qualitative research data gathering procedure example

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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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Qualitative Data Collection: What it is + Methods to do it

Qualitative data collection is vital in qualitative research. It helps researchers understand individuals’ attitudes, beliefs, and behaviors in a specific context.

Several methods are used to collect qualitative data, including interviews, surveys, focus groups, and observations. Understanding the various methods used for gathering qualitative data is essential for successful qualitative research.

In this post, we will discuss qualitative data and its collection methods of it.

Content Index

What is Qualitative Data?

What is qualitative data collection, what is the need for qualitative data collection, effective qualitative data collection methods, qualitative data analysis, advantages of qualitative data collection.

Qualitative data is defined as data that approximates and characterizes. It can be observed and recorded.

This data type is non-numerical in nature. This type of data is collected through methods of observations, one-to-one interviews, conducting focus groups, and similar methods.

Qualitative data in statistics is also known as categorical data – data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.

It’s pretty easy to understand the difference between qualitative and quantitative data. Qualitative data does not include numbers in its definition of traits, whereas quantitative research data is all about numbers.

• The cake is orange, blue, and black in color (qualitative).
• Females have brown, black, blonde, and red hair (qualitative).

Qualitative data collection is gathering non-numerical information, such as words, images, and observations, to understand individuals’ attitudes, behaviors, beliefs, and motivations in a specific context. It is an approach used in qualitative research. It seeks to understand social phenomena through in-depth exploration and analysis of people’s perspectives, experiences, and narratives. In statistical analysis , distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

The data collected through qualitative methods are often subjective, open-ended, and unstructured and can provide a rich and nuanced understanding of complex social phenomena.

Qualitative research is a type of study carried out with a qualitative approach to understand the exploratory reasons and to assay how and why a specific program or phenomenon operates in the way it is working. A researcher can access numerous qualitative data collection methods that he/she feels are relevant.

LEARN ABOUT: Best Data Collection Tools

Qualitative data collection methods serve the primary purpose of collecting textual data for research and analysis , like the thematic analysis. The collected research data is used to examine:

• Knowledge around a specific issue or a program, experience of people.
• Meaning and relationships.
• Social norms and contextual or cultural practices demean people or impact a cause.

The qualitative data is textual or non-numerical. It covers mostly the images, videos, texts, and written or spoken words by the people. You can opt for any digital data collection methods , like structured or semi-structured surveys, or settle for the traditional approach comprising individual interviews, group discussions, etc.

Data at hand leads to a smooth process ensuring all the decisions made are for the business’s betterment. You will be able to make informed decisions only if you have relevant data.

Well! With quality data, you will improve the quality of decision-making. But you will also enhance the quality of the results expected from any endeavor.

Qualitative data collection methods are exploratory. Those are usually more focused on gaining insights and understanding the underlying reasons by digging deeper.

Although quantitative data cannot be quantified, measuring it or analyzing qualitative data might become an issue. Due to the lack of measurability, collection methods of qualitative data are primarily unstructured or structured in rare cases – that too to some extent.

Let’s explore the most common methods used for the collection of qualitative data:

Individual interview

It is one of the most trusted, widely used, and familiar qualitative data collection methods primarily because of its approach. An individual or face-to-face interview is a direct conversation between two people with a specific structure and purpose.

The interview questionnaire is designed in the manner to elicit the interviewee’s knowledge or perspective related to a topic, program, or issue.

At times, depending on the interviewer’s approach, the conversation can be unstructured or informal but focused on understanding the individual’s beliefs, values, understandings, feelings, experiences, and perspectives on an issue.

More often, the interviewer chooses to ask open-ended questions in individual interviews. If the interviewee selects answers from a set of given options, it becomes a structured, fixed response or a biased discussion.

The individual interview is an ideal qualitative data collection method. Particularly when the researchers want highly personalized information from the participants. The individual interview is a notable method if the interviewer decides to probe further and ask follow-up questions to gain more insights.

Qualitative surveys

To develop an informed hypothesis, many researchers use qualitative research surveys for data collection or to collect a piece of detailed information about a product or an issue. If you want to create questionnaires for collecting textual or qualitative data, then ask more open-ended questions .

LEARN ABOUT: Research Process Steps

To answer such qualitative research questions , the respondent has to write his/her opinion or perspective concerning a specific topic or issue. Unlike other collection methods, online surveys have a wider reach. People can provide you with quality data that is highly credible and valuable.

Paper surveys

Online surveys, focus group discussions.

Focus group discussions can also be considered a type of interview, but it is conducted in a group discussion setting. Usually, the focus group consists of 8 – 10 people (the size may vary depending on the researcher’s requirement). The researchers ensure appropriate space is given to the participants to discuss a topic or issue in a context. The participants are allowed to either agree or disagree with each other’s comments. 

With a focused group discussion, researchers know how a particular group of participants perceives the topic. Researchers analyze what participants think of an issue, the range of opinions expressed, and the ideas discussed. The data is collected by noting down the variations or inconsistencies (if any exist) in the participants, especially in terms of belief, experiences, and practice. 

The participants of focused group discussions are selected based on the topic or issues for which the researcher wants actionable insights. For example, if the research is about the recovery of college students from drug addiction. The participants have to be college students studying and recovering from drug addiction.

Other parameters such as age, qualification, financial background, social presence, and demographics are also considered, but not primarily, as the group needs diverse participants. Frequently, the qualitative data collected through focused group discussion is more descriptive and highly detailed.

Record keeping

This method uses reliable documents and other sources of information that already exist as the data source. This information can help with the new study. It’s a lot like going to the library. There, you can look through books and other sources to find information that can be used in your research.

Case studies

In this method, data is collected by looking at case studies in detail. This method’s flexibility is shown by the fact that it can be used to analyze both simple and complicated topics. This method’s strength is how well it draws conclusions from a mix of one or more qualitative data collection methods.

Observations

Observation is one of the traditional methods of qualitative data collection. It is used by researchers to gather descriptive analysis data by observing people and their behavior at events or in their natural settings. In this method, the researcher is completely immersed in watching people by taking a participatory stance to take down notes.

There are two main types of observation:

• Covert: In this method, the observer is concealed without letting anyone know that they are being observed. For example, a researcher studying the rituals of a wedding in nomadic tribes must join them as a guest and quietly see everything. 
• Overt: In this method, everyone is aware that they are being watched. For example, A researcher or an observer wants to study the wedding rituals of a nomadic tribe. To proceed with the research, the observer or researcher can reveal why he is attending the marriage and even use a video camera to shoot everything around him. 

Observation is a useful method of qualitative data collection, especially when you want to study the ongoing process, situation, or reactions on a specific issue related to the people being observed.

When you want to understand people’s behavior or their way of interaction in a particular community or demographic, you can rely on the observation data. Remember, if you fail to get quality data through surveys, qualitative interviews , or group discussions, rely on observation.

It is the best and most trusted collection method of qualitative data to generate qualitative data as it requires equal to no effort from the participants.

LEARN ABOUT: Behavioral Research

You invested time and money acquiring your data, so analyze it. It’s necessary to avoid being in the dark after all your hard work. Qualitative data analysis starts with knowing its two basic techniques, but there are no rules.

• Deductive Approach: The deductive data analysis uses a researcher-defined structure to analyze qualitative data. This method is quick and easy when a researcher knows what the sample population will say.
• Inductive Approach: The inductive technique has no structure or framework. When a researcher knows little about the event, an inductive approach is applied.

Whether you want to analyze qualitative data from a one-on-one interview or a survey, these simple steps will ensure a comprehensive qualitative data analysis.

Step 1: Arrange your Data

After collecting all the data, it is mostly unstructured and sometimes unclear. Arranging your data is the first stage in qualitative data analysis. So, researchers must transcribe data before analyzing it.

Step 2: Organize all your Data

After transforming and arranging your data, the next step is to organize it. One of the best ways to organize the data is to think back to your research goals and then organize the data based on the research questions you asked.

Step 3: Set a Code to the Data Collected

Setting up appropriate codes for the collected data gets you one step closer. Coding is one of the most effective methods for compressing a massive amount of data. It allows you to derive theories from relevant research findings.

Step 4: Validate your Data

Qualitative data analysis success requires data validation. Data validation should be done throughout the research process, not just once. There are two sides to validating data:

• The accuracy of your research design or methods.
• Reliability—how well the approaches deliver accurate data.

Step 5: Concluding the Analysis Process

Finally, conclude your data in a presentable report. The report should describe your research methods, their pros and cons, and research limitations. Your report should include findings, inferences, and future research.

QuestionPro is a comprehensive online survey software that offers a variety of qualitative data analysis tools to help businesses and researchers in making sense of their data. Users can use many different qualitative analysis methods to learn more about their data.

Users of QuestionPro can see their data in different charts and graphs, which makes it easier to spot patterns and trends. It can help researchers and businesses learn more about their target audience, which can lead to better decisions and better results.

LEARN ABOUT: Steps in Qualitative Research

Qualitative data collection has several advantages, including:

• In-depth understanding: It provides in-depth information about attitudes and behaviors, leading to a deeper understanding of the research.
• Flexibility: The methods allow researchers to modify questions or change direction if new information emerges.
• Contextualization: Qualitative research data is in context, which helps to provide a deep understanding of the experiences and perspectives of individuals.
• Rich data: It often produces rich, detailed, and nuanced information that cannot capture through numerical data.
• Engagement: The methods, such as interviews and focus groups, involve active meetings with participants, leading to a deeper understanding.
• Multiple perspectives: This can provide various views and a rich array of voices, adding depth and complexity.
• Realistic setting: It often occurs in realistic settings, providing more authentic experiences and behaviors.

LEARN ABOUT: 12 Best Tools for Researchers

Qualitative research is one of the best methods for identifying the behavior and patterns governing social conditions, issues, or topics. It spans a step ahead of quantitative data as it fails to explain the reasons and rationale behind a phenomenon, but qualitative data quickly does. 

Qualitative research is one of the best tools to identify behaviors and patterns governing social conditions. It goes a step beyond quantitative data by providing the reasons and rationale behind a phenomenon that cannot be explored quantitatively.

With QuestionPro, you can use it for qualitative data collection through various methods. Using Our robust suite correctly, you can enhance the quality and integrity of the collected data.

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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.

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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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Pritha Bhandari

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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.

• 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.

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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.

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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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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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Qualitative Study Design and Data Collection

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While the prior chapter set the stage for an understanding of the nature of qualitative evaluation, this chapter will offer strategies for planning a study and making decisions about how to gather data. The process is depicted as an iterative looping through steps beginning with idea generation to dissemination of results. It is critical that strategies for rigor be incorporated throughout the process. This chapter outlines methods for data collection utilizing interviews, focus groups, observation, and naturally occurring data, and then it also describes combinations often used together, which constitute toolkits of complementary techniques.

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Answers to Self-Tests

Self-test 15.1.

Which of the strategies to ensure study rigor is primarily employed in the qualitative study scenarios below:

Data from interviews about the usability of a resource are analyzed thematically. The evaluation study team looks to see if and how similar themes have arisen in earlier meetings of the team.

Audit trail

A member of the study team, who has recently participated in another study of a similar kind of resource, becomes concerned that that person’s views about the current study are being shaped by that previous experience. That person sits with another member of the study team to share that person’s concerns and put them in perspective.

Reflexivity

At a “town hall” meeting called to present the results of a qualitative study, the sponsor of the study raises deep and serious questions about the validity of the findings. The study team returns to notes from their team meetings to review how and based on what data they came to this conclusion.

Member checking

During an evaluation project team meeting, one of the study team members finds themselves deeply repelled by off-color comments made by one of the project staff. The team member makes a note of this personal response as part of field notes.

After interviewing 10 patients participating in a study, a study team member perceives that they are hearing the same points raised by all interviewees. The team member requests a study team meeting to consider reducing the total number of interviews from 20, as previously planned, to 12.

Data saturation

A study team member “corners” a participant in a system development effort following a meeting and asks for the participant’s impressions on what transpired in the meeting.

Self-Test 15.2

Label each of the following interview scenarios, conducted as part of a qualitative study, as representing the fully structured, semi-structured or unstructured approach.

A study team member “corners” a participant in a system development project following a meeting and asks for that person’s impressions on what transpired in the meeting.

A study team member schedules time with a patient who is using an information resource to acquire specific information about the patient’s medical history.

Likely fully structured, though it could generate discussion, in which case it could veer towards semi-structured.

A study team member works with partners on the study team to develop a set of questions to be asked to all interviewees. Each question is to be followed up with the question: “Why do you think this is the case?”. At the end of the interview, subjects will be asked: “What else would you like to tell us to shed light on these matters?”

Semi-structured

An interview begins with the statement: “In general, what has been your experience using this EHR?” The remaining questions depend on how the interviewee answers this opening question.

Unstructured

A set of specific questions are read verbatim from an interview guide. No other questions are asked. The interviewees’ responses are recorded.

Fully structured

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Friedman, C.P., Wyatt, J.C., Ash, J.S. (2022). Qualitative Study Design and Data Collection. In: Evaluation Methods in Biomedical and Health Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-86453-8_15

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8 Essential Qualitative Data Collection Methods

Qualitative data methods allow you to dive deep into the mindset of your audience to discover areas for growth, development, and improvement. 

British mathematician and marketing mastermind Clive Humby once famously stated that “Data is the new oil.”  He has a point. Without data, nonprofit organizations are left second-guessing what their clients and supporters think, how their brand compares to others in the market, whether their messaging is on-point, how their campaigns are performing, where improvements can be made, and how overall results can be optimized. 

There are two primary data collection methodologies: qualitative data collection and quantitative data collection. At UpMetrics, we believe that just relying on quantitative, static data is no longer an option to drive effective impact. In this guide, we’ll focus on qualitative data collection methods and how they can help you gather, analyze, and collate information that can help drive your organization forward. 

What is Qualitative Data? 

Data collection in qualitative research focuses on gathering contextual information. Unlike quantitative data, which focuses primarily on numbers to establish ‘how many’ or ‘how much,’ qualitative data collection tools allow you to assess the ‘why’s’ and ‘how’s’ behind those statistics. This is vital for nonprofits as it enables organizations to determine:

• Existing knowledge surrounding a particular issue.
• How social norms and cultural practices impact a cause.
• What kind of experiences and interactions people have with your brand.
• Trends in the way people change their opinions.
• Whether meaningful relationships are being established between all parties.

In short, qualitative data collection methods collect perceptual and descriptive information that helps you understand the reasoning and motivation behind particular reactions and behaviors. For that reason, qualitative data methods are usually non-numerical and center around spoken and written words rather than data extrapolated from a spreadsheet or report. 

Qualitative vs. Quantitative Data 

Quantitative and qualitative data represent both sides of the same coin. There will always be some degree of debate over the importance of quantitative vs. qualitative research, data, and collection. However, successful organizations should strive to achieve a balance between the two. 

Organizations can track their performance by collecting quantitative data based on metrics including dollars raised, membership growth, number of people served, overhead costs, etc. This is all essential information to have. However, the data lacks value without the additional details provided by qualitative research because it doesn’t tell you anything about how your target audience thinks, feels, and acts. 

Qualitative data collection is particularly relevant in the nonprofit sector as the relationships people have with the causes they support are fundamentally personal and cannot be expressed numerically. Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement. 

8 Types of Qualitative Data Collection Methods  

As we have firmly established the need for qualitative data, it’s time to answer the next big question: how to collect qualitative data. 

Here is a list of the most common qualitative data collection methods. You don’t need to use them all in your quest for gathering information. However, a foundational understanding of each will help you refine your research strategy and select the methods that are likely to provide the highest quality business intelligence for your organization. 

1. Interviews

One-on-one interviews are one of the most commonly used data collection methods in qualitative research because they allow you to collect highly personalized information directly from the source. Interviews explore participants' opinions, motivations, beliefs, and experiences and are particularly beneficial in gathering data on sensitive topics because respondents are more likely to open up in a one-on-one setting than in a group environment. 

Interviews can be conducted in person or by online video call. Typically, they are separated into three main categories:

• Structured Interviews - Structured interviews consist of predetermined (and usually closed) questions with little or no variation between interviewees. There is generally no scope for elaboration or follow-up questions, making them better suited to researching specific topics. 
• Unstructured Interviews – Conversely, unstructured interviews have little to no organization or preconceived topics and include predominantly open questions. As a result, the discussion will flow in completely different directions for each participant and can be very time-consuming. For this reason, unstructured interviews are generally only used when little is known about the subject area or when in-depth responses are required on a particular subject.
• Semi-Structured Interviews – A combination of the two interviews mentioned above, semi-structured interviews comprise several scripted questions but allow both interviewers and interviewees the opportunity to diverge and elaborate so more in-depth reasoning can be explored. 

While each approach has its merits, semi-structured interviews are typically favored as a way to uncover detailed information in a timely manner while highlighting areas that may not have been considered relevant in previous research efforts. Whichever type of interview you utilize, participants must be fully briefed on the format, purpose, and what you hope to achieve. With that in mind, here are a few tips to follow: 

• Give them an idea of how long the interview will last
• If you plan to record the conversation, ask permission beforehand
• Provide the opportunity to ask questions before you begin and again at the end. 

2. Focus Groups

Focus groups share much in common with less structured interviews, the key difference being that the goal is to collect data from several participants simultaneously. Focus groups are effective in gathering information based on collective views and are one of the most popular data collection instruments in qualitative research when a series of one-on-one interviews proves too time-consuming or difficult to schedule. 

Focus groups are most helpful in gathering data from a specific group of people, such as donors or clients from a particular demographic. The discussion should be focused on a specific topic and carefully guided and moderated by the researcher to determine participant views and the reasoning behind them. 

Feedback in a group setting often provides richer data than one-on-one interviews, as participants are generally more open to sharing when others are sharing too. Plus, input from one participant may spark insight from another that would not have come to light otherwise. However, here are a couple of potential downsides:

• If participants are uneasy with each other, they may not be at ease openly discussing their feelings or opinions.
• If the topic is not of interest or does not focus on something participants are willing to discuss, data will lack value. 

The size of the group should be carefully considered. Research suggests over-recruiting to avoid risking cancellation, even if that means moderators have to manage more participants than anticipated. The optimum group size is generally between six and eight for all participants to be granted ample opportunity to speak. However, focus groups can still be successful with as few as three or as many as fourteen participants. 

3. Observation

Observation is one of the ultimate data collection tools in qualitative research for gathering information through subjective methods. A technique used frequently by modern-day marketers, qualitative observation is also favored by psychologists, sociologists, behavior specialists, and product developers. 

The primary purpose is to gather information that cannot be measured or easily quantified. It involves virtually no cognitive input from the participants themselves. Researchers simply observe subjects and their reactions during the course of their regular routines and take detailed field notes from which to draw information. 

Observational techniques vary in terms of contact with participants. Some qualitative observations involve the complete immersion of the researcher over a period of time. For example, attending the same church, clinic, society meetings, or volunteer organizations as the participants. Under these circumstances, researchers will likely witness the most natural responses rather than relying on behaviors elicited in a simulated environment. Depending on the study and intended purpose, they may or may not choose to identify themselves as a researcher during the process. 

Regardless of whether you take a covert or overt approach, remember that because each researcher is as unique as every participant, they will have their own inherent biases. Therefore, observational studies are prone to a high degree of subjectivity. For example, one researcher’s notes on the behavior of donors at a society event may vary wildly from the next. So, each qualitative observational study is unique in its own right. 

4. Open-Ended Surveys and Questionnaires

Open-ended surveys and questionnaires allow organizations to collect views and opinions from respondents without meeting in person. They can be sent electronically and are considered one of the most cost-effective qualitative data collection tools. Unlike closed question surveys and questionnaires that limit responses, open-ended questions allow participants to provide lengthy and in-depth answers from which you can extrapolate large amounts of data. 

The findings of open-ended surveys and questionnaires can be challenging to analyze because there are no uniform answers. A popular approach is to record sentiments as positive, negative, and neutral and further dissect the data from there. To gather the best business intelligence, carefully consider the presentation and length of your survey or questionnaire. Here is a list of essential considerations:

• Number of questions : Too many can feel intimidating, and you’ll experience low response rates. Too few can feel like it’s not worth the effort. Plus, the data you collect will have limited actionability. The consensus on how many questions to include varies depending on which sources you consult. However, 5-10 is a good benchmark for shorter surveys that take around 10 minutes and 15-20 for longer surveys that take approximately 20 minutes to complete. 
• Personalization: Your response rate will be higher if you greet patients by name and demonstrate a historical knowledge of their interactions with your brand. 
• Visual elements : Recipients can be easily turned off by poorly designed questionnaires. Besides, it’s a good idea to customize your survey template to include brand assets like colors, logos, and fonts to increase brand loyalty and recognition.
• Reminders : Sending survey reminders is the best way to improve your response rate. You don’t want to hassle respondents too soon, nor do you want to wait too long. Sending a follow-up at around the 3-7 mark is usually the most effective. 
• Building a feedback loop : Adding a tick-box requesting permission for further follow-ups is a proven way to elicit more in-depth feedback. Plus, it gives respondents a voice and makes their opinion feel valued.

5. Case Studies

Case studies are often a preferred method of qualitative research data collection for organizations looking to generate incredibly detailed and in-depth information on a specific topic. Case studies are usually a deep dive into one specific case or a small number of related cases. As a result, they work well for organizations that operate in niche markets.

Case studies typically involve several qualitative data collection methods, including interviews, focus groups, surveys, and observation. The idea is to cast a wide net to obtain a rich picture comprising multiple views and responses. When conducted correctly, case studies can generate vast bodies of data that can be used to improve processes at every client and donor touchpoint. 

The best way to demonstrate the purpose and value of a case study is with an example: A Longitudinal Qualitative Case Study of Change in Nonprofits – Suggesting A New Approach to the Management of Change . 

The researchers established that while change management had already been widely researched in commercial and for-profit settings, little reference had been made to the unique challenges in the nonprofit sector. The case study examined change and change management at a single nonprofit hospital from the viewpoint of all those who witnessed and experienced it. To gain a holistic view of the entire process, research included interviews with employees at every level, from nursing staff to CEOs, to identify the direct and indirect impacts of change. Results were collated based on detailed responses to questions about preparing for change, experiencing change, and reflecting on change.

6. Text Analysis

Text analysis has long been used in political and social science spheres to gain a deeper understanding of behaviors and motivations by gathering insights from human-written texts. By analyzing the flow of text and word choices, relationships between other texts written by the same participant can be identified so that researchers can draw conclusions about the mindset of their target audience. Though technically a qualitative data collection method, the process can involve some quantitative elements, as often, computer systems are used to scan, extract, and categorize information to identify patterns, sentiments, and other actionable information. 

You might be wondering how to collect written information from your research subjects. There are many different options, and approaches can be overt or covert. 

Examples include:

• Investigating how often certain cause-related words and phrases are used in client and donor social media posts.
• Asking participants to keep a journal or diary.
• Analyzing existing interview transcripts and survey responses.

By conducting a detailed analysis, you can connect elements of written text to specific issues, causes, and cultural perspectives, allowing you to draw empirical conclusions about personal views, behaviors, and social relations. With small studies focusing on participants' subjective experience on a specific theme or topic, diaries and journals can be particularly effective in building an understanding of underlying thought processes and beliefs. 

7. Audio and Video Recordings

Similarly to how data is collected from a person’s writing, you can draw valuable conclusions by observing someone’s speech patterns, intonation, and body language when you watch or listen to them interact in a particular environment or within specific surroundings. 

Video and audio recordings are helpful in circumstances where researchers predict better results by having participants be in the moment rather than having them think about what to write down or how to formulate an answer to an email survey. 

You can collect audio and video materials for analysis from multiple sources, including:

• Previously filmed records of events
• Interview recordings
• Video diaries

Utilizing audio and video footage allows researchers to revisit key themes, and it's possible to use the same analytical sources in multiple studies – providing that the scope of the original recording is comprehensive enough to cover the intended theme in adequate depth. 

It can be challenging to present the results of audio and video analysis in a quantifiable form that helps you gauge campaign and market performance. However, results can be used to effectively design concept maps that extrapolate central themes that arise consistently. Concept Mapping offers organizations a visual representation of thought patterns and how ideas link together between different demographics. This data can prove invaluable in identifying areas for improvement and change across entire projects and organizational processes. 

8. Hybrid Methodologies

It is often possible to utilize data collection methods in qualitative research that provide quantitative facts and figures. So if you’re struggling to settle on an approach, a hybrid methodology may be a good starting point. For instance, a survey format that asks closed and open questions can collect and collate quantitative and qualitative data. 

A Net Promoter Score (NPS) survey is a great example. The primary goal of an NPS survey is to collect quantitative ratings of various factors on a score of 1-10. However, they also utilize open-ended follow-up questions to collect qualitative data that helps identify insights into the trends, thought processes, reasoning, and behaviors behind the initial scoring. 

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• Volume 21, Issue 3
• Data collection in qualitative research
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• David Barrett 1 ,
• http://orcid.org/0000-0003-1130-5603 Alison Twycross 2
• 1 Faculty of Health Sciences , University of Hull , Hull , UK
• 2 School of Health and Social Care , London South Bank University , London , UK
• Correspondence to Dr David Barrett, Faculty of Health Sciences, University of Hull, Hull HU6 7RX, UK; D.I.Barrett{at}hull.ac.uk

https://doi.org/10.1136/eb-2018-102939

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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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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

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Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

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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.

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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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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.

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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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• Set-up & Conduct

Qualitative research

• Quality control procedures checklist

To describe the different methods that are used in qualitative research and provide relevant literature for more information on these types of methods.

Documentation

• Field notes
• Topic lists
• Coding information for transcripts

Qualitative research methods

Different methods of data-collection are used in qualitative research. The most common are interviews, focus group discussions, observational methods and document analysis. A relatively new method is an art-based data-collection method. Combining two or more data collection methods, for instance interviews as well as focus groups (‘data triangulation’) enhances the credibility of the study. Irrespective of the data collection method applied, it is important to keep a diary during the study, with reflections on the process (e.g. regarding method and participant selection) and the role and influence of the researcher (‘reflexivity’).

Example using methodological triangulation:

• Sewdas, R. et al. (2017) Why older workers work beyond the retirement age: a qualitative study. BMC Public Health, 17: 672. This study is based on a combination of semi-structured telephone interviews and focus groups.

Interviews are useful to explore experiences, views, opinions, or beliefs on specific matters. Transcripts of interviews can be explored and compared to others, to develop an understanding of the underlying structures of beliefs (See chapter 4 in Green & Thorogood, 2010). There are different grades of structuring the interview: structured, semi-structured or open/in-depth. Often the researcher develops a topic list before the start of the interview, which can be used in a flexible manner. As the interview is a product of interaction between the researcher and the interviewee, the setting and skills of the researcher are of importance (e.g. the ability to build a sense of trust (developing rapport), the way of phrasing questions, give the interviewee room to tell a story, (body language). Furthermore, it is important to think about the type of transcription of audio tapes. Finally, it may be important to make notes after each interview on the type of setting, non-verbal signals (body language) of an interviewee and other important things/events that may have happened during the interview and might play a role on the gathered data.

• Bakker, M. et al. (2015) Need and value of case management in multidisciplinary ALS care: A qualitative study on the perspectives of patients, spousal caregivers and professionals. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 16(3-4), 180-186.
• Bakker, M. et al. (2016) Experiences and perspectives of patients with post-polio syndrome and therapists with exercise and cognitive behavioural therapy. BMC neurology, 16(1), 23-34.
• Ockhuijsen, H.D.L. et al. (2014) Pregnancy After Miscarriage: Balancing between Loss of Control and Searching for Control. Research in Nursing & Health, 37, 267-275.

Focus group discussions

A focus group is a meeting where a group of people discuss a certain topic to examine their views/experiences on this particular topic. These discussions are useful to examine underlying reasons, motives, values and beliefs. The researcher stimulates discussion in order to examine how knowledge and ideas develop and operate in a given group. Most of the time, a facilitator guides a discussion about a particular topic in a group of usually 6-12 people. Some sensitive issues might be easier to discuss within a group, although other (personal) information might be withheld, for instance when persons are not acquainted with each other or because of hierarchical relations within the group. Therefore, a group of people need to have a certain homogeneity to be as comfortable as possible for discussing a certain topic within a group.

The role of the facilitator is to create an open atmosphere, involve participants in the discussion and manage this discussion. The organization of a focus group requires careful attention. This includes the sampling and recruitment of participants, the composition of the topic list and how the data will be collected. Each focus group has a unique design (a script instead of a topic list for an interview) that involve various exercises to stimulate discussion and reflection between participants. Exercises can also stimulate creative ideas and getting someone to tell a story.

Next to the facilitator, it might also be useful to include an observer. The observer could be useful for taking notes on non-verbal signs (body language), take notes of the most important things that have been said, support the facilitator in the discussion, and keep track of the time. In this way, the facilitator can concentrate on guiding the discussion.

• Middelweerd, A. et al. (2015) What features do Dutch university students prefer in a smartphone application for promotion of physical activity? A qualitative approach. International Journal of Behavioral Nutrition and Physical Activity, 12: 31.
• Pitts MJ, Adams Tufts K. (2013) ‘Implications of the Virginia human papillomavirus vaccine mandate for parental vaccine acceptance.’ Qualitative Health Research. 2013.
• Tausch AP, Menold N. Methodological Aspects of Focus Groups in Health Research: Results of Qualitative Interviews With Focus Group Moderators Global Qualitative Nursing Research. 2016;3:1-12. DOI: 10.1177/2333393616630466 https://doi-org.vunl.idm.oclc.org/10.1177/2333393616630466

Observational methods

Observational methods are used to understand phenomena by studying people’s accounts and actions in an everyday context. There are different types of observations, with various degrees of research participation, like non-participating observation (e.g. by using video recordings), and participant observation or ethnography. Ethnography ‘usually involves the researcher participating, overtly or covertly, in people’s daily lives for an extended period of time, watching what happens, listening to what is said, and/or asking questions through informal and formal interviews, collecting documents and artefacts’ (Hammersley & Atkinson, 2007: 3).

Observations are focused on gaining an in-depth understanding of a specific context. Observations can, just as interviews, be conducted at the continuum of open-ended and focused observations (Mortelmans, 2020). An “observation matrix” or “observation guide” is used to guide the observations. Observations are often conducted by one researcher who can build up relations with people in the field they are researching. Triangulation of researchers can contribute to the quality of the study, as it enables researchers to critically reflect upon their perspectives and how they make meaning of “the observed”. 

• Pasman, H.R.W. et al. (2003) Feeding nursing home patients with severe dementia: A qualitative study. Journal of Advanced Nursing, 42, 3, 304-311. This paper is based on participant observation by two researchers in two Dutch nursing homes.

Document analysis

Document analysis is based on existing sources, like government reports, personal documents, articles in newspapers, medical curricula, books or medical records. Document analysis is often employed to conduct a discourse analysis or to conduct a policy analysis. Document analysis can also be done to support or inform other methods of data collection such as interviews or observations (i.e. data-triangulation).

• Stuij, M. & Stokvis, R. (2015) Sport, health and the genesis of a physical activity policy in the Netherlands. International Journal of Sport Policy and Politics. 7 (2): 217-232. This paper is based on an analysis of policy documents and existing surveys on sport participation.
• Muntinga, M. E., Krajenbrink, V. Q. E., Peerdeman, S. M., Croiset, G., & Verdonk, P. (2016). Toward diversity-responsive medical education: taking an intersectionality-based approach to a curriculum evaluation.  Advances in Health Sciences Education ,  21 (3), 541-559. This paper is based on an analysis of medical curricula from an intersectional perspective.

Arts-based methods of data-collection

Arts-based methods of data-collection receive growing attention from qualitative health scholars (Mitchell et al, 2017). Arts-based methods allow participants in qualitative research to express their lived experiences in non-verbal ways, which can support an understanding of their lived experiences in other ways than interviews, focus groups of observations can do. One example of arts-based research is photovoice (Wang, 1997), which is increasingly often used in different groups such as children (Abma & Schrijver, 2020). Photovoice can be used alone, or in combination with interviews or focus groups (Sarti et al, 2019). 

• Abma, T. A., & Schrijver, J. (2020). ‘Are we famous or something?’ Participatory Health Research with children using photovoice.  Educational Action Research ,  28 (3), 405-426.
• Sarti, A., Dedding, C., & Bunders, J. F. (2019). Beyond a deficiencies approach: Towards a more integral representation of the everyday life of children growing up in contexts of poverty.  Qualitative Social Work ,  18 (5), 818-833.
• Vaart, G. V. D., Hoven, B. V., & Huigen, P. P. (2018). Creative and arts-based research methods in academic research: Lessons from a participatory research project in the Netherlands. Forum: Qualitative Social Research , 19 (2), 30.

Quality procedures

Devers (1999) formulated several strategies for enhancing the rigor of qualitative research:

Documenting your Quality control procedures

To ensure proper data collection including quality control and change control procedures are applied, see Checklist Quality control procedures and have your DMP checked by a (research) data management consultant.

Responsibilities and ethics

It is important to carefully reflect on and think about ethical dilemmas related to the practice of qualitative research as well as responsibilities of the researchers, especially regarding respondents. Procedures related to informed consent, protecting the privacy of respondents often require additional reflection from researchers, for example when qualitative research is conducted in a specific setting (ethnographic research or case studies) and standard procedures such as pseudonimizing or encoding the data are not sufficient to ensure privacy. Also, the nature of qualitative research requires personal contact between researcher and researched, for example during interviews or observations. This can also yield dilemmas that cannot be anticipated upon with standard ethics procedures, but require moral reflection during the research process. Please consult the  guideline on privacy . For specifics in a study, or contact the privacy officer /data protection officer and read into ‘procedural’ ‘situational’ and ‘relational ethics’ in qualitative research (e.g. Wijngaarden et al, 2018; Mortelmans, 2018).

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Data Collection Methods

Qualitative data-collection methods

5 Essential qualitative data collection methods

One-on-one interviews, open-ended surveys and questionnaires, focus groups, observation, case studies.

Data collection is an important tool for understanding the behavior and motivations of your audience. It helps you gather intel on the kinds of products, services, and initiatives they’d like to see. Good data also makes it easier for you to identify ways to improve the experience they have with your organization at every touchpoint.

There are two main kinds of data collection — qualitative data collection and quantitative data collection . We’re going to focus on qualitative data-collection methods here.

Learn how to gather data the right way – check out free tips on data collection from Jotform.

What’s the difference between qualitative and quantitative data?

Quantitative data is numerical data that can be ranked, categorized, and measured — like the number of customers who spent over $50 last year, the gross or net profit for a specific time period, or the number of subscribers who converted into buyers from an email marketing campaign.

This kind of data answers closed-ended questions, such as “how many” or “how much,” and you can collect it through surveys, polls , and questionnaires.

Qualitative data is descriptive rather than numerical, and it looks for context — it’s about people’s perceptions. You gather it to understand the reasons and motivations that drive certain behavior. For example, qualitative data can reveal people’s feelings and opinions about your organization, and you can use it to determine why customers buy your products (or don’t) .

Quantitative data can tell you about your market share, the demographics of your customers, and how often they buy your products or use your services. Basically, qualitative data can give you the story behind the story. One way to gather this data is through open-ended surveys and questionnaires , but let’s look at a few more.

Face-to-face interviews are one of the most common qualitative data-collection methods; they’re a great approach when you need to gather highly personalized information.

Informal, conversational interviews are ideal for asking open-ended questions , which allow you to gain rich, detailed context and gather in-depth insights into participants’ opinions, experiences, and behavior. And because the interview format provides an opportunity to ask follow-up questions, you’ll be able to fill in any information gaps and gather additional insights.

You can also apply a mixed method by gathering information beforehand using one of Jotform’s interview forms .

Open-ended surveys and questionnaires allow participants to answer freely at length, rather than choosing from a set number of responses, so you get more nuanced and detailed answers. For example, you might ask an open-ended question like “Why don’t you eat ABC brand pizza?”

You would then provide space for people to answer narratively with their opinion or perspective, rather than simply giving them a specific selection of responses to choose from like “I’m a vegan,” “It’s too expensive,” or “I don’t like pizza.”

And because surveys and questionnaires can be executed online, they’re easier to distribute, allow for wider reach, and give you the ability to collect more qualitative data, more easily.

You can easily create a survey using Jotform’s free online survey maker . Simply add your questions, set up conditional logic, and share your custom survey online to start collecting responses instantly.

Focus groups are similar to interviews, except that you conduct them in a group format. You might use a focus group when one-on-one interviews are too difficult or time-consuming to schedule. A focus group will typically consist of eight to 10 people.

Focus group participants are chosen based on the topic the researcher wants to gather insights about. For example, a focus group might be formed to provide feedback on a new marketing campaign from a number of demographically similar people in your target market or to allow people to share their views on a new product.

Qualitative data collected through focus groups can be highly detailed and descriptive, providing the ideal opportunity to gather nuanced information in a short period of time.

Observation is a traditional qualitative data-collection method in which researchers observe subjects in the course of their regular routines, take detailed field notes, and/or record subjects via video or audio. This method is useful when you want to gain a more refined understanding of participants in their natural environment.

The two main types of observation are covert and overt.

As the name suggests, covert observation is when researchers are concealed, and participants don’t know they’re being observed. For example, researchers may embed themselves in public places like stores or restaurants, or in online chat rooms, in order to gather data in a natural setting.

Overt observation is when participants are aware they’re being studied. This method allows researchers to ask questions and record conversations.

Use Jotform’s free, customizable online observation form to get started.

In the case study method, you analyze a combination of multiple qualitative data sources to draw inferences and come to conclusions.Case studies are typically structured to explore a problem, a solution, and the impact of the solution.

If you’re in the marketing space, you’ve likely seen case studies used to highlight the impact of a company’s service within a specific target market. For example, an advertising agency might use a case study to showcase how it helped a healthcare client improve the customer service experience across digital channels, or how it helped a technology client launch a campaign for a new laptop.

The case study would include the situation before and after the agency’s intervention, demonstrating the successful results of the agency’s work.

When should you use qualitative vs quantitative data-collection methods?

To get the best results, you need to rely on both quantitative and qualitative data-collection methods. You’ll get deeper insights when you use a combination of the two.

Qualitative research offers context and nuance, and it can help you develop a fuller understanding of the complexities of human behavior related to your organization’s products or services. It’s data wrapped in a rich contextual story.

That said, qualitative data research can take a lot of time to collect and analyze, and it can sometimes result in biased conclusions. That’s why you should pair it with quantitative data collection to get the best, most accurate information.

For businesses and organizations that want to improve their marketing and outreach game, these qualitative data research methods can help:

• Determine obstacles to purchasing your company’s products or using your company’s services.
• Gain insights into how clients and customers perceive your brand.
• Identify areas for improvement across the entire brand discovery and buying cycle.
• Pinpoint where your messaging may be muddled or confusing.
• Discover in-demand product features to implement.
• Understand how your brand compares to others in the market.
• Gauge marketing campaign performance.
• Identify how your website , apps, and other online assets are performing.

Jotform is a great resource for creating the kind of open-ended surveys and questionnaires that you need for qualitative data collection. And you can use Jotform for quantitative data collection as well. Whichever data-collection method you choose, Jotform can help with a wide range of surveys and questionnaires .

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4 Comments:

More than a year ago

Clearly explained and strait to the point.Thank you

simple and clear English, easy to understand. Thank you.

I have learnt the difference between the qualitative and quantitative data and also tools of collecting such data. Simple clear language used. Thanks a lot.

THANKS TO JOTFORM FOR SUCH A HIGHLY INFORMATIVE ARTICLE, I HAVE LEARNT ALOT FROM IT. I NOW HAVE ADEQUATE ANSWERS FOR ME TO TACKLE MY ASSIGNMENTS ON QUALITATIVE AND QUANTITAVE DATA.