how to write results in qualitative research

How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). 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. 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
• Free results chapter template

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 for writing 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!

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

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

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.

• Privacy Policy

Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Theoretical Framework – Types, Examples and...

Research Design – Types, Methods and Examples

Research Paper Conclusion – Writing Guide and...

Research Paper Outline – Types, Example, Template

Data Interpretation – Process, Methods and...

Thesis Format – Templates and Samples

Three principles for writing an effective qualitative results section

• Sayra Cristancho Centre for Education Research & Innovation. Western University
• Christopher Watling Centre for Education Research & Innovation. Western University
• Lorelei Lingard Centre for Education Research & Innovation. Western University

Apramian, T., Cristancho, S., Watling, C., Ott, M., & Lingard, L. (2015). Thresholds of principle and preference: Exploring procedural variation in postgraduate surgical education. Academic Medicine, 90, S70–S76. https://doi.org/10.1097/ACM.0000000000000909

Bynum, W. E., Varpio, L., Lagoo, J., & Teunissen, P. W. (2021). "I’m unworthy of being in this space": The origins of shame in medical students. Medical Education, 55(2), 185–197. https://doi.org/10.1111/medu.14354

Corden, A., & Sainsbury, R. (2006). Using verbatim quotations in reporting qualitative social research: Researchers’ views. University of York. http://www.york.ac.uk/inst/spru/pubs/pdf/verbquotresearch.pdf

Emmerton-Coughlin, H., Schlachta, C., & Lingard, L. (2017). "The other right": Control strategies and the role of language use in laparoscopic training. Medical Education, 51(12), 1269–1276. https://doi.org/10.1111/medu.13420

Ginsburg, S., Regehr, G., Lingard, L., & Eva, K. W. (2015). Reading between the lines: Faculty interpretations of narrative evaluation comments. Medical Education, 49(3), 296–306. https://doi.org/10.1111/medu.12637

Graff, G., & Birkenstein, C. (2018). “They say/I say”: The moves that matter in academic writing (4th ed.). W. W. Norton & Company.

Greenhalgh, T., Seyan, K., & Boynton, P. (2004). “Not a university type”: Focus group study of social class, ethnic, and sex differences in school pupils’ perceptions about medical school. BMJ, 328(7455), 1541. https://doi.org/10.1136/bmj.328.7455.1541

Lingard, L. (2004). Communication failures in the operating room: An observational classification of recurrent types and effects. Quality and Safety in Health Care, 13(5), 330–334. https://doi.org/10.1136/qshc.2003.008425

Lingard, L. (2019). Beyond the default colon: Effective use of quotes in qualitative research. Perspectives on Medical Education, 8, 360–364. https://doi.org/10.1007/s40037-019-00550-7

Lingard, L., & Watling, C. (2016). It’s a story, not a study: Writing an effective research paper. Academic Medicine, 91(12), e12. https://doi.org/10.1097/ACM.0000000000001389

McElhinney, Z., & Kennedy, C. (2021). By accident or design? An exploration of the career pathways, experiences and identities of academic GPs using composite narratives. Education for Primary Care, 32(5), 266–271. https://doi.org/10.1080/14739879.2021.1894991

Morrow, S. L. (2005). Quality and trustworthiness in qualitative research in counseling psychology. Journal of Counseling Psychology, 52(2), 250–260. https://doi.org/10.1037/0022-0167.52.2.250

Pack, R., Lingard, L., Watling, C., & Cristancho, S. (2020). Beyond summative decision making: Illuminating the broader roles of competence committees. Medical Education, 54(6), 517–527. https://doi.org/10.1111/medu.14072

Pratt, M. G. (2008). Fitting oval pegs into round holes: Tensions in evaluating and publishing qualitative research in top-tier North American journals. Organizational Research Methods, 11(3), 481–509. https://doi.org/10.1177/1094428107303349

Pratt, M. G. (2009). From the editors: For the lack of a boilerplate: Tips on writing up (and reviewing) qualitative research. Academy of Management Journal, 52(5), 856–862. https://doi.org/10.5465/amj.2009.44632557

Should scientists tell stories? (2013). Nature Methods, 10(11), 1037–1037. https://doi.org/10.1038/nmeth.2726

Simmons, A. (2019). The story factor: Inspiration, influence, and persuasion through the art of storytelling (Revised ed.). Basic Books.

Tottie, G. (2016). Planning what to say: Uh and um among the pragmatic markers. In G. Kaltenböck, E. Keizer, & A. Lohmann (Eds.), Studies in language companion series (Vol. 178, pp. 97–122). John Benjamins. https://doi.org/10.1075/slcs.178.04tot

Varpio, L., Ajjawi, R., Monrouxe, L. V., O’Brien, B. C., & Rees, C. E. (2017). Shedding the cobra effect: Problematising thematic emergence, triangulation, saturation and member checking. Medical Education, 51(1), 40–50. https://doi.org/10.1111/medu.13124

Watling, C., Driessen, E., van der Vleuten, C. P. M., & Lingard, L. (2012). Learning from clinical work: The roles of learning cues and credibility judgements. Medical Education, 46(2), 192–200. https://doi.org/10.1111/j.1365-2923.2011.04126.x

Willis, R. (2019). The use of composite narratives to present interview findings. Qualitative Research, 19(4), 471–480. https://doi.org/10.1177/1468794118787711

How to Cite

• Endnote/Zotero/Mendeley (RIS)

On acceptance for publication in  FoHPE the copyright of the manuscript is signed over to ANZAHPE, the publisher of  FoHPE.

Make a Submission

Anzahpe logo.

Tweets by fohpe_journal

The Australian & New Zealand Association for Health Professional Educators (ANZAHPE), is the peak organisation for practitioners who educate and train health professionals in Australia and New Zealand.

• Search Menu
• Sign in through your institution
• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Archaeology
• Late Antiquity
• Religion in the Ancient World
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Acquisition
• Language Variation
• Language Families
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Modernism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Religion
• Music and Culture
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Science
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Ethics
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business Strategy
• Business History
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Systems
• Economic Methodology
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• Ethnic Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Theory
• Politics and Law
• Politics of Development
• Public Policy
• Public Administration
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

A newer edition of this book is available.

• < Previous chapter
• Next chapter >

31 Writing Up Qualitative Research

Jane F. Gilgun, School of Social Work, University of Minnesota, Twin Cities

• Published: 04 August 2014
• Cite Icon Cite
• Permissions Icon Permissions

This chapter provides guidelines for writing journal articles based on qualitative approaches. The guidelines are part of the tradition of the Chicago School of Sociology and the author’s experience as a writer and reviewer. The guidelines include understanding experiences in context, immersion, interpretations grounded in accounts of informants’ lived experiences, and research as action-oriented. The chapter also covers writing articles that report findings based on ethnographies, autoethnographies, performances, poetry, and photography and other graphic media.

How researchers write up results for journal publications depends on the purposes of the research and the methodologies they use. Some topics are standard, such as statements about methods and methodologies, but how to represent other topics, like related research and theory, reflexivity, and informants’ accounts, may vary. For example, articles based on ethnographic research may be structured differently from writing up research whose purpose is theory development. Journal editors and reviewers often are familiar with variations in style of write-ups, but, when they are not, they may ask for modifications that violate the methodological principles of the research. A common reviewer request is for percentages, which has little meaning in almost all forms of qualitative research because the purpose of the research is to identify patterns of meanings and not distributions of variables. For example, Irvine’s (2013) ethnography of the meanings of pets to homeless people shows a variety of meaning without giving the number of participants from which she drew.

Authors sometimes move easily through the review process, but most often they do not, not only because reviewers might not “get it,” but also because authors have left out, underemphasized, or been less than clear about aspects of their research that reviewers and editors believe are important. Working with editors and reviewers frequently results in improved articles.

The purpose of this chapter is to provide guidelines for writing journal articles based on qualitative approaches. My intended audience is composed of researchers, reviewers for journals, and journal editors. Reviewers for funding agencies may also find this chapter useful. I use the terms “journal article” and “research report” as synonyms, even though some journal articles are not reports of research. I have derived the guidelines from ideas associated with the Chicago School of Sociology and my experience as an author and reviewer. Although the Chicago School was, as Becker (1999) wrote, “open to various ways of doing sociology” (p. 10), the ideas in this chapter are part of the tradition, but they are not representative of the entire tradition. Furthermore, the ideas are not fixed but are open-ended because they evolve over time. I have followed the principles of the Chicago School of Sociology throughout my career, augmented by updates to these ideas, experiments with other traditions, and the sense I make of my own experiences as researcher, author, and reviewer.

The ideas on which I draw include understanding experiences in context, immersion, interpretations grounded in accounts of informants’ lived experiences, and research as action-oriented ( Bulmer, 1984 ; Faris, 1967 ; Gilgun, 1999 d ; 2005 a ; 2012 a ; 2013 b ). To follow these principles, researchers do in-depth studies that take into account the multiple contextual factors that influence meanings and interpretations, seek multiple points of view, and often use multiple methods such as interviews, observations, and document analysis. Researchers do this style of research not only because what they learn is interesting, but because they want to do useful research; that is, research that leads to social actions and even transformations in policies, programs, and interventions. Authors and reviewers pay attention to these principles. Authors convey them in their write-ups, and reviewers look for them as they develop their appraisals.

Excellent writing up of qualitative research matches these principles. In other words, write-ups convey lived experience within multiple contexts, multiple points of view, and analyses that deepen understandings. In addition, if the research is applied, then authors write about how findings may contribute to quality of life. Qualitative researchers from other traditions may follow similar or different guidelines in their write-ups, and I sometimes note other styles of write-ups. Often these variations are related to terminology and not procedures. The reach of the Chicago School of Sociology is wide and deep.

Following these guidelines does not guarantee an easy review process, but this article will be helpful to researchers as they plan and craft their articles and as they respond to reviewers’ and editors’ comments. After almost thirty years of publishing research based on qualitative approaches, almost as many years as a reviewer, and the editing of three collections of qualitative research reports ( Gilgun, Daly, & Handel, 1992 ; Gilgun & Sussman, 1996 : Gilgun & Sands, 2012 ), I am positioned to offer helpful guidelines, not only to authors but also to reviewers and journal editors.

I begin this chapter with a discussion of general principles and then cover the content of typical sections of research reports. Some of the general material fits into various sections of reports, such as methods and findings. In those cases, I do not repeat material already covered and assume that my writing is clear enough so that readers know how the general material fits into particular sections of articles.

Although most of this chapter addresses the writing of conventional research reports, I also cover writing articles that report findings through ethnographies, autoethnographies, performances, poetry, and photography and other graphic media. Ethnographies are based on researchers’ immersion in the field, where they do extensive observations, interviews, and often document analysis (see Block, 2012 ). Geertz’s (1973) notion of “thick description” is associated with ethnographies. Thick description is characterized by research reports that show the matrix of meanings that researchers identify and attempt to represent in their reports. Autoethnographies are in-depth reflective accounts of individual lives that the narrators themselves write ( Ellis, 2009 ). Ethnographies and autoethnographies involve reflections on meanings, contexts, and other wider influences on individual lives. They are studies of intersections of individual lives and wider cultural themes and practices. Reports of these types of research can look different from conventional research reports in that they appear less formal; the usual sections of methods, literature review, findings, and analysis may have different names; and the sections may be in places that fit the logical flow of the research and not the typical structure of introductory material, methods, results, and discussion. Despite these superficial differences, researchers who write these kinds of articles seek to deepen understandings and hope to move audiences to action through conveying lived experience in context and through multiple points of view. They also typically seek transformations of persons and societies. Links between these forms of research and Chicago School traditions are self-evident.

Some General Principles

Research reports that have these characteristics depend on the quality of the data on which the reports are based, the quality of the analysis, and the skills of researchers in conveying the analysis concisely and with “grab” ( Glaser, 1978 ), which means writing that is vivid and memorable ( Gilgun, 2005 b ). Grab brings findings to life. With grab, human experiences jump off the page. Priority is given to the voices of research participants, whom I call informants, with citations and the wisdom of other researchers providing important contextual information. The voices and analyses of researchers do not dominate ( Gilgun, 2005 c ), except in some articles whose purpose is theory development or the presentation of a theory. Researcher analyses often are important, especially in putting forth social action recommendations that stem from the experiences of informants.

A well-done report shows consistency between research traditions and the writing-up of research. For example, reflexivity statements, writing with grab, and copious excerpts from fieldnotes, interviews, and documents of various sorts are consistent with phenomenological approaches whose emphasis is on lived experience and interpretations that informants make of their experiences. Researchers new to qualitative research, however, often mix their traditions without realizing it, which works when the traditions are compatible. When the traditions are not compatible, the write-ups can be confusing and even contradictory ( Gilgun, 2005 d ). Some authors may write in distanced, third-person styles while attempting to convey informants’ lived experiences. These scholars may, therefore, have difficulty getting their articles accepted. Hopefully, this chapter will facilitate the writing of research reports that show consistency across their many parts and save scholars from rejections of work over which they have taken much care.

Details on These General Principles

In this section, I provide more detail on writing up qualitative research. I begin with a discussion of the need for high-quality data, high-quality analysis, and grab. I then move on to the details of the report, such as the place of prior research and theory, contents of methods sections, organization of findings, and the balance between descriptive material and authors’ interpretations. Dilemmas abound. Writing up qualitative research is not for the faint of heart.

High-Quality Data

Since qualitative researchers seek to understand the subjective experiences of research informants in various contexts, high-quality data result in large part from the degree that researchers practice immersion and to the degree that both researchers and informants develop rapport and engage with each other. Through active engagement, informants share their experiences with the kind of detail that brings their experiences to life. How to develop rapport is beyond the scope of this article, but openness and acceptance of whatever informants say are fundamental to engagement. Interviewers do not have to agree with the values that informants’ accounts convey, as when I interview murderers and rapists ( Gilgun, 2008 ), but we do maintain a neutrality that allows the dialogue to continue ( Gilgun & Anderson, 2013 ). The content of interviews is not about us and our preferences, but about understanding informants.

Prolonged engagement can result in quality data. In interview research, prolonged engagement allows for informants’ multiple perspectives to emerge, including inconsistencies, contradictions, ambiguities, and ambivalences. In addition, prolonged engagement facilitates the kind of trust needed for informants to share personal, sensitive information in detail, which are the kinds of data that qualitative researchers seek. Prolonged engagement also gives researchers time to reflect on what they are learning and experiencing through the interviews. This provides opportunities to develop new understandings and test new understandings through subsequent research. Their understandings thus deepen and broaden. Informants, too, can reflect, reconsider, and deepen the accounts they share.

Prolonged engagement means in-depth interviews, typically multiple interviews of more than an hour each. As mentioned earlier, time between interviews allows researchers and informants to reflect on the previous interview and prepare for the next. Researchers can do background reading, discuss emerging ideas with others, and formulate pertinent new questions. Informants may retrieve long-forgotten memories and interpretations through interviews. If they have only one interview, they have no opportunity to share with researchers the material that arises after the single interview is concluded.

There are exceptions to multiple interviews as necessary for immersion and high-quality data. When researchers have expertise in interviewing and when the topic is focused, one interview of between ninety minutes to two hours could provide some depth. Even under these conditions, however, more than one interview is ideal. I did a study that involved one ninety-minute interview with perpetrators of child sexual abuse in order to understand the circumstances under which their abusive behaviors became known to law enforcement. Thus, the interview was focused. The interviewees were volunteers who had talked about the topic many times in the course of their involvement in sex abuse treatment programs. They shared their stories with depth and breadth. I, too, was well-prepared. By then, I had had about twenty-five years of experience interviewing people about personal, sensitive topics. The informants provided accounts not only because the topic was focused, but because they were willing to share and I was willing to listen and to ask questions about their sexually abusive behaviors. With one interview, however, I knew relatively little about their social histories and general worldviews. Thus, I did not have the specifics necessary to place their accounts into context. The material they provided remained valuable and resulted in one publication ( Sharma & Gilgun, 2008 ) and others in planning stages. I prefer two or more interviews because of the importance of contextual data.

In observational studies, prolonged engagement means that researchers do multiple observations over time to obtain the nuances and details that compose human actions. Observational studies often have interview components and also may have document analysis as well. In document analysis, prolonged engagement means researchers base their analyses on an ample storehouse of documents and not just flit in and out of the documents. The quality of document analysis depends on whether the analysis shows multiple perspectives, patterns, and variations within patterns. Ethnographies have these characteristics. Block’s (2012) ethnographic research on AIDS orphans in Lesotho, Africa, is an example of a well-done ethnography.

Sample Size

In principle, the size of the sample and the depth of the interview affect whether researchers can claim immersion. The more depth and breadth each case in a study has, the smaller the sample size can be. For example, researchers can engage in immersion through a single in-depth case study when they do multiple interviews and if multiple facets of the case are examined. Case studies are investigations of single units. The case can be composed of an individual, a couple, a family, a group, a nation, or a region. Single case studies are useful in the illustration, development, and testing of theories, as well as in in-depth descriptions.

The more focused the questions, the larger the sample will be. A study on long-term marriage would require a minimum of two or three interviews because the topic is complicated. The sample would include at least ten participants and up to twenty or thirty, depending on the number of interviews, to account for some of the many patterns that are likely to emerge in a study of a topic this complex. In the one-interview study I did of how sexual abuse came to the notice of law enforcement, one interview was adequate because of the tight focus of the question. Yet, I used a sample size of thirty-two to maximize the possibility of identifying a variety of patterns, which the study accomplished. As mentioned, the one interview, however, did not allow me to contextualize the stories the informants told. Fortunately, I have another large sample that involved multiple, in-depth interviews in which informants discussed multiple contexts over time. This other study was helpful to me in understanding the accounts from the single-interview study.

Recruitment can be difficult. When it is, researchers may not be able to obtain an adequate sample. For example, a sample of seven participants engaging in a single sixty- to ninety-minute interview may not provide enough data on which to base a credible analysis. In a similar vein, articles based on a single or even a few focus groups may not provide enough depth to be informative. Some depth is possible if, in a single-interview study of less than fifteen or twenty interviewees, researchers meet with informants a second time to go over what researchers understand about informants’ accounts. This sometimes is called member-checking , and it provides additional data on which to base the analysis. In summary, the more depth and breadth to a study, the smaller the sample size can be—even as small as one or two—depending on the questions and the complexity of the cases.

Quality of the Analysis

A quality analysis begins with initial planning of the research and continues until the article is accepted for publication. An excellent research report has transparency , meaning the write-up is clear in what researchers did, how they did it, and why. I often tell students they can do almost anything reasonable and ethical, as long as they make a clear account in the write-up.

During planning, some researchers identify those concepts that they can use as sensitizing concepts once in the field. Transparency about the sources of sensitizing concepts characterizes well-done reports. The sources are literature reviews and reflexivity statements. Most researchers, however, have only a limited awareness of the importance of being clear about the sources of sensitizing concepts and other notions that become part of research coding schemes. Sensitizing concepts are notions that researchers identify before beginning their research and that help researchers notice and name social processes that they might not have noticed otherwise ( Blumer, 1986 ). Other researchers wait until data analysis to begin to identify concepts that they may use as codes and that may also become core concepts that organize findings. Either approach is acceptable and depends on purpose and methodologies.

During data collection, researchers reflect on what they are learning, typically talk to other researchers about their emerging understandings, and read relevant research and theory to enlarge and deepen their understandings. Researchers also keep fieldnotes that are a form of reflection. Based on their various reflections, researchers can reformulate interview and research questions and formulate new ones, do within—and across—case comparisons while in the field, and develop new insights into the meanings of the material.

Also, while in the field, researchers identify promising patterns of meanings and identify tentative core concepts, sometimes called categories , which are ideas that organize the copious material that they amass. Once researchers identify tentative core concepts, they seek to test whether they hold up, and, when they do, they further develop the patterns and concepts. Sometimes researchers think they have “struck gold” when they identify a possible core concept or pattern, only to find that the data—or metaphorical vein of gold—peter out (Phyllis Stern, personal communication, November 2002). They then go on to identify and follow-up on other concepts and patterns that show promise of becoming viable.

Core concepts become viable when researchers are able to dimensionalize them ( Schatzman, 1991 ) through selective coding ( Corbin & Strauss, 2008 ). This means that researchers have found data that show the multiple facets of concepts, such as patterns and exceptions to any general patterns. Authors may use other terms to describe what they did, such as thematic analysis. What is important is to describe the processes and produces; and what researchers call them is of less importance.

Core concepts may begin as sensitizing concepts. Researchers sometimes identify, name, and code core concepts through notions that are part of their general stores of knowledge but were not part of the literature review or reflexivity statement. Glaser (1978) called the practice “theoretical sensitivity.” The names researchers choose may be words or phrases informants have used. However derived, core concepts are central to the organization of findings ( Gilgun, 2012 a ).

At some point, data collection stops, but analysis does not. Researchers carry analysis that occurred in the field into the next phases of the research. Immersion at this point means that researchers read and code transcripts of interviews, observations, and any documentary material they find useful. They carry forward the core concepts they identified in the field. An example of a core concept is “resilience,” which in my own research organized a great deal of interview material. The concept of resilience has been an organizing idea in several of the articles I have written and plan to write ( Gilgun, 1996 a ; 1996 b ; 2002 a ; 2002 b ; 2004 a ; 2004 b ; 2005 a ; 2006 , 2008 ; 2010 ; Gilgun & Abrams, 2005 ; Gilgun, Keskinen, Marti, & Rice, 1999 ; Gilgun, Klein, & Pranis, 2000 ).

Corbin and Strauss (2008) stated that selective coding helps researchers to decide if a concept can become a core concept, meaning it organizes a great deal of data that have multiple dimensions. An example of dimensionalization is a study of social workers in Australia whose clients were Aboriginal people. The researchers identified several core concepts, among them critical self-awareness ( Bennet, Zubrzycki, & Bacon, 2011 ). The dimensions of critical self-awareness included understanding motivations to work with Aboriginal people, fears of working with Aboriginal people, and personalization and internalization of the anger that some Aboriginal people express.

Like many other researchers, Bennet et al. (2011) were not working within an explicit Chicago School tradition. They therefore do not use terms such as core concepts, dimensionalization, and selective coding. Instead, they described their procedures as thematic analysis, conceptual mapping, and a search for meaning. However, they did use the term “saturation,” which is part of the Chicago School tradition.

A single core concept or multiple related core concepts compose research reports. The Bennet et al. (2011) article, for example, linked multiple core concepts. The authors showed how critical self-awareness leads to meaningful relationships that in turn connect to “acquiring Aboriginal knowledge” (p. 30).

With viable core concepts and rich data, researchers are positioned to present their findings in ways that are memorable and interesting; that is, with “grab” ( Glaser, 1978 ). “Grab” requires compelling descriptive material: excerpts from interviews, field notes, and various types of documents, as well as researchers’ paraphrases of these materials. An example of a research report with grab is Irvine’s (2013) account of her study of the meanings of pets to homeless people. She provided vivid descriptions of her interactions with the participants and compelling quotes that show what pets mean. Here, an example from Denise’s account of her relationship with her cat Ivy:

I have a history with depression up to suicide ideation, and Ivy, I refer to her as my suicide barrier. And I don’t say that in any light way. I would say, most days, she’s the reason why I keep going.... She is the only source of daily, steady affection and companionship that I have. (p. 19)

These and other quotes, as well as Irvine’s well-written, detailed descriptive material, show what grab means.

Grab equates with excellence in writing. Irvine’s (2013) article is an example. In terms of the grab of her article, her work is in the Chicago School tradition. She wrote in the first person. She told complete stories in which she quoted extensively from the interviews, described the persons she interviewed and the settings in which she interviewed them, and provided biographical sketches. Robert Park and Ernest Burgess, both of whom trained generations of graduate students in qualitative research at the University of Chicago in the first quarter of the twentieth century, held seminars on the use of literary techniques, such as those used in novels and autobiographies, in writing up research ( Bulmer, 1984 ; Gilgun, 1999 d ; 2012 a ). These educators wanted researchers to report on their “first-hand observation.” Park told a class of graduate students to

[g]o and sit in the lounges of the luxury hotels and on the doorsteps of the flophouses; sit on the Gold Coast settees and on the slum shakedowns; sit in the Orchestra Hall and in the Star and Garter Burlesk. In short, gentlemen [sic], go get the seat of your pants dirty. ( McKinney, 1966 , p. 71)

Park suggested to Pauline Young (1928 ; 1932) to “think and feel” like the residents of Russian Town, the subject of her dissertation, published in 1932 ( Faris, 1967 ). Irvine’s work shows these qualities. She immersed herself in the settings, she conducted in-depth interviews, and she conveyed her first-hand experiences in vivid terms.

The Chicago School also encouraged students to write in the first person. A good example is a report by Dollard (1937) , who was concerned about the racial practices of the Southern town where he was doing fieldwork. He said he was afraid that other white people watched as he talked to “Negroes” on his front porch, when he knew that custom regarding the “proper” place of “Negroes” was at the back door. He wrote

My Negro friend brought still another Negro up on the porch to meet me. Should we shake hands? Would he be insulted if I did not, or would he accept the situation? I kept my hands in pockets and did not do it, a device that was often useful in resolving such a situation. (p. 7)

This description is a portrait of a pivotal moment in Dollard’s fieldwork, and it is full of connotations about the racist practices of the time ( Gilgun, 1999 d ; 2012 a ).

Irvine (2013) also wrote in the first person. Here’s an example:

I met Trish on a cold December day in Boulder. She stood on the median at the exit of a busy shopping center with her Jack Russell Terrier bundled up in a dog bed beside her. She was “flying a sign,” or panhandling, with a piece of cardboard neatly lettered in black marker to read, “Sober. Doing the best I can. Please help.” (p. 14)

These two excerpts illustrate a methodological point Small (1916) made in his chapter on the first fifty years of sociological research in the United States: namely, the importance of going beyond “technical treatises” and providing first-person “frank judgments” that can help future generations interpret sociology. Without such contexts, “the historical significance of treatises will be misunderstood” (p. 722). Throughout his chapter, Small wrote in the first-person and provided his views—or frank judgments—on the events he narrated. From then until now, research reports in the Chicago tradition are vivid and contextual, conveying to the extent possible what it was like to be persons in situations.

There are many other examples of well-done research reports. Eck’s (2013) article on never-married men includes the basic elements that are present in almost all reports based on qualitative methods. It is transparent in its procedures, situated within scholarly traditions, well-organized, vivid, and instructive both for those new to qualitative research and for long-term researchers like me. The other articles I cite in this chapter also show many desirable qualities in research reports.

Research Report Sections

The main sections of standard reports based on qualitative methods are the same as for articles based on other types of methods: Introduction, Methods, Findings, and Discussion. The American Psychological Association (APA) manual (2009) provides information on what goes into each of these sections. Research reports in sociology journals follow a similar format, although the citation style is slightly different. The American Sociological Association uses first and last names in the reference section, a practice I support. In articles based on qualitative approaches, researchers sometimes change the names of sections, add or omit some, or reorder them. When changes are made, the general guideline is whether the changes make sense and are consistent with the purpose of the research. As Saldaña (2003) pointed out, researchers choose how to present their findings on the basis of credibility, vividness, and persuasive qualities and not for the sake of novelty. Because some articles report findings as fictionalized accounts, poetry, plays, songs, and performances (including plays), it makes sense that the sections on these findings vary from the standard format that I discuss here.

Although there are no rigid rules about how to write journal articles based on qualitative research, much depends on the methodological perspectives, purposes of the research, and the editorial guidelines of particular journals. For example, if researchers want to develop a theory, it is important to be clear from the beginning of the article to state this as the purpose of the research. The entire article should then focus on how the authors developed the theory. Research and theory cited in the literature review should have direct relevance to the substantive area on which the authors theorized. The methods section should explain what the researchers did to develop the theory. The findings section should begin with a statement of the theory that the researchers developed. The rest of the findings section should usually be composed of three parts. The first is composed of excerpts from those data that support the concepts of the theory. This is the grounding of the theory in something clear and concrete. The second is the authors’ thinking or interpretation of the meanings of each of the concepts. The third is an analysis of how the theory contributes to what is already known, such as how the findings elaborate on and call into question what is known. Thus, a research report on the development of a theory should contain a lot of scholarship that others have developed.

A report based on narrative principles or one based on an ethnography should contain copious excerpts from interviews, citing less scholarship than an article whose purpose is to develop theory. However, it is good practice to bring in related research and theory in the results section when this literature helps in interpretation, when findings have connections to other bodies of thought, and when findings are facets of a larger issue. In my now older publication on incest perpetrators ( Gilgun, 1995 ), the editors suggested that I show that when therapists engage in sexual relationships with clients, they are engaging in abuses of power similar to those of incest perpetrators. I was at first indignant that the editors wanted me to do even more work on the article, but I soon was glad they did. It is important to show that incest or any human phenomenon is not isolated from other phenomenon but is part of a larger picture. Doing so fit my purposes, which was to show how to do theory-testing/theory-guided qualitative research. Showing how findings fit into related research and theory is part of this type of research.

Whenever researchers are ready to submit an article for publication, it is wise to read recent issues of journals in which they would like to publish. If they can identify an article whose structure, methodologies, and general purpose are similar to theirs, they could study how those authors presented their material. If, for example, in a report on narrative research, the introductory material is relatively brief, and the findings and discussion sections compose most of the pages, researchers would do well to format their articles in similar ways. I study journals in which I have interest and model much of my own articles after those published in these journals. I make sure, however, that I cover topics that in my judgment are important to cover.

Prior Research and Theory

In my experience, something as simple as the place of prior research and theory can get complicated in the writing of reports based on qualitative research, even when the purpose of the article is primarily descriptive and is not to construct an explicit theory. In general, related research and theory literature can be presented at the beginning of a report as part of a review of pertinent research and theory, in the findings section when prior work helps in the interpretation and analysis of findings, or in the discussion section, where authors may reflect on how their findings add to, undermine, or correct what is known and even add something new.

Readers expect and journal editors typically want articles to begin with literature review, with some exceptions. A perusal of journals that publish qualitative studies shows this. Yet there are exceptions. Valásquez (2011) began her report on her encounter with scientology with an extended and rather meandering first-person narrative. Her literature review began toward the end of the article. She tailored the review to the report that preceded it. In this article and others, the literature review helped in the interpretation of findings and helped to situate the report in its scholarly contexts. In other articles, the literature review appears in the introductory section. This sets the scholarly context of the research, highlights the significance of topics, and identifies gaps in knowledge. Neither authors nor reviewers should have rigid expectations about where the scholarship of others belongs. It belongs where it makes the most sense and has the most impact.

For many, the placement of literature reviews seems self-evident. Yet, some well-known approaches, such as grounded theory, can set authors up for confusion about where the literature review belongs. This can result in delays in writing up their results. The procedures of grounded theory are open-ended and designed to find new aspects of phenomena—often underresearched—and then develop theories from the findings. At the outset of their work, researchers cannot anticipate what they will find. Therefore, teachers such as Strauss and Glaser advised students not to do literature reviews until they had identified basic social processes that become the focus of the research ( Covan, 2007 ; Glaser & Strauss, 1967 ).

How, then, do researchers write up research reports when they are doing an open-ended study that, by definition, will culminate in unanticipated findings? Do they write their reports as records on how they proceeded chronologically, or do they follow APA style and the dominant tradition that says the literature review comes first? For the most part, I follow the tradition, as, apparently, do most researchers. However, to structure reports in this way sometimes feels strained and artificial. I would prefer to write a more chronological account, in which I can share with readers the lines of inquiry and procedures I followed. The literature review at the beginning of the report, therefore, would be brief. The methods section is quite detailed in how I went about developing the theory. The findings section would have the three-part format I discussed earlier: statement of the theory, presentations of excerpts that support assertions that certain concepts compose the theory, my interpretation of the meanings of the concepts and the excerpts that support them, and then the use of related research and theory to further develop the theory and to situate it in its scholarly traditions.

In all but one of the research reports that I have published, I did the literature after I had identified findings. The one exception was research I did based on the method of analytic induction, in which researchers can use literature reviews to focus their research from the outset ( Gilgun, 1995 , 2007 ). In this research, I used concepts from theories on justice and care to analyze transcripts of interviews I had previously conducted on how perpetrators view child sexual abuse. Even though I was familiar with the transcripts, I found that the concepts of justice and care and their definitions sensitized me to see things in the material that I had not noticed as I did data collection and during previous analyses of the data.

Furthermore, in writing up the results, I brought in research that was not part of the literature review to help me to interpret findings and to show how findings fit with and added to what was already known. I did not place this material in the introductory literature review. Placing related research and theory as parts of the results and discussion sections is common and may be necessary in articles that are reporting on a theory that the authors developed. For descriptive studies whose purpose is not theory-building, such as ethnographies, some findings sections include the addition of research and theory not present in the introductory section. Often, however, authors do not follow this pattern. An example is found in Ahmed (2013) , who described how migrants experience settling into a new country. She presents excerpts from interviews and her interpretation of them, including organizing them into a typology, but she does not bring additional research and theory into her interpretations.

Tensions can arise between how much space to give to literature reviews and how much to allot to presentation of informants’ accounts/findings ( Gilgun, 2005 c ). This happened in the most recent article I co-wrote, which is on mothers’ perspectives on the signs of child sexual abuse ( Gilgun & Anderson, 2013 ). We believed the literature review was important because it not only set up our research but summarized a great deal of information that was important to our intended audience of social service professionals. We also wanted to anticipate the expectations of reviewers and the journal editor. Yet, we put much effort into making the literature review as concise as possible in order to have reasonable space for findings. We wrote the literature review before we did data analysis. When we wrote up the results, the first draft was probably three times longer than any journal article could be.

We had written case studies first to be sure that we understood each case in detail. We had wanted to share what the women said in the kind of detail that had helped us deepen our own understandings, so we cut back on the case material. The article was still too long. We decided to exclude the few instances we had in which women knew of the abuse but tried to handle it themselves or did not believe the children when told. We did more summarizing of the literature review. We eliminated many references.

After much effort, we finally had a manuscript that was the required length of twenty-two pages. It included a literature review that set up the research in good form, an adequate accounting of the method, and findings that conveyed with grab the complexities of the signs and lack of signs of child sexual abuse. We wove points made in the literature review into our interpretations, yet we had to leave out important patterns for the sake of space. The editor’s decision was a revise and resubmit, which we did. The main recommendation was to elaborate on applications. This was a great suggestion, and we dug deep to think about this. We are pleased with the results. We had to do further reading on topics we had not anticipated at the onset of our project, and we squeezed in a few new citations in the discussion section that related to implications of the research. This additional material greatly enhanced the meanings and usefulness of the research.

There is much more to say about qualitative research and literature reviews. Sometimes researchers get stuck, as I have more than once. I have research that I have not yet published because I have been unable to figure out how to do the multiple literature reviews I think I must show how my theory builds on, adds to, and challenges what is already known. I have written up this research as conference papers, where expectations about literature reviews are more relaxed ( Gilgun, 1996c , 1998 , 1999c , 2000 ). One of these. papers was on a comprehensive theory of interpersonal violence ( Gilgun, 2000 ). I wanted to write my theory first and then show how the findings contribute to what is already known. Doing so doesn’t seem so outlandish today, and I now can imagine writing it up exactly as I would want to. At the same time, I wonder if I would? I really don’t know if any journal that would publish a theory of violence would also accept an article that places a literature review after findings. Furthermore, my writing up of the theory would take so many pages that I would not have enough space to do a comprehensive literature review. As of today, the theory I am developing has links to sixteen or more bodies of literature. No way can I publish a journal-length article that will accommodate that much research and theory!

So, here I am, many years into the development of a comprehensive theory, still reflecting on how to create journal articles out of my analysis. I have published many articles in social media outlets exploring ideas that are the basis for the theory. I have put these articles into collections that are available on the internet ( Gilgun, 2012 b ; 2012 c ; 2013 a ). The theory is so complex that writing bits and pieces over the years and having a place to put them have been very helpful.

Finally, some articles may cite few if any related research and theory. This may fit articles whose purpose is to convey lived experience that stands on its own. These articles feature performances, plays, autoethnographies, fictionalized accounts, poetry, and song, among others. Egbe (2013) wrote two poems that she explained were accounts of her experiences of doing research in Nigeria with young smokers. She said she was “dazed by the vast opportunity this method gives a researcher to dig deep into a research problem and be submerged into the world of participants” (p. 353). Her two-page article is composed of two poems and her explanation. The article showed grab, evidence of immersion, experiences in contexts, and multiple perspectives. Her work, therefore, followed well-established guidelines for writing up qualitative research. Egbe not only omitted a literature review, but she did not write about how to use the results of her research, assuming that its uses are self-evident. Obviously, she thought a literature review unnecessary; the reviewers and journal editors agreed with her.

Reflexivity Statements

A growing number of journals encourage researchers to include reflexivity statements in research reports. Researchers may place these in the introductory material of an article, after the literature review and before the methods section; this probably is the most important place to put them because reflexivity statements often influence the focus and design of the research, including the choice of sensitizing concepts and codes. Reflexivity statements may also appear in the methods and findings and methods sections when important. Reflexivity statements are accounts of researchers’ experiences with the topic of research; accounts of their expectations regarding informant issues and their relationships to informants, especially in regard to power differentials and other ethical concerns; and accounts of their reflections on various issues related to possible experiences that informants may have had. They also may include the experience they had while participating in the research ( D’Cruz, Gillingham, & Melendez, 2007 ; Presser, 2005 ). My article on doing research on violence is an extended reflexivity statement ( Gilgun, 2008 ). There appears to be no standard content for reflexivity statements and no standard places for them to appear. Personal and professional experiences and reflections on power differentials may be the emergent standard. Whatever decisions researchers make about reflexivity statements, they alert audiences to researchers’ perspectives, which can be helpful to readers as they attempt to make sense of research reports.

An example of a reflexivity statement is found in Winter (2010) work. Winter is a practitioner turned researcher who had a previous relationship as a guardian ad litem with the children with whom she later conducted the research that she was reporting. Winter was reflexive about the implications of her prior relationship with these children. I imagine, based on my own experience, that she put only a fraction of her thinking into her article. Not only did she write in her reflexivity statement that she had a prior relationship with the children, but she also wrote about the ethical issues involved.

Ethical issues have a place in reflexivity statements. I have run into ethical questions over the course of my research career. One situation that stands out is the encounter I had with a mother and her eleven-year-old daughter who had participated in my dissertation research on child sexual abuse ( Gilgun, 1983 ). The mother cried and told her daughter how sorry she was that she had been unable to protect her from sexual abuse. The girl was touched but did not seem to know what to do. I suggested that she go stand by her mother. When she got close, the mother and daughter hugged each other and cried. This is a significant event with ethical implications that I included in the findings section of my dissertation and in a subsequent research report ( Gilgun, 1984 ). The ethical issue is, first, whether I should have stepped out of my role as detached researcher and guided the girl to go to her mother, and, second, whether I should have made my blurring of boundaries public by publishing them.

As far as the placement of reflexivity statements, the initial statement has a logical location after the literature review because the reflexivity statement contributes to the development of the research questions, the identification of sensitizing concepts, the interview schedule, and the overall design of research procedures. Accounts of ongoing reflexivity could be part the findings section and of the discussion section. Reflexivity statements are not a standard part of research reports, but they can contribute to readers’ understandings of the research.

Along with the literature review, reflexivity statements contribute to practical and applied significance statements and may also help to identify gaps in knowledge. Literature reviews and reflexivity statements contain key concepts. The concepts that researchers define at the end of introductory sections typically become codes during analysis, although researchers may not label the concepts as codes either in the introductory section or in the methods section. I am unsure why such labeling has not become routine. When concepts carry the label code , this clarifies where codes come from. Without naming codes and stating where they come from, much of analysis is mystified. Many reports read as if the codes appear out of nowhere during analysis. Even Glaser’s (1978) notion of theoretical sensitivity mystifies the origins of codes. How, for example, do researchers become theoretically sensitive? What if researchers are beginning their scholarly careers? How theoretically sensitive are they ( Covan, 2007 )? What are the implications for the quality of the analysis?

Research Questions, Hypotheses, and Definitions

The final part of the introductory section of a research report is devoted to research questions, hypotheses to be tested (if any), and definitions of core concepts. In general, in qualitative research, hypotheses are statements of relationships between concepts. Theories usually are composed of two or more hypotheses, although, at times, some researchers may use the term theory to designate a single hypothesis ( Gilgun, 2005 b ). Concepts are extractions from concrete data. Sometimes concepts are called second-order concepts and data first-order concepts .

Research questions may be absent. In their place are purpose statements that make the focus of the report clear. Hypotheses are rarely present in qualitative research. When they are, the purpose of the research is to test them and typically to develop them more fully. This type of research has in the past been called analytic induction ( Gilgun, 1995 e), whereas a more up-to-date version of qualitative hypothesis testing and theory-guided research is called deductive qualitative analysis ( Gilgun, 2005 d ; 2013 ). Analytic induction and deductive qualitative analysis are part of the Chicago School tradition.

Methods Section

Most methods sections for reports based on qualitative approaches have the same elements as any other research report. Descriptions of the sample, recruitment, interview schedule, and plans for data analysis are standard. The APA manual provides guidelines ( American Psychological Association, 2009 ) that fit many types of qualitative research reports. However, reports based on autoethnographies, poetry, and performances may have brief or no methods sections. As is clear by now, the report’s contents depend on the purposes and methodologies of the research and on the editorial requirements of journals.

Accounts of Methodologies

In writing up qualitative research, methods sections usually contain a brief overview of the research methodology, which is the set of principles that guided the research. The following is an account of the methodology used in a research report on cancer treatment in India:

For this project we drew upon interpretive traditions within qualitative research. This involved us taking an in-depth exploratory approach to data collection, aimed at documenting the subjective and complex experiences of the respondents. Our aim was to achieve a detailed understanding of the varying positions adhered to, and to locate those within a broader spectrum underlying beliefs and/or agendas. ( Broom & Doron, 2013 , p. 57)

Sometimes, statements of methodology are much more elaborate, but in research reports, such a statement is sufficient, again depending on the editorial policies of particular journals. A few citations, which this article had, round out an adequate statement of methodology.

However, many reports are written in a clear and straightforward way with scant or no account of methodologies. Examples are the work of Eck (2013) and Spermon, Darlington, and Gibney (2013) . These kinds of well-done write-ups might eventually be considered generic. Spermon et al. said their study was phenomenological, which sets up assumptions that the report will be primarily descriptive. In actuality, the intent was to develop theory. Such mixing of methodologies may be the wave of the future; in many ways, distinctions between phenomenological studies whose purposes are descriptive and those whose purposes are to build theory are blurred. Such blurring may have been the case for decades because it is possible and often desirable to build theories based on phenomenological perspectives; that is, in-depth descriptions of lived experience. However, authors are wise to state in one place what their methodologies are and how they put them to use, such as for descriptive purposes or for theory-building.

Description of Sample

Placing descriptions of sample size and the demographics of the sample in the methods sections is typical. As mentioned earlier, evaluation of sample size depends on the depth and breadth of the study. The more depth a study has, the smaller the number of cases can be. The more breadth and the sharper the focus, the larger sample sizes typically are. Samples on which a study is based must provide enough material on which to base a credible article. A sample size of one may be adequate if researchers show their work demonstrates the basic principles of almost all forms of qualitative research: perspectives of persons who participate in the research, researcher immersion into the settings or the life stories of persons interviewed, multiple perspectives, contextual information of various types, and applications. Autoethnographies often have an n of one, but joint autoethnographies are possible. Ethnographies may not give a sample size, as was the case in the performance ethnography of Valásquez (2011) who wrote in the first person about her experience with scientology. In her first-person ethnography, Irvine (2013) also did not mention sample size. She said that the narratives she used for the article were from a larger study on the meanings of animals to people who have no homes. She did not describe the usual demographics of age, gender, social class, and ethnicity.

Most articles describe the demographics of the sample. In a recently accepted article ( Gilgun & Anderson, 2013 ), I saw no relevance in mentioning the size of the larger sample from which we drew in order to tell the stories of how mothers responded to their learning that their husbands or life partners had sexually abused their children. We included an exact count of the larger sample because we assumed that it would be the journal’s expectations. We also gave particulars of the demographics. Except for social class and ethnicity, we saw little relevance for the other descriptors. These status variables were relevant to us because most of the sample was white and middle or upper class. This is important because much research on child sexual abuse is done with poor people, and there are stereotypes that poor families and families of color are more likely to experience incest than are white middle and upper class families. Overall, as with some other issues related to writing, the adequacy of the sample description depends on the methodological principles of the research and the journal’s editorial policies.

Recruitment

Accounts of recruitment procedures are important because researchers want to show that their work is ethical. Respect for the autonomy or freedom of choice of participants needs to be demonstrated. In addition, often the persons in whose lives we are interested have vulnerabilities. To show that the research procedures have not exploited these vulnerabilities is part of ethical considerations. Most articles have these accounts. Furthermore, when there are accounts of recruitment procedures, it becomes obvious why the sample is not randomly selected. Irvine’s (2013) account of recruitment is exemplary. She recruited through veterinary clinics that took care of the pets of homeless persons. She did not approach potential participants herself. Doing so risked making refusals difficult. The staff informed persons of the research and its purposes. If individuals said they were interested, they gave permission for the staff to give their names to researchers. The research interviews took place in the clinics.

The ethics of recruitment revolve around values, such as respect for autonomy, dignity, and worth. Other ethical issues that are important to mention in reports include the use of incentives for participation. Although many human subjects committees now require monetary incentives for participation, this has ethical implications. Irvine (2013) solved this by giving gift cards after the interviews were completed. Reports on ethical issues have a place in methods sections.

Data Collection and Analysis

Accounts of data collection and analysis are part of the methods section. Data collection procedures should be detailed for many reasons. Primary among them is the need for transparency in terms of the ethical standards the researchers followed, as well as the need to allow for replication of the study. Such details also provide guidelines for others who might be interested in using the methods. In addition, there are many different schools of thought and procedures for each of the methods used with the three general types of data collection: interviews, observations, and documents. It is helpful to state which particular data collection procedures the researchers used. Researchers often provide examples of the kinds of questions asked and procedures used for recording observations and excerpts from documents. Some researchers may omit such an accounting, as with some autoethnographies and articles that turn research material into performances.

How researchers analyzed data is part of the methods sections. As with data collection, there are so many types of analysis that researchers need to describe the particular forms that they used. For figuring out how to report on data analysis, researchers would do well to study articles in journals in which they want to publish. Irvine (2013) used a method of analysis I have never heard of called “personal narrative analysis” (p. 8). She gave enough detail to provide the general idea of what she did and a sufficient number of citations for additional information.

The level of detail can vary. In some sociology journals, for example, researchers may say little about analysis and sometimes little about data collection. This is because the journal editors, reviewers, and those who publish in and read the articles have assumptions that they for the most part take for granted. Even in these journals, however, researchers may want to account for their analytic procedures, especially if they are writing on topics outside of what is usual in such journals.

Other journals require a great deal of detail. In those instances, researchers first decide what they think is essential and then shape their accounts to fit what appears to be usual practice in the journal. The following paragraphs describe data analysis in a recently accepted article on signs of child sexual abuse in families ( Gilgun & Anderson, 2013 ).

Data Analysis

In the analysis of data, the first author read the transcripts multiple times and coded them for instances related to disclosures of child sexual abuse and associated signs of the abuse, such as how and when the women first learned of the abuse or suspected it was occurring in their families, their responses, and their reflections on the signs of abuse they might have missed, as well as child and perpetrator behaviors that they did not realize were related to child sexual abuse. Their initial and longer term responses and reflections were also coded. The second author independently read and coded about one-third of the transcripts using this coding scheme to arrive at a 100 percent agreement.

Sources of the codes were our professional experiences in the area of child sexual abuse, the review of research, and the first author’s familiarity with the content of the interviews because she had been the interviewer. These codes served as sensitizing concepts, which, as Blumer (1986) explained, are ideas that guide researchers to see aspects of phenomena that they might otherwise not notice. Although altering researchers’ ideas to what might be significant serves an obvious useful purpose, sensitizing concepts might also may blind researchers to other aspects of phenomena that might be important. Therefore, we also used negative case analysis, which is a procedure that guides researchers to look for aspects of phenomena that contradict or do not fit with emerging understandings. In this way, researchers are positioned to see patterns, variations within patterns, exceptions, and contradictions in findings ( Becker et al., 1961 ; Bogdan & Biklen, 2007 ; Cressey, 1953 ; Lindesmith, 1947 ).

As we wrote this section, we were aware of the limited space that we had to fill. Yet we were committed to accounting for where our codes came from for reviewers and editors who may be unfamiliar with pre-established codes. As discussed earlier, many reports are written as if codes appear by magic. We decided that, in this report, we would be as clear as possible about where our codes came from. We also reasoned that we would have to call on the authority of well-respected methodologists if reviewers and editors had questions about what we had done. Furthermore, we were aware of the dated nature of the references; we could do nothing about that because there has not been much written recently about pre-established codes. I have written about this quite a bit, but as one of the authors, I not only had to be anonymous during the review process, but I could not be the sole authority.

Generalizability

Many reviewers and editors have questions about the generalizability of the results of qualitative research. Authors themselves sometimes question the generalizability of their own findings. That’s why it remains important to provide clear guidelines in research reports about how the authors view the usefulness of their findings. The following ideas may be helpful to authors as they write their reports and to reviewers who are positioned as gatekeepers. The results of qualitative research are not meant to be generalized in a probabilistic sense. But because dropouts and refusals limit the randomness of samples, most forms of research can’t be generalized in a probabilistic sense.

Conversely, as Cronbach (1975) wrote almost forty years ago, the results of any form of research are working hypotheses that must be tested in local settings. Thus, the applicability of qualitative or any other kind of research can be demonstrated only through attempts at application. Do the findings illuminate other situations? Do the results provide researchers, policy makers, and direct practitioners with ideas on how to proceed? Those who apply the research expect to have to adjust findings to fit particular new situations. Many researchers and some journal editors and reviewers know through common sense and everyday experience how to use the results of qualitative research. Our personal lives are extended case studies. What we learn in one situation, we carry over into another. We know we have to test what we have learned in past situations for fit with new situations. If we do not, we impose our ideas on situations that may demand new perspectives. This common practice of applying results to all situations is disrespectful of local conditions and autonomy of persons. We want to avoid such disrespect in how we suggest readers use the results of our research.

Trustworthiness and Authenticity

Pointing out the trustworthiness of procedures and the findings that result from them sometimes are parts of methods sections. Related to trustworthiness are issues of authenticity ( Guba & Lincoln, 2005 ). Both trustworthiness and authenticity arise from immersion, seeking to understand the perspectives of others in context, reflexivity, and seeking multiple points of view. Researchers who have applied these principles will produce reports that are trustworthy and authentic. In addition, the reports will have grab. Extended discussions related to these issues are beyond the scope of this chapter and the scope of research reports as well.

I get more requests for revisions of methods sections, especially for accounts of data collection and analysis, than for any other parts of a manuscript. This is not surprising, given the multiple possible variations. I never know who the reviewers will be and what their expectations are. I rely first on my beliefs about what I want in the procedures section and then I study articles the journal has already publishes. I include what journal editors appear to expect, but I also add information that I think is important, even when it is not part of what I see in methods sections.

Findings Sections

Findings sections in research reports include both descriptive and conceptual material. Descriptive material is composed of researchers’ paraphrasing and summarizing of what they found and excerpts from interviews, fieldnotes, and documents. The descriptive material, at its best, is detailed and lively; it not only is informative, it has grab. This material contributes to understandings of human experiences in context. In addition, descriptive material is the basis of researchers’ theorizing and it also provides documentation and illustrations of assertions that researchers make.

Conceptual material comprises the analysis and is made up of inferences such as the general statements, concepts, and hypotheses that researchers develop from the material (data). One way to think about the relationship between descriptive and conceptual material is to think of descriptive material as composed of first-order concepts and conceptual material as composed of second-order concepts. Each type depends on the other. Credible conceptual material is based on descriptive material, some of which is contained in the article. Qualitative research yields mountains of data, a fraction of which can be placed into a published article.

As with other sections of research reports, findings sections have many possible variations that depend on the purpose of the research and the methodologies on which the research is based. Thus, the findings can range from heavily descriptive to heavily conceptual. Heavily conceptual research reports arise from research whose purpose is theoretical, in which researchers set out to test, refine, reformulate, or develop theory. Theoretical reports require some descriptive material to show the basis of theoretical statements, but they are often relatively short on descriptive material.

Reports that are primarily descriptive are composed of excerpts from data. Theoretical material appears in often subtle ways, such as in the form of concepts that organize findings. Irvine’s (2013) study of homeless people and their pets is largely descriptive, composed of excerpts from the interviews and Irvine’s paraphrases and narration of what she did, how, and when. The findings were narrative case studies based on interviews and observations. The details of the narratives were vivid and had the kind of grab that Glaser (1978) recommended. They showed multiples perspectives and variations on what it meant to homeless informants to have pets in their lives. The first three pages were a review of relevant literature and a presentation of method. The last five pages were a discussion of the findings.

As lengthy as the descriptive material is, conceptual material frames the entire report. In the literature review, Irvine introduced notions of positive identity, generativity, and redemption. She used them to analyze her data and organize findings, which were the narrative case studies. She used the concept of redemption as the core or organizing concept, going into some detail about how the research material supports the significance of this idea of pets as redemptive for homeless people.

This analysis is based squarely on the descriptive material. For instance, Irvine wrote that in the stories she presented in her article, “animals provide the vehicle for redemption.” She illustrated this point with a quote from one of the narratives and then reminded readers that the narratives “contain variations on the theme” of “ life is better because this animal is in it ” (p. 20; emphasis in original). Readers do not take this on faith because the basis of this general statement in presented multiple times in the case studies. Irvine has much more material on which she based these ideas, but there is not enough room in a journal-length article to show all of her evidence.

An example of an article that is theoretical in purpose and short on descriptive material is found in the work of Cordeau (2012) . She developed a grounded theory of the “transition from student to professional nurse” when student nurses work with “mannequins as simulated patients” (p. 90). Based on interviews, observations, and reports that the students wrote on their clinical experiences, the study was composed of about 10 percent descriptive material. This material included excerpts interviews and student reports. In the results section, she used this descriptive material to illustrate and possibly document the grounded theory she constructed. The theory’s “core category” was “linking,” which had four components, called properties. She documented the properties, primarily with her own thinking about her research material and also with excerpts from interviews, observations, and student reports.

Like Irvine’s (2013) study, the purpose of Cordeau’s (2012) work was applied where she wanted to build theory that would contribute to the development of clinical expertise in nursing students. She also devoted about one page of her study to applications.

Core Concepts

I’ve previously provided an extended discussion of core concepts. This section highlights some key points and illustrates them. Core concepts, often called core categories , organize findings. I prefer the term concept because concept is the term used in discussing theory, such as “concepts are the building blocks of theory,” and theory is one of several possible products of qualitative research. Researchers decide on which concepts are core in the course of analysis. Researchers are ready to write up their reports when they have settled on, named, and dimensionalized one or more core concepts. The terms “core concepts” and “core categories” are associated with grounded theory ( Charmaz, 2006 ; Corbin & Strauss, 2008 ), but they are useful in other types of qualitative research, such as interpretive phenomenology and narrative analysis. Core concepts both organize findings and, typically, bring together a great deal of information. The term “dimension” means that researchers account for as many aspects of the core concepts as they can in order to show the multiple perspectives and patterns that typically compose concepts.

In reporting on core concepts, I recommend that researchers name them, introduce them, describe them using excerpts from the research material, comment on them, and then situate each of the concepts and their commentaries within their scholarly contexts. As discussed earlier, this shows how the findings fit with what is already known, or add to, force modification of, or refute what is known. Although many researchers, do not situate findings in their scholarly contexts, they usually cover the other topics.

No matter how authors report findings, they should do so with grab. An example of a report exemplary for its grab is the work of Scott (2003) on what it means to be a professional with a physical disability. Scott began her article not with a literature review but with three reviewer comments on other articles she had written. She then stated that the present article was a response to these comments. She followed up with a description of three male students who waited to speak to her after class about her disability and the notion of embodiment that she discussed in class. She brought in related literature throughout the article. Through her own reflections, reports on how others have responded to her, reports on the accounts that three other women with disabilities gave to her as a person with cerebral palsy, and her literature review, Scott not only showed the meanings of disabilities to persons who have them, but also what others say about their own disabilities, what some people who are able-bodied say about women with disabilities, and how all of this connects to what is known about disabilities and to wide-spread beliefs about disabilities. Her article is full of grab, such as the header that read, “The Day I Became Human.” With the authors’ own experience as the centerpiece, this article exemplifies write-ups that demonstrate the meanings of lived experience in various contexts, immersion, grab, and implications for social action. The analysis she presented as part of her findings is exemplary.

In the production of quality research, no matter the type of write-up, there are no short cuts. Research reports based on poetry, for example, are held to the same standards as any other article: grab, immersion, lived experience in context, and implications for action. In addition, such research reports typically locate themselves within social and human sciences traditions. Furman’s (2007) reflections and analysis of poetry that he wrote over the course of many years provide an example of how poetry can be used in qualitative analysis. This kind of research is a type of document analysis. In performance studies, researchers create a theater production of informant’s accounts of their experiences whose purpose is to transform audiences and move them to action ( Saldaña, 2003 ). The performances are the equivalent of research reports and when they are effective, they have the four characteristics of qualitative research under discussion.

Discussion Sections

In traditional research reports, the discussion section follows the results section. In discussion sections, authors reflect on findings, including what the findings are, how findings contribute to understandings of phenomena of interest, the lines of inquiry the results open up, and implications for policy and practice. Other generic topics to consider are those related to the focus of the journal. For example, if the journal’s focus is related to health, then authors show how findings are related to health.

Discussion sections present the author with opportunities to advocate for how his or her research can be used. The applied purposes of Irvine’s (2013) research come through when she devoted an entire page to make observations about implications. She pointed out how her research contributes to a transformation of images of homeless persons as isolated to images of them as engaged in relationships not only with their pets but with other persons, too. She noted that rehousing homeless persons requires a change in policy that would allow them to have pets. Furthermore, she said that caring for a pet “can turn things around” (p. 24).

In the discussion section I wrote with Anderson ( Gilgun & Anderson, 2013 ), we addressed methodological issues, such as the probable existence of other patterns in addition to those we identified and the nonrandom nature of our sample. We also acknowledged the difficulties in working with families in which child sexual abuse has occurred. Since qualitative researchers want to understand lived experiences, we had to prepare ourselves to deal effectively in research areas that are difficult emotionally for us as researchers. Although we may acknowledge the emotional challenges of some topics in reflexivity statements, discussion sections are opportunities for authors to acknowledge the difficulties of using the results we produce. In the article I wrote with Anderson, we made such an acknowledgment, one that we hoped would facilitate more effective practice. We wrote

Practitioners themselves may experience shock, rage, and disgust. The practice of neutrality, in its therapeutic sense, is important in these cases ( Gil & Johnson, 1993 ; Rober, 2011 ). Neutrality means that practitioners maintain their analytic stances while at the same time they remain attuned not only to service users but also to themselves. When practicing neutrality, service providers regulate their own emotional responses in order to remain emotionally available to service users. Neutrality also means that service providers remain open-minded so that they can hear stories that they may not expect to hear; in other words, to make room for the unexpected ( Rober, 2011 ). Attunement to inner processes is a form of reflection that can facilitate the development of trust between service users and providers. When providers are reflective, they are less likely to tune out, close down, and otherwise stop listening to what services users express. When they listen and hear what service users say, they are more likely to facilitate the best possible outcomes in difficult situations ( Weingarten, 2012 ).

Doing research on lived experience can be difficult for informants and for researchers. Acknowledgment of the implications of these difficulties for users of the research has a place in discussion sections.

In summary, most articles are fairly straightforward in their write-ups: focused literature reviews, reflexivity statements in many cases, clear statements of purpose, clarity about sources of research questions and/or hypotheses, identification and definition of key concepts, identification of codes the researcher develops from literature reviews and reflexivity statements, succinct accounting of methods, and findings organized logically by core concepts around which the researcher organizes the multiple dimensions of those concepts. Excellent writing makes articles interesting and accessible. Some kinds of write-ups deviate from these components, but they are held to the same standards of immersion, experiences in context, multiple perspectives, and implications for action and other applications. When authors have the good fortune to have a recommendation to revise and resubmit, suggestions for revisions often improve the quality of the article.

The seemingly endless variations that are possible in the write-up of qualitative research makes writing and reviewing manuscripts challenging, especially when compared to traditions in which rigid rules prevail. However, it is important that approaches to qualitative research continue to evolve to meet with our ever-changing understandings of human phenomena. The clarity and transparency of reports are the fundamental guidelines for making judgments about quality. I often tell my students that the guidelines for doing qualitative research are flexible, and what is important is to be clear about what you did, why you did it, and what you came up with.

The notion of grab is central to write-up. Since qualitative research seeks to understand lived experiences, it is logical that findings report on the lived experiences in vivid terms, replete with quotes from data. This is not to undermine the importance of analysis, but grab is possible even in write-ups that require a great deal of analysis. Grab becomes possible because researchers must provide the evidence for the theories and concepts they develop.

When there are questions about priorities related to informants’ voices, researchers’ interpretations, and prior research, I hope that authors, reviewers, and editors remember that as important as analysis and previous work may be, the voices of informants bring these other important parts of manuscripts to life. Researchers make decisions about whose voices take priority.

There is no one way to respond to these dilemmas. Authors must make their own decisions about what is important to them and then search for journals that will welcome what they want to convey. It’s important to consider pushing the boundaries and writing an article in a way that the researcher thinks will best convey his or her findings.

The importance of quality data, quality analysis, and “grab” are foundational. I began this chapter with a discussion of the balance between description and analysis. I then considered core concepts as organizers of findings, the place of literature reviews, styles of presenting methods and methodologies, and the balance between the voices of informants and researchers. I concluded with the many variations in types of reports that result from the various purposes that qualitative research projects can have. There are many different types of qualitative research and many styles of write-ups. This chapter may sensitize readers to enduring issues in the writing of research reports. Like qualitative research itself, there are multiple points of view on how to write up qualitative research.

Ahmed, A. ( 2013 ). Structural narrative analysis: Understanding experiences of lifestyle migration through two plot typologies.   Qualitative Inquiry , 19 , 231–243.

Google Scholar

American Psychological Association. ( 2009 ). The publication manual of the American Psychological Association (6th ed.). Washington, DC: American Psychological Association.

Google Preview

Becker, H. S. ( 1999 ). The Chicago School, so-called.   Qualitative Sociology , 22 (1), 3–12.

Becker, H. S. , Geer, B. , Hughes, E. C. , & Strauss, A. L. ( 1961 ). Boys in white: Student culture in medical school . Chicago: University of Chicago Press.

Block, E. ( 2012 ). “That’s what I see:” Enhancing AIDS intervention research through deep ethnography.   Qualitative Social Work 14 ( 4 ), 379–394.

Bogdan, R. C. , & Biklen, S. K. ( 2007 ). Qualitative research for education: An introduction to theories and methods (5th ed.). Boston, MA: Allyn & Bacon.

Bennet, B. , Zubrzycki, J. , & Bacon, V. ( 2011 ). What do we know? The experiences of social workers working alongside aboriginal people.   Australian Social Work , 64 (1), 20–37.

Blumer, H. ( 1986 ). The methodological position of symbolic interactionism. In H. Blumer (Ed.), Symbolic interactionism: Perspective and method (pp. 1–60). Berkeley: University of California Press.

Bulmer, M. ( 1984 ). The Chicago School of Sociology: Institutionalization, diversity, and the rise of sociological research . Chicago: University of Chicago Press.

Broom, A. F. , & Doron, A. ( 2013 ). Traditional medicines, collective negotiation, and representations of risks in Indian cancer care.   Qualitative Health Research , 23 (1), 54–65.

Charmaz, K. ( 2006 ). Constructing Grounded Theory: A practical guide through qualitative analysis . London: Sage.

Corbin, J. , & Straus, A. ( 2008 ). Basics of qualitative research (3rd. ed.). Thousand Oaks, CA: Sage.

Cordeau, M. A. ( 2012 ). Linking the transition: A substantive theory of high stakes clinical simulation.   Advances in Nursing Science , 35 (3), E90–E102.

Covan, E. K. ( 2007 ). The discovery of grounded theory in practice: The legacy of multiple methods. In A. Bryant & K. Charmaz (Eds.), The Sage handbook of grounded theory (pp. 58–93). Thousand Oaks, CA: Sage.

Cressey, D. R. ( 1953 ). Other people’s money . Belmont, CA: Wadsworth.

Cronbach, L. ( 1975 ). Beyond the two disciplines of scientific psychology.   American Psychologist , 30 , 116–127.

D’Cruz, H. , Gillingham, P. , & Melendez, S. ( 2007 ). Reflexivity, its meanings and relevance for social work: A critical review of the literature.   British Journal of Social Work , 37 (1), 73–90.

Dollard, J. ( 1937 ). Caste and class in a southern town . New Haven, CT: Yale University Press.

Eck, B. ( 2013 ). How never-married men make sense of an unintended identity.   Journal of Contemporary Ethnography , 42 (1), 31–63.

Egbe, C. O. ( 2013 ). Descriptive poems on a qualitative research with young smokers in southern Nigeria (2013).   Qualitative Inquiry , 19 (5), 353–354.

Ellis, C. ( 2009 ). Revision: Autoethnographic reflections on life and work . Walnut Creek, CA: Left Coast Press.

Faris, R. E. L. ( 1967 ). Chicago sociology 1920-1932 . Chicago: University of Chicago Press.

Furman, R. ( 2007 ). Poetry and narrative as qualitative data: Explorations into existential theory.   The Indo-Pacific Journal of Phenomenology , 7 , 1–9.

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

Gil, E. , & Johnson, T. C. ( 1993 ). Sexualized children: Assessment and treatment of sexualized children and children who molest . Rockville, MD: Launch Press.

Gilgun, J. F. ( 1983 ). The sexual abuse of the young female in life course perspective. 3058 . Ann Arbor, MI: Dissertations Abstracts International.

Gilgun, J. F. (1984). A non-coercive method of helping children discuss their own sexual abuse. Part II: An example of intervention by multiple qualitative case studies. Scribd.com . Retrieved from http://www.scribd.com/doc/117051267/A-Non-Coercive-Method-of-Helping-Children-Discuss-Their-Own-Sexual-Abuse .

Gilgun, J. F. ( 1995 ). “We shared something special”: The moral discourse of incest perpetrators.   Journal of Marriage and the Family , 57 , 265–281.

Gilgun, J. F. ( 1996 a). Human development and adversity in ecological perspective: Part 1: A conceptual framework.   Families in Society , 77 , 395–402.

Gilgun, J. F. ( 1996 b). Human development and adversity in ecological perspective, Part 2: Three patterns.   Families in Society , 77 , 459–576.

Gilgun, Jane F. (l996c November). The phenomenology of family violence. Paper presented at the Preconference Workshop on Theory Construction and Research Methodology, National Council on Family Relations, Kansas City, MO, November 5.

Gilgun, J. F. (1998, November). A comprehensive theory of family violence. Paper presented at the 28th Preconference Workshop on Theory Construction and Research Methodology, National Council on Family Relations , Milwaukee, WI.

Gilgun, J. F. ( 1999 a). CASPARS: New tools for assessing client risks and strengths.   Families in Society , 80 (5), 450–459.

Gilgun, J. F. (1999c, November). In their own words: Men talk about their violence . Paper presented at the 29th Preconference Workshop on Theory Construction and Research Methodology, National Council on Family Relations , Irvine, CA.

Gilgun, Jane F. ( 1999 d ). Methodological pluralism and qualitative family research. In Suzanne K. Steinmetz , Marvin B. Sussman , and Gary W. Peterson (Eds.), Handbook of Marriage and the Family (2nd ed.) (pp. 219–261). New York: Plenum.

Gilgun, J. F. (2000, June 25–28). A comprehensive theory of interpersonal violence . Paper presented at the Victimization of Children and Youth: An International Research Conference, Durham, NH .

Gilgun, J. F. ( 2002 a). Completing the circle: American Indian medicine wheels and the promotion of resilience in children and youth in care.   Journal of Human Behavior and the Social Environment , 6 (2), 65–84.

Gilgun, J. F. ( 2002 b). Social work and the assessment of the potential for violence. In T. N. Tiong & I. Dodds (Eds.), Social work around the world II (pp. 58–74). Berne, Switzerland: International Federation of Social Workers.

Gilgun, J. F. ( 2004 a). A strengths-based approach to child and family assessment. In D. R. Catheral (Ed.), Handbook of stress, trauma and the family (pp. 307–324). New York: Bruner-Routledge.

Gilgun, J. F. ( 2004 b). The 4-D: Strengths-based assessments for youth who’ve experienced adversities.   Journal of Human Behavior in the Social Environment , 10 (4), 51–73 .

Gilgun, J. F. ( 2005 a). Evidence-based practice, descriptive research, and the resilience-schema-gender-brain (RSGB) assessment.   British Journal of Social Work , 35 (6), 843–862.

Gilgun, J. F. ( 2005 b). “ Grab” and good science: Writing up the results of qualitative research.   Qualitative Health Research , 15 (2), 256–262.

Gilgun, J. F. ( 2005 c). Lighten up! The citation dilemma in qualitative research.   Qualitative Health Research , 15 (5), 721–725.

Gilgun, J. F. ( 2005 d). Qualitative research and family psychology.   Journal of Family Psychology , 19 (1), 40–50.

Gilgun, J. F. ( 2006 ). Children and adolescents with problematic sexual behaviors: Lessons from research on resilience. In R. Longo & D. Prescott (Eds.), Current perspectives on working with sexually aggressive youth and youth with sexual behavior problems (pp. 383–394). Holyoke, MA: Neari Press.

Gilgun, Jane F. (2007, November). The legacy of the Chicago School of Sociology for family theory-building, Pre-Conference Workshop on Theory Construction and Research Methodology, National Council on Family Relation, Pittsburgh, PA, November 7.

Gilgun, J. F. ( 2008 ). Lived experience, reflexivity, and research on perpetrators of interpersonal violence.   Qualitative Social Work , 7 (2), 181–197.

Gilgun, J. F. ( 2010 ). Reflections on 25 years of research on violence.   Reflections: Narratives of Professional Helping , 16 (4), 50–59.

Gilgun, J. F. ( 2012 a). Enduring themes in qualitative family research.   Journal of Family Theory and Review , 4 , 80–95.

Gilgun, J. F. (2012b). School shootings are not senseless and other essays on violence . Amazon. http://www.amazon.com/s/ref=nb_sb_noss?url=search-alias%3Ddigital-text&field-keywords=school+shootings+are+not+senseless . Retrieved 2 January 2014.

Gilgun, J. F. (2012c). The thin blue line of police brutality & other essays . Amazon. http://www.amazon.com/s/ref=nb_sb_noss?url=search-alias%3Ddigital-text&field-keywords=the+thin+blue+line+of+police+brutality&rh=n%3A133140011%2Ck%3Athe+thin+blue+line+of+police+brutality . Retrieved 2 January 2014.

Gilgun, J. F. (2013a). The logic of murderous rampages & other essays on violence and its prevention (2nd ed.). Amazon. http://www.amazon.com/Murderous-Rampages-Essays-Violence-Prevention-ebook/dp/B00AYPR18A/ref=sr_1_2?s=digital-text&ie=UTF8&qid=1388681278&sr=1-2&keywords=the+logic+of+murderous+rampages . Retrieved 2 January 2014.

Gilgun, J. F. ( 2013 b). Qualitative family research: Enduring themes and contemporary variations. In G. F. Peterson & K. Bush (Eds.), Handbook of marriage and the family (3rd ed., pp. 91–119). New York: Plenum.

Gilgun, J. F. , & Abrams, L. S. ( 2005 ). Gendered adaptations, resilience, and the perpetration of violence. In M. Ungar (Ed.), Handbook for working with children and Youth: Pathways to resilience across cultures and context (pp. 57–70). Toronto: University of Toronto Press.

Gilgun, J. F. , & Anderson, G. ( 2013 ). Mothers’ perspectives on signs of child sexual abuse in their families.   Families in Society , 94 (4), 259–267.

Gilgun, J. F. , Daly, K. , & Handel, G. (Eds.). ( 1992 ). Qualitative methods in family research . Newbury Park, CA: SAGE Publications.

Gilgun, J. F. , Keskinen, S. , Marti, D. J. , & Rice, K. ( 1999 ). Clinical applications of the CASPARS instruments: Boys who act out sexually.   Families in Society , 80 (6), 629–641.

Gilgun, J. F. , Klein, C. , & Pranis, K. ( 2000 ). The significance of resources in models of risk.   Journal of Interpersonal Violence , 14 , 627–646.

Gilgun, J. F. , & Sands, R. G. (Eds.). ( 2012 ). Special issue: The contributions of qualitative approaches to developmental intervention research.   Qualitative Social Work: Research and Practice , 11 (4), 80–95.

Gilgun, J. F. , & Sussman, M. B. (Eds.). ( 1996 ). The methods and methodologies of qualitative family research . New York: Haworth. Also published as a special issue of Marriage and Family Review , 41 (1–2).

Glaser, B. ( 1978 ). Theoretical sensitivity . Mill Valley, CA: Sociology Press.

Glaser, B. , & Strauss, A. ( 1967 ). The discovery of grounded theory . Chicago: Aldine.

Guba, E. G. , & Lincoln, Y. S. ( 2005 ). Paradigmatic controversies, contradictions, and emerging confluences. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed.). Thousand Oaks, CA: Sage.

Irvine, L. ( 2013 ). Animals as lifechangers and lifesavers: Pets in the redemptive narratives of homeless people.   Journal of Contemporary Ethnography , 42 (1), 3–30.

Lindesmith, A. ( 1947 ). Opiate addictions . Bloomington, IN: Principia.

McKinney, J. C. ( 1966 ). Constructive typology and social theory . New York: Appleton-Century-Crofts.

Presser, L. ( 2005 ). Negotiating power and narrative in research: Implications for feminist methodology.   Signs: Journal of Women in Culture and Society , 30 (4), 2067–2090.

Rober, P. ( 2011 ). The therapist’s experiencing in family therapy practice.   Journal of Family Therapy , 33 , 233–255.

Saldaña, J. ( 2003 ). Dramatizing data: A primer.   Qualitative Inquiry , 9 , 218–236.

Schatzman, L. ( 1991 ). Dimensional analysis: Notes on an alternative approach to the grounding of theory in qualitative research. In D. R. Maines (Ed.), Social organizations and social process: Essays in honor of Anselm Strauss (pp. 303–314). New York: Aldine de Gruyter.

Scott, J. A. ( 2003 ). Problematizing a researcher’s performance of “insider status:” An autoethnography of “designer disabled” identity.   Qualitative Inquiry , 19 , 101–115.

Sharma, A. , & Gilgun, J. F. ( 2008 ). What perpetrators say about child sexual abuse.   Indian Journal of Social Work , 69 (3), 321–338.

Small, A. W. ( 1916 ). Fifty years of sociology in the United States, 1865–1915.   American Journal of Sociology , 21 , 712–864.

Spermon, D.   Darlington, Y , & Gibney, P. ( 2013 ). Complex posttraumatic stress disorder: Voice of healing.   Qualitative Health Research , 24 (1), 43–53.

Velásquez, G. ( 2011 ). Inside the Church of Scientology: An ethnographic performance script.   Qualitative Inquiry , 17 , 824–836.

Weingarten, K. ( 2012 ). Sorrow: A therapist’s reflection on the inevitable and the unknowable.   Family Process , 51 (4), 440–455.

Winter, K. ( 2010 ). The perspectives of young children in care.   Child and Family Social Work , 15 , 186–195.

Young, P. V. (l928). The reorganization of Jewish family life in America.   Social Forces , 7 , 238–243.

Young, P. V. (l932). The Pilgrims of Russian Town . Chicago: University of Chicago Press.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 7. The Results
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE:   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

• << Previous: Insiderness
• Next: Using Non-Textual Elements >>
• Last Updated: May 30, 2024 9:38 AM
• URL: https://libguides.usc.edu/writingguide

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 ]

6 Qualitative data examples for thorough market researchers

Types of qualitative data in market research, 6 qualitative data examples, get nuanced insights from qualitative market research.

There are plenty of ways to gather consumer insights for fresh campaigns and better products, but qualitative research is up there with the best sources of insight.

This guide is packed with examples of how to turn qualitative data into actionable insights, to spark your creativity and sharpen your research strategy. You’ll see how qualitative data, especially through surveys, opens doors to deeper understanding by inviting consumers to share their experiences and thoughts freely, in their own words — and how qualitative data can transform your brand.

Before we dig into some examples of how qualitative data can empower your teams to make focused, confident and quick decisions on anything from product to marketing, let’s go back to basics. We can categorize qualitative data into roughly three categories: binary, nominal and ordinal data. Here’s how each of them is used in qualitative data analysis.

Binary data

Binary data represents a choice between two distinct options, like ‘yes’ or ‘no’. In market research, this type of qualitative data is useful for filtering responses or making clear distinctions in consumer preferences.

Binary data in qualitative research is great for straightforward insights, but has its limits. Here’s a quick guide on when to use it and when to opt for qualitative data that is more detailed:

Binary data is great for:

• Quick Yes/No questions : like “Have you used our app? Yes or No.”
• Initial screening : to quickly sort participants for further studies.
• Clear-cut answers : absolute factors, such as ownership or usage.

Avoid binary data for:

• Understanding motivations : it lacks the depth to explore why behind actions.
• Measuring intensity : can’t show how much someone likes or uses something.
• Detail needed for product development : misses the nuanced feedback necessary for innovations.

Nominal data

Nominal data categorizes responses without implying any order. For example, when survey respondents choose their favorite brand from a list, the data collected is nominal, offering insights into brand preferences among different demographics.

Some other examples of qualitative data that can be qualified as nominal are asking participants to name their primary information source about products in categories like social media, friends, or online reviews. Or in focus groups, discussing brand perceptions could classify brands into categories such as luxury, budget-friendly, or eco-conscious, based on participant descriptions.

Nominal data is great for:

• Categorizing responses : such as types of consumer complaints (product quality, customer service, delivery issues).
• Identifying preferences : like favorite product categories (beverages, electronics, apparel).
• Segmentation : grouping participants based on attributes (first-time buyers, loyal customers).

Nominal data is not for:

• Measuring quantities : it can’t quantify how much more one category is preferred over another.
• Ordering or ranking responses : it doesn’t indicate which category is higher or lower in any hierarchy.
• Detailed behavioral analysis : While it can group behaviors, it doesn’t delve into the frequency or intensity of those behaviors.

Ordinal data

Ordinal data introduces a sense of order, ranking preferences or satisfaction levels. In qualitative analysis, it’s particularly useful for understanding how consumers prioritize features or products, giving researchers a clearer picture of market trends.

Other examples of qualitative data analyses that use ordinal data, are for instance a study on consumer preferences for coffee flavors, participants might rank flavors in order of preference, providing insights into flavor trends. You can also get ordinal data from focus groups on things like customer satisfaction surveys or app usability, by asking users to rate their ease of use or happiness on an ordinal scale.

Ordinal data is great for:

• Ranking preferences : asking participants to rank product features from most to least important.
• Measuring satisfaction levels : using scales like “very satisfied,” “satisfied,” “neutral,” “dissatisfied,” “very dissatisfied.”
• Assessing Agreement : with statements on a scale from “strongly agree” to “strongly disagree.”

Ordinal data is not for:

• Quantifying differences : it doesn’t show how much more one rank is preferred over another, just the order.
• Precise measurements : can’t specify the exact degree of satisfaction or agreement, only relative positions.

This mix of qualitative and quantitative data will give you a well-rounded view of participant attitudes and preferences.

The things you can do with qualitative data are endless. But this article shouldn’t turn into a work of literature, so we’ll highlight six ways to collect qualitative data and give you examples of how to use these qualitative research methods to get actionable results.

How to get qual insights with Attest

You can get to the heart of what your target customers think, with reliable qualitative insights from Attest Video Responses

1. Highlighting brand loyalty drivers with open-ended surveys and questionnaires

Open-ended surveys and questionnaires are great at finding out what makes customers choose and stick with a brand. Here’s why this qualitative data analysis tool is so good for gathering qualitative data on things like brand loyalty and customer experience:

Straight from the source

Open-ended survey responses show the actual thoughts and feelings of your target audience in their own words, while still giving you structure in your data analysis.

Understanding ‘why’

Numbers can show us how many customers are loyal; open-ended survey responses explain why they are. You can also easily add thematic analysis to the mix by counting certain keywords or phrases.

Guiding decisions

The insights from these surveys can help a brand decide where to focus its efforts, from making sure their marketing highlights what customers love most to improving parts of their product.

Surveys are one of the most versatile and efficient qualitative data collection methods out there. We want to bring the power of qualitative data analysis to every business and make it easy to gather qualitative data from the people who matter most to your brand. Check out our survey templates to hit the ground running. And you’re not limited to textual data as your only data source — we also enable you to gather video responses to get additional context from non verbal cues and more.

2. Trend identification with observation notes

Observation notes are a powerful qualitative data analysis tool for spotting trends as they naturally unfold in real-world settings. Here’s why they’re particularly valuable insights and effective for identifying new trends:

Real behavior

Observing people directly shows us how they actually interact with products or services, not just how they say they do. This can highlight emerging trends in consumer behavior or preferences before people can even put into words what they are doing and why.

Immediate insights

By watching how people engage with different products, we can quickly spot patterns or changes in behavior. This immediate feedback is invaluable for catching trends as they start.

Context matters

Observations give you context. You can see not just what people do, but where and how they do it. This context can be key to understanding why a trend is taking off.

Unprompted reactions

Since people don’t know they’re being observed for these purposes, their actions are genuine. This leads to authentic insights about what’s really catching on.

3. Understanding consumer sentiments through semi-structured interviews

Semi-structured interviews for qualitative data analysis are an effective method for data analysts to get a deep understanding of consumer sentiments. It provides a structured yet flexible approach to gather in-depth insights. Here’s why they’re particularly useful for this type of research question:

Personal connection

These interviews create a space for a real conversation, allowing consumers to share their feelings, experiences, and opinions about a brand or product in a more personal setting.

Flexibility

The format lets the interviewer explore interesting points that come up during the conversation, diving deeper into unexpected areas of discussion. This flexibility uncovers richer insights than strictly structured interviews.

Depth of understanding

By engaging in detailed discussions, brands can understand not just what consumers think but why they think that way and what stations their train of thought passes by.

Structure and surprise

Semi-structured interviews can be tailored to explore specific areas of interest while still allowing for new insights to emerge.

4. Using focus groups for informing market entry strategies

Using a focus group to inform market entry strategies provides a dynamic way to discover your potential customers’ needs, preferences, and perceptions before launching a product or entering a new market. Here’s how focus groups can be particularly effective for this kind of research goal:

Real conversations

Focus groups allow for real-time, interactive discussions, giving you a front-row seat to hear what your potential customers think and feel about your product or service idea.

Diverse Perspectives

By bringing together people from various backgrounds, a focus group can offer a wide range of views and insights, highlighting different consumer needs and contextual information that you might miss out on in a survey.

Spotting opportunities and challenges

The dynamic nature of focus groups can help uncover unique market opportunities or potential challenges that might not be evident through other research methods, like cultural nuances.

Testing ideas

A focus group is a great way to test and compare reactions to different market entry strategies, from pricing models to distribution channels, providing clear direction on what approach might work best.

5. Case studies to gain a nuanced understanding of consumers on a broad level

Case studies in qualitative research zoom in on specific stories from customers or groups using a product or service, great for gaining a nuanced understanding of consumers at a broad level. Here’s why case studies are a particularly effective qualitative data analysis tool for this type of research goal:

In-depth analysis

Case studies can provide a 360-degree look at the consumer experience, from initial awareness to post-purchase feelings.

This depth of insight reveals not just what consumers do, but why they do it, uncovering motivations, influences, and decision-making processes.

Longitudinal insight

Case studies can track changes in consumer behavior or satisfaction over time, offering a dynamic view of how perceptions evolve.

This longitudinal perspective is crucial for giving context to the lifecycle of consumer engagement with a brand.

Storytelling power

The narrative nature of case studies — when done right — makes them powerful tools for communicating complex consumer insights in an accessible and engaging way, which can be especially useful for internal strategy discussions or external marketing communications.

6. Driving product development with diary studies

Diary studies are a unique qualitative research method that involves participants recording their thoughts, experiences, or behaviors over a period of time, related to using a product or service. This qualitative data analysis method is especially valuable for driving product development for several reasons:

Real-time insights

Diary studies capture real-time user experiences and feedback as they interact with a product in their daily lives.

This ongoing documentation provides a raw, unfiltered view of how a product fits into the user’s routine, highlighting usability issues or unmet needs that might not be captured in a one-time survey or interview.

Realistic user journey mapping

By analyzing diary entries, you can map out the entire user journey, identifying critical touch points where users feel delighted, frustrated, or indifferent.

This then enables you to implement targeted improvements and innovations at the moments that matter most.

Identifying patterns

Over the course of a diary study, patterns in behavior, preferences, and challenges can emerge, which is great for thematic analysis.

It can guide product developers to prioritize features or fixes that will have the most significant impact on user satisfaction, which is especially great if they don’t know what areas to focus on first.

Qualitative research brings your consumers’ voices directly to your strategy table. The examples we’ve explored show how qualitative data analysis methods like surveys, interviews, and case studies illuminate the ‘why’ behind consumer choices, guiding more informed decisions. Using these insights means crafting products and messages that resonate deeply, ensuring your brand not only meets but exceeds consumer expectations.

Customer Research Manager 

Related articles

Making it personal – using tech to build connections with consumers | diageo, panel discussion – future-proofing your brand with consumer insights, attest product release: multi-market | attest’s alyssa stringer, subscribe to our newsletter.

Fill in your email and we’ll drop fresh insights and events info into your inbox each week.

* I agree to receive communications from Attest. Privacy Policy .

You're now subscribed to our mailing list to receive exciting news, reports, and other updates!

A systematic literature review of empirical research on ChatGPT in education

• Open access
• Published: 26 May 2024
• Volume 3 , article number  60 , ( 2024 )

Cite this article

You have full access to this open access article

• Yazid Albadarin   ORCID: orcid.org/0009-0005-8068-8902 1 ,
• Mohammed Saqr 1 ,
• Nicolas Pope 1 &
• Markku Tukiainen 1  

555 Accesses

Explore all metrics

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli’s (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell’s (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Similar content being viewed by others

Empowering learners with ChatGPT: insights from a systematic literature exploration

Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners

Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT

Avoid common mistakes on your manuscript.

1 Introduction

Educational technology, a rapidly evolving field, plays a crucial role in reshaping the landscape of teaching and learning [ 82 ]. One of the most transformative technological innovations of our era that has influenced the field of education is Artificial Intelligence (AI) [ 50 ]. Over the last four decades, AI in education (AIEd) has gained remarkable attention for its potential to make significant advancements in learning, instructional methods, and administrative tasks within educational settings [ 11 ]. In particular, a large language model (LLM), a type of AI algorithm that applies artificial neural networks (ANNs) and uses massively large data sets to understand, summarize, generate, and predict new content that is almost difficult to differentiate from human creations [ 79 ], has opened up novel possibilities for enhancing various aspects of education, from content creation to personalized instruction [ 35 ]. Chatbots that leverage the capabilities of LLMs to understand and generate human-like responses have also presented the capacity to enhance student learning and educational outcomes by engaging students, offering timely support, and fostering interactive learning experiences [ 46 ].

The ongoing and remarkable technological advancements in chatbots have made their use more convenient, increasingly natural and effortless, and have expanded their potential for deployment across various domains [ 70 ]. One prominent example of chatbot applications is the Chat Generative Pre-Trained Transformer, known as ChatGPT, which was introduced by OpenAI, a leading AI research lab, on November 30th, 2022. ChatGPT employs a variety of deep learning techniques to generate human-like text, with a particular focus on recurrent neural networks (RNNs). Long short-term memory (LSTM) allows it to grasp the context of the text being processed and retain information from previous inputs. Also, the transformer architecture, a neural network architecture based on the self-attention mechanism, allows it to analyze specific parts of the input, thereby enabling it to produce more natural-sounding and coherent output. Additionally, the unsupervised generative pre-training and the fine-tuning methods allow ChatGPT to generate more relevant and accurate text for specific tasks [ 31 , 62 ]. Furthermore, reinforcement learning from human feedback (RLHF), a machine learning approach that combines reinforcement learning techniques with human-provided feedback, has helped improve ChatGPT’s model by accelerating the learning process and making it significantly more efficient.

This cutting-edge natural language processing (NLP) tool is widely recognized as one of today's most advanced LLMs-based chatbots [ 70 ], allowing users to ask questions and receive detailed, coherent, systematic, personalized, convincing, and informative human-like responses [ 55 ], even within complex and ambiguous contexts [ 63 , 77 ]. ChatGPT is considered the fastest-growing technology in history: in just three months following its public launch, it amassed an estimated 120 million monthly active users [ 16 ] with an estimated 13 million daily queries [ 49 ], surpassing all other applications [ 64 ]. This remarkable growth can be attributed to the unique features and user-friendly interface that ChatGPT offers. Its intuitive design allows users to interact seamlessly with the technology, making it accessible to a diverse range of individuals, regardless of their technical expertise [ 78 ]. Additionally, its exceptional performance results from a combination of advanced algorithms, continuous enhancements, and extensive training on a diverse dataset that includes various text sources such as books, articles, websites, and online forums [ 63 ], have contributed to a more engaging and satisfying user experience [ 62 ]. These factors collectively explain its remarkable global growth and set it apart from predecessors like Bard, Bing Chat, ERNIE, and others.

In this context, several studies have explored the technological advancements of chatbots. One noteworthy recent research effort, conducted by Schöbel et al. [ 70 ], stands out for its comprehensive analysis of more than 5,000 studies on communication agents. This study offered a comprehensive overview of the historical progression and future prospects of communication agents, including ChatGPT. Moreover, other studies have focused on making comparisons, particularly between ChatGPT and alternative chatbots like Bard, Bing Chat, ERNIE, LaMDA, BlenderBot, and various others. For example, O’Leary [ 53 ] compared two chatbots, LaMDA and BlenderBot, with ChatGPT and revealed that ChatGPT outperformed both. This superiority arises from ChatGPT’s capacity to handle a wider range of questions and generate slightly varied perspectives within specific contexts. Similarly, ChatGPT exhibited an impressive ability to formulate interpretable responses that were easily understood when compared with Google's feature snippet [ 34 ]. Additionally, ChatGPT was compared to other LLMs-based chatbots, including Bard and BERT, as well as ERNIE. The findings indicated that ChatGPT exhibited strong performance in the given tasks, often outperforming the other models [ 59 ].

Furthermore, in the education context, a comprehensive study systematically compared a range of the most promising chatbots, including Bard, Bing Chat, ChatGPT, and Ernie across a multidisciplinary test that required higher-order thinking. The study revealed that ChatGPT achieved the highest score, surpassing Bing Chat and Bard [ 64 ]. Similarly, a comparative analysis was conducted to compare ChatGPT with Bard in answering a set of 30 mathematical questions and logic problems, grouped into two question sets. Set (A) is unavailable online, while Set (B) is available online. The results revealed ChatGPT's superiority in Set (A) over Bard. Nevertheless, Bard's advantage emerged in Set (B) due to its capacity to access the internet directly and retrieve answers, a capability that ChatGPT does not possess [ 57 ]. However, through these varied assessments, ChatGPT consistently highlights its exceptional prowess compared to various alternatives in the ever-evolving chatbot technology.

The widespread adoption of chatbots, especially ChatGPT, by millions of students and educators, has sparked extensive discussions regarding its incorporation into the education sector [ 64 ]. Accordingly, many scholars have contributed to the discourse, expressing both optimism and pessimism regarding the incorporation of ChatGPT into education. For example, ChatGPT has been highlighted for its capabilities in enriching the learning and teaching experience through its ability to support different learning approaches, including adaptive learning, personalized learning, and self-directed learning [ 58 , 60 , 91 ]), deliver summative and formative feedback to students and provide real-time responses to questions, increase the accessibility of information [ 22 , 40 , 43 ], foster students’ performance, engagement and motivation [ 14 , 44 , 58 ], and enhance teaching practices [ 17 , 18 , 64 , 74 ].

On the other hand, concerns have been also raised regarding its potential negative effects on learning and teaching. These include the dissemination of false information and references [ 12 , 23 , 61 , 85 ], biased reinforcement [ 47 , 50 ], compromised academic integrity [ 18 , 40 , 66 , 74 ], and the potential decline in students' skills [ 43 , 61 , 64 , 74 ]. As a result, ChatGPT has been banned in multiple countries, including Russia, China, Venezuela, Belarus, and Iran, as well as in various educational institutions in India, Italy, Western Australia, France, and the United States [ 52 , 90 ].

Clearly, the advent of chatbots, especially ChatGPT, has provoked significant controversy due to their potential impact on learning and teaching. This indicates the necessity for further exploration to gain a deeper understanding of this technology and carefully evaluate its potential benefits, limitations, challenges, and threats to education [ 79 ]. Therefore, conducting a systematic literature review will provide valuable insights into the potential prospects and obstacles linked to its incorporation into education. This systematic literature review will primarily focus on ChatGPT, driven by the aforementioned key factors outlined above.

However, the existing literature lacks a systematic literature review of empirical studies. Thus, this systematic literature review aims to address this gap by synthesizing the existing empirical studies conducted on chatbots, particularly ChatGPT, in the field of education, highlighting how ChatGPT has been utilized in educational settings, and identifying any existing gaps. This review may be particularly useful for researchers in the field and educators who are contemplating the integration of ChatGPT or any chatbot into education. The following research questions will guide this study:

What are students' and teachers' initial attempts at utilizing ChatGPT in education?

What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?

2 Methodology

To conduct this study, the authors followed the essential steps of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) and Okoli’s [ 54 ] steps for conducting a systematic review. These included identifying the study’s purpose, drafting a protocol, applying a practical screening process, searching the literature, extracting relevant data, evaluating the quality of the included studies, synthesizing the studies, and ultimately writing the review. The subsequent section provides an extensive explanation of how these steps were carried out in this study.

2.1 Identify the purpose

Given the widespread adoption of ChatGPT by students and teachers for various educational purposes, often without a thorough understanding of responsible and effective use or a clear recognition of its potential impact on learning and teaching, the authors recognized the need for further exploration of ChatGPT's impact on education in this early stage. Therefore, they have chosen to conduct a systematic literature review of existing empirical studies that incorporate ChatGPT into educational settings. Despite the limited number of empirical studies due to the novelty of the topic, their goal is to gain a deeper understanding of this technology and proactively evaluate its potential benefits, limitations, challenges, and threats to education. This effort could help to understand initial reactions and attempts at incorporating ChatGPT into education and bring out insights and considerations that can inform the future development of education.

2.2 Draft the protocol

The next step is formulating the protocol. This protocol serves to outline the study process in a rigorous and transparent manner, mitigating researcher bias in study selection and data extraction [ 88 ]. The protocol will include the following steps: generating the research question, predefining a literature search strategy, identifying search locations, establishing selection criteria, assessing the studies, developing a data extraction strategy, and creating a timeline.

2.3 Apply practical screen

The screening step aims to accurately filter the articles resulting from the searching step and select the empirical studies that have incorporated ChatGPT into educational contexts, which will guide us in answering the research questions and achieving the objectives of this study. To ensure the rigorous execution of this step, our inclusion and exclusion criteria were determined based on the authors' experience and informed by previous successful systematic reviews [ 21 ]. Table 1 summarizes the inclusion and exclusion criteria for study selection.

2.4 Literature search

We conducted a thorough literature search to identify articles that explored, examined, and addressed the use of ChatGPT in Educational contexts. We utilized two research databases: Dimensions.ai, which provides access to a large number of research publications, and lens.org, which offers access to over 300 million articles, patents, and other research outputs from diverse sources. Additionally, we included three databases, Scopus, Web of Knowledge, and ERIC, which contain relevant research on the topic that addresses our research questions. To browse and identify relevant articles, we used the following search formula: ("ChatGPT" AND "Education"), which included the Boolean operator "AND" to get more specific results. The subject area in the Scopus and ERIC databases were narrowed to "ChatGPT" and "Education" keywords, and in the WoS database was limited to the "Education" category. The search was conducted between the 3rd and 10th of April 2023, which resulted in 276 articles from all selected databases (111 articles from Dimensions.ai, 65 from Scopus, 28 from Web of Science, 14 from ERIC, and 58 from Lens.org). These articles were imported into the Rayyan web-based system for analysis. The duplicates were identified automatically by the system. Subsequently, the first author manually reviewed the duplicated articles ensured that they had the same content, and then removed them, leaving us with 135 unique articles. Afterward, the titles, abstracts, and keywords of the first 40 manuscripts were scanned and reviewed by the first author and were discussed with the second and third authors to resolve any disagreements. Subsequently, the first author proceeded with the filtering process for all articles and carefully applied the inclusion and exclusion criteria as presented in Table  1 . Articles that met any one of the exclusion criteria were eliminated, resulting in 26 articles. Afterward, the authors met to carefully scan and discuss them. The authors agreed to eliminate any empirical studies solely focused on checking ChatGPT capabilities, as these studies do not guide us in addressing the research questions and achieving the study's objectives. This resulted in 14 articles eligible for analysis.

2.5 Quality appraisal

The examination and evaluation of the quality of the extracted articles is a vital step [ 9 ]. Therefore, the extracted articles were carefully evaluated for quality using Fink’s [ 24 ] standards, which emphasize the necessity for detailed descriptions of methodology, results, conclusions, strengths, and limitations. The process began with a thorough assessment of each study's design, data collection, and analysis methods to ensure their appropriateness and comprehensive execution. The clarity, consistency, and logical progression from data to results and conclusions were also critically examined. Potential biases and recognized limitations within the studies were also scrutinized. Ultimately, two articles were excluded for failing to meet Fink’s criteria, particularly in providing sufficient detail on methodology, results, conclusions, strengths, or limitations. The review process is illustrated in Fig.  1 .

The study selection process

2.6 Data extraction

The next step is data extraction, the process of capturing the key information and categories from the included studies. To improve efficiency, reduce variation among authors, and minimize errors in data analysis, the coding categories were constructed using Creswell's [ 15 ] coding techniques for data extraction and interpretation. The coding process involves three sequential steps. The initial stage encompasses open coding , where the researcher examines the data, generates codes to describe and categorize it, and gains a deeper understanding without preconceived ideas. Following open coding is axial coding , where the interrelationships between codes from open coding are analyzed to establish more comprehensive categories or themes. The process concludes with selective coding , refining and integrating categories or themes to identify core concepts emerging from the data. The first coder performed the coding process, then engaged in discussions with the second and third authors to finalize the coding categories for the first five articles. The first coder then proceeded to code all studies and engaged again in discussions with the other authors to ensure the finalization of the coding process. After a comprehensive analysis and capturing of the key information from the included studies, the data extraction and interpretation process yielded several themes. These themes have been categorized and are presented in Table  2 . It is important to note that open coding results were removed from Table  2 for aesthetic reasons, as it included many generic aspects, such as words, short phrases, or sentences mentioned in the studies.

2.7 Synthesize studies

In this stage, we will gather, discuss, and analyze the key findings that emerged from the selected studies. The synthesis stage is considered a transition from an author-centric to a concept-centric focus, enabling us to map all the provided information to achieve the most effective evaluation of the data [ 87 ]. Initially, the authors extracted data that included general information about the selected studies, including the author(s)' names, study titles, years of publication, educational levels, research methodologies, sample sizes, participants, main aims or objectives, raw data sources, and analysis methods. Following that, all key information and significant results from the selected studies were compiled using Creswell’s [ 15 ] coding techniques for data extraction and interpretation to identify core concepts and themes emerging from the data, focusing on those that directly contributed to our research questions and objectives, such as the initial utilization of ChatGPT in learning and teaching, learners' and educators' familiarity with ChatGPT, and the main findings of each study. Finally, the data related to each selected study were extracted into an Excel spreadsheet for data processing. The Excel spreadsheet was reviewed by the authors, including a series of discussions to ensure the finalization of this process and prepare it for further analysis. Afterward, the final result being analyzed and presented in various types of charts and graphs. Table 4 presents the extracted data from the selected studies, with each study labeled with a capital 'S' followed by a number.

This section consists of two main parts. The first part provides a descriptive analysis of the data compiled from the reviewed studies. The second part presents the answers to the research questions and the main findings of these studies.

3.1 Part 1: descriptive analysis

This section will provide a descriptive analysis of the reviewed studies, including educational levels and fields, participants distribution, country contribution, research methodologies, study sample size, study population, publication year, list of journals, familiarity with ChatGPT, source of data, and the main aims and objectives of the studies. Table 4 presents a comprehensive overview of the extracted data from the selected studies.

3.1.1 The number of the reviewed studies and publication years

The total number of the reviewed studies was 14. All studies were empirical studies and published in different journals focusing on Education and Technology. One study was published in 2022 [S1], while the remaining were published in 2023 [S2]-[S14]. Table 3 illustrates the year of publication, the names of the journals, and the number of reviewed studies published in each journal for the studies reviewed.

3.1.2 Educational levels and fields

The majority of the reviewed studies, 11 studies, were conducted in higher education institutions [S1]-[S10] and [S13]. Two studies did not specify the educational level of the population [S12] and [S14], while one study focused on elementary education [S11]. However, the reviewed studies covered various fields of education. Three studies focused on Arts and Humanities Education [S8], [S11], and [S14], specifically English Education. Two studies focused on Engineering Education, with one in Computer Engineering [S2] and the other in Construction Education [S3]. Two studies focused on Mathematics Education [S5] and [S12]. One study focused on Social Science Education [S13]. One study focused on Early Education [S4]. One study focused on Journalism Education [S9]. Finally, three studies did not specify the field of education [S1], [S6], and [S7]. Figure  2 represents the educational levels in the reviewed studies, while Fig.  3 represents the context of the reviewed studies.

Educational levels in the reviewed studies

Context of the reviewed studies

3.1.3 Participants distribution and countries contribution

The reviewed studies have been conducted across different geographic regions, providing a diverse representation of the studies. The majority of the studies, 10 in total, [S1]-[S3], [S5]-[S9], [S11], and [S14], primarily focused on participants from single countries such as Pakistan, the United Arab Emirates, China, Indonesia, Poland, Saudi Arabia, South Korea, Spain, Tajikistan, and the United States. In contrast, four studies, [S4], [S10], [S12], and [S13], involved participants from multiple countries, including China and the United States [S4], China, the United Kingdom, and the United States [S10], the United Arab Emirates, Oman, Saudi Arabia, and Jordan [S12], Turkey, Sweden, Canada, and Australia [ 13 ]. Figures  4 and 5 illustrate the distribution of participants, whether from single or multiple countries, and the contribution of each country in the reviewed studies, respectively.

The reviewed studies conducted in single or multiple countries

The Contribution of each country in the studies

3.1.4 Study population and sample size

Four study populations were included: university students, university teachers, university teachers and students, and elementary school teachers. Six studies involved university students [S2], [S3], [S5] and [S6]-[S8]. Three studies focused on university teachers [S1], [S4], and [S6], while one study specifically targeted elementary school teachers [S11]. Additionally, four studies included both university teachers and students [S10] and [ 12 , 13 , 14 ], and among them, study [S13] specifically included postgraduate students. In terms of the sample size of the reviewed studies, nine studies included a small sample size of less than 50 participants [S1], [S3], [S6], [S8], and [S10]-[S13]. Three studies had 50–100 participants [S2], [S9], and [S14]. Only one study had more than 100 participants [S7]. It is worth mentioning that study [S4] adopted a mixed methods approach, including 10 participants for qualitative analysis and 110 participants for quantitative analysis.

3.1.5 Participants’ familiarity with using ChatGPT

The reviewed studies recruited a diverse range of participants with varying levels of familiarity with ChatGPT. Five studies [S2], [S4], [S6], [S8], and [S12] involved participants already familiar with ChatGPT, while eight studies [S1], [S3], [S5], [S7], [S9], [S10], [S13] and [S14] included individuals with differing levels of familiarity. Notably, one study [S11] had participants who were entirely unfamiliar with ChatGPT. It is important to note that four studies [S3], [S5], [S9], and [S11] provided training or guidance to their participants before conducting their studies, while ten studies [S1], [S2], [S4], [S6]-[S8], [S10], and [S12]-[S14] did not provide training due to the participants' existing familiarity with ChatGPT.

3.1.6 Research methodology approaches and source(S) of data

The reviewed studies adopted various research methodology approaches. Seven studies adopted qualitative research methodology [S1], [S4], [S6], [S8], [S10], [S11], and [S12], while three studies adopted quantitative research methodology [S3], [S7], and [S14], and four studies employed mixed-methods, which involved a combination of both the strengths of qualitative and quantitative methods [S2], [S5], [S9], and [S13].

In terms of the source(s) of data, the reviewed studies obtained their data from various sources, such as interviews, questionnaires, and pre-and post-tests. Six studies relied on interviews as their primary source of data collection [S1], [S4], [S6], [S10], [S11], and [S12], four studies relied on questionnaires [S2], [S7], [S13], and [S14], two studies combined the use of pre-and post-tests and questionnaires for data collection [S3] and [S9], while two studies combined the use of questionnaires and interviews to obtain the data [S5] and [S8]. It is important to note that six of the reviewed studies were quasi-experimental [S3], [S5], [S8], [S9], [S12], and [S14], while the remaining ones were experimental studies [S1], [S2], [S4], [S6], [S7], [S10], [S11], and [S13]. Figures  6 and 7 illustrate the research methodologies and the source (s) of data used in the reviewed studies, respectively.

Research methodologies in the reviewed studies

Source of data in the reviewed studies

3.1.7 The aim and objectives of the studies

The reviewed studies encompassed a diverse set of aims, with several of them incorporating multiple primary objectives. Six studies [S3], [S6], [S7], [S8], [S11], and [S12] examined the integration of ChatGPT in educational contexts, and four studies [S4], [S5], [S13], and [S14] investigated the various implications of its use in education, while three studies [S2], [S9], and [S10] aimed to explore both its integration and implications in education. Additionally, seven studies explicitly explored attitudes and perceptions of students [S2] and [S3], educators [S1] and [S6], or both [S10], [S12], and [S13] regarding the utilization of ChatGPT in educational settings.

3.2 Part 2: research questions and main findings of the reviewed studies

This part will present the answers to the research questions and the main findings of the reviewed studies, classified into two main categories (learning and teaching) according to AI Education classification by [ 36 ]. Figure  8 summarizes the main findings of the reviewed studies in a visually informative diagram. Table 4 provides a detailed list of the key information extracted from the selected studies that led to generating these themes.

The main findings in the reviewed studies

4 Students' initial attempts at utilizing ChatGPT in learning and main findings from students' perspective

4.1 virtual intelligent assistant.

Nine studies demonstrated that ChatGPT has been utilized by students as an intelligent assistant to enhance and support their learning. Students employed it for various purposes, such as answering on-demand questions [S2]-[S5], [S8], [S10], and [S12], providing valuable information and learning resources [S2]-[S5], [S6], and [S8], as well as receiving immediate feedback [S2], [S4], [S9], [S10], and [S12]. In this regard, students generally were confident in the accuracy of ChatGPT's responses, considering them relevant, reliable, and detailed [S3], [S4], [S5], and [S8]. However, some students indicated the need for improvement, as they found that answers are not always accurate [S2], and that misleading information may have been provided or that it may not always align with their expectations [S6] and [S10]. It was also observed by the students that the accuracy of ChatGPT is dependent on several factors, including the quality and specificity of the user's input, the complexity of the question or topic, and the scope and relevance of its training data [S12]. Many students felt that ChatGPT's answers were not always accurate and most of them believed that it requires good background knowledge to work with.

4.2 Writing and language proficiency assistant

Six of the reviewed studies highlighted that ChatGPT has been utilized by students as a valuable assistant tool to improve their academic writing skills and language proficiency. Among these studies, three mainly focused on English education, demonstrating that students showed sufficient mastery in using ChatGPT for generating ideas, summarizing, paraphrasing texts, and completing writing essays [S8], [S11], and [S14]. Furthermore, ChatGPT helped them in writing by making students active investigators rather than passive knowledge recipients and facilitated the development of their writing skills [S11] and [S14]. Similarly, ChatGPT allowed students to generate unique ideas and perspectives, leading to deeper analysis and reflection on their journalism writing [S9]. In terms of language proficiency, ChatGPT allowed participants to translate content into their home languages, making it more accessible and relevant to their context [S4]. It also enabled them to request changes in linguistic tones or flavors [S8]. Moreover, participants used it to check grammar or as a dictionary [S11].

4.3 Valuable resource for learning approaches

Five studies demonstrated that students used ChatGPT as a valuable complementary resource for self-directed learning. It provided learning resources and guidance on diverse educational topics and created a supportive home learning environment [S2] and [S4]. Moreover, it offered step-by-step guidance to grasp concepts at their own pace and enhance their understanding [S5], streamlined task and project completion carried out independently [S7], provided comprehensive and easy-to-understand explanations on various subjects [S10], and assisted in studying geometry operations, thereby empowering them to explore geometry operations at their own pace [S12]. Three studies showed that students used ChatGPT as a valuable learning resource for personalized learning. It delivered age-appropriate conversations and tailored teaching based on a child's interests [S4], acted as a personalized learning assistant, adapted to their needs and pace, which assisted them in understanding mathematical concepts [S12], and enabled personalized learning experiences in social sciences by adapting to students' needs and learning styles [S13]. On the other hand, it is important to note that, according to one study [S5], students suggested that using ChatGPT may negatively affect collaborative learning competencies between students.

4.4 Enhancing students' competencies

Six of the reviewed studies have shown that ChatGPT is a valuable tool for improving a wide range of skills among students. Two studies have provided evidence that ChatGPT led to improvements in students' critical thinking, reasoning skills, and hazard recognition competencies through engaging them in interactive conversations or activities and providing responses related to their disciplines in journalism [S5] and construction education [S9]. Furthermore, two studies focused on mathematical education have shown the positive impact of ChatGPT on students' problem-solving abilities in unraveling problem-solving questions [S12] and enhancing the students' understanding of the problem-solving process [S5]. Lastly, one study indicated that ChatGPT effectively contributed to the enhancement of conversational social skills [S4].

4.5 Supporting students' academic success

Seven of the reviewed studies highlighted that students found ChatGPT to be beneficial for learning as it enhanced learning efficiency and improved the learning experience. It has been observed to improve students' efficiency in computer engineering studies by providing well-structured responses and good explanations [S2]. Additionally, students found it extremely useful for hazard reporting [S3], and it also enhanced their efficiency in solving mathematics problems and capabilities [S5] and [S12]. Furthermore, by finding information, generating ideas, translating texts, and providing alternative questions, ChatGPT aided students in deepening their understanding of various subjects [S6]. It contributed to an increase in students' overall productivity [S7] and improved efficiency in composing written tasks [S8]. Regarding learning experiences, ChatGPT was instrumental in assisting students in identifying hazards that they might have otherwise overlooked [S3]. It also improved students' learning experiences in solving mathematics problems and developing abilities [S5] and [S12]. Moreover, it increased students' successful completion of important tasks in their studies [S7], particularly those involving average difficulty writing tasks [S8]. Additionally, ChatGPT increased the chances of educational success by providing students with baseline knowledge on various topics [S10].

5 Teachers' initial attempts at utilizing ChatGPT in teaching and main findings from teachers' perspective

5.1 valuable resource for teaching.

The reviewed studies showed that teachers have employed ChatGPT to recommend, modify, and generate diverse, creative, organized, and engaging educational contents, teaching materials, and testing resources more rapidly [S4], [S6], [S10] and [S11]. Additionally, teachers experienced increased productivity as ChatGPT facilitated quick and accurate responses to questions, fact-checking, and information searches [S1]. It also proved valuable in constructing new knowledge [S6] and providing timely answers to students' questions in classrooms [S11]. Moreover, ChatGPT enhanced teachers' efficiency by generating new ideas for activities and preplanning activities for their students [S4] and [S6], including interactive language game partners [S11].

5.2 Improving productivity and efficiency

The reviewed studies showed that participants' productivity and work efficiency have been significantly enhanced by using ChatGPT as it enabled them to allocate more time to other tasks and reduce their overall workloads [S6], [S10], [S11], [S13], and [S14]. However, three studies [S1], [S4], and [S11], indicated a negative perception and attitude among teachers toward using ChatGPT. This negativity stemmed from a lack of necessary skills to use it effectively [S1], a limited familiarity with it [S4], and occasional inaccuracies in the content provided by it [S10].

5.3 Catalyzing new teaching methodologies

Five of the reviewed studies highlighted that educators found the necessity of redefining their teaching profession with the assistance of ChatGPT [S11], developing new effective learning strategies [S4], and adapting teaching strategies and methodologies to ensure the development of essential skills for future engineers [S5]. They also emphasized the importance of adopting new educational philosophies and approaches that can evolve with the introduction of ChatGPT into the classroom [S12]. Furthermore, updating curricula to focus on improving human-specific features, such as emotional intelligence, creativity, and philosophical perspectives [S13], was found to be essential.

5.4 Effective utilization of CHATGPT in teaching

According to the reviewed studies, effective utilization of ChatGPT in education requires providing teachers with well-structured training, support, and adequate background on how to use ChatGPT responsibly [S1], [S3], [S11], and [S12]. Establishing clear rules and regulations regarding its usage is essential to ensure it positively impacts the teaching and learning processes, including students' skills [S1], [S4], [S5], [S8], [S9], and [S11]-[S14]. Moreover, conducting further research and engaging in discussions with policymakers and stakeholders is indeed crucial for the successful integration of ChatGPT in education and to maximize the benefits for both educators and students [S1], [S6]-[S10], and [S12]-[S14].

6 Discussion

The purpose of this review is to conduct a systematic review of empirical studies that have explored the utilization of ChatGPT, one of today’s most advanced LLM-based chatbots, in education. The findings of the reviewed studies showed several ways of ChatGPT utilization in different learning and teaching practices as well as it provided insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of the reviewed studies came from diverse fields of education, which helped us avoid a biased review that is limited to a specific field. Similarly, the reviewed studies have been conducted across different geographic regions. This kind of variety in geographic representation enriched the findings of this review.

In response to RQ1 , "What are students' and teachers' initial attempts at utilizing ChatGPT in education?", the findings from this review provide comprehensive insights. Chatbots, including ChatGPT, play a crucial role in supporting student learning, enhancing their learning experiences, and facilitating diverse learning approaches [ 42 , 43 ]. This review found that this tool, ChatGPT, has been instrumental in enhancing students' learning experiences by serving as a virtual intelligent assistant, providing immediate feedback, on-demand answers, and engaging in educational conversations. Additionally, students have benefited from ChatGPT’s ability to generate ideas, compose essays, and perform tasks like summarizing, translating, paraphrasing texts, or checking grammar, thereby enhancing their writing and language competencies. Furthermore, students have turned to ChatGPT for assistance in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks, which fosters a supportive home learning environment, allowing them to take responsibility for their own learning and cultivate the skills and approaches essential for supportive home learning environment [ 26 , 27 , 28 ]. This finding aligns with the study of Saqr et al. [ 68 , 69 ] who highlighted that, when students actively engage in their own learning process, it yields additional advantages, such as heightened motivation, enhanced achievement, and the cultivation of enthusiasm, turning them into advocates for their own learning.

Moreover, students have utilized ChatGPT for tailored teaching and step-by-step guidance on diverse educational topics, streamlining task and project completion, and generating and recommending educational content. This personalization enhances the learning environment, leading to increased academic success. This finding aligns with other recent studies [ 26 , 27 , 28 , 60 , 66 ] which revealed that ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities. Ultimately, fostering students' performance, engagement, and motivation, leading to increase students' academic success [ 14 , 44 , 58 ]. This ultimate outcome is in line with the findings of Saqr et al. [ 68 , 69 ], which emphasized that learning strategies are important catalysts of students' learning, as students who utilize effective learning strategies are more likely to have better academic achievement.

Teachers, too, have capitalized on ChatGPT's capabilities to enhance productivity and efficiency, using it for creating lesson plans, generating quizzes, providing additional resources, generating and preplanning new ideas for activities, and aiding in answering students’ questions. This adoption of technology introduces new opportunities to support teaching and learning practices, enhancing teacher productivity. This finding aligns with those of Day [ 17 ], De Castro [ 18 ], and Su and Yang [ 74 ] as well as with those of Valtonen et al. [ 82 ], who revealed that emerging technological advancements have opened up novel opportunities and means to support teaching and learning practices, and enhance teachers’ productivity.

In response to RQ2 , "What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?", the findings from this review provide profound insights and raise significant concerns. Starting with the insights, chatbots, including ChatGPT, have demonstrated the potential to reshape and revolutionize education, creating new, novel opportunities for enhancing the learning process and outcomes [ 83 ], facilitating different learning approaches, and offering a range of pedagogical benefits [ 19 , 43 , 72 ]. In this context, this review found that ChatGPT could open avenues for educators to adopt or develop new effective learning and teaching strategies that can evolve with the introduction of ChatGPT into the classroom. Nonetheless, there is an evident lack of research understanding regarding the potential impact of generative machine learning models within diverse educational settings [ 83 ]. This necessitates teachers to attain a high level of proficiency in incorporating chatbots, such as ChatGPT, into their classrooms to create inventive, well-structured, and captivating learning strategies. In the same vein, the review also found that teachers without the requisite skills to utilize ChatGPT realized that it did not contribute positively to their work and could potentially have adverse effects [ 37 ]. This concern could lead to inequity of access to the benefits of chatbots, including ChatGPT, as individuals who lack the necessary expertise may not be able to harness their full potential, resulting in disparities in educational outcomes and opportunities. Therefore, immediate action is needed to address these potential issues. A potential solution is offering training, support, and competency development for teachers to ensure that all of them can leverage chatbots, including ChatGPT, effectively and equitably in their educational practices [ 5 , 28 , 80 ], which could enhance accessibility and inclusivity, and potentially result in innovative outcomes [ 82 , 83 ].

Additionally, chatbots, including ChatGPT, have the potential to significantly impact students' thinking abilities, including retention, reasoning, analysis skills [ 19 , 45 ], and foster innovation and creativity capabilities [ 83 ]. This review found that ChatGPT could contribute to improving a wide range of skills among students. However, it found that frequent use of ChatGPT may result in a decrease in innovative capacities, collaborative skills and cognitive capacities, and students' motivation to attend classes, as well as could lead to reduced higher-order thinking skills among students [ 22 , 29 ]. Therefore, immediate action is needed to carefully examine the long-term impact of chatbots such as ChatGPT, on learning outcomes as well as to explore its incorporation into educational settings as a supportive tool without compromising students' cognitive development and critical thinking abilities. In the same vein, the review also found that it is challenging to draw a consistent conclusion regarding the potential of ChatGPT to aid self-directed learning approach. This finding aligns with the recent study of Baskara [ 8 ]. Therefore, further research is needed to explore the potential of ChatGPT for self-directed learning. One potential solution involves utilizing learning analytics as a novel approach to examine various aspects of students' learning and support them in their individual endeavors [ 32 ]. This approach can bridge this gap by facilitating an in-depth analysis of how learners engage with ChatGPT, identifying trends in self-directed learning behavior, and assessing its influence on their outcomes.

Turning to the significant concerns, on the other hand, a fundamental challenge with LLM-based chatbots, including ChatGPT, is the accuracy and quality of the provided information and responses, as they provide false information as truth—a phenomenon often referred to as "hallucination" [ 3 , 49 ]. In this context, this review found that the provided information was not entirely satisfactory. Consequently, the utilization of chatbots presents potential concerns, such as generating and providing inaccurate or misleading information, especially for students who utilize it to support their learning. This finding aligns with other findings [ 6 , 30 , 35 , 40 ] which revealed that incorporating chatbots such as ChatGPT, into education presents challenges related to its accuracy and reliability due to its training on a large corpus of data, which may contain inaccuracies and the way users formulate or ask ChatGPT. Therefore, immediate action is needed to address these potential issues. One possible solution is to equip students with the necessary skills and competencies, which include a background understanding of how to use it effectively and the ability to assess and evaluate the information it generates, as the accuracy and the quality of the provided information depend on the input, its complexity, the topic, and the relevance of its training data [ 28 , 49 , 86 ]. However, it's also essential to examine how learners can be educated about how these models operate, the data used in their training, and how to recognize their limitations, challenges, and issues [ 79 ].

Furthermore, chatbots present a substantial challenge concerning maintaining academic integrity [ 20 , 56 ] and copyright violations [ 83 ], which are significant concerns in education. The review found that the potential misuse of ChatGPT might foster cheating, facilitate plagiarism, and threaten academic integrity. This issue is also affirmed by the research conducted by Basic et al. [ 7 ], who presented evidence that students who utilized ChatGPT in their writing assignments had more plagiarism cases than those who did not. These findings align with the conclusions drawn by Cotton et al. [ 13 ], Hisan and Amri [ 33 ] and Sullivan et al. [ 75 ], who revealed that the integration of chatbots such as ChatGPT into education poses a significant challenge to the preservation of academic integrity. Moreover, chatbots, including ChatGPT, have increased the difficulty in identifying plagiarism [ 47 , 67 , 76 ]. The findings from previous studies [ 1 , 84 ] indicate that AI-generated text often went undetected by plagiarism software, such as Turnitin. However, Turnitin and other similar plagiarism detection tools, such as ZeroGPT, GPTZero, and Copyleaks, have since evolved, incorporating enhanced techniques to detect AI-generated text, despite the possibility of false positives, as noted in different studies that have found these tools still not yet fully ready to accurately and reliably identify AI-generated text [ 10 , 51 ], and new novel detection methods may need to be created and implemented for AI-generated text detection [ 4 ]. This potential issue could lead to another concern, which is the difficulty of accurately evaluating student performance when they utilize chatbots such as ChatGPT assistance in their assignments. Consequently, the most LLM-driven chatbots present a substantial challenge to traditional assessments [ 64 ]. The findings from previous studies indicate the importance of rethinking, improving, and redesigning innovative assessment methods in the era of chatbots [ 14 , 20 , 64 , 75 ]. These methods should prioritize the process of evaluating students' ability to apply knowledge to complex cases and demonstrate comprehension, rather than solely focusing on the final product for assessment. Therefore, immediate action is needed to address these potential issues. One possible solution would be the development of clear guidelines, regulatory policies, and pedagogical guidance. These measures would help regulate the proper and ethical utilization of chatbots, such as ChatGPT, and must be established before their introduction to students [ 35 , 38 , 39 , 41 , 89 ].

In summary, our review has delved into the utilization of ChatGPT, a prominent example of chatbots, in education, addressing the question of how ChatGPT has been utilized in education. However, there remain significant gaps, which necessitate further research to shed light on this area.

7 Conclusions

This systematic review has shed light on the varied initial attempts at incorporating ChatGPT into education by both learners and educators, while also offering insights and considerations that can facilitate its effective and responsible use in future educational contexts. From the analysis of 14 selected studies, the review revealed the dual-edged impact of ChatGPT in educational settings. On the positive side, ChatGPT significantly aided the learning process in various ways. Learners have used it as a virtual intelligent assistant, benefiting from its ability to provide immediate feedback, on-demand answers, and easy access to educational resources. Additionally, it was clear that learners have used it to enhance their writing and language skills, engaging in practices such as generating ideas, composing essays, and performing tasks like summarizing, translating, paraphrasing texts, or checking grammar. Importantly, other learners have utilized it in supporting and facilitating their directed and personalized learning on a broad range of educational topics, assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. Educators, on the other hand, found ChatGPT beneficial for enhancing productivity and efficiency. They used it for creating lesson plans, generating quizzes, providing additional resources, and answers learners' questions, which saved time and allowed for more dynamic and engaging teaching strategies and methodologies.

However, the review also pointed out negative impacts. The results revealed that overuse of ChatGPT could decrease innovative capacities and collaborative learning among learners. Specifically, relying too much on ChatGPT for quick answers can inhibit learners' critical thinking and problem-solving skills. Learners might not engage deeply with the material or consider multiple solutions to a problem. This tendency was particularly evident in group projects, where learners preferred consulting ChatGPT individually for solutions over brainstorming and collaborating with peers, which negatively affected their teamwork abilities. On a broader level, integrating ChatGPT into education has also raised several concerns, including the potential for providing inaccurate or misleading information, issues of inequity in access, challenges related to academic integrity, and the possibility of misusing the technology.

Accordingly, this review emphasizes the urgency of developing clear rules, policies, and regulations to ensure ChatGPT's effective and responsible use in educational settings, alongside other chatbots, by both learners and educators. This requires providing well-structured training to educate them on responsible usage and understanding its limitations, along with offering sufficient background information. Moreover, it highlights the importance of rethinking, improving, and redesigning innovative teaching and assessment methods in the era of ChatGPT. Furthermore, conducting further research and engaging in discussions with policymakers and stakeholders are essential steps to maximize the benefits for both educators and learners and ensure academic integrity.

It is important to acknowledge that this review has certain limitations. Firstly, the limited inclusion of reviewed studies can be attributed to several reasons, including the novelty of the technology, as new technologies often face initial skepticism and cautious adoption; the lack of clear guidelines or best practices for leveraging this technology for educational purposes; and institutional or governmental policies affecting the utilization of this technology in educational contexts. These factors, in turn, have affected the number of studies available for review. Secondly, the utilization of the original version of ChatGPT, based on GPT-3 or GPT-3.5, implies that new studies utilizing the updated version, GPT-4 may lead to different findings. Therefore, conducting follow-up systematic reviews is essential once more empirical studies on ChatGPT are published. Additionally, long-term studies are necessary to thoroughly examine and assess the impact of ChatGPT on various educational practices.

Despite these limitations, this systematic review has highlighted the transformative potential of ChatGPT in education, revealing its diverse utilization by learners and educators alike and summarized the benefits of incorporating it into education, as well as the forefront critical concerns and challenges that must be addressed to facilitate its effective and responsible use in future educational contexts. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Data availability

The data supporting our findings are available upon request.

Abbreviations

• Artificial intelligence

AI in education

Large language model

Artificial neural networks

Chat Generative Pre-Trained Transformer

Recurrent neural networks

Long short-term memory

Reinforcement learning from human feedback

Natural language processing

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

AlAfnan MA, Dishari S, Jovic M, Lomidze K. ChatGPT as an educational tool: opportunities, challenges, and recommendations for communication, business writing, and composition courses. J Artif Intell Technol. 2023. https://doi.org/10.37965/jait.2023.0184 .

Article   Google Scholar  

Ali JKM, Shamsan MAA, Hezam TA, Mohammed AAQ. Impact of ChatGPT on learning motivation. J Engl Stud Arabia Felix. 2023;2(1):41–9. https://doi.org/10.56540/jesaf.v2i1.51 .

Alkaissi H, McFarlane SI. Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus. 2023. https://doi.org/10.7759/cureus.35179 .

Anderson N, Belavý DL, Perle SM, Hendricks S, Hespanhol L, Verhagen E, Memon AR. AI did not write this manuscript, or did it? Can we trick the AI text detector into generated texts? The potential future of ChatGPT and AI in sports & exercise medicine manuscript generation. BMJ Open Sport Exerc Med. 2023;9(1): e001568. https://doi.org/10.1136/bmjsem-2023-001568 .

Ausat AMA, Massang B, Efendi M, Nofirman N, Riady Y. Can chat GPT replace the role of the teacher in the classroom: a fundamental analysis. J Educ. 2023;5(4):16100–6.

Google Scholar  

Baidoo-Anu D, Ansah L. Education in the Era of generative artificial intelligence (AI): understanding the potential benefits of ChatGPT in promoting teaching and learning. Soc Sci Res Netw. 2023. https://doi.org/10.2139/ssrn.4337484 .

Basic Z, Banovac A, Kruzic I, Jerkovic I. Better by you, better than me, chatgpt3 as writing assistance in students essays. 2023. arXiv preprint arXiv:2302.04536 .‏

Baskara FR. The promises and pitfalls of using chat GPT for self-determined learning in higher education: an argumentative review. Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai. 2023;2:95–101. https://doi.org/10.47435/sentikjar.v2i0.1825 .

Behera RK, Bala PK, Dhir A. The emerging role of cognitive computing in healthcare: a systematic literature review. Int J Med Inform. 2019;129:154–66. https://doi.org/10.1016/j.ijmedinf.2019.04.024 .

Chaka C. Detecting AI content in responses generated by ChatGPT, YouChat, and Chatsonic: the case of five AI content detection tools. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.2.12 .

Chiu TKF, Xia Q, Zhou X, Chai CS, Cheng M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Comput Educ Artif Intell. 2023;4:100118. https://doi.org/10.1016/j.caeai.2022.100118 .

Choi EPH, Lee JJ, Ho M, Kwok JYY, Lok KYW. Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Educ Today. 2023;125:105796. https://doi.org/10.1016/j.nedt.2023.105796 .

Cotton D, Cotton PA, Shipway JR. Chatting and cheating: ensuring academic integrity in the era of ChatGPT. Innov Educ Teach Int. 2023. https://doi.org/10.1080/14703297.2023.2190148 .

Crawford J, Cowling M, Allen K. Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). J Univ Teach Learn Pract. 2023. https://doi.org/10.53761/1.20.3.02 .

Creswell JW. Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook]. 4th ed. London: Pearson Education; 2015.

Curry D. ChatGPT Revenue and Usage Statistics (2023)—Business of Apps. 2023. https://www.businessofapps.com/data/chatgpt-statistics/

Day T. A preliminary investigation of fake peer-reviewed citations and references generated by ChatGPT. Prof Geogr. 2023. https://doi.org/10.1080/00330124.2023.2190373 .

De Castro CA. A Discussion about the Impact of ChatGPT in education: benefits and concerns. J Bus Theor Pract. 2023;11(2):p28. https://doi.org/10.22158/jbtp.v11n2p28 .

Deng X, Yu Z. A meta-analysis and systematic review of the effect of Chatbot technology use in sustainable education. Sustainability. 2023;15(4):2940. https://doi.org/10.3390/su15042940 .

Eke DO. ChatGPT and the rise of generative AI: threat to academic integrity? J Responsib Technol. 2023;13:100060. https://doi.org/10.1016/j.jrt.2023.100060 .

Elmoazen R, Saqr M, Tedre M, Hirsto L. A systematic literature review of empirical research on epistemic network analysis in education. IEEE Access. 2022;10:17330–48. https://doi.org/10.1109/access.2022.3149812 .

Farrokhnia M, Banihashem SK, Noroozi O, Wals AEJ. A SWOT analysis of ChatGPT: implications for educational practice and research. Innov Educ Teach Int. 2023. https://doi.org/10.1080/14703297.2023.2195846 .

Fergus S, Botha M, Ostovar M. Evaluating academic answers generated using ChatGPT. J Chem Educ. 2023;100(4):1672–5. https://doi.org/10.1021/acs.jchemed.3c00087 .

Fink A. Conducting research literature reviews: from the Internet to Paper. Incorporated: SAGE Publications; 2010.

Firaina R, Sulisworo D. Exploring the usage of ChatGPT in higher education: frequency and impact on productivity. Buletin Edukasi Indonesia (BEI). 2023;2(01):39–46. https://doi.org/10.56741/bei.v2i01.310 .

Firat, M. (2023). How chat GPT can transform autodidactic experiences and open education.  Department of Distance Education, Open Education Faculty, Anadolu Unive .‏ https://orcid.org/0000-0001-8707-5918

Firat M. What ChatGPT means for universities: perceptions of scholars and students. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.1.22 .

Fuchs K. Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse? Front Educ. 2023. https://doi.org/10.3389/feduc.2023.1166682 .

García-Peñalvo FJ. La percepción de la inteligencia artificial en contextos educativos tras el lanzamiento de ChatGPT: disrupción o pánico. Educ Knowl Soc. 2023;24: e31279. https://doi.org/10.14201/eks.31279 .

Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor A, Chartash D. How does ChatGPT perform on the United States medical Licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9: e45312. https://doi.org/10.2196/45312 .

Hashana AJ, Brundha P, Ayoobkhan MUA, Fazila S. Deep Learning in ChatGPT—A Survey. In   2023 7th international conference on trends in electronics and informatics (ICOEI) . 2023. (pp. 1001–1005). IEEE. https://doi.org/10.1109/icoei56765.2023.10125852

Hirsto L, Saqr M, López-Pernas S, Valtonen T. (2022). A systematic narrative review of learning analytics research in K-12 and schools.  Proceedings . https://ceur-ws.org/Vol-3383/FLAIEC22_paper_9536.pdf

Hisan UK, Amri MM. ChatGPT and medical education: a double-edged sword. J Pedag Educ Sci. 2023;2(01):71–89. https://doi.org/10.13140/RG.2.2.31280.23043/1 .

Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ. Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectr. 2023. https://doi.org/10.1093/jncics/pkad010 .

Househ M, AlSaad R, Alhuwail D, Ahmed A, Healy MG, Latifi S, Sheikh J. Large Language models in medical education: opportunities, challenges, and future directions. JMIR Med Educ. 2023;9: e48291. https://doi.org/10.2196/48291 .

Ilkka T. The impact of artificial intelligence on learning, teaching, and education. Minist de Educ. 2018. https://doi.org/10.2760/12297 .

Iqbal N, Ahmed H, Azhar KA. Exploring teachers’ attitudes towards using CHATGPT. Globa J Manag Adm Sci. 2022;3(4):97–111. https://doi.org/10.46568/gjmas.v3i4.163 .

Irfan M, Murray L, Ali S. Integration of Artificial intelligence in academia: a case study of critical teaching and learning in Higher education. Globa Soc Sci Rev. 2023;8(1):352–64. https://doi.org/10.31703/gssr.2023(viii-i).32 .

Jeon JH, Lee S. Large language models in education: a focus on the complementary relationship between human teachers and ChatGPT. Educ Inf Technol. 2023. https://doi.org/10.1007/s10639-023-11834-1 .

Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT—Reshaping medical education and clinical management. Pak J Med Sci. 2023. https://doi.org/10.12669/pjms.39.2.7653 .

King MR. A conversation on artificial intelligence, Chatbots, and plagiarism in higher education. Cell Mol Bioeng. 2023;16(1):1–2. https://doi.org/10.1007/s12195-022-00754-8 .

Kooli C. Chatbots in education and research: a critical examination of ethical implications and solutions. Sustainability. 2023;15(7):5614. https://doi.org/10.3390/su15075614 .

Kuhail MA, Alturki N, Alramlawi S, Alhejori K. Interacting with educational chatbots: a systematic review. Educ Inf Technol. 2022;28(1):973–1018. https://doi.org/10.1007/s10639-022-11177-3 .

Lee H. The rise of ChatGPT: exploring its potential in medical education. Anat Sci Educ. 2023. https://doi.org/10.1002/ase.2270 .

Li L, Subbareddy R, Raghavendra CG. AI intelligence Chatbot to improve students learning in the higher education platform. J Interconnect Netw. 2022. https://doi.org/10.1142/s0219265921430325 .

Limna P. A Review of Artificial Intelligence (AI) in Education during the Digital Era. 2022. https://ssrn.com/abstract=4160798

Lo CK. What is the impact of ChatGPT on education? A rapid review of the literature. Educ Sci. 2023;13(4):410. https://doi.org/10.3390/educsci13040410 .

Luo W, He H, Liu J, Berson IR, Berson MJ, Zhou Y, Li H. Aladdin’s genie or pandora’s box For early childhood education? Experts chat on the roles, challenges, and developments of ChatGPT. Early Educ Dev. 2023. https://doi.org/10.1080/10409289.2023.2214181 .

Meyer JG, Urbanowicz RJ, Martin P, O’Connor K, Li R, Peng P, Moore JH. ChatGPT and large language models in academia: opportunities and challenges. Biodata Min. 2023. https://doi.org/10.1186/s13040-023-00339-9 .

Mhlanga D. Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. Soc Sci Res Netw. 2023. https://doi.org/10.2139/ssrn.4354422 .

Neumann, M., Rauschenberger, M., & Schön, E. M. (2023). “We Need To Talk About ChatGPT”: The Future of AI and Higher Education.‏ https://doi.org/10.1109/seeng59157.2023.00010

Nolan B. Here are the schools and colleges that have banned the use of ChatGPT over plagiarism and misinformation fears. Business Insider . 2023. https://www.businessinsider.com

O’Leary DE. An analysis of three chatbots: BlenderBot, ChatGPT and LaMDA. Int J Intell Syst Account, Financ Manag. 2023;30(1):41–54. https://doi.org/10.1002/isaf.1531 .

Okoli C. A guide to conducting a standalone systematic literature review. Commun Assoc Inf Syst. 2015. https://doi.org/10.17705/1cais.03743 .

OpenAI. (2023). https://openai.com/blog/chatgpt

Perkins M. Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. J Univ Teach Learn Pract. 2023. https://doi.org/10.53761/1.20.02.07 .

Plevris V, Papazafeiropoulos G, Rios AJ. Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard. arXiv (Cornell University) . 2023. https://doi.org/10.48550/arxiv.2305.18618

Rahman MM, Watanobe Y (2023) ChatGPT for education and research: opportunities, threats, and strategies. Appl Sci 13(9):5783. https://doi.org/10.3390/app13095783

Ram B, Verma P. Artificial intelligence AI-based Chatbot study of ChatGPT, google AI bard and baidu AI. World J Adv Eng Technol Sci. 2023;8(1):258–61. https://doi.org/10.30574/wjaets.2023.8.1.0045 .

Rasul T, Nair S, Kalendra D, Robin M, de Oliveira Santini F, Ladeira WJ, Heathcote L. The role of ChatGPT in higher education: benefits, challenges, and future research directions. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.1.29 .

Ratnam M, Sharm B, Tomer A. ChatGPT: educational artificial intelligence. Int J Adv Trends Comput Sci Eng. 2023;12(2):84–91. https://doi.org/10.30534/ijatcse/2023/091222023 .

Ray PP. ChatGPT: a comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Phys Syst. 2023;3:121–54. https://doi.org/10.1016/j.iotcps.2023.04.003 .

Roumeliotis KI, Tselikas ND. ChatGPT and Open-AI models: a preliminary review. Future Internet. 2023;15(6):192. https://doi.org/10.3390/fi15060192 .

Rudolph J, Tan S, Tan S. War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.1.23 .

Ruiz LMS, Moll-López S, Nuñez-Pérez A, Moraño J, Vega-Fleitas E. ChatGPT challenges blended learning methodologies in engineering education: a case study in mathematics. Appl Sci. 2023;13(10):6039. https://doi.org/10.3390/app13106039 .

Sallam M, Salim NA, Barakat M, Al-Tammemi AB. ChatGPT applications in medical, dental, pharmacy, and public health education: a descriptive study highlighting the advantages and limitations. Narra J. 2023;3(1): e103. https://doi.org/10.52225/narra.v3i1.103 .

Salvagno M, Taccone FS, Gerli AG. Can artificial intelligence help for scientific writing? Crit Care. 2023. https://doi.org/10.1186/s13054-023-04380-2 .

Saqr M, López-Pernas S, Helske S, Hrastinski S. The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: a full program study. Comput Educ. 2023;199:104787. https://doi.org/10.1016/j.compedu.2023.104787 .

Saqr M, Matcha W, Uzir N, Jovanović J, Gašević D, López-Pernas S. Transferring effective learning strategies across learning contexts matters: a study in problem-based learning. Australas J Educ Technol. 2023;39(3):9.

Schöbel S, Schmitt A, Benner D, Saqr M, Janson A, Leimeister JM. Charting the evolution and future of conversational agents: a research agenda along five waves and new frontiers. Inf Syst Front. 2023. https://doi.org/10.1007/s10796-023-10375-9 .

Shoufan A. Exploring students’ perceptions of CHATGPT: thematic analysis and follow-up survey. IEEE Access. 2023. https://doi.org/10.1109/access.2023.3268224 .

Sonderegger S, Seufert S. Chatbot-mediated learning: conceptual framework for the design of Chatbot use cases in education. Gallen: Institute for Educational Management and Technologies, University of St; 2022. https://doi.org/10.5220/0010999200003182 .

Book   Google Scholar  

Strzelecki A. To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interact Learn Environ. 2023. https://doi.org/10.1080/10494820.2023.2209881 .

Su J, Yang W. Unlocking the power of ChatGPT: a framework for applying generative AI in education. ECNU Rev Educ. 2023. https://doi.org/10.1177/20965311231168423 .

Sullivan M, Kelly A, McLaughlan P. ChatGPT in higher education: Considerations for academic integrity and student learning. J ApplLearn Teach. 2023;6(1):1–10. https://doi.org/10.37074/jalt.2023.6.1.17 .

Szabo A. ChatGPT is a breakthrough in science and education but fails a test in sports and exercise psychology. Balt J Sport Health Sci. 2023;1(128):25–40. https://doi.org/10.33607/bjshs.v127i4.1233 .

Taecharungroj V. “What can ChatGPT do?” analyzing early reactions to the innovative AI chatbot on Twitter. Big Data Cognit Comput. 2023;7(1):35. https://doi.org/10.3390/bdcc7010035 .

Tam S, Said RB. User preferences for ChatGPT-powered conversational interfaces versus traditional methods. Biomed Eng Soc. 2023. https://doi.org/10.58496/mjcsc/2023/004 .

Tedre M, Kahila J, Vartiainen H. (2023). Exploration on how co-designing with AI facilitates critical evaluation of ethics of AI in craft education. In: Langran E, Christensen P, Sanson J (Eds).  Proceedings of Society for Information Technology and Teacher Education International Conference . 2023. pp. 2289–2296.

Tlili A, Shehata B, Adarkwah MA, Bozkurt A, Hickey DT, Huang R, Agyemang B. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn Environ. 2023. https://doi.org/10.1186/s40561-023-00237-x .

Uddin SMJ, Albert A, Ovid A, Alsharef A. Leveraging CHATGPT to aid construction hazard recognition and support safety education and training. Sustainability. 2023;15(9):7121. https://doi.org/10.3390/su15097121 .

Valtonen T, López-Pernas S, Saqr M, Vartiainen H, Sointu E, Tedre M. The nature and building blocks of educational technology research. Comput Hum Behav. 2022;128:107123. https://doi.org/10.1016/j.chb.2021.107123 .

Vartiainen H, Tedre M. Using artificial intelligence in craft education: crafting with text-to-image generative models. Digit Creat. 2023;34(1):1–21. https://doi.org/10.1080/14626268.2023.2174557 .

Ventayen RJM. OpenAI ChatGPT generated results: similarity index of artificial intelligence-based contents. Soc Sci Res Netw. 2023. https://doi.org/10.2139/ssrn.4332664 .

Wagner MW, Ertl-Wagner BB. Accuracy of information and references using ChatGPT-3 for retrieval of clinical radiological information. Can Assoc Radiol J. 2023. https://doi.org/10.1177/08465371231171125 .

Wardat Y, Tashtoush MA, AlAli R, Jarrah AM. ChatGPT: a revolutionary tool for teaching and learning mathematics. Eurasia J Math, Sci Technol Educ. 2023;19(7):em2286. https://doi.org/10.29333/ejmste/13272 .

Webster J, Watson RT. Analyzing the past to prepare for the future: writing a literature review. Manag Inf Syst Quart. 2002;26(2):3.

Xiao Y, Watson ME. Guidance on conducting a systematic literature review. J Plan Educ Res. 2017;39(1):93–112. https://doi.org/10.1177/0739456x17723971 .

Yan D. Impact of ChatGPT on learners in a L2 writing practicum: an exploratory investigation. Educ Inf Technol. 2023. https://doi.org/10.1007/s10639-023-11742-4 .

Yu H. Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Front Psychol. 2023;14:1181712. https://doi.org/10.3389/fpsyg.2023.1181712 .

Zhu C, Sun M, Luo J, Li T, Wang M. How to harness the potential of ChatGPT in education? Knowl Manag ELearn. 2023;15(2):133–52. https://doi.org/10.34105/j.kmel.2023.15.008 .

Download references

The paper is co-funded by the Academy of Finland (Suomen Akatemia) Research Council for Natural Sciences and Engineering for the project Towards precision education: Idiographic learning analytics (TOPEILA), Decision Number 350560.

Author information

Authors and affiliations.

School of Computing, University of Eastern Finland, 80100, Joensuu, Finland

Yazid Albadarin, Mohammed Saqr, Nicolas Pope & Markku Tukiainen

You can also search for this author in PubMed   Google Scholar

Contributions

YA contributed to the literature search, data analysis, discussion, and conclusion. Additionally, YA contributed to the manuscript’s writing, editing, and finalization. MS contributed to the study’s design, conceptualization, acquisition of funding, project administration, allocation of resources, supervision, validation, literature search, and analysis of results. Furthermore, MS contributed to the manuscript's writing, revising, and approving it in its finalized state. NP contributed to the results, and discussions, and provided supervision. NP also contributed to the writing process, revisions, and the final approval of the manuscript in its finalized state. MT contributed to the study's conceptualization, resource management, supervision, writing, revising the manuscript, and approving it.

Corresponding author

Correspondence to Yazid Albadarin .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

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

See Table  4

The process of synthesizing the data presented in Table  4 involved identifying the relevant studies through a search process of databases (ERIC, Scopus, Web of Knowledge, Dimensions.ai, and lens.org) using specific keywords "ChatGPT" and "education". Following this, inclusion/exclusion criteria were applied, and data extraction was performed using Creswell's [ 15 ] coding techniques to capture key information and identify common themes across the included studies.

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

Albadarin, Y., Saqr, M., Pope, N. et al. A systematic literature review of empirical research on ChatGPT in education. Discov Educ 3 , 60 (2024). https://doi.org/10.1007/s44217-024-00138-2

Download citation

Received : 22 October 2023

Accepted : 10 May 2024

Published : 26 May 2024

DOI : https://doi.org/10.1007/s44217-024-00138-2

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

• Large language models
• Educational technology
• Systematic review

Advertisement

• Find a journal
• Publish with us
• Track your research

Graduate Teacher Education Students Use and Evaluate ChatGPT as an Essay-Writing Tool

• Anthony G Picciano City University of New York, Hunter College

Artificial intelligence (AI) has been evolving since the mid-20 th Century when luminaries such as Alan Turing, Herbert Simon and Marvin Minsky began developing rudimentary AI applications. For decades, AI programs remained pretty much in the realm of computer science and experimental game playing.  This changed radically in the 2020s when commercial vendors such as OpenAI  and Google developed generative AI programs (ChatGPT) and (Bard) using large, language modelling (LLM).  As a result, generative AI  is now being considered for use in all walks of life including education.

     In Spring 2023, when ChatGPT burst into the public psyche, twenty-five education students in the author’s graduate seminar were invited to participate in a qualitative study using ChatGPT as a tool for completing an essay assignment.  Fifteen (N=15) accepted the offer. The purpose in doing this was to give students in this seminar the opportunity to use ChatGPT in a supportive environment and to collect qualitative data from them on their experiences using ChatGPT.

     All of these students have master’s degrees in education and experience as teachers in New York City schools. Their training and experience give them keen insights into pedagogical practice making them ideally suited to evaluate ChatGPT as an instructional tool. This article reports on the results of this study.

Keywords:  Artificial Intelligence, AI, ChatGPT,  Graduate Teacher Education, Qualitative Research

Author Biography

Anthony g picciano, city university of new york, hunter college.

Copyright (c) 2024 Anthony G Picciano

This work is licensed under a Creative Commons Attribution 4.0 International License .

As a condition of publication, the author agrees to apply the Creative Commons – Attribution International 4.0 (CC-BY) License to OLJ articles. See: https://creativecommons.org/licenses/by/4.0/ .

This licence allows anyone to reproduce OLJ articles at no cost and without further permission as long as they attribute the author and the journal. This permission includes printing, sharing and other forms of distribution.

Author(s) hold copyright in their work, and retain publishing rights without restrictions

The DOAJ Seal is awarded to journals that demonstrate best practice in open access publishing

OLC Membership

OLC Research Center

Information

• For Readers
• For Authors
• For Librarians