how to present results in qualitative research

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

• The particular approach to data collection used in the study;
• Any theoretical perspective(s) that shaped your data collection and analytical approach;
• When the study occurred, over how long, and where (concealing identifiable details as needed);
• A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
• The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
• The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

• Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
• Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
• Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
• Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
• Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
• Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

• AI Templates
• Get a demo Sign up for free Log in Log in

Buttoning up research: How to present and visualize qualitative data

15 Minute Read

There is no doubt that data visualization is an important part of the qualitative research process. Whether you're preparing a presentation or writing up a report, effective visualizations can help make your findings clear and understandable for your audience. 

In this blog post, we'll discuss some tips for creating effective visualizations of qualitative data. 

First, let's take a closer look at what exactly qualitative data is.

What is qualitative data?

Qualitative data is information gathered through observation, questionnaires, and interviews. It's often subjective, meaning that the researcher has to interpret it to draw meaningful conclusions from it. 

The difference between qualitative data and quantitative data

When researchers use the terms qualitative and quantitative, they're referring to two different types of data. Qualitative data is subjective and descriptive, while quantitative data is objective and numerical.

Qualitative data is often used in research involving psychology or sociology. This is usually where a researcher may be trying to identify patterns or concepts related to people's behavior or attitudes. It may also be used in research involving economics or finance, where the focus is on numerical values such as price points or profit margins. 

Before we delve into how best to present and visualize qualitative data, it's important that we highlight how to be gathering this data in the first place. ‍

How best to gather qualitative data

In order to create an effective visualization of qualitative data, ensure that the right kind of information has been gathered. 

Here are six ways to gather the most accurate qualitative data:

• Define your research question: What data is being set out to collect? A qualitative research question is a definite or clear statement about a condition to be improved, a project’s area of concern, a troubling question that exists, or a difficulty to be eliminated. It not only defines who the participants will be but guides the data collection methods needed to achieve the most detailed responses.
• ‍ Determine the best data collection method(s): The data collected should be appropriate to answer the research question. Some common qualitative data collection methods include interviews, focus groups, observations, or document analysis. Consider the strengths and weaknesses of each option before deciding which one is best suited to answer the research question.  ‍
• Develop a cohesive interview guide: Creating an interview guide allows researchers to ask more specific questions and encourages thoughtful responses from participants. It’s important to design questions in such a way that they are centered around the topic of discussion and elicit meaningful insight into the issue at hand. Avoid leading or biased questions that could influence participants’ answers, and be aware of cultural nuances that may affect their answers.
• ‍ Stay neutral – let participants share their stories: The goal is to obtain useful information, not to influence the participant’s answer. Allowing participants to express themselves freely will help to gather more honest and detailed responses. It’s important to maintain a neutral tone throughout interviews and avoid judgment or opinions while they are sharing their story. 
• ‍ Work with at least one additional team member when conducting qualitative research: Participants should always feel comfortable while providing feedback on a topic, so it can be helpful to have an extra team member present during the interview process – particularly if this person is familiar with the topic being discussed. This will ensure that the atmosphere of the interview remains respectful and encourages participants to speak openly and honestly.
• ‍ Analyze your findings: Once all of the data has been collected, it’s important to analyze it in order to draw meaningful conclusions. Use tools such as qualitative coding or content analysis to identify patterns or themes in the data, then compare them with prior research or other data sources. This will help to draw more accurate and useful insights from the results. 

By following these steps, you will be well-prepared to collect and analyze qualitative data for your research project. Next, let's focus on how best to present the qualitative data that you have gathered and analyzed.

Create your own AI-powered templates for better, faster research synthesis. Discover new customer insights from data instantly.

The top 10 things Notably shipped in 2023 and themes for 2024.

How to visually present qualitative data.

When it comes to how to present qualitative data visually, the goal is to make research findings clear and easy to understand. To do this, use visuals that are both attractive and informative. 

Presenting qualitative data visually helps to bring the user’s attention to specific items and draw them into a more in-depth analysis. Visuals provide an efficient way to communicate complex information, making it easier for the audience to comprehend. 

Additionally, visuals can help engage an audience by making a presentation more interesting and interactive.

Here are some tips for creating effective visuals from qualitative data:

• ‍ Choose the right type of visualization: Consider which type of visual would best convey the story that is being told through the research. For example, bar charts or line graphs might be appropriate for tracking changes over time, while pie charts or word clouds could help show patterns in categorical data. 
• ‍ Include contextual information: In addition to showing the actual numbers, it's helpful to include any relevant contextual information in order to provide context for the audience. This can include details such as the sample size, any anomalies that occurred during data collection, or other environmental factors.
• ‍ Make it easy to understand: Always keep visuals simple and avoid adding too much detail or complexity. This will help ensure that viewers can quickly grasp the main points without getting overwhelmed by all of the information. 
• ‍ Use color strategically: Color can be used to draw attention to certain elements in your visual and make it easier for viewers to find the most important parts of it. Just be sure not to use too many different colors, as this could create confusion instead of clarity. 
• ‍ Use charts or whiteboards: Using charts or whiteboards can help to explain the data in more detail and get viewers engaged in a discussion. This type of visual tool can also be used to create storyboards that illustrate the data over time, helping to bring your research to life. 

Visualizing qualitative data in Notably

Notably helps researchers visualize their data on a flexible canvas, charts, and evidence based insights. As an all-in-one research platform, Notably enables researchers to collect, analyze and present qualitative data effectively.

Notably provides an intuitive interface for analyzing data from a variety of sources, including interviews, surveys, desk research, and more. Its powerful analytics engine then helps you to quickly identify insights and trends in your data . Finally, the platform makes it easy to create beautiful visuals that will help to communicate research findings with confidence. 

Research Frameworks in Analysis

The canvas in Analysis is a multi-dimensional workspace to play with your data spatially to find likeness and tension. Here, you may use a grounded theory approach to drag and drop notes into themes or patterns that emerge in your research. Utilizing the canvas tools such as shapes, lines, and images, allows researchers to build out frameworks such as journey maps, empathy maps, 2x2's, etc. to help synthesize their data.

Going one step further, you may begin to apply various lenses to this data driven canvas. For example, recoloring by sentiment shows where pain points may distributed across your customer journey. Or, recoloring by participant may reveal if one of your participants may be creating a bias towards a particular theme.

Exploring Qualitative Data through a Quantitative Lens

Once you have begun your analysis, you may visualize your qualitative data in a quantitative way through charts. You may choose between a pie chart and or a stacked bar chart to visualize your data. From here, you can segment your data to break down the ‘bar’ in your bar chart and slices in your pie chart one step further.

To segment your data, you can choose between ‘Tag group’, ‘Tag’, ‘Theme’, and ‘Participant'. Each group shows up as its own bar in the bar chart or slice in the pie chart. For example, try grouping data as ‘Participant’ to see the volume of notes assigned to each person. Or, group by ‘Tag group’ to see which of your tag groups have the most notes.

Depending on how you’ve grouped or segmented your charts will affect the options available to color your chart. Charts use colors that are a mix of sentiment, tag, theme, and default colors. Consider color as a way of assigning another layer of meaning to your data. For example, choose a red color for tags or themes that are areas of friction or pain points. Use blue for tags that represent opportunities.

AI Powered Insights and Cover Images

One of the most powerful features in Analysis is the ability to generate insights with AI. Insights combine information, inspiration, and intuition to help bridge the gap between knowledge and wisdom. Even before you have any tags or themes, you may generate an AI Insight from your entire data set. You'll be able to choose one of our AI Insight templates that are inspired by trusted design thinking frameworks to stimulate generative, and divergent thinking. With just the click of a button, you'll get an insight that captures the essence and story of your research. You may experiment with a combination of tags, themes, and different templates or, create your own custom AI template. These insights are all evidence-based, and are centered on the needs of real people. You may package these insights up to present your research by embedding videos, quotes and using AI to generate unique cover image.

You can sign up to run an end to end research project for free and receive tips on how to make the most out of your data. Want to chat about how Notably can help your team do better, faster research? Book some time here for a 1:1 demo with your whole team.

Introducing Notably + Miro Integration: 3 Tips to Analyze Miro Boards with AI in Notably

5 Steps to turn data into insights with Notably

Give your research synthesis superpowers..

Try Teams for 7 days

Free for 1 project

blog @ precision

Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD Cand). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter – exciting! But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step.  

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips and tricks for an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

The Ultimate Guide to Qualitative Research - Part 3: Presenting Qualitative Data

• Introduction

How do you present qualitative data?

Data visualization.

• Research paper writing
• Transparency and rigor in research
• How to publish a research paper

Table of contents

• Transparency and rigor

Navigate to other guide parts:

Part 1: The Basics or Part 2: Handling Qualitative Data

• Presenting qualitative data

In the end, presenting qualitative research findings is just as important a skill as mastery of qualitative research methods for the data collection and data analysis process . Simply uncovering insights is insufficient to the research process; presenting a qualitative analysis holds the challenge of persuading your audience of the value of your research. As a result, it's worth spending some time considering how best to report your research to facilitate its contribution to scientific knowledge.

When it comes to research, presenting data in a meaningful and accessible way is as important as gathering it. This is particularly true for qualitative research , where the richness and complexity of the data demand careful and thoughtful presentation. Poorly written research is taken less seriously and left undiscussed by the greater scholarly community; quality research reporting that persuades its audience stands a greater chance of being incorporated in discussions of scientific knowledge.

Qualitative data presentation differs fundamentally from that found in quantitative research. While quantitative data tend to be numerical and easily lend themselves to statistical analysis and graphical representation, qualitative data are often textual and unstructured, requiring an interpretive approach to bring out their inherent meanings. Regardless of the methodological approach , the ultimate goal of data presentation is to communicate research findings effectively to an audience so they can incorporate the generated knowledge into their research inquiry.

As the section on research rigor will suggest, an effective presentation of your research depends on a thorough scientific process that organizes raw data into a structure that allows for a thorough analysis for scientific understanding.

Preparing the data

The first step in presenting qualitative data is preparing the data. This preparation process often begins with cleaning and organizing the data. Cleaning involves checking the data for accuracy and completeness, removing any irrelevant information, and making corrections as needed. Organizing the data often entails arranging the data into categories or groups that make sense for your research framework.

Coding the data

Once the data are cleaned and organized, the next step is coding , a crucial part of qualitative data analysis. Coding involves assigning labels to segments of the data to summarize or categorize them. This process helps to identify patterns and themes in the data, laying the groundwork for subsequent data interpretation and presentation. Qualitative research often involves multiple iterations of coding, creating new and meaningful codes while discarding unnecessary ones , to generate a rich structure through which data analysis can occur.

Uncovering insights

As you navigate through these initial steps, keep in mind the broader aim of qualitative research, which is to provide rich, detailed, and nuanced understandings of people's experiences, behaviors, and social realities. These guiding principles will help to ensure that your data presentation is not only accurate and comprehensive but also meaningful and impactful.

While this process might seem intimidating at first, it's an essential part of any qualitative research project. It's also a skill that can be learned and refined over time, so don't be discouraged if you find it challenging at first. Remember, the goal of presenting qualitative data is to make your research findings accessible and understandable to others. This requires careful preparation, a clear understanding of your data, and a commitment to presenting your findings in a way that respects and honors the complexity of the phenomena you're studying.

In the following sections, we'll delve deeper into how to create a comprehensive narrative from your data, the visualization of qualitative data , and the writing and publication processes . Let's briefly excerpt some of the content in the articles in this part of the guide.

ATLAS.ti helps you make sense of your data

Find out how with a free trial of our powerful data analysis interface.

How often do you read a research article and skip straight to the tables and figures? That's because data visualizations representing qualitative and quantitative data have the power to make large and complex research projects with thousands of data points comprehensible when authors present data to research audiences. Researchers create visual representations to help summarize the data generated from their study and make clear the pathways for actionable insights.

In everyday situations, a picture is always worth a thousand words. Illustrations, figures, and charts convey messages that words alone cannot. In research, data visualization can help explain scientific knowledge, evidence for data insights, and key performance indicators in an orderly manner based on data that is otherwise unstructured.

For all of the various data formats available to researchers, a significant portion of qualitative and social science research is still text-based. Essays, reports, and research articles still rely on writing practices aimed at repackaging research in prose form. This can create the impression that simply writing more will persuade research audiences. Instead, framing research in terms that are easy for your target readers to understand makes it easier for your research to become published in peer-reviewed scholarly journals or find engagement at scholarly conferences. Even in market or professional settings, data visualization is an essential concept when you need to convince others about the insights of your research and the recommendations you make based on the data.

Importance of data visualization

Data visualization is important because it makes it easy for your research audience to understand your data sets and your findings. Also, data visualization helps you organize your data more efficiently. As the explanation of ATLAS.ti's tools will illustrate in this section, data visualization might point you to research inquiries that you might not even be aware of, helping you get the most out of your data. Strictly speaking, the primary role of data visualization is to make the analysis of your data , if not the data itself, clear. Especially in social science research, data visualization makes it easy to see how data scientists collect and analyze data.

Prerequisites for generating data visualizations

Data visualization is effective in explaining research to others only if the researcher or data scientist can make sense of the data in front of them. Traditional research with unstructured data usually calls for coding the data with short, descriptive codes that can be analyzed later, whether statistically or thematically. These codes form the basic data points of a meaningful qualitative analysis. They represent the structure of qualitative data sets, without which a scientific visualization with research rigor would be extremely difficult to achieve. In most respects, data visualization of a qualitative research project requires coding the entire data set so that the codes adequately represent the collected data.

A successfully crafted research study culminates in the writing of the research paper . While a pilot study or preliminary research might guide the research design , a full research study leads to discussion that highlights avenues for further research. As such, the importance of the research paper cannot be overestimated in the overall generation of scientific knowledge.

The physical and natural sciences tend to have a clinical structure for a research paper that mirrors the scientific method: outline the background research, explain the materials and methods of the study, outline the research findings generated from data analysis, and discuss the implications. Qualitative research tends to preserve much of this structure, but there are notable and numerous variations from a traditional research paper that it's worth emphasizing the flexibility in the social sciences with respect to the writing process.

Requirements for research writing

While there aren't any hard and fast rules regarding what belongs in a qualitative research paper , readers expect to find a number of pieces of relevant information in a rigorously-written report. The best way to know what belongs in a full research paper is to look at articles in your target journal or articles that share a particular topic similar to yours and examine how successfully published papers are written.

It's important to emphasize the more mundane but equally important concerns of proofreading and formatting guidelines commonly found when you write a research paper. Research publication shouldn't strictly be a test of one's writing skills, but acknowledging the importance of convincing peer reviewers of the credibility of your research means accepting the responsibility of preparing your research manuscript to commonly accepted standards in research.

As a result, seemingly insignificant things such as spelling mistakes, page numbers, and proper grammar can make a difference with a particularly strict reviewer. Even when you expect to develop a paper through reviewer comments and peer feedback, your manuscript should be as close to a polished final draft as you can make it prior to submission.

Qualitative researchers face particular challenges in convincing their target audience of the value and credibility of their subsequent analysis. Numbers and quantifiable concepts in quantitative studies are relatively easier to understand than their counterparts associated with qualitative methods . Think about how easy it is to make conclusions about the value of items at a store based on their prices, then imagine trying to compare those items based on their design, function, and effectiveness.

Qualitative research involves and requires these sorts of discussions. The goal of qualitative data analysis is to allow a qualitative researcher and their audience to make such determinations, but before the audience can accept these determinations, the process of conducting research that produces the qualitative analysis must first be seen as trustworthy. As a result, it is on the researcher to persuade their audience that their data collection process and subsequent analysis is rigorous.

Qualitative rigor refers to the meticulousness, consistency, and transparency of the research. It is the application of systematic, disciplined, and stringent methods to ensure the credibility, dependability, confirmability, and transferability of research findings. In qualitative inquiry, these attributes ensure the research accurately reflects the phenomenon it is intended to represent, that its findings can be understood or used by others, and that its processes and results are open to scrutiny and validation.

Transparency

It is easier to believe the information presented to you if there is a rigorous analysis process behind that information, and if that process is explicitly detailed. The same is true for qualitative research results, making transparency a key element in qualitative research methodologies. Transparency is a fundamental aspect of rigor in qualitative research. It involves the clear, detailed, and explicit documentation of all stages of the research process. This allows other researchers to understand, evaluate, replicate, and build upon the study. Transparency in qualitative research is essential for maintaining rigor, trustworthiness, and ethical integrity. By being transparent, researchers allow their work to be scrutinized, critiqued, and improved upon, contributing to the ongoing development and refinement of knowledge in their field.

Research papers are only as useful as their audience in the scientific community is wide. To reach that audience, a paper needs to pass the peer review process of an academic journal. However, the idea of having research published in peer-reviewed journals may seem daunting to newer researchers, so it's important to provide a guide on how an academic journal looks at your research paper as well as how to determine what is the right journal for your research.

In simple terms, a research article is good if it is accepted as credible and rigorous by the scientific community. A study that isn't seen as a valid contribution to scientific knowledge shouldn't be published; ultimately, it is up to peers within the field in which the study is being considered to determine the study's value. In established academic research, this determination is manifest in the peer review process. Journal editors at a peer-reviewed journal assign papers to reviewers who will determine the credibility of the research. A peer-reviewed article that completed this process and is published in a reputable journal can be seen as credible with novel research that can make a profound contribution to scientific knowledge.

The process of research publication

The process has been codified and standardized within the scholarly community to include three main stages. These stages include the initial submission stage where the editor reviews the relevance of the paper, the review stage where experts in your field offer feedback, and, if reviewers approve your paper, the copyediting stage where you work with the journal to prepare the paper for inclusion in their journal.

Publishing a research paper may seem like an opaque process where those involved with academic journals make arbitrary decisions about the worthiness of research manuscripts. In reality, reputable publications assign a rubric or a set of guidelines that reviewers need to keep in mind when they review a submission. These guidelines will most likely differ depending on the journal, but they fall into a number of typical categories that are applicable regardless of the research area or the type of methods employed in a research study, including the strength of the literature review , rigor in research methodology , and novelty of findings.

Choosing the right journal isn't simply a matter of which journal is the most famous or has the broadest reach. Many universities keep lists of prominent journals where graduate students and faculty members should publish a research paper , but oftentimes this list is determined by a journal's impact factor and their inclusion in major academic databases.

Guide your research to publication with ATLAS.ti

Turn insights into visualizations with our easy-to-use interface. Download a free trial today.

This section is part of an entire guide. Use this table of contents to jump to any page in the guide.

Part 1: The Basics

• What is qualitative data?
• 10 examples of qualitative data
• Qualitative vs. quantitative research
• What is mixed methods research?
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research questions
• Conceptual framework
• Conceptual vs. theoretical framework
• Focus groups
• Observational research
• Case studies
• Survey research
• What is ethnographic research?
• Confidentiality and privacy in research
• Bias in research
• Power dynamics in research
• Reflexivity

Part 2: Handling Qualitative Data

• Research transcripts
• Field notes in research
• Research memos
• Survey data
• Images, audio, and video in qualitative research
• Coding qualitative data
• Coding frame
• Auto-coding and smart coding
• Organizing codes
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• What is inductive reasoning?
• Inductive vs. deductive reasoning
• What is data interpretation?
• Qualitative analysis software

Part 3: Presenting Qualitative Data

• Data visualization - What is it and why is it important?

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 26 April 2008

Analysing and presenting qualitative data

• P. Burnard 1 ,
• P. Gill 2 ,
• K. Stewart 3 ,
• E. Treasure 4 &
• B. Chadwick 5  

British Dental Journal volume  204 ,  pages 429–432 ( 2008 ) Cite this article

355k Accesses

619 Citations

5 Altmetric

Metrics details

Analysing and presenting qualitative data is one of the most confusing aspects of qualitative research.

This paper provides a pragmatic approach using a form of thematic content analysis. Approaches to presenting qualitative data are also discussed.

The process of qualitative data analysis is labour intensive and time consuming. Those who are unsure about this approach should seek appropriate advice.

This paper provides a pragmatic approach to analysing qualitative data, using actual data from a qualitative dental public health study for demonstration purposes. The paper also critically explores how computers can be used to facilitate this process, the debate about the verification (validation) of qualitative analyses and how to write up and present qualitative research studies.

You have full access to this article via your institution.

Similar content being viewed by others

Realizing the full potential of behavioural science for climate change mitigation

Kristian S. Nielsen, Viktoria Cologna, … Kimberly S. Wolske

An overview of clinical decision support systems: benefits, risks, and strategies for success

Reed T. Sutton, David Pincock, … Karen I. Kroeker

Interviews in the social sciences

Eleanor Knott, Aliya Hamid Rao, … Chana Teeger

Introduction

Previous papers in this series have introduced readers to qualitative research and identified approaches to collecting qualitative data. However, for those new to this approach, one of the most bewildering aspects of qualitative research is, perhaps, how to analyse and present the data once it has been collected. This final paper therefore considers a method of analysing and presenting textual data gathered during qualitative work. boxed-text

Box 1: Qualitative research in dentistry

Qualitative research in dentistry

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

Conducting qualitative interviews with school children in dental research

Approaches to analysing qualitative data

There are two fundamental approaches to analysing qualitative data (although each can be handled in a variety of different ways): the deductive approach and the inductive approach. 1 , 2 Deductive approaches involve using a structure or predetermined framework to analyse data. Essentially, the researcher imposes their own structure or theories on the data and then uses these to analyse the interview transcripts. 3

This approach is useful in studies where researchers are already aware of probable participant responses. For example, if a study explored patients' reasons for complaining about their dentist, the interview may explore common reasons for patients' complaints, such as trauma following treatment and communication problems. The data analysis would then consist of examining each interview to determine how many patients had complaints of each type and the extent to which complaints of each type co-occur. 3 However, while this approach is relatively quick and easy, it is inflexible and can potentially bias the whole analysis process as the coding framework has been decided in advance, which can severely limit theme and theory development.

Conversely, the inductive approach involves analysing data with little or no predetermined theory, structure or framework and uses the actual data itself to derive the structure of analysis. This approach is comprehensive and therefore time-consuming and is most suitable where little or nothing is known about the study phenomenon. Inductive analysis is the most common approach used to analyse qualitative data 2 and is, therefore, the focus of this paper.

Whilst a variety of inductive approaches to analysing qualitative data are available, the method of analysis described in this paper is that of thematic content analysis , and is, perhaps, the most common method of data analysis used in qualitative work. 4 , 5 This method arose out of the approach known as grounded theory, 6 although the method can be used in a range of other types of qualitative work, including ethnography and phenomenology (see the first paper in this series 7 for definitions). Indeed, the process of thematic content analysis is often very similar in all types of qualitative research, in that the process involves analysing transcripts, identifying themes within those data and gathering together examples of those themes from the text.

Data collection and data analysis

Interview transcripts, field notes and observations provide a descriptive account of the study, but they do not provide explanations. 4 It is the researcher who has to make sense of the data that have been collected by exploring and interpreting them.

Quantitative and qualitative research differ somewhat in their approach to data analysis. In quantitative research, data analysis often only occurs after all or much of data have been collected. However, in qualitative research, data analysis often begins during, or immediately after, the first data are collected, although this process continues and is modified throughout the study. Initial analysis of the data may also further inform subsequent data collection. For example, interview schedules may be slightly modified in light of emerging findings, where additional clarification may be required.

Computer software for data analysis

The method of analysis described in this paper involves managing the data 'by hand'. However, there are several computer-assisted qualitative data analysis software (CAQDAS) packages available that can be used to manage and help in the analysis of qualitative data. Common programmes include ATLAS. ti and NVivo. It should be noted, however, that such programs do not 'analyse' the data – that is the task of the researcher – they simply manage the data and make handling of them easier.

For example, computer packages can help to manage, sort and organise large volumes of qualitative data, store, annotate and retrieve text, locate words, phrases and segments of data, prepare diagrams and extract quotes. 8 However, whilst computer programmes can facilitate data analysis, making the process easier and, arguably, more flexible, accurate and comprehensive, they do not confirm or deny the scientific value or quality of qualitative research, as they are merely instruments, as good or as bad as the researcher using them.

Stages in the process

Regardless of whether data are analysed by hand or using computer software, the process of thematic content analysis is essentially the same, in that it involves identifying themes and categories that 'emerge from the data'. This involves discovering themes in the interview transcripts and attempting to verify, confirm and qualify them by searching through the data and repeating the process to identify further themes and categories. 4

In order to do this, once the interviews have been transcribed verbatim, the researcher reads each transcript and makes notes in the margins of words, theories or short phrases that sum up what is being said in the text. This is usually known as open coding. The aim, however, is to offer a summary statement or word for each element that is discussed in the transcript. The exception to this is when the respondent has clearly gone off track and begun to move away from the topic under discussion. Such deviations (as long as they really are deviations) can simply be uncoded. Such 'off the topic' material is sometimes known as 'dross'. 9

Table 1 is an example of the initial coding framework used in the data generated from an actual interview with a child in a qualitative dental public health study, exploring primary school children's understanding of food. 10

In the second stage, the researcher collects together all of the words and phrases from all of the interviews onto a clean set of pages. These can then be worked through and all duplications crossed out. This will have the effect of reducing the numbers of 'categories' quite considerably. 11 , 12 Using a section of the initial coding framework from the above study, 10 such a list of categories might read as follows:

Children's perception of food

Positive notions of food and their consequences

Negative notions of food and their consequences

Peer influence

Healthy/unhealthy foods

Effects of sweets and chocolates

Effects of 'junk food'

Food choices in school

Diet in childhood

Food preferences

Expected diet as a 'grown up'

Food choices and preferences of friendship groups

Effects of fizzy drinks

Perceptions of adult/child diets

The need to be 'healthy' as an adult.

Once this second, shorter list of categories has been compiled, the researcher goes a stage further and looks for overlapping or similar categories. Informed by the analytical and theoretical ideas developed during the research, these categories are further refined and reduced in number by grouping them together. 4 A list of several categories (perhaps up to a maximum of twelve) can then be compiled. If we consider the above example, we might eventually come up with the reduced list shown in Table 2 .

This reduced list forms the final category system that can be used to divide up all of the interviews. 12 The next stage is to allocate each of the categories its own coloured marking pen and then each transcript is worked through and data that fit under a particular category are marked with the according colour. Finally, all of the sections of data, under each of the categories (and thus assigned a particular colour) are cut out and pasted onto the A4 sheets. Subject dividers can then be labelled with each category label and the corresponding coloured snippets, on each of the pages, are filed in a lever arch file. What the researcher has achieved is an organised dataset, filed in one folder. It is from this folder that the report of the findings can be written.

As discussed earlier, computer programmes can be used to manage this process and may be particularly useful in qualitative studies with larger datasets. However, researchers wishing to use such software should first undertake appropriate training and should be aware that most programmes often do not abide by normal MS Windows conventions (eg, most interview transcripts have to be converted from MS Word into rich text format before they can be imported into the programme for analysis).

Verification

The analysis of qualitative data does, of course, involve interpreting the study findings. However, this process is arguably more subjective than the process normally associated with quantitative data analysis, since a common belief amongst social scientists is that a definitive, objective view of social reality does not exist. For example, some quantitative researchers claim that qualitative accounts cannot be held straightforwardly to represent the social world, thus different researchers may interpret the same data somewhat differently. 4 Consequently, this leads to the issue of the verifiability of qualitative data analysis.

There is, therefore, a debate as to whether qualitative researchers should have their analyses verified or validated by a third party. 13 , 14 It has been argued that this process can make the analysis more rigorous and reduce the element bias. There are two key ways of having data analyses validated by others: respondent validation (or member check) – returning to the study participants and asking them to validate analyses – and peer review (or peer debrief, also referred to as inter-rater reliability) – whereby another qualitative researcher analyses the data independently. 13 , 14 , 15

Participant validation involves returning to respondents and asking them to carefully read through their interview transcripts and/or data analysis for them to validate, or refute, the researcher's interpretation of the data. Whilst this can arguably help to refine theme and theory development, the process is hugely time consuming and, if it does not occur relatively soon after data collection and analysis, participants may have also changed their perceptions and views because of temporal effects and potential changes in their situation, health, and perhaps even as a result of participation in the study. 15

Some respondents may also want to modify their opinions on re-presentation of the data if they now feel that, on reflection, their original comments are not 'socially desirable'. There is also the problem of how to present such information to people who are likely to be non-academics. Furthermore, it is possible that some participants will not recognise some of the emerging theories, as each of them will probably have contributed only a portion of the data. 16

The process of peer review involves at least one other suitably experienced researcher independently reviewing and exploring interview transcripts, data analysis and emerging themes. It has been argued that this process may help to guard against the potential for lone researcher bias and help to provide additional insights into theme and theory development. 14 , 16 , 17 However, many researchers also feel that the value of this approach is questionable, since it is possible that each researcher may interpret the data, or parts of it, differently. 8 Also, if both perspectives are grounded in and supported by the data, is one interpretation necessarily stronger or more valid than the other?

Unfortunately, despite perpetual debate, there is no definitive answer to the issue of validity in qualitative analysis. However, to ensure that the analysis process is systematic and rigorous, the whole corpus of collected data must be thoroughly analysed. Therefore, where appropriate, this should also include the search for and identification of relevant 'deviant or contrary cases' – ie, findings that are different or contrary to the main findings, or are simply unique to some or even just one respondent. Qualitative researchers should also utilise a process of 'constant comparison' when analysing data. This essentially involves reading and re-reading data to search for and identify emerging themes in the constant search for understanding and the meaning of the data. 18 , 19 Where appropriate, researchers should also provide a detailed explication in published reports of how data was collected and analysed, as this helps the reader to critically assess the value of the study.

It should also be noted that qualitative data cannot be usefully quantified given the nature, composition and size of the sample group, and ultimately the epistemological aim of the methodology.

Writing and presenting qualitative research

There are two main approaches to writing up the findings of qualitative research. 20 The first is to simply report key findings under each main theme or category, using appropriate verbatim quotes to illustrate those findings. This is then accompanied by a linking, separate discussion chapter in which the findings are discussed in relation to existing research (as in quantitative studies). The second is to do the same but to incorporate the discussion into the findings chapter. Below are brief examples of the two approaches, using actual data from a qualitative dental public health study that explored primary school children's understanding of food. 10

Example a (the traditional approach):

Contrasts and contradictions

The interviews demonstrated that children are able to operate contrasts and contradictions about food effortlessly. These contradictions are both sophisticated and complex, incorporating positive and negative notions relating to food and its health and social consequences, which they are able to fluently adopt when talking about food:

'My mother says drink juice because it's healthy and she says if you don't drink it you won't get healthy and you won't have any sweets and you'll end up having to go to hospital if you don't eat anything like vegetables because you'll get weak' . (Girl, school 3, age 11 years).

If this approach was used, the findings chapter would subsequently be followed by a separate supporting discussion and conclusion section in which the findings would be critically discussed and compared to the appropriate existing research. As in quantitative research, these supporting chapters would also be used to develop theories or hypothesise about the data and, if appropriate, to make realistic conclusions and recommendations for practice and further research.

Example b (combined findings and discussion chapter):

Copying friends

In this study, as with others (eg Ludvigsen & Sharma 21 and Watt & Sheiham 22 ), peer influence is a strong factor, with children copying each other's food choices at school meal times:

Girl: 'They say “copy me and what I have.”'

Interviewer: 'And do you copy them if they say that?'

Girl: 'Yes.'

Interviewer: 'Why do you copy them if they say that?'

Girl: 'Because they are my friends.'

(Girl, school 1, age 7).

Children also identified friendship groups according to the school meal type they have. Children have been known to have school dinners, or packed lunches if their friends also have the same. 21

If this approach was used, the combined findings and discussion section would simply be followed by a concluding chapter. Further guidance on writing up qualitative reports can be found in the literature. 20

This paper has described a pragmatic process of thematic content analysis as a method of analysing qualitative data generated by interviews or focus groups. Other approaches to analysis are available and are discussed in the literature. 23 , 24 , 25 The method described here offers a method of generating categories under which similar themes or categories can be collated. The paper also briefly illustrates two different ways of presenting qualitative reports, having analysed the data.

This analysis process, when done properly, is systematic and rigorous and therefore labour-intensive and time consuming. 4 Consequently, for those undertaking this process for the first time, we recommend seeking advice from experienced qualitative researchers.

Spencer L, Ritchie J, O'Connor W . Analysis: practices, principles and processes. In Ritchie J, Lewis J (eds) Qualitative research practice . pp 199–218. London: Sage Publications, 2004.

Google Scholar  

Lathlean J . Qualitative analysis. In Gerrish K, Lacy A (eds) The research process in nursing . pp 417–433. Oxford: Blackwell Science, 2006.

Williams C, Bower E J, Newton J T . Research in primary dental care part 6: data analysis. Br Dent J 2004; 197 : 67–73.

Article   Google Scholar  

Pope C, Ziebland S, Mays N . Analysing qualitative data. In Pope C, Mays N (eds) Qualitative research in health care . 2nd ed. pp 75–88. London: BMJ Books, 1999.

Ritchie J, Spencer L, O'Connor W . Carrying out qualitative analysis. In Ritchie J, Lewis J (eds) Qualitative research practice . pp 219–262. London: Sage Publications, 2004.

Glaser B G, Strauss A L . The discovery of grounded theory: strategies for qualitative research . Chicago: Aldine Publishing Company, 1967.

Stewart K, Gill P, Chadwick B, Treasure E . Qualitative research in dentistry. Br Dent J 2008; 204 : 235–239.

Seale C . Analysing your data. In Silverman D (ed) Doing qualitative research . pp 154–174. London: Sage Publications, 2000.

Morse J M, Field P . Nursing research: the application of qualitative approaches . Cheltenham: Stanley Thornes, 1996.

Book   Google Scholar  

Stewart K, Gill P, Treasure E, Chadwick B . Understanding about food among 6-11 year olds in South Wales. Food Cult Soc 2006; 9 : 317–333.

Burnard P . A method of analysing interview transcripts in qualitative research. Nurse Educ Today 1991; 11 : 461–466.

Burnard P . A pragmatic approach to qualitative data analysis. In Newell R, Burnard P (eds). Research for evidence based practice . pp 97–107. Oxford: Blackwell Publishing, 2006.

Mays N, Pope C . Rigour and qualitative research. BMJ 1995; 311 : 109–112.

Barbour R S . Checklists for improving rigour in qualitative research: a case of the tail wagging the dog? BMJ 2001; 322 : 1115–1117.

Long T, Johnson M . Rigour, reliability and validity in qualitative research. Clin Eff Nurs 2000; 4 : 30–37.

Cutcliffe J R, McKenna H P . Establishing the credibility of qualitative research findings: the plot thickens. J Adv Nurs 1999; 30 : 374–380.

Andrews M, Lyne P, Riley E . Validity in qualitative health care research: an exploration of the impact of individual researcher perspectives within collaborative enquiry. J Adv Nurs 1996; 23 : 441–447.

Silverman D . Doing qualitative research . London: Sage Publications, 2000.

Polit D F, Beck C T . Essentials of nursing research: methods, appraisal, and utilization . 6th ed. Philadelphia: Lippincott Williams & Wilkins, 2006.

Burnard P . Writing a qualitative research report. Nurse Educ Today 2004; 24 : 174–179.

Ludvigsen A, Sharma N . Burger boy and sporty girl; children and young people's attitudes towards food in school . Barkingside: Barnardo's, 2004.

Watt R G, Sheiham A . Towards an understanding of young people's conceptualisation of food and eating. Health Educ J 1997; 56 : 340–349.

Bryman A, Burgess R (eds). Analysing qualitative data . London: Routledge, 1993.

Miles M, Huberman A . Qualitative data analysis . 2nd ed. Thousand Oaks: Sage Publications, 1994.

Silverman D . Interpreting qualitative data: methods for analysing talk, text and interaction . 3rd ed. Thousand Oaks: Sage Publications, 2006.

Download references

Author information

Authors and affiliations.

Professor of Nursing, Cardiff School of Nursing and Midwifery Studies, Ty Dewi Sant, Heath Park, Cardiff, CF14 4XY,

Senior Research Fellow, Faculty of Health, Sport and Science, University of Glamorgan, Pontypridd, CF37 1DL,

Research Fellow, Academic Unit of Primary Care, University of Bristol, Bristol, BS8 2AA,

Dean, School of Dentistry/Professor of Dental Public Health, Cardiff University, Heath Park, CF14 4XY, Cardiff

E. Treasure

Professor of Paediatric Dentistry, Dental Health and Biological Sciences, School of Dentistry, Cardiff University, Heath Park, Cardiff, CF14 4XY,

B. Chadwick

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to P. Gill .

Additional information

Refereed paper

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Burnard, P., Gill, P., Stewart, K. et al. Analysing and presenting qualitative data. Br Dent J 204 , 429–432 (2008). https://doi.org/10.1038/sj.bdj.2008.292

Download citation

Published : 26 April 2008

Issue Date : 26 April 2008

DOI : https://doi.org/10.1038/sj.bdj.2008.292

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Primary-school-aged children inspire their peers and families to eat more vegetables in the kiidsay project: a qualitative descriptive study.

• Karpouzis F.
• Lindberg R.

BMC Pediatrics (2024)

Nurse assistants’ perception of caring for older persons who are dying in their own home

• Magdalena Annersten Gershater
• Josefin Brenner

BMC Palliative Care (2024)

COVID-19 and Challenging Working Environments: Experiences of Black Sub-Saharan African (BSSA) Front-Line Health Care Professionals Amid of COVID-19 Pandemic in the English Midlands Region

• Nyashanu Mathew
• Pfende Farai
• Mandu Stephen Ekpenyong

Journal of Racial and Ethnic Health Disparities (2024)

Ethical and Regulatory Gaps in Aesthetic Medical Practice in Top Asian Medical Tourism Destinations

• Nishakanthi Gopalan

Asian Bioethics Review (2024)

Subjective Experiences of Pregnancy, Delivery, and Nursing in Transgender Men and Non-Binary Individuals: A Qualitative Analysis of Gender and Mental Health Concerns

• Felicitas A. O. K. Falck
• Cecilia M. U. Dhejne
• Gabriela M. Armuand

Archives of Sexual Behavior (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Dissertation
• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

Prevent plagiarism. Run a free check.

I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Survivorship bias
• Self-serving bias
• Availability heuristic
• Halo effect
• Hindsight bias
• Deep learning
• Generative AI
• Machine learning
• Reinforcement learning
• Supervised vs. unsupervised learning

 (AI) Tools

• Grammar Checker
• Paraphrasing Tool
• Text Summarizer
• AI Detector
• Plagiarism Checker
• Citation Generator

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, July 18). How to Write a Results Section | Tips & Examples. Scribbr. Retrieved March 20, 2024, from https://www.scribbr.com/dissertation/results/

Is this article helpful?

Tegan George

Other students also liked, what is a research methodology | steps & tips, how to write a discussion section | tips & examples, how to write a thesis or dissertation conclusion, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Presenting and evaluating qualitative research

Affiliation.

• 1 Univeristy of Nottingham, Nottingham, United Kingdom. [email protected]
• PMID: 21179252
• PMCID: PMC2987281
• DOI: 10.5688/aj7408141

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education. It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

Keywords: American Journal of Pharmaceutical Education; qualitative research; research papers.

• Data Interpretation, Statistical
• Education, Pharmacy / standards*
• Focus Groups
• Interviews as Topic
• Publishing / standards*
• Qualitative Research*
• Reproducibility of Results
• United Kingdom

How to present the analysis of qualitative data within interdisciplinary studies for readers in the life and natural sciences

• Open access
• Published: 25 May 2021
• Volume 56 , pages 967–984, ( 2022 )

Cite this article

You have full access to this open access article

• Gerda Casimir 1 ,
• Hilde Tobi 1 &
• Peter Andrew Tamás   ORCID: orcid.org/0000-0002-5409-1273 1  

5611 Accesses

5 Citations

1 Altmetric

Explore all metrics

Research that addresses complex challenges often requires contributions from the social, life and natural sciences. The disciplines that contribute subject response data, and more specifically qualitative analyses of subject response data, to interdisciplinary studies are characterised by low consensus with respect to methods they use a diversity of terms to describe those methods and they often work from assumptions that are foreign to readers in the natural and life sciences. The first contribution this paper makes is to demonstrate that the forms of reporting that may be adequate for communicating quantitative analysis do not provide teams that include members from natural, life and social sciences with useful accounts of qualitative analysis. Our second contribution is to discuss and model how to report four methods appropriate for qualitative contributions to interdisciplinary projects.

Similar content being viewed by others

A worked example of Braun and Clarke’s approach to reflexive thematic analysis

David Byrne

Criteria for Good Qualitative Research: A Comprehensive Review

Drishti Yadav

How to use and assess qualitative research methods

Loraine Busetto, Wolfgang Wick & Christoph Gumbinger

Avoid common mistakes on your manuscript.

1 Introduction

There are strong arguments to combine quantitative analysis and qualitative analysis within the social sciences (Babbie 1989 ; Creswell and Clark 2000 ; Johnson Onwuegbuzie and Turner 2007 ). Research that addresses complex challenges, such as adaptation to the effects of climate change, often involves teams from the social, life and natural sciences. These interdisciplinary studies frequently demand teams to integrate qualitative analysis of subject response data with quantitative analysis of direct measures of natural phenomena. Further, reports of these studies are often presented in journals whose reporting formats anticipate quantitative analysis of direct measurements for natural and life science readers. We have found specific guidance on the design of interdisciplinary research (e.g. Tobi & Kampen 2018 ), on how to make it meaningful for policy (e.g. Kampen and Tamás 2014 ) and we have found a large number of guidelines for the reporting of both quantitative and qualitative analysis for both disciplinary researchers and for those times when interdisciplinarity is limited to the social sciences. Despite our own and our peers’ efforts, we have not found guidelines for the presentation of the qualitative analysis of subject response data that well serve integration into the reports of interdisciplinary studies published in journals that are read outside of the social sciences.

Our purpose with this paper is to strengthen inter-disciplinary science by improving the adequacy of the reports of analysis of qualitative subject response data within reports of interdisciplinary studies. In the next section we demonstrate the need for these guidelines by describing and faulting existing reporting practices. The guidance we then offer is presented through the use of a model case. The analysis methods we present in this model case were selected for their relevance to interdisciplinary research addressing environmental challenges.

2 The transparency of reporting in interdisciplinary research

In preparation for this manuscript we downloaded four years of papers that contained both ‘interdisciplinary’ and ‘interview’ in their titles, keywords and abstracts (N = 1160 papers). Footnote 1 The term ‘interdisciplinary’ was selected as we were certain that authors’ self-identification would be a strong indicator of interdisciplinarity and the term ‘interview’ was selected as the alternatives we considered, such as ‘qualitative’ produced high false-positive rates. We recognize that this search strategy likely excluded many studies which compromises the generalisability of our findings. We then used automatic coding in Atlas.ti to identify all paragraphs that contained both words ‘analysis’ and ‘interview’ (n = 1033 paragraphs) to quickly identify those papers that contained a substantial discussion of the methods used to analyse interview data and a location within papers where that discussion is certain to be found. We then used random selection from these paragraphs to identify papers for examination. We continued to randomly select papers for examination until five in a row produced no novel observations (n = 79 papers).

In all of the papers examined, researchers reported that they identified and aggregated themes in order to present patterns. The description given these efforts generally mirrored the account given of their analysis of quantitative data. For example, many reported ‘thematic content analysis’ which appears to be as informative as ‘multiple logistic regression.’ These two are neither equivalent nor are they similarly informative. The term ‘multiple logistic regression’ references a specific set of analysis procedures and assumptions about which there is well-known consensus. Thematic content analysis, however, involves two distinct steps neither of which benefits from the consensus supporting interpretation of the term ‘multiple logistic regression’. The first step in thematic content analysis is the attachment of codes to text that capture meaning. This step, coding, is akin to measurement or data processing in the natural and life sciences. The codes applied are the equivalent to the pH value recorded by a researcher when using litmus strips to measure acidity in surface water or the calculation of BMI based on data provided on weight and height.

Staying with the step of coding, which tended to be far better discussed than synthesis, in the articles we reviewed it was consistently clear that researchers identified themes, but it was not clear where those themes came from. Unlike chemistry, where budding scientists are taught consistently how to read litmus strips so the reader knows what procedures lie behind a stated pH value, there is no consensus in the social sciences that we know of that allows a reader to infer from ‘thematic content analysis’ an unequivocal understanding of how researchers identified units of text as meaningful and then determined what speakers meant by what they said. Certainly, many of the articles we reviewed used multiple raters and negotiation to improve reliability, but inter-rater agreement does not improve transparency in the manner required to shed light on validity.

Turning now to synthesis, we did not often find interpretable discussion of mechanisms by which the text strings coded by researchers were combined so as to produce the patterns that were reported. In the quantitative world, this would be the same as presenting the manipulation of data as a ‘cluster analysis’ without any further specification of the math and the criterion used to support identification of the patterns reported. Those cases that provided an interpretable account tended to be informed by well referenced use of grounded theory in which the processes and logic behind a line of argument and refutational synthesis are clearly stated.

In summary, analysis of qualitative subject response data and quantitative direct measures data arise from disciplines that vary dramatically in their level of consensus. Therefore, qualitative analysis requires far more detailed reporting than is normally found in the accounts given of quantitative analysis. In the following section we introduce and then provide and discuss model reports for the analysis of narrative subject-response data in research that is both mixed-methods and interdisciplinary.

3 Model case

3.1 material and methods, 3.1.1 material.

To demonstrate appropriate presentation of the qualitative analysis of subject response data within reports of interdisciplinary studies we used transcripts of eleven semi-structured interviews that were part of an interdisciplinary study in the domain of socio-technical studies. The questions that we use to provide model presentations here are (a) how do international graduate students use ICT technology to maintain ties with their household members and (b) what is the meaning of ‘household’ as experienced by those students. This material, relatively unstructured interview data, is typical of that subjected to qualitative analysis in interdisciplinary research.

Both the interviews and the transcripts were done in Spring 2012 by Jarkyn Shadymanova, at the time research fellow at the Sociology of Consumption and Households Group, Wageningen University, the Netherlands. Interviewees were African graduate students of Wageningen University who were interviewed in English. In the remainder of this paper the numbers P1 to P11 are used to refer to these interviewees. As should be found in such reports, a demographic description of the interviewees is presented in Annex one.

3.1.2 Methods

For this paper we exercised simple forms of four methods of analysis, aspects of which we have often found to be silently combined by researchers who are contributing to interdisciplinary studies: content analysis, metaphor analysis, domain analysis and membership categorization analysis. For each method we provide exemplar texts for a ‘materials and methods’ and for a ‘results’ section that are preceded by an introduction to the method and followed by a discussion of the method and its reporting.

Each of the methods we have chosen to model and discuss is understood and used in diverse ways. Silverman ( 2015 ), for instance, mentions content analysis, membership categorization analysis, conversation analysis, discourse analysis, semiotics and workplace studies. Flick ( 2014 ) speaks of grounded theory coding, thematic coding and content analysis, conversation, discourse and hermeneutic analysis. Coffey and Atkinson ( 1996 ) distinguish narrative analysis, metaphor analysis, and domain analysis. Bernard ( 1988 ) addresses narrative, discourse and content analysis. In addition, often ethnography or feminist research is mentioned (Bernard 1988 ; Grbich 2012 ; Silverman 2015 ).

There are also inconsistencies in discussion of coding. Most authors see coding as essential for qualitative analysis. However, they differ in the way they see coding in relation to analysis. Miles and Huberman explicitly state: “Coding is analysis” (Miles and Huberman 1994 p. 56). Others see coding and analysis as two distinct phases, where the latter is of a higher level of abstraction. Flick, citing Strauss and Corbin, 1990, distinguishes coding and ‘axial coding’, where the “Axial coding is the process of relating subcategories to a category” (Flick 2014 p. 311).

In our many years of instruction at the graduate level, our students have consistently recognized on their own that these taxonomies overlap, that the terms included in each are not mutually exclusive and that each taxonomy partitions practice in slightly different ways. In addition to making it impossible to infer from a label such as ‘thematic content analysis’ what was actually done, this lack of consensus also makes it impossible for an author who has transparently reported their analysis to defend against a detractor who argues, from a different definition of the method named, that their analysis is lacking some crucial dimension. The lack of consensus that characterizes methodological texts on qualitative data analysis brings us to our most basic recommendation: transparent report of qualitative analysis requires justification and detailed description of each of the analytic steps followed and the assessment of the appropriateness of such analysis must turn on examination of analytic steps in context and not the label assumed.

3.2 Content Analysis

3.2.1 introduction.

Content analysis is a ‘technique for making inferences by systematically and objectively identifying special characteristics of messages’ (Holsti 1969 , p. 608). As used in this study, content analysis aimed to examine textual data through the systematic application of pre-determined categorization codes and then determining frequencies of text fragments in each category (Silverman 2015 ).

Content analysis is typically used to answer questions of the form ‘who, what, when, where, how and how often’. All kinds of qualitative data can be subjected to content analysis: newspaper clippings, literary works, e-mails, pictures, audio clips, blogs, movies, scientific articles, answers to open questions in a survey, and, of course, also interview or focus groups discussion transcripts.

Coding within content analysis is done top-down, on the basis of a predefined protocol with a coding scheme, derived from the theoretical framework of the researcher as informed by a review of relevant literature. Initial coding schemes are often tested and amended based on their performance in a sample of the data.

Coding consistently imposes an operationalization of the conceptual framework of the researcher on the data in a manner that may be inconsistent with the framing used by research subjects. If repondents consistently use the same terms to describe analytically relevant concepts, automatic coding may be used. Compared to manual coding, automatic coding has three advantages. It allows for the coding of larger data sets, it increases completeness and it eliminates human error. Nonetheless, automatic coding requires careful consideration. For instance, the term ‘internet’ can appear in semantic units indicating a problem (lack of access) as well as a mode of–successful–communication.

In our example the research question for content analysis was: What are the characteristics of each respondent’s household and how do they communicate–with what tools, how often and how long–with their relatives back home?

3.2.2 Report of method

In order to determine household characteristics we used top-down content analysis. The coding frame used was based on an earlier scheme (Casimir and Tobi 2011 ) and extended with a list of ICT (Information Communication Technology) devices derived from Shadymanova’s interview guide. The coding scheme was segmented following the research questions: which people are part of the household, what is shared (resources, activities, expenditures), which ICT tools are used to communicate with the household, and how often are they used. The coding protocol was tested on two randomly chosen interviews, found inadequate and modified such that it adequately anticipated the full diversity of ICT tools used and household compositions. Manual coding was used rather than automatic as tests of automatic coding did not identify all relevant text strings and did not consistently associate appropriate codes with found text strings. For instance, the search string ‘internet access’ could indicate both the possibility of internet access and the absence of it.

3.2.3 Report of results

3.2.3.1 coding.

The coding phase resulted in Table 1 , where the first two columns contain the coding scheme. The third column gives a summary of results.

3.2.3.2 Analysis

Analysis consisted of an overview of frequencies of the codes applied (column 3 of Table 1 ). Six of the interviewees had one or more children, five of the interviewees were single. Most frequently mentioned as shared within the household were: sharing a roof, sharing consumption (food), sharing income and expenditures.

To communicate with their household back home, all interviewees used a mobile phone, e-mail and instant messaging (Skype). Six of the eleven interviewees had contact with their household back home every day, two interviewees twice or three times per week, and three interviewees once a week. The remaining interviewee–who was single without children–had contact with her relatives once per month. Duration of communications varied from a few minutes to three hours or more. The latter only when Skype was available.

3.2.4 Discussion of content analysis and its reporting

Top-down content analysis provided a description of manifest features within the data identified by the researchers at the outset as relevant to their study. The method allowed the researchers to extend the initial coding scheme that appeared adequate with respect to the research question, such that it became adequate with respect to the data. Results however are limited to the deductively imposed framework used and will not report findings that call into question the appropriateness of that framework.

3.3 Metaphor analysis

3.3.1 introduction.

Metaphor analysis uses systematic examination of elicited or spontaneous metaphors to identify latent conceptualizations (Schmitt 2005 ). According to Coffey and Atkinson ( 1996 ) metaphors are grounded in socially shared knowledge: “Particular metaphors may help to identify cultural domains that are familiar to the members of a given culture or subculture; they express specific values, collective identities, shared knowledge, and common vocabularies” (Coffey and Atkinson 1996 , p. 86). Metaphors require and reflect shared meanings. “In terms of data analysis (…) we can explore the intent (or function) of the metaphor, the cultural context of the metaphor, and the semantic mode of the metaphor” (Coffey and Atkinson 1996 , p. 85). Metaphor analysis is well suited for questions such as: ‘how do people depict a situation?’, or: ‘how do they describe a process?’ Data could be any kind of text, talk or visual. Metaphor analysis is particularly relevant when research questions require researchers to identify and make explicit implicit aspects of data, for example, when communication is highly coded as is often found in exploration of sensitive topics.

A metaphor analysis involves identification, classification and inductive examination of metaphors to draw inferences regarding the structure and significance of the conceptual metaphors of which they are an instance (Low and Cameron 1999 ). As metaphors are manifested in ways that are context-dependent, their analysis often starts with bottom-up coding. Top-down coding for metaphors is only indicated when researchers have a specific interest in pre-determined forms of metaphors (e.g. path metaphors, battle metaphors, animal metaphors).

The research question for our example is: what implicit perceptions and/or feelings do respondents have with respect to their households and their travel from that household, expressed through flowery language.

3.3.2 Report of method

Following Coffey and Atkinson ( 1996 ), we started metaphor analysis with building a protocol that would allow multiple researchers reliably to identify instances of flowery language. We tested this protocol and then coded the text for instances of flowery language. Once so identified, we then examined text coded as flowery in detail for instances of metaphors. For this coding we operationalized ‘metaphor’ as any instance where a term used can also be used in a different context and where hearers’ knowledge of that use in that different context alters their interpretation. Once all instances of flowery language were scrutinized for metaphors, we identified the source context for metaphorical terms, detailed the additional meanings that may be implied through use of that term, and then selected from those possibilities the one that was most probable.

3.3.3 Report of results

3.3.3.1 coding.

Coding identified metaphors in more than half of the transcripts. Respondents used non-literal descriptions when discussing topics that may involve emotion, such as distance (e.g. ‘another planet’), connection (e.g. ‘blood ties’) and surprise or impact (e.g. ‘shock’). The complete list of metaphors found is presented in Table 2 .

3.3.3.2 Analysis

On review, we decided that it was inappropriate to undertake analysis beyond the identification of potential metaphors. Each of our respondents came from a distinct cultural context so each could be expected to have their own distinct repertoire of metaphors. The narratives examined did not arise in natural conversation within their context but in interaction with an interviewer who comes from a different context, so it is not clear that respondents would have drawn on the repertoire found in their home context. As metaphor use is tied to both language and context, and interviews were held in a foreign country in both respondents’ and interviewer’s second language, we could not identify possible, let alone the most probable, meaning.

3.3.4 Discussion of metaphor analysis and its reporting

Metaphor analysis is useful when research questions require interpretation of a narrative that goes beyond the strictly literal meaning of terms. Since we did not have enough information at our disposal–as explained in Sect.  4.1 ., we cannot discuss results or come to conclusions.

3.4 Domain analysis

3.4.1 introduction.

Domain analysis was created by ethnographers to help them understand how the communities they were studying structured their world. “Domain analysis involves a search for the larger units of cultural knowledge called domains (…). In doing this kind of analysis we will search for cultural symbols which are included in larger categories (domains) by virtue of some similarity.” (Spradley 1979 , p. 94). Spradley distinguished four elements in the domain structure. The first is the so-called folk terms that informants use. These terms have semantic relationships–the second element–with ‘cover terms’, a name for a category of cultural knowledge, which are the third feature of domains. Finally, every domain has a boundary: informants know what is part of the domain and what is not (Carballo-Cárdenas Mol and Tobi 2013 ).

Codes are derived from the ‘folk’ terms used by the respondents in interviews (Borgatti and Halgin 1999 ) using in-vivo coding. “The systematic use of in-vivo codes can be used to develop a ‘bottom-up’ approach to the derivation of categories from the content of the data” (Coffey and Atkinson 1996 , p. 32). Data can be any kind of text or script, both naturally occurring and elicited text, talk and visuals.

The research question in our example was: How do respondents talk about their ‘household’ and their communication with home?

3.4.2 Report of method

Domain analysis was created in order to allow researchers to describe how respondents structure their worlds on their terms. Following Coffey and Atkinson ( 1996 ) our first step was to code the ‘folk terms’ with which the interviewees expressed their ideas about their household and the communication with that household. The second step was to identify those words or expressions that clearly indicated distinct domains (cover terms). Cover terms were inductively identified through clustering of folk terms. Clustering was indicated, in the first instance, by proximity between terms. For example, ‘sharing’ is subdivided into sharing a roof, sharing expenditures, etc. Once proximity associations were exhausted, terms were then clustered using refutational and then confirmatory arguments. For example, the term ‘meaning of life’ was tested against ‘sharing’ and ‘significance of household’ and found to fit least poorly and acceptably with ‘significance of household.’ Our last step was then to identify how other descriptive terms related to the cover terms just identified. Examples of such semantic relations are membership, causation or sequence.

3.4.3 Report of results

3.4.3.1 coding.

When talking about their household, interviewees referred to the sharing of several aspects–sharing a roof, food, income, expenditures, time, household organization–and to the significance of households, including feelings of connectedness and emotional aspects. Terms used were, for instance, “the people who live together”, “who share food”, “who live under the same roof”; and: “it is an emotion”, “it is protecting”, “it is important”, “it is the ‘holy thing’”. Or: “the meaning of life”. Also mentioned: “It is the place where I feel important and valuable”.

When talking about communication, the interviewees referred to the content of communications. The content of communication ranged from sharing practical information–some repair that has to be done, financial issues–to conversations about how their beloved ones are doing, in particular: how children are doing in school. Also, medical problems with children or parents were discussed, or business shared with relatives. Sometimes no specific topic was mentioned, but the interviewees indicated that they wanted to communicate with home because they felt lonely, or because they wanted to hear their mother’s voice, since he or she missed her.

Interviewees did also talk about their communication style. One of the interviewees indicated that ICT creates circumstances for communication which may ask for a different style: “You have to be more kind, support them. When I am there, I tend to be more rigorous” (P4).

3.4.3.2 Analysis

In the analysis phase, cover terms were defined and the folk terms were related with semantic relationships to these cover terms. In some cases, the cover terms were divided into sub-terms, for instance ‘sharing’ is subdivided into sharing a roof, sharing expenditures, etc. Figure  1 presents the result of both the coding phase and the analysis phase for the domain ‘Household’. In Fig.  2 , the content types (cover terms) and expressions (folk terms) of the domain ‘Communication’ are shown.

Domain household with folk terms (uncolored boxes), semantic relationships (labels on the arrows) and cover terms (filled boxes, with initial capitals)

Domain communication, in particular content of communication

3.4.4 Discussion of domain analysis and its reporting

Domain analysis allowed us to answer questions about how respondents structured their world. It was possible efficiently and reliably to identify folk terms through in-vivo coding. Decisions on categorization of folk terms and semantic relationships between folk terms and cover terms required subjective judgement that would be difficult to reproduce, but was quite easy to transparently document. For example, rather than proceed with the classification structure just presented, we could have opted for subcategories such as practical reasons (sharing information, asking where people are), communication for its own sake (when feeling lonely, for instance) and other reasons for communication. Transparent presentation of these subjective judgements should be reported as an annex within or as supplemental material accompanying a standard-length journal article.

3.5 Membership Categorization Analysis

3.5.1 introduction.

Membership categorization analysis (MCA), introduced by Sacks in 1972, identifies the categories interviewees use to classify people and how these categories are routinely attached to particular kinds of attributes and activities (Silverman 2015 ). The rules applied to attribute individuals to categories are called Membership Categorization Devices, MCDs (Schegloff 2007 ). MCA does not ask people how they categorize, but investigates how people “use social categories to account for, explain, justify and make sense of people’s actions” (Fitzgerald and Housley 2015 , p. 6). Each set of categories is a collection and categories may belong to more than one collection: professor is part of the collection students/administrators/staff, i.e. university community, but also part of the occupational collection plumber/doctor/secretary/undertaker (Schegloff 2007 ).

In the actual execution of MCA, the fundamental goal is to identify how respondents order categories and their attributes in their social world. Data analyzed through MCA may be any kind of text, talk or visuals. We used Membership Categorization Analysis to identify how respondents determined if a given individual was a member of their household.

3.5.2 Report of method

Membership categorization analysis was developed to identify from natural speech how subjects classify others and what characteristics are associated with those classes (Silverman 2015 ). In our analysis we used MCA to identify the rules by which our subjects classified others as member of their household: membership classification devices. Shadymanova elicited data appropriate for this analysis by asking respondents the following questions: “Could you tell me about your household?” and “What is a household for you?”. We identified each instance where an individual was classified as either a member or not a member of the household and then coded explanatory text. These fragments of explanatory text were then examined for justifications for the classification just given. Justifications were then clustered by similarity and each cluster of justification was then described as a membership classification device.

3.5.3 Report of results

3.5.3.1 coding.

Respondents appeared to use several terms when indicating whether individuals were members of their household or not. Interviewees used terms as ‘being family’, ‘having blood ties’. Several found sharing a roof, sharing income, or expenditures, sharing food or household chores justification to include people in the category ‘household’. In several cases, household membership was related to feeling responsible (“I am paying their school fees / their rent, because I am feeling responsible for them”). Also, emotional terms were used (“She feels like family”). Some of the interviewees realized that their cultural background influences their rules of inclusion:

If I consider household as composed of those who are close to me somehow, I will say that I have one wife; I have three kids; and I have a lady who helps us at home, who helps my wife. So that is my household, my small household. But in Africa, let’s say in my country, (…) [the] household is part of a larger household: (…) aunts, (…) one brother and some sisters. (P1)

One interviewee said: “ household is the space you are sharing and supplying for daily needs ” (P4). Since he was the one who paid for food and rent, he was part of that household, also when he was abroad at the moment. Sharing a roof was also for P3 a reason to include people; however, being absent was no reason to exclude people as a member of the household.

Several interviewees expressed awareness of the existence of different definitions of households by saying: my wife and my children constitute my nuclear household, but in our culture–or Africa, or in my country–we include siblings, aunts, grandparents. Also, nephews or nieces for whom they took care by paying rent or school fees could be included or excluded from the household, depending on the definition used.

The second column of Table 3 contains the terms used by the interviewees.

3.5.3.2 Analysis

After the coding was done, the rules of inclusion and exclusion used by the interviewees were classified into categories of Membership Categorization Devices, given in the first column of Table 3 . The third column contains further remarks.

3.5.4 Discussion of MCA and its reporting

MCA produced a list of criteria (Membership Categorization Devices) that justify classification of individuals with respect to their membership in the category ‘household’. It gave the authors a new understanding of respondents’ public construction of their understanding of relationships. Significantly, interviewees’ notions of ‘household’ were mutually inconsistent and many interviewees used more than one device (e.g. blood and familiarity) when determining membership in their household.

4 Discussion

4.1 comparison of the four methods modelled.

In the previous section we modelled and discussed what is required to usefully report individual qualitative analysis methods within interdisciplinary studies. In this sub-section we step back and comparatively discuss the methods we modelled.

Only content analysis identified the variables of interest (i.e. specified codes) before starting coding and analysis. The other three approaches identified variables inductively through ‘bottom-up coding’, either using the terms used by the interviewees (e.g. the ‘folk terms’ in the domain analysis, and the flowery language of interviewees in the metaphor analysis), or identifying rules of inclusion and exclusion in the membership categorization analysis. In those three methods, once variables were identified, they were converted into an analytic framework that was deductively applied to the remaining texts and checked in an iterative cycle with the texts already coded.

Content analysis, as conducted here, was counting the number of times the terms determined to be relevant appeared in the text. Although not presented here, analysis may be continued through the use of theoretically motivated descriptive and correlational statistics which would be then reported according to the norms governing reporting of quantitative analysis. We were able to report this analysis method transparently because its operation relied on deductive application of a clearly declared coding scheme. In our example, the content analysis gave information on household composition, what households shared, ICT-tools used, and frequency and duration of communication with those tools.

Domain analysis, as used in this example, permitted us to work from manifest features of the transcripts to identify cognitive structures used by respondents in the interview. Domain analysis may be used on a substantial set of interviews, with special attention to the presence or absence of subgroups’ use of folk terms. For the purposes of simplicity, in this example we did not examine the interaction between domain and metaphor analysis. In practice, the folk-terms identified as key within a domain analysis may be themselves or may be closely associated with metaphors. In these cases, the additional layers of meaning associated with terms by metaphor analysis must be carried forward through the domain analysis as the often-normative shadings that come with metaphors may be analytically relevant.

Membership Categorization Analysis added to the domain analysis by identifying who was a member of the household and who not, while in the domain analysis the emphasis was on the significance of the household, without taking into account who were part of it and what made them part. MCA, like domain analysis, may be used in analysis of substantially sized data sets, although both seem less suitable for large data sets than content analysis.

We were not able to produce an adequate metaphor analysis. Like domain analysis and MCA, metaphor analysis requires repeated close reading of transcripts. As the metaphors studied are found precisely at the intersection of language and culture as they collide in an interview setting that was, in this case, foreign to both, we could not apply thematic codes with the sort of confidence possible with content analysis. While we were able to identify that there was a metaphor, we could not produce an unambiguous description of the range of possible associated meanings nor could we reliably describe the rules that govern association of these meanings with the flowery language we identified. Were we reporting a metaphor analysis in a context where no part of the research aspired to be a valid account, as is appropriate in some exploratory studies or in those where the assumptions required for valid descriptions are not met, we may have chosen to proceed by associating our own meanings with identified metaphors. In the context of a project that is both inter-disciplinary and mixed-methods, however, it is not appropriate for researchers to silently include speculation as data. When there is reason to believe that the words used by respondents have connotations that are analytically relevant, it is certainly appropriate to recognize those connotations. Identification of these connotations, however, would require explicit design of a transparently reported distinct research effort to develop a formal ruleset for the identification and interpretation of the analytically relevant metaphors found in the narratives examined.

In our experience, and as suggested in the discussion of domain analysis given above, it is rarely possible to answer a socially relevant research question through use of a single method of qualitative analysis. Contrary to what we have observed to be common practice, it is not appropriate to report, for example, only that a ‘frame analysis’ was undertaken where that term describes interactive application of several constitutive methods. Each of these constitutive methods, and the means by which the data arising therefrom are combined, should be described separately. The level of detail required to support this sort of description may very well not fit either the norms or the space afforded in current publication fora. In those cases, the reporting of qualitative analysis useful for interdisciplinary teams will require publication of supplemental material.

4.2 Coding and analysis

Coding requires segmentation of a narrative into units of meaning that are hopefully compatible with the conceptual framework within which the research questions were formulated and appropriate for the sort of analysis required to answer that question. When reporting qualitative analysis of narrative data for interdisciplinary teams, this segmentation and then the association of these segments with codes should not be presented as analysis as these two steps most closely approximate the work done by a respondent when she provides a value in response to a structured survey item or when a researcher records the value displayed on an instrument. With this in mind, a transparent discussion of coding is not an adequate report of qualitative analysis. Once a narrative is segmented in a manner that fits the researcher’s conceptual framework, the texts so coded are data appropriate for analysis. How the narrative fragments are interpreted once coded is determined by the nature of the data analysis method chosen. For example, within MCA text coded as ‘category bound activity’ is interpreted and used quite differently than text coded as ‘path metaphor’ within a metaphor analysis. In the absence of a well established shared lexicon, the mechanisms and content of the interpretations made through analysis should be reported in detail. In order to be interpretable by inter-disciplinary teams, it may be better to report coding as ‘data processing’ and the manipulation and interpretation of the coded narrative fragments as ‘data analysis.’

The results of content analysis, domain analysis and MCA may usefully be presented in the form of a table or graph and in this article we showed examples of both. The graphs were produced within a qualitative data analysis program, in this case Atlas.ti. Presenting results in a way that does not solely rely on ‘typical’ quotes is recommended. When quotes are used, the justification for their selection, as well considered in discussions around annotation for transparent inquiry, must be reported ( https://qdr.syr.edu/ati ).

4.3 Transparency and appropriateness

It may not be possible to transparently report qualitative analysis of subject response data but this should not encourage use of transparent but inappropriate methods. While we strongly encourage explicit coding in order to improve transparency, we recognize that even with the systematic approaches we took, transparency in reporting analogous to that found in purely quantitative interdisciplinary studies, is not always possible. In several instances a given semantic unit could reasonably be recognized by two or more codes that the scheme used presented as mutually exclusive and we were unable to complete a metaphor analysis though subjective attribution of meaning by researchers may be necessary. In keeping with the principles of annotation for transparent inquiry ( https://qdr.syr.edu/ati ), when only one reading is carried forward, such decisions should be transparently documented through applying all possible codes within the analysis software used and then using comments to provide discussion supporting the decisions taken.

4.4 Compatibility with contributions from the natural and life sciences

Qualitative analysis of subject response data within interdisciplinary studies is, appropriately, reductive. Some authors, for instance St. Pierre and Jackson ( 2014 ) argue that coding ought to be avoided entirely. They state that lecturers “teach analysis as coding because it is teachable” (St. Pierre & Jackson, 2014 , p. 715) and reject the many textbooks and university research courses which, according to them, support the positivist, quasi-statistic analytic practice, reducing words to numbers. We agree that coding is a reductive exercise and that coding and analysis can be distinguished. We, however, think this critique is not relevant as it makes epistemic assumptions that are not appropriate for inter-disciplinary mixed-methods research. Research on environmental challenges, for example, is funded to inform practice and the measure of this research, ultimately, is predictive validity. For this, researchers must assume that the world described is somewhat stable, that descriptions thereof will converge, that the data they gather represents something more than instrument effects and that it is possible to reduce the complexity of the world sufficiently to render a useful representation. If the purpose of qualitative inquiry within interdisciplinary efforts is to complement and extend quantitative findings, it is appropriate to adopt a compatible stance. The assumptions necessary to support such reductive analysis, as long discussed (e.g. Bergdahl 2019 ; Shankman et al. 1984 ) may not hold in some circumstances and naïve combination of fundamentally different data does gross disservice to both.

5 Conclusion

In this paper we first demonstrated that the forms of reporting qualitative analysis in interdisciplinary research often do not provide readers with sufficiently detailed accounts of qualitative analysis. Secondly, to mitigate this problem we presented reporting models for four methods of analysis selected for their relevance to interdisciplinary research addressing environmental challenges. Qualitative analysis of narrative subject response data requires a high level of detail in reporting. Clear separation and transparent accounts of both coding and analysis are crucial for qualitative contributions to interdisciplinary mixed-methods research.

Further literature reviews on qualitative contributions to interdisciplinary research is needed to get a better understanding of the strengths and weaknesses of methods used and reported. By definition, literature reviews rely on publications, whether grey, white or peer-reviewed. Therefore, we think our contribution to the reporting of qualitative contributions to interdisciplinary research is essential to both the interdisciplinary readership for transparency and replication purposes, and the study of qualitative methods within interdisciplinary research.

In March of 2019 we executed the following search in Web of Science:

TOPIC: (interdisciplin* or inter-disciplinary).

Refined by: TOPIC: (interview*) AND DOCUMENT TYPES: ( ARTICLE) AND PUBLICATION YEARS: ( 2019 OR 2018 OR 2017 OR 2016).

Timespan: All years. Indexes: SCI-EXPANDED, SSCI, A&HCI, ESCI.

Babbie, E.R.: The practice of social research, 1st edn. Wadsworth Publishing Company, Beverley Hills (1989)

Google Scholar  

Bergdahl, E.: Is meta-synthesis turning rich descriptions into thin reductions? A criticism of meta-aggregation as a form of qualitative synthesis. Nurs. Inq. 26 (1), e12273 (2019)

Article   Google Scholar  

Bernard, R. H. (1988). Research methods in cultural anthropology : Sage.

Borgatti, S.P., Halgin, D.S.: Elicitation techniques for cultural domain analysis. Ethnogr. Toolkit 3 , 115–151 (1999)

Carballo-Cárdenas, E.C., Mol, A.P., Tobi, H.: Information systems for marine protected areas: how do users interpret desirable data attributes? Environ. Model. Softw. 41 , 185–198 (2013)

Casimir, G.J., Tobi, H.: Defining and using the concept of household: a systematic review. Int. J. Consum. Stud. 35 (5), 498–506 (2011)

Coffey, A., & Atkinson, P. (1996). Making sense of qualitative data : complementary research strategies . Thousand Oaks, CA. : Sage Publications.

Creswell, J. W., & Clark, V. L. P. (2000). Designing and conducting mixed methods research (1st ed.): Sage.

Fitzgerald, R., & Housley, W. (2015). Advances in membership categorisation analysis : Sage.

Flick, U. (2014). An introduction to qualitative research (4th ed.): Sage.

Grbich, C. (2012). Qualitative data analysis: An introduction (2nd ed.): Sage.

Holsti, O. R. (1969). Content analysis for the social sciences and humanities. 1969. Don Mills: Addison-Wesley .

Johnson, B.R., Onwuegbuzie, A.J., Turner, L.A.: Toward a definition of mixed methods research. J. Mixed Methods Res. 1 (2), 112–133 (2007)

Kampen, J.K., Tamás, P.: Should I take this seriously? A simple checklist for calling bullshit on policy supporting research. Qual. Quant. 48 (3), 1213–1223 (2014)

Low, G., & Cameron, L. (1999). Researching and applying metaphor : Ernst Klett Sprachen.

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2 ed.): Sage.

Schegloff, E.A.: A tutorial on membership categorization. J. Pragmat. 39 (3), 462–482 (2007)

Schmitt, R.: Systematic metaphor analysis as a method of qualitative research. Qual. Report 10 (2), 358–394 (2005)

Shankman, P., Ágh, A., Bourguignon, E., Brintnall, D.E., Cole, J.R., Connor, L., Fabian, J.: The thick and the thin: on the interpretive theoretical program of clifford geertz [and comments and reply]. Curr. Anthropol. 25 (3), 261–280 (1984)

Silverman, D. (2015). Interpreting qualitative data (5th ed.): Sage.

Spradley, J.P.: The ethnographic interview. New York: Holt, Rhinehart & Winston. LeCompte MD (2000) . Anal. Qual. Data. Theory Pract. 39 (3), 146–156 (1979)

St. Pierre, E. A., & Jackson, A. Y. (2014). Qualitative data analysis after coding. In: Sage Publications Sage CA: Los Angeles, CA.

Tobi, H., Kampen, J.K.: Research design: the methodology for interdisciplinary research framework. Qual. Quant. 52 (3), 1209–1225 (2018)

Download references

Acknowledgements

We thank Jarkyn Shadymanova for making available her interviews for this research.

Author information

Authors and affiliations.

Wageningen University, Wageningen, Netherlands

Gerda Casimir, Hilde Tobi & Peter Andrew Tamás

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Peter Andrew Tamás .

Ethics declarations

Conflict of interest.

The authors declare that they have no competing interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1: demographic description of interviewees

A basic demographic description of the interviewees is provided in Table

4 . The order of the data analysis methods was randomized and differed across interviewees to reduce order effects. For the analysis, we used Atlas.ti, Computer-Aided Qualitative Data Analysis Software, version 7.5.6—18.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Casimir, G., Tobi, H. & Tamás, P.A. How to present the analysis of qualitative data within interdisciplinary studies for readers in the life and natural sciences. Qual Quant 56 , 967–984 (2022). https://doi.org/10.1007/s11135-021-01162-2

Download citation

Accepted : 03 May 2021

Published : 25 May 2021

Issue Date : June 2022

DOI : https://doi.org/10.1007/s11135-021-01162-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Qualitative
• Inter-disciplinary
• Mixed-methods
• Transparency
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Pediatr Psychol

Commentary: Writing and Evaluating Qualitative Research Reports

Yelena p. wu.

1 Division of Public Health, Department of Family and Preventive Medicine, University of Utah,

2 Cancer Control and Population Sciences, Huntsman Cancer Institute,

Deborah Thompson

3 Department of Pediatrics-Nutrition, USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine,

Karen J. Aroian

4 College of Nursing, University of Central Florida,

Elizabeth L. McQuaid

5 Department of Psychiatry and Human Behavior, Brown University, and

Janet A. Deatrick

6 School of Nursing, University of Pennsylvania

Objective  To provide an overview of qualitative methods, particularly for reviewers and authors who may be less familiar with qualitative research. Methods  A question and answer format is used to address considerations for writing and evaluating qualitative research. Results and Conclusions  When producing qualitative research, individuals are encouraged to address the qualitative research considerations raised and to explicitly identify the systematic strategies used to ensure rigor in study design and methods, analysis, and presentation of findings. Increasing capacity for review and publication of qualitative research within pediatric psychology will advance the field’s ability to gain a better understanding of the specific needs of pediatric populations, tailor interventions more effectively, and promote optimal health.

The Journal of Pediatric Psychology (JPP) has a long history of emphasizing high-quality, methodologically rigorous research in social and behavioral aspects of children’s health ( Palermo, 2013 , 2014 ). Traditionally, research published in JPP has focused on quantitative methodologies. Qualitative approaches are of interest to pediatric psychologists given the important role of qualitative research in developing new theories ( Kelly & Ganong, 2011 ), illustrating important clinical themes ( Kars, Grypdonck, de Bock, & van Delden, 2015 ), developing new instruments ( Thompson, Bhatt, & Watson, 2013 ), understanding patients’ and families’ perspectives and needs ( Bevans, Gardner, Pajer, Riley, & Forrest, 2013 ; Lyons, Goodwin, McCreanor, & Griffin, 2015 ), and documenting new or rarely examined issues ( Haukeland, Fjermestad, Mossige, & Vatne, 2015 ; Valenzuela et al., 2011 ). Further, these methods are integral to intervention development ( Minges et al., 2015 ; Thompson et al., 2007 ) and understanding intervention outcomes ( de Visser et al., 2015 ; Hess & Straub, 2011 ). For example, when designing an intervention, qualitative research can identify patient and family preferences for and perspectives on desirable intervention characteristics and perceived needs ( Cassidy et al., 2013 ; Hess & Straub, 2011 ; Thompson, 2014 ), which may lead to a more targeted, effective intervention.

Both qualitative and quantitative approaches are concerned with issues such as generalizability of study findings (e.g., to whom the study findings can be applied) and rigor. However, qualitative and quantitative methods have different approaches to these issues. The purpose of qualitative research is to contribute knowledge or understanding by describing phenomenon within certain groups or populations of interest. As such, the purpose of qualitative research is not to provide generalizable findings. Instead, qualitative research has a discovery focus and often uses an iterative approach. Thus, qualitative work is often foundational to future qualitative, quantitative, or mixed-methods studies.

At the time of this writing, three of six current calls for papers for special issues of JPP specifically note that manuscripts incorporating qualitative approaches would be welcomed. Despite apparent openness to broadening JPP’s emphasis beyond its traditional quantitative approach, few published articles have used qualitative methods. For example, of 232 research articles published in JPP from 2012 to 2014 (excluding commentaries and reviews), only five used qualitative methods (2% of articles).

The goal of the current article is to present considerations for writing and evaluating qualitative research within the context of pediatric psychology to provide a framework for writing and reviewing manuscripts reporting qualitative findings. The current article may be especially useful to reviewers and authors who are less familiar with qualitative methods. The tenets presented here are grounded in the well-established literature on reporting and evaluating qualitative research, including guidelines and checklists ( Eakin & Mykhalovskiy, 2003 ; Elo et al., 2014 ; Mays & Pope, 2000 ; Tong, Sainsbury, & Craig, 2007 ). For example, the Consolidated Criteria for Reporting Qualitative Research checklist describes essential elements for reporting qualitative findings ( Tong et al., 2007 ). Although the considerations presented in the current manuscript have broad applicability to many fields, examples were purposively selected for the field of pediatric psychology.

Our goal is that this article will stimulate publication of more qualitative research in pediatric psychology and allied fields. More specifically, the goal is to encourage high-quality qualitative research by addressing key issues involved in conducting qualitative studies, and the process of conducting, reporting, and evaluating qualitative findings. Readers interested in more in-depth information on designing and implementing qualitative studies, relevant theoretical frameworks and approaches, and analytic approaches are referred to the well-developed literature in this area ( Clark, 2003 ; Corbin & Strauss, 2008 ; Creswell, 1994 ; Eakin & Mykhalovskiy, 2003 ; Elo et al., 2014 ; Mays & Pope, 2000 ; Miles, Huberman, & Saldaña, 2013 ; Ritchie & Lewis, 2003 ; Saldaña, 2012 ; Sandelowski, 1995 , 2010 ; Tong et al., 2007 ; Yin, 2015 ). Researchers new to qualitative research are also encouraged to obtain specialized training in qualitative methods and/or to collaborate with a qualitative expert in an effort to ensure rigor (i.e., validity).

We begin the article with a definition of qualitative research and an overview of the concept of rigor. While we recognize that qualitative methods comprise multiple and distinct approaches with unique purposes, we present an overview of considerations for writing and evaluating qualitative research that cut across qualitative methods. Specifically, we present basic principles in three broad areas: (1) study design and methods, (2) analytic considerations, and (3) presentation of findings (see Table 1 for a summary of the principles addressed in each area). Each area is addressed using a “question and answer” format. We present a brief explanation of each question, options for how one could address the issue raised, and a suggested recommendation. We recognize, however, that there are no absolute “right” or “wrong” answers and that the most “right” answer for each situation depends on the specific study and its purpose. In fact, our strongest recommendation is that authors of qualitative research manuscripts be explicit about their rationale for design, analytic choices, and strategies so that readers and reviewers can evaluate the rationale and rigor of the study methods.

Summary of Overarching Principles to Address in Qualitative Research Manuscripts

What Is Qualitative Research?

Qualitative methods are used across many areas of health research, including health psychology ( Gough & Deatrick, 2015 ), to study the meaning of people’s lives in their real-world roles, represent their views and perspectives, identify important contextual conditions, discover new or additional insights about existing social and behavioral concepts, and acknowledge the contribution of multiple perspectives ( Yin, 2015 ). Qualitative research is a family of approaches rather than a single approach. There are multiple and distinct qualitative methodologies or stances (e.g., constructivism, post-positivism, critical theory), each with different underlying ontological and epistemological assumptions ( Lincoln, Lynham, & Guba, 2011 ). However, certain features are common to most qualitative approaches and distinguish qualitative research from quantitative research ( Creswell, 1994 ).

Key to all qualitative methodologies is that multiple perspectives about a phenomenon of interest are essential, and that those perspectives are best inductively derived or discovered from people with personal experience regarding that phenomenon. These perspectives or definitions may differ from “conventional wisdom.” Thus, meanings need to be discovered from the population under study to ensure optimal understanding. For instance, in a recent qualitative study about texting while driving, adolescents said that they did not approve of texting while driving. The investigators, however, discovered that the respondents did not consider themselves driving while a vehicle was stopped at a red light. In other words, the respondents did approve of texting while stopped at a red light. In addition, the adolescents said that they highly valued being constantly connected via texting. Thus, what is meant by “driving” and the value of “being connected” need to be considered when approaching the issue of texting while driving with adolescents ( McDonald & Sommers, 2015 ).

Qualitative methods are also distinct from a mixed-method approach (i.e., integration of qualitative and quantitative approaches; Creswell, 2013b ). A mixed-methods study may include a first phase of quantitative data collection that provides results that inform a second phase of the study that includes qualitative data collection, or vice versa. A mixed-methods study may also include concurrent quantitative and qualitative data collection. The timing, priority, and stage of integration of the two approaches (quantitative and qualitative) are complex and vary depending on the research question; they also dictate how to attend to differing qualitative and quantitative principles ( Creswell et al., 2011 ). Understanding the basic tenets of qualitative research is preliminary to integrating qualitative research with another approach that has different tenets. A full discussion of the integration of qualitative and quantitative research approaches is beyond the scope of this article. Readers interested in the topic are referred to one of the many excellent resources on the topic ( Creswell, 2013b ).

What Are Typical Qualitative Research Questions?

Qualitative research questions are typically open-ended and are framed in the spirit of discovery and exploration and to address existing knowledge gaps. The current manuscript provides exemplar pediatric qualitative studies that illustrate key issues that arise when reporting and evaluating qualitative studies. Example research questions that are contained in the studies cited in the current manuscript are presented in Table 2 .

Example Qualitative Research Questions From the Pediatric Literature

What Are Rigor and Transparency in Qualitative Research?

There are several overarching principles with unique application in qualitative research, including definitions of scientific rigor and the importance of transparency. Quantitative research generally uses the terms reliability and validity to describe the rigor of research, while in qualitative research, rigor refers to the goal of seeking to understand the tacit knowledge of participants’ conception of reality ( Polanyi, 1958 ). For example, Haukeland and colleagues (2015) used qualitative analysis to identify themes describing the emotional experiences of a unique and understudied population—pediatric siblings of children with rare medical conditions such as Turner syndrome and Duchenne muscular dystrophy. Within this context, the authors’ rendering of the diverse and contradictory emotions experienced by siblings of children with these rare conditions represents “rigor” within a qualitative framework.

While debate exists regarding the terminology describing and strategies for strengthening scientific rigor in qualitative studies ( Guba, 1981 ; Morse, 2015a , 2015b ; Sandelowski, 1993a ; Whittemore, Chase, & Mandle, 2001 ), little debate exists regarding the importance of explaining strategies used to strengthen rigor. Such strategies should be appropriate for the specific study; therefore, it is wise to clearly describe what is relevant for each study. For example, in terms of strengthening credibility or the plausibility of data analysis and interpretation, prolonged engagement with participants is appropriate when conducting an observational study (e.g., observations of parent–child mealtime interactions; Hughes et al., 2011 ; Power et al., 2015 ). For an interview-only study, however, it would be more practical to strengthen credibility through other strategies (e.g., keeping detailed field notes about the interviews included in the analysis).

Dependability is the stability of a data analysis protocol. For instance, stepwise development of a coding system from an “a priori” list of codes based on the underlying conceptual framework or existing literature (e.g., creating initial codes for potential barriers to medication adherence based on prior studies) may be essential for analysis of data from semi-structured interviews using multiple coders. But this may not be the ideal strategy if the purpose is to inductively derive all possible coding categories directly from data in an area where little is known. For some research questions, the strategy may be to strengthen confirmability or to verify a specific phenomenon of interest using different sources of data before generating conclusions. This process, which is commonly referred to in the research literature as triangulation, may also include collecting different types of data (e.g., interview data, observational data), using multiple coders to incorporate different ways of interpreting the data, or using multiple theories ( Krefting, 1991 ; Ritchie & Lewis, 2003 ). Alternatively, another investigator may use triangulation to provide complementarity data ( Krefting, 1991 ) to garner additional information to deepen understanding. Because the purpose of qualitative research is to discover multiple perspectives about a phenomenon, it is not necessarily appropriate to attain concordance across studies or investigators when independently analyzing data. Some qualitative experts also believe that it is inappropriate to use triangulation to confirm findings, but this debate has not been resolved within the field ( Ritchie & Lewis, 2003 ; Tobin & Begley, 2004 ). More agreement exists, however, regarding the value of triangulation to complement, deepen, or expand understanding of a particular topic or issue ( Ritchie & Lewis, 2003 ). Finally, instead of basing a study on a sample that allows for generalizing statistical results to other populations, investigators in qualitative research studies are focused on designing a study and conveying the results so that the reader understands the transferability of the results. Strategies for transferability may include explanations of how the sample was selected and descriptive characteristics of study participants, which provides a context for the results and enables readers to decide if other samples share critical attributes. A study is deemed transferable if relevant contextual features are common to both the study sample and the larger population.

Strategies to enhance rigor should be used systematically across each phase of a study. That is, rigor needs to be identified, managed, and documented throughout the research process: during the preparation phase (data collection and sampling), organization phase (analysis and interpretation), and reporting phase (manuscript or final report; Elo et al., 2014 ). From this perspective, the strategies help strengthen the trustworthiness of the overall study (i.e., to what extent the study findings are worth heeding; Eakin & Mykhalovskiy, 2003 ; Lincoln & Guba, 1985 ).

A good example of managing and documenting rigor and trustworthiness can be found in a study of family treatment decisions for children with cancer ( Kelly & Ganong, 2011 ). The researchers describe how they promoted the rigor of the study and strengthening its credibility by triangulating data sources (e.g., obtaining data from children’s custodial parents, stepparents, etc.), debriefing (e.g., holding detailed conversations with colleagues about the data and interpretations of the data), member checking (i.e., presenting preliminary findings to participants to obtain their feedback and interpretation), and reviewing study procedure decisions and analytic procedures with a second party.

Transparency is another key concept in written reports of qualitative research. In other words, enough detail should be provided for the reader to understand what was done and why ( Ritchie & Lewis, 2003 ). Examples of information that should be included are a clear rationale for selecting a particular population or people with certain characteristics, the research question being investigated, and a meaningful explanation of why this research question was selected (i.e., the gap in knowledge or understanding that is being investigated; Ritchie & Lewis, 2003 ). Clearly describing recruitment, enrollment, data collection, and data analysis or extraction methods are equally important ( Dixon-Woods, Shaw, Agarwal, & Smith, 2004 ). Coherency among methods and transparency about research decisions adds to the robustness of qualitative research ( Tobin & Begley, 2004 ) and provides a context for understanding the findings and their implications.

Study Design and Methods

Is qualitative research hypothesis driven.

In contrast to quantitative research, qualitative research is not typically hypothesis driven ( Creswell, 1994 ; Ritchie & Lewis, 2003 ). A risk associated with using hypotheses in qualitative research is that the findings could be biased by the hypotheses. Alternatively, qualitative research is exploratory and typically guided by a research question or conceptual framework rather than hypotheses ( Creswell, 1994 ; Ritchie & Lewis, 2003 ). As previously stated, the goal of qualitative research is to increase understanding in areas where little is known by developing deeper insight into complex situations or processes. According to Richards and Morse (2013) , “If you know what you are likely to find, …  you should not be working qualitatively” (p. 28). Thus, we do not recommend that a hypothesis be stated in manuscripts presenting qualitative data.

What Is the Role of Theory in Qualitative Research?

Consistent with the exploratory nature of qualitative research, one particular qualitative method, grounded theory, is used specifically for discovering substantive theory (i.e., working theories of action or processes developed for a specific area of concern; Bryant & Charmaz, 2010 ; Glaser & Strauss, 1967 ). This method uses a series of structured steps to break down qualitative data into codes, organize the codes into conceptual categories, and link the categories into a theory that explains the phenomenon under study. For example, Kelly and Ganong (2011) used grounded theory methods to produce a substantive theory about how single and re-partnered parents (e.g., households with a step-parent) made treatment decisions for children with childhood cancer. The theory of decision making developed in this study included “moving to place,” which described the ways in which parents from different family structures (e.g., single and re-partnered parents) were involved in the child’s treatment decision-making. The resulting theory also delineated the causal conditions, context, and intervening factors that contributed to the strategies used for moving to place.

Theories may be used in other types of qualitative research as well, serving as the impetus or organizing framework for the study ( Sandelowski, 1993b ). For example, Izaguirre and Keefer (2014) used Social Cognitive Theory ( Bandura, 1986 ) to investigate self-efficacy among adolescents with inflammatory bowel disease. The impetus for selecting the theory was to inform the development of a self-efficacy measure for adolescent self-management. In another study on health care transition in youth with Type 1 Diabetes ( Pierce, Wysocki, & Aroian, 2016 ), the investigators adapted a social-ecological model—the Socio-ecological Model of Adolescent and Young Adult Transition Readiness (SMART) model ( Schwartz, Tuchman, Hobbie, & Ginsberg, 2011 )—to their study population ( Pierce & Wysocki, 2015 ). Pierce et al. (2016) are currently using the adapted SMART model to focus their data collection and structure the preliminary analysis of their data about diabetes health care transition.

Regardless of whether theory is induced from data or selected in advance to guide the study, consistent with the principle of transparency , its role should be clearly identified and justified in the research publication ( Bradbury-Jones, Taylor, & Herber, 2014 ; Kelly, 2010 ). Methodological congruence is an important guiding principle in this regard ( Richards & Morse, 2013 ). If a theory frames the study at the outset, it should guide and direct all phases. The resulting publication(s) should relate the phenomenon of interest and the research question(s) to the theory and specify how the theory guided data collection and analysis. The publication(s) should also discuss how the theory fits with the finished product. For instance, authors should describe how the theory provided a framework for the presentation of the findings and discuss the findings in context with the relevant theoretical literature.

A study examining parents’ motivations to promote vegetable consumption in their children ( Hingle et al., 2012 ) provides an example of methodological congruence. The investigators adapted the Model of Goal Directed Behavior ( Bagozzi & Pieters, 1998 ) for parenting practices relevant to vegetable consumption (Model of Goal Directed Vegetable Parenting Practices; MGDVPP). Consistent with the adapted theoretical model and in keeping with the congruence principle, interviews were guided by the theoretical constructs contained within the MGDVPP, including parents’ attitudes, subjective norms, and perceived behavioral control related to promoting vegetable consumption in children ( Hingle et al., 2012 ). The study discovered that the adapted model successfully identified parents’ motivations to encourage their children to eat more vegetables.

The use of the theory should be consistent with the basic goal of qualitative research, which is discovery. Alternatively stated, theories should be used as broad orienting frameworks for exploring topical areas without imposing preconceived ideas and biases. The theory should be consistent with the study findings and not be used to force-fit the researcher’s interpretation of the data ( Sandelowski, 1993b ). Divergence from the theory when it does not fit the study findings is illustrated in a qualitative study of hypertension prevention beliefs in Hispanics ( Aroian, Peters, Rudner, & Waser, 2012 ). This study used the Theory of Planned Behavior as a guiding theoretical framework but found that coding separately for normative and control beliefs was not the best organizing schema for presenting the study findings. When divergence from the original theory occurs, the research report should explain and justify how and why the theory was modified ( Bradbury-Jones et al., 2014 ).

What Are Typical Sampling Methods in Qualitative Studies?

Qualitative sampling methods should be “purposeful” ( Coyne, 1997 ; Patton, 2015 ; Tuckett, 2004 ). Purposeful sampling is based on the study purpose and investigator judgments about which people and settings will provide the richest information for the research questions. The logic underlying this type of sampling differs from the logic underlying quantitative sampling ( Patton, 2015 ). Quantitative research strives for empirical generalization. In qualitative studies, generalizability beyond the study sample is typically not the intent; rather, the focus is on deriving depth and context-embedded meaning for the relevant study population.

Purposeful sampling is a broad term. Theoretical sampling is one particular type of purposeful sampling unique to grounded theory methods ( Coyne, 1997 ). In theoretical sampling, study participants are chosen according to theoretical categories that emerge from ongoing data collection and analyses ( Bryant & Charmaz, 2010 ). Data collection and analysis are conducted concurrently to allow generating and testing hypotheses that emerge from analyzing incoming data. The following example from the previously mentioned qualitative interview study about transition from pediatric to adult care in adolescents with type 1 diabetes ( Pierce et al., 2016 ) illustrates the process of theoretical sampling: An adolescent study participant stated that he was “turned off” by the “childish” posters in his pediatrician’s office. He elaborated that he welcomed transitioning to adult care because his diabetes was discovered when he was 18, an age when he reportedly felt more “mature” than most pediatric patients. These data were coded as “developmental misfit” and prompted a tentative hypothesis about developmental stage at entry for pediatric diabetes care and readiness for health care transition. Examining this hypothesis prompted seeking study participants who varied according to age or developmental stage at time of diagnosis to examine the theoretical relevance of an emerging theme about developmental fit.

Not all purposeful sampling, however, is “theoretical.” For example, ethnographic studies typically seek to understand a group’s cultural beliefs and practices ( Creswell, 2013a ). Consistent with this purpose, researchers conducting an ethnographic study might purposefully select study participants according to specific characteristics that reflect the social roles and positions in a given group or society (e.g., socioeconomic status, education; Johnson, 1990 ).

Random sampling is generally not used in qualitative research. Random selection requires a sufficiently large sample to maximize the potential for chance and, as will be discussed below, sample size is intentionally small in qualitative studies. However, random sampling may be used to verify or clarify findings ( Patton, 2015 ). Validating study findings with a randomly selected subsample can be used to address the possibility that a researcher is inadvertently giving greater attention to cases that reinforce his or her preconceived ideas.

Regardless of the sampling method used, qualitative researchers should clearly describe the sampling strategy and justify how it fits the study when reporting study findings (transparency). A common error is to refer to theoretical sampling when the cases were not chosen according to emerging theoretical concepts. Another common error is to apply sampling principles from quantitative research (e.g., cluster sampling) to convince skeptical reviewers about the rigor or validity of qualitative research. Rigor is best achieved by being purposeful, making sound decisions, and articulating the rationale for those decisions. As mentioned earlier in the discussion of transferability , qualitative researchers are encouraged to describe their methods of sample selection and descriptive characteristics about their sample so that readers and reviewers can judge how the current sample may differ from others. Understanding the characteristics of each qualitative study sample is essential for the iterative nature of qualitative research whereby qualitative findings inform the development of future qualitative, quantitative, or mixed-methods studies. Reviewers should evaluate sampling decisions based on how they fit the study purpose and how they influence the quality of the end product.

What Sample Size Is Needed for Qualitative Research?

No definitive rules exist about sample size in qualitative research. However, sample sizes are typically smaller than those in quantitative studies ( Patton, 2015 ). Small samples often generate a large volume of data and information-rich cases, ultimately leading to insight regarding the phenomenon under study ( Patton, 2015 ; Ritchie & Lewis, 2003 ). Sample sizes of 20–30 cases are typical, but a qualitative sample can be even smaller under some circumstances ( Mason, 2010 ).

Sample size adequacy is evaluated based on the quality of the study findings, specifically the full development of categories and inter-relationships or the adequacy of information about the phenomenon under study ( Corbin & Strauss, 2008 ; Ritchie & Lewis, 2003 ). Small sample sizes are of concern if they do not result in these outcomes. Data saturation (i.e., the point at which no new information, categories, or themes emerge) is often used to judge informational adequacy ( Morgan, 1998 ; Ritchie & Lewis, 2003 ). Although enough participants should be included to obtain saturation ( Morgan, 1998 ), informational adequacy pertains to more than sample size. It is also a function of the quality of the data, which is influenced by study participant characteristics (e.g., cognitive ability, knowledge, representativeness) and the researcher’s data-gathering skills and analytical ability to generate meaningful findings ( Morse, 2015b ; Patton, 2015 ).

Sample size is also influenced by type of qualitative research, the study purpose, the sample, the depth and complexity of the topic investigated, and the method of data collection. In general, the more heterogeneous the sample, the larger the sample size, particularly if the goal is to investigate similarities and differences by specific characteristics ( Ritchie & Lewis, 2003 ). For instance, in a study to conduct an initial exploration of factors underlying parents’ motivations to use good parenting practices, theoretical saturation (i.e., the point at which no new information, categories, or themes emerge) was obtained with a small sample ( n  = 15), most likely because the study was limited to parents of young children ( Hingle et al., 2012 ). If the goal of the study had been, for example, to identify racial/ethnic, gender, or age differences in food parenting practices, a larger sample would likely be needed to obtain saturation or informational adequacy.

Studies that seek to understand maximum variation in a phenomenon might also need a larger sample than one that is seeking to understand extreme or atypical cases. For example, a qualitative study of diet and physical activity in young Australian men conducted focus groups to identify perceived motivators and barriers to healthy eating and physical activity and examine the influence of body weight on their perceptions. Examining the influence of body weight status required 10 focus groups to allow for group assignment based on body mass index ( Ashton et al., 2015 ). More specifically, 61 men were assigned to a healthy-weight focus group ( n  = 3), an overweight/obese focus group ( n  = 3), or a mixed-weight focus group ( n  = 4). Had the researcher not been interested in whether facilitators and barriers differed by weight status, its likely theoretical saturation could have been obtained with fewer groups. Depth of inquiry also influences sample size ( Sandelowski, 1995 ). For instance, an in-depth analysis of an intervention for children with cancer and their families included 16 family members from three families. Study data comprised 52 hrs of videotaped intervention sessions and 10 interviews ( West, Bell, Woodgate, & Moules, 2015 ). Depth was obtained through multiple data points and types of data, which justified sampling only a few families.

Authors of publications describing qualitative findings should show evidence that the data were “saturated” by a sample with sufficient variation to permit detailing shared and divergent perspectives, meanings, or experiences about the topic of inquiry. Decisions related to the sample (e.g., targeted recruitment) should be detailed in publications so that peer reviewers have the context for evaluating the sample and determining how the sample influenced the study findings ( Patton, 2015 ).

Qualitative Data Analysis

When conducting qualitative research, voluminous amounts of data are gathered and must be prepared (i.e., transcribed) and managed. During the analytic process, data are systematically transformed through identifying, defining, interpreting, and describing findings that are meant to comprehensively describe the phenomenon or the abstract qualities that they have in common. The process should be systematic ( dependability ) and well-documented in the analysis section of a qualitative manuscript. For example, Kelly and Ganong (2011) , in their study of medical treatment decisions made by families of children with cancer, described their analytic procedure by outlining their approach to coding and use of memoing (e.g., keeping careful notes about emerging ideas about the data throughout the analytic process), comparative analysis (e.g., comparing data against one another and looking for similarities and differences), and diagram drawing (e.g., pictorially representing the data structure, including relationships between codes).

How Should Researchers Document Coding Reliability?

Because the intent of qualitative research is to account for multiple perspectives, the goal of qualitative analysis is to comprehensively incorporate those perspectives into discernible findings. Researchers accustomed to doing quantitative studies may expect authors to quantify interrater reliability (e.g., kappa statistic) but this is not typical in qualitative research. Rather, the emphasis in qualitative research is on (1) training those gathering data to be rigorous and produce high-quality data and on (2) using systematic processes to document key decisions (e.g., code book), clear direction, and open communication among team members during data analysis. The goal is to make the most of the collective insight of the investigative team to triangulate or complement each other’s efforts to process and interpret the data. Instead of evaluating if two independent raters came to the same numeric rating, reviewers of qualitative manuscripts should judge to what extent the overall process of coding, data management, and data interpretation were systematic and rigorous. Authors of qualitative reports should articulate their coding procedures for others to evaluate. Together, these strategies promote trustworthiness of the study findings.

An example of how these processes are described in the report of a qualitative study is as follows:

The first two authors independently applied the categories to a sample of two interviews and compared their application of the categories to identify lack of clarity and overlap in categories. The investigators created a code book that contained a definition of categories, guidelines for their application, and excerpts of data exemplifying the categories. The first two authors independently coded the data and compared how they applied the categories to the data and resolved any differences during biweekly meetings. ATLAS.ti, version 6.2, was used to document and accommodate ongoing changes and additions to the coding structure ( Palma et al., 2015 , p. 224).

Do I Need to Use a Specialized Qualitative Data Software Program for Analysis?

Multiple computer software packages for qualitative data analysis are currently available ( Silver & Lewins, 2014 ; Yin, 2015 ). These packages allow the researcher to import qualitative data (e.g., interview transcripts) into the software program and organize data segments (e.g., delineate which interview excerpts are relevant to particular themes). Qualitative analysis software can be useful for organizing and sorting through data, including during the analysis phase. Some software programs also offer sophisticated coding and visualization capabilities that facilitate and enhance interpretation and understanding. For example, if data segments are coded by specific characteristics (e.g., gender, race/ethnicity), the data can be sorted and analyzed by these characteristics, which may contribute to an understanding of whether and/or how a particular phenomenon may vary by these characteristics.

The strength of computer software packages for qualitative data analysis is their potential to contribute to methodological rigor by organizing the data for systematic analyses ( John & Johnson, 2000 ; MacMillan & Koenig, 2004 ). However, the programs do not replace the researchers’ analyses. The researcher or research team is ultimately responsible for analyzing the data, identifying the themes and patterns, and placing the findings within the context of the literature. In other words, qualitative data analysis software programs contribute to, but do not ensure scientific rigor or “objectivity” in, the analytic process. In fact, using a software program for analysis is not essential if the researcher demonstrates the use of alternative tools and procedures for rigor.

Presentation of Findings

Should there be overlap between presentation of themes in the results and discussion sections.

Qualitative papers sometimes combine results and discussion into one section to provide a cohesive presentation of the findings along with meaningful linkages to the existing literature ( Burnard, 2004 ; Burnard, Gill, Stewart, Treasure, & Chadwick, 2008 ). Although doing so is an acceptable method for reporting qualitative findings, some journals prefer the two sections to be distinct.

When the journal style is to distinguish the two sections, the results section should describe the findings, that is, the themes, while the discussion section should pull the themes together to make larger-level conclusions and place the findings within the context of the existing literature. For instance, the findings section of a study of how rural African-American adolescents, parents, and community leaders perceived obesity and topics for a proposed obesity prevention program, contained a description of themes about adolescent eating patterns, body shape, and feedback on the proposed weight gain prevention program according to each subset of participants (i.e., adolescents, parents, community leaders). The discussion section then put these themes within the context of findings from prior qualitative and intervention studies in related populations ( Cassidy et al., 2013 ). In the Discussion, when making linkages to the existing literature, it is important to avoid the temptation to extrapolate beyond the findings or to over-interpret them ( Burnard, 2004 ). Linkages between the findings and the existing literature should be supported by ample evidence to avoid spurious or misleading connections ( Burnard, 2004 ).

What Should I Include in the Results Section?

The results section of a qualitative research report is likely to contain more material than customary in quantitative research reports. Findings in a qualitative research paper typically include researcher interpretations of the data as well as data exemplars and the logic that led to researcher interpretations ( Sandelowski & Barroso, 2002 ). Interpretation pertains to the researcher breaking down and recombining the data and creating new meanings (e.g., abstract categories, themes, conceptual models). Select quotes from interviews or other types of data (e.g., participant observation, focus groups) are presented to illustrate or support researcher interpretations. Researchers trained in the quantitative tradition, where interpretation is restricted to the discussion section, may find this surprising; however, in qualitative methods, researcher interpretations represent an important component of the study results. The presentation of the findings, including researcher interpretations (e.g., themes) and data (e.g., quotes) supporting those interpretations, adds to the trustworthiness of the study ( Elo et al., 2014 ).

The Results section should contain a balance between data illustrations (i.e., quotes) and researcher interpretations ( Lofland & Lofland, 2006 ; Sandelowski, 1998 ). Because interpretation arises out of the data, description and interpretation should be combined. Description should be sufficient to support researcher interpretations, and quotes should be used judiciously ( Morrow, 2005 ; Sandelowski, 1994 ). Not every theme needs to be supported by multiple quotes. Rather, quotes should be carefully selected to provide “voice” to the participants and to help the reader understand the phenomenon from the participant’s perspective within the context of the researcher’s interpretation ( Morrow, 2005 ; Ritchie & Lewis, 2003 ). For example, researchers who developed a grounded theory of sexual risk behavior of urban American Indian adolescent girls identified desire for better opportunities as a key deterrent to neighborhood norms for early sexual activity. They illustrated this theme with the following quote: “I don’t want to live in the ‘hood and all that…My sisters are stuck there because they had babies. That isn’t going to happen to me” ( Saftner, Martyn, Momper, Loveland-Cherry, & Low, 2015 , p. 372).

There is no precise formula for the proportion of description to interpretation. Both descriptive and analytic excess should be avoided ( Lofland & Lofland, 2006 ). The former pertains to presentation of unedited field notes or interview transcripts rather than selecting and connecting data to analytic concepts that explain or summarize the data. The latter pertains to focusing on the mechanics of analysis and interpretation without substantiating researcher interpretations with quotes. Reviewer requests for methodological rigor can result in researchers writing qualitative research papers that suffer from analytic excess ( Sandelowski & Barroso, 2002 ). Page limitations of most journals provide a safeguard against descriptive excess, but page limitations should not circumvent researchers from providing the basis for their interpretations.

Additional potential problems with qualitative results sections include under-elaboration, where themes are too few and not clearly defined. The opposite problem, over-elaboration, pertains to too many analytic distinctions that could be collapsed under a higher level of abstraction. Quotes can also be under- or over-interpreted. Care should be taken to ensure the quote(s) selected clearly support the theme to which they are attached. And finally, findings from a qualitative study should be interesting and make clear contributions to the literature ( Lofland & Lofland, 2006 ; Morse, 2015b ).

Should I Quantify My Results? (e.g., Frequency With Which Themes Were Endorsed)

There is controversy over whether to quantify qualitative findings, such as providing counts for the frequency with which particular themes are endorsed by study participants ( Morgan, 1993 ; Sandelowski, 2001 ). Qualitative papers usually report themes and patterns that emerge from the data without quantification ( Dey, 1993 ). However, it is possible to quantify qualitative findings, such as in qualitative content analysis. Qualitative content analysis is a method through which a researcher identifies the frequency with which a phenomenon, such as specific words, phrases, or concepts, is mentioned ( Elo et al., 2014 ; Morgan, 1993 ). Although this method may appeal to quantitative reviewers, it is important to note that this method only fits specific study purposes, such as studies that investigate the language used by a particular group when communicating about a specific topic. In addition, results may be quantified to provide information on whether themes appeared to be common or atypical. Authors should avoid using imprecise language, such as “some participants” or “many participants.” A good example of quantification of results to illustrate more or less typical themes comes from a manuscript describing a qualitative study of school nurses’ perceived barriers to addressing obesity with students and their families. The authors described that all but one nurse reported not having the resources they needed to discuss weight with students and families whereas one-quarter of nurses reported not feeling competent to discuss weight issues ( Steele et al., 2011 ). If quantification of findings is used, authors should provide justification that explains how quantification is consistent with the aims or goals of the study ( Sandelowski, 2001 ).

Conclusions

This article highlighted key theoretical and logistical considerations that arise in designing, conducting, and reporting qualitative research studies (see Table 1 for a summary). This type of research is vital for obtaining patient, family, community, and other stakeholder perspectives about their needs and interests, and will become increasingly critical as our models of health care delivery evolve. For example, qualitative research could contribute to the study of health care providers and systems with the goal of optimizing our health care delivery models. Given the increasing diversity of the populations we serve, qualitative research will also be critical in providing guidance in how to tailor health interventions to key characteristics and increase the likelihood of acceptable, effective treatment approaches. For example, applying qualitative research methods could enhance our understanding of refugee experiences in our health care system, clarify treatment preferences for emerging adults in the midst of health care transitions, examine satisfaction with health care delivery, and evaluate the applicability of our theoretical models of health behavior changes across racial and ethnic groups. Incorporating patient perspectives into treatment is essential to meeting this nation’s priority on patient-centered health care ( Institute of Medicine Committee on Quality of Health Care in America, 2001 ). Authors of qualitative studies who address the methodological choices addressed in this review will make important contributions to the field of pediatric psychology. Qualitative findings will lead to a more informed field that addresses the needs of a wide range of patient populations and produces effective and acceptable population-specific interventions to promote health.

Acknowledgments

The authors thank Bridget Grahmann for her assistance with manuscript preparation.

This work was supported by National Cancer Institute of the National Institutes of Health (K07CA196985 to Y.W.). This work is a publication of the United States Department of Agriculture/Agricultural Research Center (USDA/ARS), Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas. It is also a publication of the USDA/ARS, Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, and funded in part with federal funds from the USDA/ARS under Cooperative Agreement No. 58‐6250‐0‐008 (to D.T.). The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products, or organizations imply endorsement from the U.S. government. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflicts of interest : None declared.

• Aroian K. J., Peters R. M., Rudner N., Waser L. (2012). Hypertension prevention beliefs of hispanics . Journal of Transcultural Nursing , 23 , 134–142. doi:10.1177/1043659611433871. [ PubMed ] [ Google Scholar ]
• Ashton L. M., Hutchesson M. J., Rollo M. E., Morgan P. J., Thompson D. I., Collins C. E. (2015). Young adult males’ motivators and perceived barriers towards eating healthily and being active: A qualitative study . The International Journal of Behavioral Nutrition and Physical Activity , 12 , 93 doi:10.1186/s12966‐015‐0257‐6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bagozzi R., Pieters R. (1998). Goal-directed emotions . Cognition & Emotion , 12 ( 1 ), 1–26. [ Google Scholar ]
• Bandura A. (1986). Social foundations of thought and action: A social cognitive theory . Englewood Cliffs, NJ: Prentice-Hall Inc. [ Google Scholar ]
• Bevans K. B., Gardner W., Pajer K., Riley A. W., Forrest C. B. (2013). Qualitative development of the PROMIS ® pediatric stress response item banks . Journal of Pediatric Psychology , 38 , 173–191. doi:10.1093/jpepsy/jss107. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bradbury-Jones C., Taylor J., Herber O. (2014). How theory is used and articulated in qualitative research: Development of a new typology . Social Science and Medicine , 120 , 135–141. doi:10.1016/j.socscimed.2014.09.014. [ PubMed ] [ Google Scholar ]
• Bryant A., Charmaz K. (2010). The Sage handbook of grounded theory . Thousand Oaks, CA: Sage. [ Google Scholar ]
• Burnard P. (2004). Writing a qualitative research report . Nurse Education Today , 24 , 174–179. doi:10.1016/j.nedt.2003.11.005. [ PubMed ] [ Google Scholar ]
• Burnard P., Gill P., Stewart K., Treasure E., Chadwick B. (2008). Analysing and presenting qualitative data . British Dental Journal , 204 , 429–432. doi:10.1038/sj.bdj.2008.292. [ PubMed ] [ Google Scholar ]
• Cassidy O., Sbrocco T., Vannucci A., Nelson B., Jackson-Bowen D., Heimdal J., Heimdal J., Mirza N., Wilfley D. E., Osborn R., Shomaker L. B., Young J. F., Waldron H., Carter M., Tanofsky-Kraff M., (2013). Adapting interpersonal psychotherapy for the prevention of excessive weight gain in rural African American girls . Journal of Pediatric Psychology , 38 , 965–977. doi:10.1093/jpepsy/jst029. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Clark J. (2003). How to peer review a qualitative manuscript . Peer Review in Health Sciences , 2 , 219–235. [ Google Scholar ]
• Corbin S., Strauss A. (2008). Basics of qualitative research (3rd ed.). Los Angeles, CA: Sage Publications. [ Google Scholar ]
• Coyne I. T. (1997). Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? Journal of Advanced Nursing , 26 , 623–630. doi:10.1046/j.1365‐2648.1997.t01‐25‐00999.x. [ PubMed ] [ Google Scholar ]
• Creswell J. W. (1994). Research design: Qualitative & quantitative approaches . Journal of Marketing Research , 33 , 252 doi:10.2307/3152153. [ Google Scholar ]
• Creswell J. W. (2013a). Qualitative inquiry and research design: Choosing among five approaches . Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Creswell J. W. (2013b). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Creswell J. W., Klassen A. C., Plano Clark V. L., Smith K. C.;for the Office of Behavioral and Social Sciences Research. (2011). Best practices for mixed methods research in the health sciences . Retrieved from National Institutes of Health: http://obssr.od.nih.gov/mixed_methods_research .
• de Visser R. O., Graber R., Hart A., Abraham C., Scanlon T., Watten P., Memon A. (2015). Using qualitative methods within a mixed-methods approach to developing and evaluating interventions to address harmful alcohol use among young people . Health Psychology , 34 , 349–360. doi:10.1037/hea0000163. [ PubMed ] [ Google Scholar ]
• Dey I. (1993). Qualitative data analysis: A user-friendly guide for social scientists . New York, NY: Routledge. [ Google Scholar ]
• Dixon-Woods M., Shaw R. L., Agarwal S., Smith J. A. (2004). The problem of appraising qualitative research . Quality and Safety in Health Care , 13 , 223–225. doi:10.1136/qhc.13.3.223. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Eakin J. M., Mykhalovskiy E. (2003). Reframing the evaluation of qualitative health research: Reflections on a review of appraisal guidelines in the health sciences . Journal of Evaluation in Clinical Practice , 9 , 187–194. doi:10.1046/j.1365‐2753.2003.00392.x. [ PubMed ] [ Google Scholar ]
• Elo S., Kääriäinen M., Kanste O., Pölkki T., Utriainen K., Kyngäs H. (2014). Qualitative content analysis: A focus on trustworthiness . SAGE Open , 4 ( 1 ), 1–10. doi:10.1177/2158244014522633. [ Google Scholar ]
• Glaser B., Strauss A. (1967). The discovery grounded theory: Strategies for qualitative inquiry . Nursing Research , 17 , 364 doi:10.1097/00006199‐196807000‐00014. [ Google Scholar ]
• Gough B., Deatrick J. A. (2015). Qualitative health psychology research: Diversity, power, and impact . Health Psychology , 34 , 289–292. doi:10.1037/hea0000206. [ PubMed ] [ Google Scholar ]
• Guba E. G. (1981). Criteria for assessing the trustworthiness of naturalistic inquiries . Educational Communication and Technology , 29 , 75–91. doi:10.1007/BF02766777. [ Google Scholar ]
• Haukeland Y. B., Fjermestad K. W., Mossige S., Vatne T. M. (2015). Emotional experiences among siblings of children with rare disorders . Journal of Pediatric Psychology , 40 , 12–20. doi:10.1093/jpepsy/jsv022. [ PubMed ] [ Google Scholar ]
• Hess J. S., Straub D. M. (2011). Brief report: Preliminary findings from a pilot health care transition education intervention for adolescents and young adults with special health care needs . Journal of Pediatric Psychology , 36 , 172–178. doi:10.1093/jpepsy/jsq091. [ PubMed ] [ Google Scholar ]
• Hingle M., Beltran A., O’Connor T., Thompson D., Baranowski J., Baranowski T. (2012). A model of goal directed vegetable parenting practices . Appetite , 58 , 444–449. doi:10.1016/j.appet.2011.12.011. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hughes S. O., Power T. G., Papaioannou M. A., Cross M. B., Nicklas T. A., Hall S. K., Shewchuk R. M. (2011). Emotional climate, feeding practices, and feeding styles: An observational analysis of the dinner meal in Head Start families . The International Journal of Behavavioral Nutrition and Physical Activity , 8 , 60 doi:10.1186/1479‐5868‐8‐60. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Institute of Medicine Committee on Quality of Health Care in America. (2001). Crossing the quality chasm: A new health system for the 21st century . National Academies Press. Washington, DC. [ Google Scholar ]
• Izaguirre M. R., Keefer L. (2014). Development of a self-efficacy scale for adolescents and young adults with inflammatory bowel disease . Journal of Pediatric Gastroenterology and Nutrition , 59 , 29–32. doi:10.1097/mpg.0000000000000357. [ PubMed ] [ Google Scholar ]
• John W. S., Johnson P. (2000). The pros and cons of data analysis software for qualitative research . Journal of Nursing Scholarship , 32 , 393–397. [ PubMed ] [ Google Scholar ]
• Johnson J. C. (1990). Selecting ethnographic informants . Sage Publications. Thousand Oaks, CA. [ Google Scholar ]
• Kars M. C., Grypdonck M. H., de Bock L. C., van Delden J. J. (2015). The parents’ ability to attend to the “voice of their child” with incurable cancer during the palliative phase . Health Psychology , 34 , 446–452. doi:10.1037/hea0000166. [ PubMed ] [ Google Scholar ]
• Kelly K., Ganong L. (2011). Moving to place: Childhood cancer treatment decision making in single-parent and repartnered family structures . Qualitative Health Research , 21 , 349–364. doi:10.1177/1049732310385823. [ PubMed ] [ Google Scholar ]
• Kelly M. (2010). The role of theory in qualitative health research . Family Practice , 27 , 285–290. doi:10.1093/fampra/cmp077. [ PubMed ] [ Google Scholar ]
• Krefting L. (1991). Rigor in qualitative research: The assessment of trustworthiness . The American Journal of Occupational Therapy , 45 , 214–222. doi:10.5014/ajot.45.3.214. [ PubMed ] [ Google Scholar ]
• Lincoln Y. S., Guba E. G. (1985). Naturalistic inquiry . Newbury Park, CA: Sage Publications. [ Google Scholar ]
• Lincoln Y. S., Lynham S. A., Guba E. G. (2011). Paradigmatic controversies, contradictions, and emerging confluences, revisited . In Denzin N. K., Lincoln Y. S. (Eds.), The Sage handbook of qualitative research (4th ed., pp. 97–128). Thousand Oaks, CA: Sage. [ Google Scholar ]
• Lofland J., Lofland L. H. (2006). Analyzing social settings: A guide to qualitative observation and analysis . Belmont, CA: Wadsworth Publishing Company. [ Google Scholar ]
• Lyons A. C., Goodwin I., McCreanor T., Griffin C. (2015). Social networking and young adults’ drinking practices: Innovative qualitative methods for health behavior research . Health Psychology , 34 , 293–302. doi:10.1037/hea0000168. [ PubMed ] [ Google Scholar ]
• MacMillan K., Koenig T. (2004). The wow factor: Preconceptions and expectations for data analysis software in qualitative research . Social Science Computer Review , 22 , 179–186. doi:10.1177/0894439303262625. [ Google Scholar ]
• Mason M. (Producer). (2010). Sample size and saturation in PhD studies using qualitative interviews . Forum: Qualitative Social Research . Retrieved from http://nbn-resolving.de/urn:nbn:de:0114-fqs100387 .
• Mays N., Pope C. (2000). Qualitative research in health care: Assessing quality in qualitative research . British Medical Journal , 320 , 50 doi:10.1136/bmj.320.7226.50. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• McDonald C. C., Sommers M. S. (2015). Teen drivers’ perceptions of inattention and cell phone use while eriving . Traffic Injury Prevention , 16 ( Suppl 2 ), S52–S58. doi:10.1080/15389588.2015.1062886. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Miles M. B., Huberman A. M., Saldaña J. (2013). Qualitative data analysis: A methods sourcebook (3rd ed.). Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Minges K. E., Owen N., Salmon J., Chao A., Dunstan D. W., Whittemore R. (2015). Reducing youth screen time: Qualitative metasynthesis of findings on barriers and facilitators . Health Psychology , 34 , 381–397. doi:10.1037/hea0000172. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Morgan D. L. (1993). Qualitative content analysis: A guide to paths not taken . Qualitative Health Research , 3 , 112–121. doi:10.1177/104973239300300107. [ PubMed ] [ Google Scholar ]
• Morgan D. L. (1998). Planning Focus Groups: Focus Group Kit #2 . Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Morrow S. (2005). Quality and trustworthiness in qualitative research in counseling psychology . Journal of Counseling Psychology , 52 , 250–260. doi:10.1037/0022‐0167.52.2.250. [ Google Scholar ]
• Morse J. M. (2015a). Critical analysis of strategies for determining rigor in qualitative inquiry . Qualitative Health Research , 25 , 1212–1222. doi:10.1177/1049732315588501. [ PubMed ] [ Google Scholar ]
• Morse J. M. (2015b). Data were saturated . Qualitative Health Research , 25 , 587–588. doi:10.1177/1049732315576699. [ PubMed ] [ Google Scholar ]
• Palermo T. M. (2013). New guidelines for publishing review articles in JPP: Systematic reviews and topical reviews . Journal of Pediatric Psychology , 38 , 5–9. doi:10.1093/jpepsy/jss124. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Palermo T. M. (2014). Evidence-based interventions in pediatric psychology: Progress over the decades . Journal of Pediatric Psychology , 39 , 753–762. doi:10.1093/jpepsy/jsu048. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Palma E., Deatrick J., Hobbie W., Ogle S., Kobayashi K., Maldonado L. (2015). Maternal caregiving demands for adolescent and young adult survivors of pediatric brain tumors . Oncology Nursing Forum , 42 , 222–229. doi:10.1188/15.ONF.. [ PubMed ] [ Google Scholar ]
• Patton M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Pierce J. S., Wysocki T. (2015). Topical Review: Advancing research on the transition to adult care for type 1 diabetes . Journal of Pediatric Psychology , 40 , 1041–1047. doi:10.1093/jpepsy/jsv064. [ PubMed ] [ Google Scholar ]
• Pierce J. S., Wysocki T., Aroian K. (2016). Multiple stakeholder perspectives on health care transition outcomes in Type 1 Diabetes . Unpublished data. [ Google Scholar ]
• Polanyi M. (1958). Personal knowledge . New York, NY: Harper & Row. [ Google Scholar ]
• Power T. G., Hughes S. O., Goodell L. S., Johnson S. L., Duran J. A., Williams K., Beck A. D., Frankel L. A. (2015). Feeding practices of low-income mothers: How do they compare to current recommendations? The International Journal of Behavioral Nutrition and Physical Activity , 12 , 34 doi:10.1186/s12966‐015‐0179‐3. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Richards L., Morse J. M. (2013). Readdme first for a user’s guide to qualitative methods (3rd ed.). Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Ritchie J., Lewis J. (Eds.). (2003). Qualitative research practice: A guide for social science students and researchers . Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Saftner M. A., Martyn K. K., Momper S. L., Loveland-Cherry C. J., Low L. K. (2015). Urban American Indian adolescent girls framing sexual risk behavior . Journal of Transcultural Nursing , 26 , 365–375. doi:10.1177/1043659614524789. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Saldaña J. (2012). The coding manual for qualitative researchers . Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Sandelowski M. (1993a). Rigor or rigor mortis: The problem of rigor in qualitative research revisited . Advances in Nursing Science , 16 , 1–8. [ PubMed ] [ Google Scholar ]
• Sandelowski M. (1993b). Theory unmasked: The uses and guises of theory in qualitative research . Research in Nursing & Health , 16 , 213–218. doi:10.1002/nur.4770160308. [ PubMed ] [ Google Scholar ]
• Sandelowski M. (1994). The use of quotes in qualitative research . Research in Nursing and Health , 17 , 479–482. [ PubMed ] [ Google Scholar ]
• Sandelowski M. (1995). Sample size in qualitative research . Research in Nursing and Health , 18 , 179–183. [ PubMed ] [ Google Scholar ]
• Sandelowski M. (1998). Writing a good read: Strategies for re-presenting qualitative data . Research in Nursing and Health , 21 , 375–382. doi:10.1016/s1361‐3111(98)80052‐6. [ PubMed ] [ Google Scholar ]
• Sandelowski M. (2001). Real qualitative researchers do not count: The use of numbers in qualitative research . Research in Nursing and Health , 24 , 230–240. [ PubMed ] [ Google Scholar ]
• Sandelowski M. (2010). What’s in a name? Qualitative description revisited . Research in Nursing and Health , 33 , 77–84. doi:10.1002/nur.20362.. [ PubMed ] [ Google Scholar ]
• Sandelowski M., Barroso J. (2002). Finding the findings in qualitative studies . Journal of Nursing Scholarship , 34 , 213–219. [ PubMed ] [ Google Scholar ]
• Schwartz L. A., Tuchman L. K., Hobbie W. L., Ginsberg J. P. (2011). A social-ecological model of readiness for transition to adult-oriented care for adolescents and young adults with chronic health conditions . Child: Care, Health, and Development , 37 , 883–895. doi:10.1111/j.1365‐2214.2011.01282.x. [ PubMed ] [ Google Scholar ]
• Silver C., Lewins A. (2014). Using software in qualitative research: A step-by-step guide (2nd ed.). London: Sage Publications. [ Google Scholar ]
• Steele R. G., Wu Y. P., Jensen C. D., Pankey S., Davis A. M., Aylward B. S. (2011). School nurses’ perceived barriers to discussing weight with children and their families: A qualitative approach . Journal of School Health , 81 , 128–137. doi:10.1111/j.1746‐1561.2010.00571.x. [ PubMed ] [ Google Scholar ]
• Thompson D. (2014). Talk to me, please!: The importance of qualitative research to games for health . Games for Health: Research, Development, and Clinical Applications , 3 , 117–118. doi:10.1089/g4h.2014.0023. [ PubMed ] [ Google Scholar ]
• Thompson D., Baranowski T., Buday R., Baranowski J., Juliano M., Frazior M., Wilsdon J., Jago R. (2007). In pursuit of change: Youth response to intensive goal setting embedded in a serious video game . Journal of Diabetes Science and Technology , 1 , 907–917. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Thompson D., Bhatt R., Watson K. (2013). Physical activity problem-solving inventory for adolescents: Development and initial validation . Pediatric Exercise Science , 25 , 448–467. [ PubMed ] [ Google Scholar ]
• Tobin G. A., Begley C. M. (2004). Methodological rigour within a qualitative framework . Journal of Advanced Nursing , 48 , 388–396. doi:10.1111/j.1365‐2648.2004.03207.x. [ PubMed ] [ Google Scholar ]
• Tong A., Sainsbury P., Craig J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups . International Journal for Quality in Health Care , 19 , 349–357. doi:10.1093/intqhc/mzm042. [ PubMed ] [ Google Scholar ]
• Tuckett A. G. (2004). Qualitative research sampling: The very real complexities . Nurse Researcher , 12 , 47–61. doi:10.7748/nr2004.07.12.1.47.c5930. [ PubMed ] [ Google Scholar ]
• Valenzuela J. M., Buchanan C. L., Radcliffe J., Ambrose C., Hawkins L. A., Tanney M., Rudy B. J. (2011). Transition to adult services among behaviorally infected adolescents with HIV—a qualitative study . Journal of Pediatric Psychology , 36 , 134–140. doi:10.1093/jpepsy/jsp051. [ PubMed ] [ Google Scholar ]
• West C. H., Bell J. M., Woodgate R. L., Moules N. J. (2015). Waiting to return to normal: An exploration of family systems intervention in childhood cancer . Journal of Family Nursing , 21 , 261–294. doi:10.1177/1074840715576795. [ PubMed ] [ Google Scholar ]
• Whittemore R., Chase S. K., Mandle C. L. (2001). Validity in qualitative research . Qualitative Health Research , 11 , 522–537. doi:10.1177/104973201129119299. [ PubMed ] [ Google Scholar ]
• Yin R. K. (2015). Qualitative research from start to finish (2nd ed.). New York, NY: Guilford Press. [ Google Scholar ]

[Guide] How to Present Qualitative Research Findings in PowerPoint?

By: Author Shrot Katewa

As a researcher, it is quite pointless to do the research if we are unable to share the findings with our audience appropriately! Using PowerPoint is one of the best ways to present research outcomes. But, how does one present qualitative research findings using PowerPoint?

In order to present the qualitative research findings using PowerPoint, you need to create a robust structure for your presentation, make it engaging and visually appealing, present the patterns with explanations for it and highlight the conclusion of your research findings.

In this article, we will help you understand the structure of your presentation. Plus, we’ll share some handy tips that will make your qualitative research presentation really effective!

How to Create a Structure for your Qualitative Research Presentation?

Creating the right structure for your presentation is key to ensuring that it is correctly understood by your audience.

The structure of your Research Presentation not only makes it easier for you to create the document, it also makes it simple for the audience to understand what all will be covered in the presentation at the time of presenting it to your audience.

Furthermore, having a robust structure is a great way to ensure that you don’t miss out on any of the points while working on creating the presentation.

But, what structure should one follow?

Creating a good structure can be tricky for some. Thus, I’m sharing what has worked well for me during my previous research projects.

NOTE – It is important to note that although the following structure is highly effective for most research findings presentation, it has been generalized in order to serve a wide range of research projects. You may want to take a look at points that are very specific to the nature of your research project and include them at your discretion.

Here’s my recommended structure to create your Research Findings presentation –

1. Objective of the Research

A great way to start your presentation is to highlight the objective of your research project.

It is important to remember that merely sharing the objective may sometimes not be enough. A short backstory along with the purpose of your research project can pack a powerful punch ! It not only validates the reasoning for your project but also subtly establishes trust with your audience.

However, do make sure that you’re not reading the backstory from the slide. Let it flow naturally when you are delivering the presentation. Keep the presentation as minimalistic as possible.

2. Key Parameters Considered for Measurement

Once you’ve established the objective, the next thing that you may want to do is perhaps share the key parameters considered for the success of your project.

Every research project, including qualitative research, needs to have a few key parameters to measure against the objective of the research.

For example – If the goal of your project is to gather the sentiments of a certain group of people for a particular product, you may need to measure their feelings. Are they happy or unhappy using the product? How do they perceive the branding of the product? Is it affordable?

Make sure that you list down all such key parameters that were considered while conducting the qualitative research.

In general, laying these out before sharing the outcome can help your audience think from your perspective and look at the findings from the correct lens.

3. Research Methodology Adopted

The next thing that you may want to include in your presentation is the methodology that you adopted for conducting the research.

By knowing your approach, the audience can be better prepared for the outcome of your project. Ensure that you provide sound reasoning for the chosen methodology.

This section of your presentation can also showcase some pictures of the research being conducted. If you have captured a video, include that. Doing this provides further validation of your project.

4. Research Outcomes (Presenting Descriptive Analysis)

This is the section that will constitute the bulk of the your presentation.

Use the slides in this section to describe the observations, and the resulting outcomes on each of the key parameters that were considered for the research project.

It is usually a good idea to dedicate at least 1 or more slides for each parameter . Make sure that you present data wherever possible. However, ensure that the data presented can be easily comprehended.

Provide key learnings from the data, highlight any outliers, and possible reasoning for it. Try not to go too in-depth with the stats as this can overwhelm the audience. Remember, a presentation is most helpful when it is used to provide key highlights of the research !

Apart from using the data, make sure that you also include a few quotes from the participants.

5. Summary and Learnings from the Research

Once you’ve taken the audience through the core part of your research findings, it is a good practice to summarize the key learnings from each of the section of your project.

Make sure your touch upon some of the key learnings covered in the research outcome of your presentation.

Furthermore, include any additional observations and key points that you may have had which were previously not covered.

The summary slide also often acts as “Key Takeaways” from the research for your audience. Thus, make sure that you maintain brevity and highlight only the points that you want your audience to remember even after the presentation.

6. Inclusions and Exclusions (if any)

While this can be an optional section for some of the researchers.

However, dedicating a section on inclusions and exclusions in your presentation can be a great value add! This section helps your audience understand the key factors that were excluded (or included) on purpose!

Moreover, it creates a sense of thoroughness in the minds of your audience.

7. Conclusion of the Research

The purpose of the conclusion slide of your research findings presentation is to revisit the objective, and present a conclusion.

A conclusion may simply validate or nullify the objective. It may sometimes do neither. Nevertheless, having a conclusion slide makes your presentation come a full circle. It creates this sense of completion in the minds of your audience.

8. Questions

Finally, since your audience did not spend as much time as you did on the research project, people are bound to have a few questions.

Thus, the last part of your presentation structure should be dedicated to allowing your audience to ask questions.

Tips for Effectively Presenting Qualitative Research Findings using PowerPoint

For a presentation to be effective, it is important that the presentation is not only well structured but also that it is well created and nicely delivered!

While we have already covered the structure, let me share with you some tips that you can help you create and deliver the presentation effectively.

Tip 1 – Use Visuals

Using visuals in your presentation is a great way to keep the presentations engaging!

Visual aids not only help make the presentation less boring, but it also helps your audience in retaining the information better!

So, use images and videos of the actual research wherever possible. If these do not suffice or do not give a professional feel, there are a number of resources online from where you can source royalty-free images.

My recommendation for high-quality royalty-free images would be either Unsplash or Pexels . Both are really good. The only downside is that they often do not provide the perfect image that can be used. That said, it can get the job done for at least half the time.

If you are unable to find the perfect free image, I recommend checking out Dreamstime . They have a huge library of images and are much cheaper than most of the other image banks. I personally use Dreamstime for my presentation projects!

Tip 2 – Tell a Story (Don’t Show Just Data!)

I cannot stress enough on how important it is to give your presentation a human touch. Delivering a presentation in the form of a story does just that! Furthermore, storytelling is also a great tool for visualization .

Data can be hard-hitting, whereas a touching story can tickle the emotions of your audience on various levels!

One of the best ways to present a story with your research project is to start with the backstory of the objective. We’ve already talked about this in the earlier part of this article.

Start with why is this research project is so important. Follow a story arc that provides an exciting experience of the beginning, the middle, and a progression towards a climax; much like a plot of a soap opera.

Tip 3 – Include Quotes of the Participants

Including quotes of the participants in your research findings presentation not only provides evidence but also demonstrates authenticity!

Quotes function as a platform to include the voice of the target group and provide a peek into the mindset of the target audience.

When using quotes, keep these things in mind –

1. Use Quotes in their Unedited Form

When using quotes in your presentation, make sure that you use them in their raw unedited form.

The need to edit quotes should be only restricted to aid comprehension and sometimes coherence.

Furthermore, when editing the quotes, make sure that you use brackets to insert clarifying words. The standard format for using the brackets is to use square brackets for clarifying words and normal brackets for adding a missing explanation.

2. How to Decide which Quotes to Consider?

It is important to know which quotes to include in your presentation. I use the following 3 criteria when selecting the quote –

• Relevance – Consider the quotes that are relevant, and trying to convey the point that you want to establish.
• Length – an ideal quote should be not more than 1-2 sentences long.
• Choose quotes that are well-expressed and striking in nature.

3. Preserve Identity of the Participant

It is important to preserve and protect the identity of the participant. This can be done by maintaining confidentiality and anonymity.

Thus, refrain from using the name of the participant. An alternative could be using codes, using pseudonyms (made up names) or simply using other general non-identifiable parameters.

Do note, when using pseudonyms, remember to highlight it in the presentation.

If, however, you do need to use the name of the respondent, make sure that the participant is okay with it and you have adequate permissions to use their name.

Tip 4 – Make your Presentation Visually Appealing and Engaging

It is quite obvious for most of us that we need to create a visually appealing presentation. But, making it pleasing to the eye can be a bit challenging.

Fortunately, we wrote a detailed blog post with tips on how to make your presentation attractive. It provides you with easy and effective tips that you can use even as a beginner! Make sure you check that article.

7 EASY tips that ALWAYS make your PPT presentation attractive (even for beginners)

In addition to the tips mentioned in the article, let me share a few things that you can do which are specific to research outcome presentations.

4.1 Use a Simple Color Scheme

Using the right colors are key to make a presentation look good.

One of the most common mistakes that people make is use too many colors in their presentation!

My recommendation would be to go with a monochromatic color scheme in PowerPoint .

4.2 Make the Data Tables Simple and Visually Appealing

When making a presentation on research outcomes, you are bound to present some data.

But, when data is not presented in a proper manner, it can easily and quickly make your presentation look displeasing! The video below can be a good starting point.

Using neat looking tables can simply transform the way your presentation looks. So don’t just dump the data from excel on your PowerPoint presentation. Spend a few minutes on fixing it!

4.3 Use Graphs and Charts (wherever necessary)

When presenting data, my recommendation would be that graphs and charts should be your first preference.

Using graphs or charts make it easier to read the data, takes less time for the audience to comprehend, and it also helps to identify a trend.

However, make sure that the correct chart type is used when representing the data. The last thing that you want is to poorly represent a key piece of information.

4.4 Use Icons instead of Bullet Points

Consider the following example –

This slide could have been created just as easily using bullet points. However, using icons and representing the information in a different format makes the slide pleasing on the eye.

Thus, always try to use icons wherever possible instead of bullet points.

Tip 5 – Include the Outliers

Many times, as a research project manager, we tend to focus on the trends extracted from a data set.

While it is important to identify patterns in the data and provide an adequate explanation for the pattern, it is equally important sometimes to highlight the outliers prominently.

It is easy to forget that there may be hidden learnings even in the outliers. At times, the data trend may be re-iterating the common wisdom. However, upon analyzing the outlier data points, you may get insight into how a few participants are doing things successfully despite not following the common knowledge.

That said, not every outlier will reveal hidden information. So, do verify what to include and what to exclude.

Tip 6 – Take Inspiration from other Presentations

I admit, making any presentation can be a tough ask let alone making a presentation for showcasing qualitative research findings. This is especially hard when we don’t have the necessary skills for creating a presentation.

One quick way to overcome this challenge could be take inspiration from other similar presentations that we may have liked.

There is no shame in being inspired from others. If you don’t have any handy references, you can surely Google it to find a few examples.

One trick that almost always works for me is using Pinterest .

But, don’t just directly search for a research presentation. You will have little to no success with it. The key is to look for specific examples for inspiration. For eg. search for Title Slide examples, or Image Layout Examples in Presentation.

Tip 7 – Ask Others to Critic your Presentation

The last tip that I would want to provide is to make sure that you share the presentation with supportive colleagues or mentors to attain feedback.

This step can be critical to iron out the chinks in the armor. As research project manager, it is common for you to get a bit too involved with the project. This can lead to possibilities wherein you miss out on things.

A good way to overcome this challenge is to get a fresh perspective on your project and the presentation once it has been prepared.

Taking critical feedback before your final presentation can also prepare you to handle tough questions in an adept manner.

Final Thoughts

It is quite important to ensure that we get it right when working on a presentation that showcases the findings of our research project. After all, we don’t want to be in a situation wherein we put in all the hard-work in the project, but we fail to deliver the outcome appropriately.

I hope you will find the aforementioned tips and structure useful, and if you do, make sure that you bookmark this page and spread the word. Wishing you all the very best for your project!