanding. Discourse Analysis is often used in rhetorical studies, which is reviewed in another section of this textbook. It is also used heavily in the communication sub-field of performance studies (see for example, Carlin & Park-Fuller, 2012; Palczewski, 2001). In both of these areas the researcher is attempting to make meaning of words and often nonverbal messages to provide new insights and/or greater understanding. Conversation analysis is used in some ethnography if the focus is on studying the structure of interaction in language use/conversation (see for example, Hall, 2009).

Data Write-Up

When one reaches this phase of the research process it is easy to think the creative, critical thinking work is done, but particularly in qualitative research, it is not. Writing the results is part of the meaning making process. Writing itself, is often viewed as an analysis method (Richardson, 2003). It is an opportunity for the researcher to synthesize what was learned and once again review the meanings rendered, but this time in the larger context of previous research summarized in the literature review. Often returning to this larger body of knowledge will push the researcher to make deeper and/or more complex connections among their own themes and meanings rendered.   

Not all qualitative research reports follow the traditional social science organization, but if you are not writing an autoethnography or narrative study, we recommend the social science organization for clarity. It contains four basic parts. The first is the introduction and literature review, second is a description of the research methods selected for collection and analysis, third is a description of the results or meanings formed from the data, and fourth is a discussion of the study.

A metaphor that might be helpful for visualizing how your paper will look is an old-fashioned hourglass. The introduction and literature review begins broadly and then the paper narrows to a focus on the specific study’s design. The paper ends more broadly again where the author places h/his specific results back into the context of previous research and the larger field of study to consider how the study contributed to the larger field.

There are some basic criteria for the whole report to keep in mind. They are the same criteria proposed in the data analysis process. And, by the way, these are good criteria to look for as you assess other’s reports, as well.

• Transparency in writing about the process is key. Transparency refers to the need to be open, clear and explicit in describing the research procedures used to design the study, conduct data collection and data analysis (Seale, Gobo, Gurbrium & Silverman, 2004).The reader should be able to trace your steps and follow how you came to the conclusions you did. If the reader cannot do so, the author is hiding what may be useful information to help others understand how s/he came to the conclusions formed. 
• Representativeness requires the researcher to be sure the results are grounded in the words and experiences of the participants and not overly controlled by the researcher’s voice. Participant quotes are your data, the narrative in the report is your voice weaving these quotes together to make meaning. Thus much of the write up will include description – the participants' actual words and behaviors to support your claims (themes or conclusions). The reader should not feel the patterns, themes, or other conclusions were drawn out of thin air. The reader should be able to see 2-3 exemplars for each claim made about the results of the study.
• Reflexivity is related to transparency. It is an effort to be open with the reader and acknowledge that you as the researcher are a part of what is being researched. You are not an objective observer uninfluenced by the study or detached from its results. The reflexive author is careful to separate h/his own feelings and comments from what the participants said, and careful to note when such a clear separation is not possible. Reflexive writing is being critical of one’s own work. This does not mean always being negative, pointing out limitations, just that one recognizes h/his potential role in the process.

Introduction and Literature Review

The introduction and literature review are combined in this step. It is useful to think of them as a coherent rationale for the study you conducted or propose to conduct. The Rationale is drawn from life experiences, review of news reports, popular press, secondary sources (as in websites, textbooks or published reviews of literature and most importantly, previous original scholarship on the topic. As in quantitative research, research questions that guide original qualitative research are generally expected to be based on previous research. Some ethnographers and autoethnographers prefer not to review the literature prior to collecting original research so that their views are not tainted. In this case, the literature view portion of the final report would go in the discussion of the results of the study, rather than as a part of the rationale of the study.

The rationale is built by answering the following questions in the paper, usually in this order:    

• A statement of the problem that needs to be studied (this can be a social problem, an academic theory that needs to be developed, a lack of experience needed, etc.) The definition of problem is meant to be broadly defined here. This is the attention-getter of the paper. It should compel the reader to want to know more. (Usual length is from a paragraph to two pages, double-spaced.)
• Embedded in the statement of the problem and why it needs to be addressed, the researcher introduces for the first time any key terms that will be used repeatedly in the study. These should be briefly defined for the reader with a citation of the source. For example, if you were doing a study on how people build an intercultural relationship with someone from a different race or ethnicity, What key terms would you be using? Mostly likely you would need to define culture, race/ethnicity, prejudice, intercultural relationships and relational development. While there may be other concepts, only the most central ones need to be introduced here.
• A preview of the type of research warranted from the review of the problem.
• There are multiple ways to organize a review of the literature. You will want to select what helped you organize the information and understand it best. Topical organization is most popular, but it might be useful to organize the review in a chronological fashion. The organization of sub-headings should help lead the reader to understand why you have decided on the design of the study you selected.
• The question that is commonly asked is, “how much do I need to include from each study?” There is no specific answer – thank goodness. There is room for your thoughts. Basically include what is most relevant to the present study you will or did do and not more. Thus, if you adopted methods from a study then describe the study’s methods, but if you are only citing the study as evidence that your topic is important, then just cite the study, as in parentheses at the end of a statement you make as in: (Ellis, 2004). 
• A review of literature is not just a cut and pasting of a series of research abstracts. Your voice synthesizing the previous research should be central in the review.
• The review of previous research literature usually concludes with the proposal of one to three overall research questions the author will attempt to answer with the specific study proposed. (Length varies a great deal depending on the instructor’s assignment and/or publishers’ page limits. A rule of thumb is that it be almost as long as the reporting of results from your study, from five to sixteen pages).

Research Methods

As in quantitative research, there are two sub-headings to this: data collection and data analysis.

Data Collection.  Here the researcher describes the general research design proposed to answer the research questions stated at the end of the literature review. The criterion of transparency is key to follow here. This section may range from two to approximately six pages. This section includes the following, usually in this order:

• Participants – method used for soliciting participants, ethical guidelines followed in soliciting them and in terms of promises of confidentiality, or other concerns to not ask more of them than is necessary. Forms of consent are referenced and included in the appendices of the paper. Lastly, in qualitative research, if there are not too many participants, the researcher might include their first name or pseudonym and a brief description of each person’s demographics collected as relevant to the study.
• Data Collection – a detailed description of the methods chosen (e.g. interviews, ethnographic observation, …), how they were conducted, and why these choices are relevant for the general research questions asked. Actual interview questions are usually put in the appendices.

Data Analysis.  Here the researcher explains which analysis tools (e.g. content analysis, theme analysis) were selected and why they are a good fit for the general research questions asked and the specific data collection methods employed. This section includes:

• Description of how data immersion was accomplished (e.g. length of time taken, number of readings, etc.)
• Definition of the unit of analysis used with a few examples
• How the data was coded and what emerged as the coding scheme
• How the researcher managed the data and reduced it to a focus that was attainable and relevant

This is where the author describes what was constructed in step four and five of the general data analysis process: the conceptual development and evaluation of results or meta-analysis. The author describes what meanings were taken from the data based on the analysis process. A suggested outline:

• A list of the categories or themes and exemplar texts for each to demonstrate them
• If negative case analysis was conducted, how it changed the resulting interpretations.
• Results from the meta-analysis of themes or categories. What did the researcher find about how these might be related? What is the deeper and broader level of meaning attained from the analysis process?

This is where a criterion used in autoethnography might be useful (Ellis, 2004). The author is expected to use  evocative writing  -- writing that calls up emotional responses from readers. It compels readers to engage with the material beyond a cognitive level. By calling for writing that is compelling, we are not suggesting the author try to manipulate the readers’ emotions. It is just that in autoethnography the quality of a study is measured so to speak by how much it compels the readers, draws them into the reality being described, and helps them see the experience from an inside view. We think this is a good criterion for most qualitative research on human experience.

If you imagine the introduction and conclusion of your research report as covers on a book that are mirror opposites, the conclusion should respond to and connect with the introduction. The goal is that when the reader finishes s/he will feel like the book just snapped shut. The author connects the findings from the present study to the larger social problem being addressed and the larger body of research in the literature review. When the covers snap shut, the author has done a good job of answering the basic question of, “so what?”  “Why these research questions, why these participants, why this research design, why these interpretations of meaning, why do the results matter?” The following outline is offered to help assure you answer the question of “so what?”

• Preview of the sub-headings in the conclusion
• Summary of the key findings (themes, theory development and sub-categories supporting them  (less quotes are needed here).
• Contributions of the study – both practically (e.g. for practitioners working with students, patients, for people to apply in their daily life, etc.) and academically (e.g. contributions to theory, methods, topic of study – the larger field of literature on the subject). Note this is especially where the author returns to the literature review and selects specific sources that the results of the study might speak to.
• Limitations of the study (e.g. in participant selection, data collection and analysis methods applied.
• Suggestions for future research (often based on the limitations of the present study).

Other Aspects of the Paper

For a more detailed description of the writing process and format, also see Chapter 7 of this book: “ Conclusion: Presenting Your Results ".

There are other parts of the academic paper you should include in your final write-up. We have provided useful resources for you to consider when including these aspects as part of your paper.

For an example paper that uses the required APA format for a research paper write-up, see the following source:  http://owl.english.purdue.edu/media/pdf/20090212013008_560.pdf .  However, this example uses quantitative data, so be aware the results section will not look like your qualitative study results. Qualitative reports tend to be longer.

Abstract & Titles.

http://writing.wisc.edu/Handbook/presentations_abstracts.html   http://owl.english.purdue.edu/owl/resource/560/1/

Tables, References, & Other Materials.

http://owl.english.purdue.edu/owl/resource/560/19/   http://owl.english.purdue.edu/owl/resource/560/05/

Data Presentation

Instructors will often ask you to present an oral version of your study in addition to the written report. This is a great way to help you gain more full comprehension of what you did in your study. If you can explain it well to others, you likely understand more deeply what you accomplished. The oral presentation is also a great way to prepare you for academic or other professional conference presentations that will help add to your resume. Prior to submitting original research to be considered for publication in journals or edited books, researchers share their studies orally to gain feedback and revise the written version of a study for submission to publication.

Two of the most common venues are oral presentations as a part of a panel of speakers and poster presentations. You might also be called upon to write an executive summary of the results of your study that makes it more accessible for people to review quickly. There are good resources for doing all of these online, so we have provided these here.

Oral Presentations

http://writing.wisc.edu/Handbook/presentations_oral.html   http://writing.wisc.edu/Handbook/presentations_delivery.html

Poster Presentations

http://writing.wisc.edu/Handbook/presentations_poster.html

Executive Summary

http://www.csun.edu/~vcecn006/summary.html http://www.stanford.edu/group/gender/ResearchPrograms/DualCareer/DualCareerFinalExecSum.pdf  (an example of an executive summary for university policy makers, from a research study on dual career academic couples) http://www.kff.org/entmedia/upload/7618ES.pdf  (another example of an executive summary from a study of food advertising to children on television)

Alhojailan, M.I. (2012). Thematic analysis: A critical review of its process and evaluation.  West East Journal of Social Sciences, 1 (1), 39-47. [access at  http://www.westeastinstitute.com/journals/wp-content/uploads/2013/02/4-Mohammed-Ibrahim-Alhojailan-Full-Paper-Thematic-Analysis-A-Critical-Review-Of-Its-Process-And-Evaluation.pdf ]

Blumer, H. (1986).  Symbolic interactionism: Perspective and method. Berkeley: University of California Press.

Bochner, A., & Ellis, C. (1992). Personal narrative as a social approach to interpersonal communication.  Communication Theory, 2,  65-72.

Carlin, P. S., & Park-Fuller, L. M. (2012). Disaster narrative emergent/cies: Performing loss, identity and resistance.  Text and Performance Quarterly, 32 (1), 20-37.

Cohen, M., & Avanzino, S. (2010). We are people first: Framing organizational assimilation experiences of the physically disabled using co-cultural theory.  Communication Studies ,  61 (3), 272-303.

DeFrancisco, V., and Chatham-Carpenter, A.  (2000). Self in community: African American women’s views of self-esteem.  Howard Journal of Communication, 11  (2), 73-92.

Denscombe, M. (2010).  The good research guide for small-scale social research projects  (4th ed.). Maidenhead, England: Open University Press.

Denzin, N. K. (2006).  Sociological Methods: A Sourcebook.  Piscataway, NJ: Transaction.

Dilthey, W. (2010).  Selected works, volume II: Understanding the human world . R. A. Makkreel & Frithjof, R. (Eds.). Princeton, NJ: Princeton University Press.

Ellis, C. (2004).  The ethnographic I: A methodological novel about autoethnography.  Walnut Creek, CA: AltaMira Press

Ellis, C., & Bochner, A. (2000). Autoethnography, personal narrative, reflexivity: Research as subject. In N. Denzin & Y. Lincoln (Eds.),  Handbook of qualitative research  (2nd ed., pp. 733-768). Thousand Oaks, CA: Sage.

Gadamer, H. G. (1976).  Philosophical hermeneutics  (D. E. Linge, Trans.). Berkeley: University of California Press.

Geertz, C. (1973).  The interpretation of cultures . New York: Basic Books.

Glaser, B., & Strauss, A. (1967).  The discovery of grounded theory: Strategies for qualitative  research.  New York: Aldine.

Grbich, C. (2007).  Qualitative data analysis: An introduction.  Thousand Oaks, CA: Sage.

Hiles, D. R. (2008). Transparency. In L. M. Givens (Ed.),  The Sage encyclopedia of qualitative  research methods.  Thousand Oaks, CA:Sage.

Hundley, H. L., & Shyles, L. (2010). US teenagers’ perceptions and awareness of digital technology: A focus group approach.  New Media & Society ,  12 (3), 417-433.

Husserl, E. (1990).  On the phenomenology of the consciousness of internal time (1893–1917) , (J. B. Brough, Trans.). Dordrecht: Kluwer.

Keyton, J. (2011).  Communication research: Asking questions, finding answers  (3rd ed.). New York: McGraw Hill.

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

Lindlof, T. R. (1995).  Qualitative communication research methods.  Thousand Oaks, CA: Sage.

Lindlof, T. R., & Taylor, B. C. (2002).  Qualitative research methods  (2nd ed.). Thousand Oaks, CA: Sage.

Lofland, J. & Lofland, L. H. (1995).  Analyzing social settings: A guide to qualitative observation and analysis.  Belmont, CA: Wadsworth.

Meares, M. M., Oetzel, J.G., Torres, A., Derkacs, D., & Ginossar, T. (2004). Employee mistreatment and muted voices in the culturally diverse workplace.  Journal of Applied Communication, 32  (1), 4-27.

Owen, W. F. (1984). Interpretive themes in relational communication.  Quarterly Journal of Speech, 70 , 274-287.

Palczewski, C. H. (2001, Summer). Contesting pornography: Terministic catharsis and definitional argument.  Argument and Advocacy, 38,  1-17.

Richardson, L. (2003). Writing: A method of inquiry.In N. K. Denzin & Y. S. Lincoln (Eds.),  Collecting and interpreting qualitative materials  (pp. 499-541). Thousand Oaks, CA: Sage.

Ricoeur P. (1981).  Hermeneutics and human science: Essays on language, action and interpretation  (J. Thompson, Trans.). London: Cambridge University Press.

Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking in conversation,  Language, 50,  696-735.

Saldana, J. (2009). The coding manual for qualitative researchers. Thousand Oaks, CA: Sage.

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User Research

Aug 19, 2024 • 17 minutes read

Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

Qualitative UX research made easy

Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.

7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants.

Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Stakeholders Perspective of Integrating Female Genital Schistosomiasis into HIV Care: A Qualitative Study in Ghana

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Background In Sub-Saharan Africa (SSA), HIV infection is the main factor contributing to adult premature death. The prevalence of HIV in the region could also be associated with recent increases in Female Genital Schistosomiasis (FGS) globally. The fast-rising prevalence of FGS in SSA nations including Ghana, which has led to the emergence of dual HIV-FGS conditions, provides evidence of the trend. As such the WHO is advocating for integrated services of HIV and FGS care. This study explored stakeholders’ perspectives of the integration of prevention and control measures for Female Genital Schistosomiasis and HIV care in FGS endemic settings in Ghana. Methods The study was conducted in the Ga South Municipality in the Greater Accra region of Ghana. Using qualitative research methods, Focus Group Discussion was conducted with Community Health Officers (n=9) and Key Informant Interviews with stakeholders including health care professionals and providers at the Regional, District and community levels (n=13) to explore the feasibility, challenges, and opportunities of integrating FGS prevention and control package with HIV continuum of care in communities. In-depth interviews were also conducted among Persons with FGS and HIV (n=13), Female Households (n=10), Community Health Management Committee members and Community leader (n=7) to explore their views on the facilitators and barriers of the integration of FGS into HIV care into the Primary Health Care (PHC) in Ghana. All study participants were purposively sampled to achieve the study objective. All audio-recorded data were transcribed verbatim, a codebook developed, and the data was thematically analysed with the aid of NVivo software version 13.     Results The study identified a knowledge gap regarding Female Genital Schistosomiasis (FGS) compared to HIV. The majority of Community Health Officers (CHOs) exhibited limited knowledge about FGS. Additionally, health workers misconstrued FGS as sexually transmitted infections. Community members who expressed knowledge of FGS were about gynecological symptoms of FGS. Three main health outlets; health facilities, herbal centers, and spiritual centers are utilized either concurrently or in sequence. This health seeking behaviour negatively affected the early detection and management of FGS among HIV clients. Integration of HIV and FGS may be affected by the limited awareness and knowledge, resource constraints, stigma and discrimination, healthcare providers’ attitudes and practices, and cultural beliefs. Conclusions The study finds that knowledge of FGS was usually low among both community members and Community Health Officers. This was having a detrimental effect on regular screening of females for genital schistosomiasis. Integration of FGS and HIV has the potential to help Ghana achieve HIV eradication; however, before such a program is launched, implementation barriers such as stigma, knowledge gap, unavailability of needed logistics at health facilities, shortage of FGS and HIV drugs and issues of accessibility of drugs must be addressed. The results also imply that forming alliances and working together with various community health care professionals may help with early HIV and FGS diagnosis and treatment. Finally, there is the pressing need to develop a clinical protocol for FGS and HIV integration.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Author declarations.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The protocol for the study was reviewed and approved by the Ghana Health Service Ethics Review Committee (GHS -ERC: 001/01/24).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The data is available on request sent to the Administrator of Ghana Health Service Ethics Review Committee at [email protected]

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