presenting results in qualitative research

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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.

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.

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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?

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

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

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

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

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1 Introduction

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

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

2 The transparency of reporting in interdisciplinary research

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

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

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

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

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

3 Model case

3.1 material and methods, 3.1.1 material.

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

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

3.1.2 Methods

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

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

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

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

3.2 Content Analysis

3.2.1 introduction.

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

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

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

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

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

3.2.2 Report of method

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

3.2.3 Report of results

3.2.3.1 coding.

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

3.2.3.2 Analysis

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

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

3.2.4 Discussion of content analysis and its reporting

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

3.3 Metaphor analysis

3.3.1 introduction.

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

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

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

3.3.2 Report of method

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

3.3.3 Report of results

3.3.3.1 coding.

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

3.3.3.2 Analysis

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

3.3.4 Discussion of metaphor analysis and its reporting

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

3.4 Domain analysis

3.4.1 introduction.

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

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

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

3.4.2 Report of method

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

3.4.3 Report of results

3.4.3.1 coding.

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

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

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

3.4.3.2 Analysis

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

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

Domain communication, in particular content of communication

3.4.4 Discussion of domain analysis and its reporting

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

3.5 Membership Categorization Analysis

3.5.1 introduction.

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

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

3.5.2 Report of method

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

3.5.3 Report of results

3.5.3.1 coding.

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

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

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

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

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

3.5.3.2 Analysis

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

3.5.4 Discussion of MCA and its reporting

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

4 Discussion

4.1 comparison of the four methods modelled.

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

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

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

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

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

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

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

4.2 Coding and analysis

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

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

4.3 Transparency and appropriateness

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

4.4 Compatibility with contributions from the natural and life sciences

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

5 Conclusion

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

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

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

TOPIC: (interdisciplin* or inter-disciplinary).

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

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

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

Google Scholar  

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

Article   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Acknowledgements

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

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Appendix 1: demographic description of interviewees

A basic demographic description of the interviewees is provided in Table

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

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

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• Published: 26 April 2008

Analysing and presenting qualitative data

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

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

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

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

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

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

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Introduction.

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

Box 1: Qualitative research in dentistry

Qualitative research in dentistry

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

Conducting qualitative interviews with school children in dental research

Approaches to analysing qualitative data

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

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

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

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

Data collection and data analysis

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

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

Computer software for data analysis

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

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

Stages in the process

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

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

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

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

Children's perception of food

Positive notions of food and their consequences

Negative notions of food and their consequences

Peer influence

Healthy/unhealthy foods

Effects of sweets and chocolates

Effects of 'junk food'

Food choices in school

Diet in childhood

Food preferences

Expected diet as a 'grown up'

Food choices and preferences of friendship groups

Effects of fizzy drinks

Perceptions of adult/child diets

The need to be 'healthy' as an adult.

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

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

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

Verification

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

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

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

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

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

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

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

Writing and presenting qualitative research

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

Example a (the traditional approach):

Contrasts and contradictions

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

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

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

Example b (combined findings and discussion chapter):

Copying friends

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

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

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

Girl: 'Yes.'

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

Girl: 'Because they are my friends.'

(Girl, school 1, age 7).

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

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

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

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

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

Google Scholar  

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

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

Article   Google Scholar  

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

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

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

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

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

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

Book   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Qualitative Research Resources: Presenting Qualitative Research

Created by health science librarians.

• What is Qualitative Research?
• Qualitative Research Basics
• Special Topics
• Training Opportunities: UNC & Beyond
• Help at UNC
• Qualitative Software for Coding/Analysis
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• Finding Qualitative Studies
• Assessing Qualitative Research
• Writing Up Your Research
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• Publishing Qualitative Research

Presenting Qualitative Research, with a focus on posters

• Qualitative & Libraries: a few gems
• Data Repositories

Example posters

• The Meaning of Work for People with MS: a Qualitative Study A good example with quotes
• Fostering Empathy through Design Thinking Among Fourth Graders in Trinidad and Tobago Includes quotes, photos, diagrams, and other artifacts from qualitative study
• Examining the Use and Perception of Harm of JUULs by College Students: A Qualitative Study Another interesting example to consider
• NLM Informationist Supplement Grant: Daring to Dive into Documentation to Determine Impact An example from the Carolina Digital Repository discussed in a class more... less... Allegri, F., Hayes, B., & Renner, B. (2017). NLM Informationist Supplement Grant: Daring to Dive into Documentation to Determine Impact. https://doi.org/10.17615/bk34-p037
• Qualitative Posters in F1000 Research Archive (filtered on "qualitative" in title) Sample qualitative posters
• Qualitative Posters in F1000 Research Archive (filtered on "qualitative" in keywords) Sample qualitative posters

Michelle A. Krieger Blog (example, posts follow an APA convention poster experience with qualitative posters):

• Qualitative Data and Research Posters I
• Qualitative Data and Research Posters II

"Oldies but goodies":

• How to Visualize Qualitative Data: Ann K. Emery, September 25, 2014 Data Visualization / Chart Choosing, Color-Coding by Category, Diagrams, Icons, Photographs, Qualitative, Text, Timelines, Word Clouds more... less... Getting a little older, and a commercial site, but with some good ideas to get you think.
• Russell, C. K., Gregory, D. M., & Gates, M. F. (1996). Aesthetics and Substance in Qualitative Research Posters. Qualitative Health Research, 6(4), 542–552. Older article with much good information. Poster materials section less applicable.Link is for UNC-Chapel Hill affiliated users.

Additional resources

• CDC Coffee Break: Considerations for Presenting Qualitative Data (Mark D. Rivera, March 13, 2018) PDF download of slide presentation. Display formats section begins on slide 10.
• Print Book (Davis Library): Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook, 3rd edition From Paul Mihas, Assistant Director of Education and Qualitative Research at the Odum Institute for Research in Social Science at UNC: Qualitative Data Analysis: A Methods Sourcebook (4th ed.) by Miles, Huberman, and Saldana has a section on Displaying the Data (and a chapter on Designing Matrix, Network, and Graphic Displays) that can help students consider numerous options for visually synthesizing data and findings. Many of the suggestions can be applied to designing posters (April 15, 2021).
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• Next: Qualitative & Libraries: a few gems >>
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Presenting your qualitative analysis findings: tables to include in chapter 4.

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

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

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

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

Demographics Tables

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

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

Participant Demographics

Tables to Illustrate Initial Codes

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

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

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

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

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

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

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

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

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

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

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

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

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

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

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

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

• Chapter Seven: Presenting Your Results

This chapter serves as the culmination of the previous chapters, in that it focuses on how to present the results of one's study, regardless of the choice made among the three methods. Writing in academics has a form and style that you will want to apply not only to report your own research, but also to enhance your skills at reading original research published in academic journals. Beyond the basic academic style of report writing, there are specific, often unwritten assumptions about how quantitative, qualitative, and critical/rhetorical studies should be organized and the information they should contain. This chapter discusses how to present your results in writing, how to write accessibly, how to visualize data, and how to present your results in person.  

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

Written Presentation of Results

Once you've gone through the process of doing communication research – using a quantitative, qualitative, or critical/rhetorical methodological approach – the final step is to  communicate  it.

The major style manuals (the APA Manual, the MLA Handbook, and Turabian) are very helpful in documenting the structure of writing a study, and are highly recommended for consultation. But, no matter what style manual you may use, there are some common elements to the structure of an academic communication research paper.

Title Page :

This is simple: Your Paper's Title, Your Name, Your Institutional Affiliation (e.g., University), and the Date, each on separate lines, centered on the page. Try to make your title both descriptive (i.e., it gives the reader an idea what the study is about) and interesting (i.e., it is catchy enough to get one's attention).

For example, the title, "The uncritical idealization of a compensated psychopath character in a popular book series," would not be an inaccurate title for a published study, but it is rather vague and exceedingly boring. That study's author fortunately chose the title, "A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight ," which is more precisely descriptive, and much more interesting (Merskin, 2011). The use of the colon in academic titles can help authors accomplish both objectives: a catchy but relevant phrase, followed by a more clear explanation of the article's topic.

In some instances, you might be asked to write an abstract, which is a summary of your paper that can range in length from 75 to 250 words. If it is a published paper, it is useful to include key search terms in this brief description of the paper (the title may already have a few of these terms as well). Although this may be the last thing your write, make it one of the best things you write, because this may be the first thing your audience reads about the paper (and may be the only thing read if it is written badly). Summarize the problem/research question, your methodological approach, your results and conclusions, and the significance of the paper in the abstract.

Quantitative and qualitative studies will most typically use the rest of the section titles noted below. Critical/rhetorical studies will include many of the same steps, but will often have different headings. For example, a critical/rhetorical paper will have an introduction, definition of terms, and literature review, followed by an analysis (often divided into sections by areas of investigation) and ending with a conclusion/implications section. Because critical/rhetorical research is much more descriptive, the subheadings in such a paper are often times not generic subheads like "literature review," but instead descriptive subheadings that apply to the topic at hand, as seen in the schematic below. Because many journals expect the article to follow typical research paper headings of introduction, literature review, methods, results, and discussion, we discuss these sections briefly next.

Introduction:

As you read social scientific journals (see chapter 1 for examples), you will find that they tend to get into the research question quickly and succinctly. Journal articles from the humanities tradition tend to be more descriptive in the introduction. But, in either case, it is good to begin with some kind of brief anecdote that gets the reader engaged in your work and lets the reader understand why this is an interesting topic. From that point, state your research question, define the problem (see Chapter One) with an overview of what we do and don't know, and finally state what you will do, or what you want to find out. The introduction thus builds the case for your topic, and is the beginning of building your argument, as we noted in chapter 1.

By the end of the Introduction, the reader should know what your topic is, why it is a significant communication topic, and why it is necessary that you investigate it (e.g., it could be there is gap in literature, you will conduct valuable exploratory research, or you will provide a new model for solving some professional or social problem).

Literature Review:

The literature review summarizes and organizes the relevant books, articles, and other research in this area. It sets up both quantitative and qualitative studies, showing the need for the study. For critical/rhetorical research, the literature review often incorporates the description of the historical context and heuristic vocabulary, with key terms defined in this section of the paper. For more detail on writing a literature review, see Appendix 1.

The methods of your paper are the processes that govern your research, where the researcher explains what s/he did to solve the problem. As you have seen throughout this book, in communication studies, there are a number of different types of research methods. For example, in quantitative research, one might conduct surveys, experiments, or content analysis. In qualitative research, one might instead use interviews and observations. Critical/rhetorical studies methods are more about the interpretation of texts or the study of popular culture as communication. In creative communication research, the method may be an interpretive performance studies or filmmaking. Other methods used sometimes alone, or in combination with other methods, include legal research, historical research, and political economy research.

In quantitative and qualitative research papers, the methods will be most likely described according to the APA manual standards. At the very least, the methods will include a description of participants, data collection, and data analysis, with specific details on each of these elements. For example, in an experiment, the researcher will describe the number of participants, the materials used, the design of the experiment, the procedure of the experiment, and what statistics will be used to address the hypotheses/research questions.

Critical/rhetorical researchers rarely have a specific section called "methods," as opposed to quantitative and qualitative researchers, but rather demonstrate the method they use for analysis throughout the writing of their piece.

Helping your reader understand the methods you used for your study is important not only for your own study's credibility, but also for possible replication of your study by other researchers. A good guideline to keep in mind is  transparency . You want to be as clear as possible in describing the decisions you made in designing your study, gathering and analyzing your data so that the reader can retrace your steps and understand how you came to the conclusions you formed. A research study can be very good, but if it is not clearly described so that others can see how the results were determined or obtained, then the quality of the study and its potential contributions are lost.

After you completed your study, your findings will be listed in the results section. Particularly in a quantitative study, the results section is for revisiting your hypotheses and reporting whether or not your results supported them, and the statistical significance of the results. Whether your study supported or contradicted your hypotheses, it's always helpful to fully report what your results were. The researcher usually organizes the results of his/her results section by research question or hypothesis, stating the results for each one, using statistics to show how the research question or hypothesis was answered in the study.

The qualitative results section also may be organized by research question, but usually is organized by themes which emerged from the data collected. The researcher provides rich details from her/his observations and interviews, with detailed quotations provided to illustrate the themes identified. Sometimes the results section is combined with the discussion section.

Critical/rhetorical researchers would include their analysis often with different subheadings in what would be considered a "results" section, yet not labeled specifically this way.

Discussion:

In the discussion section, the researcher gives an appraisal of the results. Here is where the researcher considers the results, particularly in light of the literature review, and explains what the findings mean. If the results confirmed or corresponded with the findings of other literature, then that should be stated. If the results didn't support the findings of previous studies, then the researcher should develop an explanation of why the study turned out this way. Sometimes, this section is called a "conclusion" by researchers.

References:

In this section, all of the literature cited in the text should have full references in alphabetical order. Appendices: Appendix material includes items like questionnaires used in the study, photographs, documents, etc. An alphabetical letter is assigned for each piece (e.g. Appendix A, Appendix B), with a second line of title describing what the appendix contains (e.g. Participant Informed Consent, or  New York Times  Speech Coverage). They should be organized consistently with the order in which they are referenced in the text of the paper. The page numbers for appendices are consecutive with the paper and reference list.

Tables/Figures:

Tables and figures are referenced in the text, but included at the end of the study and numbered consecutively. (Check with your professor; some like to have tables and figures inserted within the paper's main text.) Tables generally are data in a table format, whereas figures are diagrams (such as a pie chart) and drawings (such as a flow chart).

Accessible Writing

As you may have noticed, academic writing does have a language (e.g., words like heuristic vocabulary and hypotheses) and style (e.g., literature reviews) all its own. It is important to engage in that language and style, and understand how to use it to  communicate effectively in an academic context . Yet, it is also important to remember that your analyses and findings should also be written to be accessible. Writers should avoid excessive jargon, or—even worse—deploying jargon to mask an incomplete understanding of a topic.

The scourge of excessive jargon in academic writing was the target of a famous hoax in 1996. A New York University physics professor submitted an article, " Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity ," to a special issue of the academic journal  Social Text  devoted to science and postmodernism. The article was designed to point out how dense academic jargon can sometimes mask sloppy thinking. As the professor, Alan Sokal, had expected, the article was published. One sample sentence from the article reads:

It has thus become increasingly apparent that physical "reality", no less than social "reality", is at bottom a social and linguistic construct; that scientific "knowledge", far from being objective, reflects and encodes the dominant ideologies and power relations of the culture that produced it; that the truth claims of science are inherently theory-laden and self-referential; and consequently, that the discourse of the scientific community, for all its undeniable value, cannot assert a privileged epistemological status with respect to counter-hegemonic narratives emanating from dissident or marginalized communities. (Sokal, 1996. pp. 217-218)

According to the journal's editor, about six reviewers had read the article but didn't suspect that it was phony. A public debate ensued after Sokal revealed his hoax. Sokal said he worried that jargon and intellectual fads cause academics to lose contact with the real world and "undermine the prospect for progressive social critique" ( Scott, 1996 ). The APA Manual recommends to avoid using technical vocabulary where it is not needed or relevant or if the technical language is overused, thus becoming jargon. In short, the APA argues that "scientific jargon...grates on the reader, encumbers the communication of information, and wastes space" (American Psychological Association, 2010, p. 68).

Data Visualization

Images and words have long existed on the printed page of manuscripts, yet, until recently, relatively few researchers possessed the resources to effectively combine images combined with words (Tufte, 1990, 1983). Communication scholars are only now becoming aware of this dimension in research as computer technologies have made it possible for many people to produce and publish multimedia presentations.

Although visuals may seem to be anathema to the primacy of the written word in research, they are a legitimate way, and at times the best way, to present ideas. Visual scholar Lester Faigley et al. (2004) explains how data visualizations have become part of our daily lives:

Visualizations can shed light on research as well. London-based David McCandless specializes in visualizing interesting research questions, or in his words "the questions I wanted answering" (2009, p. 7). His images include a graph of the  peak times of the year for breakups  (based on Facebook status updates), a  radiation dosage chart , and some  experiments with the Google Ngram Viewer , which charts the appearance of keywords in millions of books over hundreds of years.

The  public domain image  below creatively maps U.S. Census data of the outflow of people from California to other states between 1995 and 2000.

Visualizing one's research is possible in multiple ways. A simple technology, for example, is to enter data into a spreadsheet such as Excel, and select  Charts  or  SmartArt  to generate graphics. A number of free web tools can also transform raw data into useful charts and graphs.  Many Eyes , an open source data visualization tool (sponsored by IBM Research), says its goal "is to 'democratize' visualization and to enable a new social kind of data analysis" (IBM, 2011). Another tool,  Soundslides , enables users to import images and audio to create a photographic slideshow, while the program handles all of the background code. Other tools, often open source and free, can help visual academic research into interactive maps; interactive, image-based timelines; interactive charts; and simple 2-D and 3-D animations. Adobe Creative Suite (which includes popular software like Photoshop) is available on most computers at universities, but open source alternatives exist as well.  Gimp  is comparable to Photoshop, and it is free and relatively easy to use.

One online performance studies journal,  Liminalities , is an excellent example of how "research" can be more than just printed words. In each issue, traditional academic essays and book reviews are often supported photographs, while other parts of an issue can include video, audio, and multimedia contributions. The journal, founded in 2005, treats performance itself as a methodology, and accepts contribution in html, mp3, Quicktime, and Flash formats.

For communication researchers, there is also a vast array of visual digital archives available online. Many of these archives are located at colleges and universities around the world, where digital librarians are spearheading a massive effort to make information—print, audio, visual, and graphic—available to the public as part of a global information commons. For example, the University of Iowa has a considerable digital archive including historical photos documenting American railroads and a database of images related to geoscience. The University of Northern Iowa has a growing Special Collections Unit that includes digital images of every UNI Yearbook between 1905 and 1923 and audio files of UNI jazz band performances. Researchers at he University of Michigan developed  OAIster , a rich database that has joined thousands of digital archives in one searchable interface. Indeed, virtually every academic library is now digitizing all types of media, not just texts, and making them available for public viewing and, when possible, for use in presenting research. In addition to academic collections, the  Library of Congress  and the  National Archives  offer an ever-expanding range of downloadable media; commercial, user-generated databases such as Flickr, Buzznet, YouTube and Google Video offer a rich resource of images that are often free of copyright constraints (see Chapter 3 about Creative Commons licenses) and nonprofit endeavors, such as the  Internet Archive , contain a formidable collection of moving images, still photographs, audio files (including concert recordings), and open source software.

Presenting your Work in Person

As Communication students, it's expected that you are not only able to communicate your research project in written form but also in person.

Before you do any oral presentation, it's good to have a brief "pitch" ready for anyone who asks you about your research. The pitch is routine in Hollywood: a screenwriter has just a few minutes to present an idea to a producer. Although your pitch will be more sophisticated than, say, " Snakes on a Plane " (which unfortunately was made into a movie), you should in just a few lines be able to explain the gist of your research to anyone who asks. Developing this concise description, you will have some practice in distilling what might be a complicated topic into one others can quickly grasp.

Oral presentation

In most oral presentations of research, whether at the end of a semester, or at a research symposium or conference, you will likely have just 10 to 20 minutes. This is probably not enough time to read the entire paper aloud, which is not what you should do anyway if you want people to really listen (although, unfortunately some make this mistake). Instead, the point of the presentation should be to present your research in an interesting manner so the listeners will want to read the whole thing. In the presentation, spend the least amount of time on the literature review (a very brief summary will suffice) and the most on your own original contribution. In fact, you may tell your audience that you are only presenting on one portion of the paper, and that you would be happy to talk more about your research and findings in the question and answer session that typically follows. Consider your presentation the beginning of a dialogue between you and the audience. Your tone shouldn't be "I have found everything important there is to find, and I will cram as much as I can into this presentation," but instead "I found some things you will find interesting, but I realize there is more to find."

Turabian (2007) has a helpful chapter on presenting research. Most important, she emphasizes, is to remember that your audience members are listeners, not readers. Thus, recall the lessons on speech making in your college oral communication class. Give an introduction, tell them what the problem is, and map out what you will present to them. Organize your findings into a few points, and don't get bogged down in minutiae. (The minutiae are for readers to find if they wish, not for listeners to struggle through.) PowerPoint slides are acceptable, but don't read them. Instead, create an outline of a few main points, and practice your presentation.

Turabian  suggests an introduction of not more than three minutes, which should include these elements:

• The research topic you will address (not more than a minute).
• Your research question (30 seconds or less)
• An answer to "so what?" – explaining the relevance of your research (30 seconds)
• Your claim, or argument (30 seconds or less)
• The map of your presentation structure (30 seconds or less)

As Turabian (2007) suggests, "Rehearse your introduction, not only to get it right, but to be able to look your audience in the eye as you give it. You can look down at notes later" (p. 125).

Poster presentation

In some symposiums and conferences, you may be asked to present at a "poster" session. Instead of presenting on a panel of 4-5 people to an audience, a poster presenter is with others in a large hall or room, and talks one-on-one with visitors who look at the visual poster display of the research. As in an oral presentation, a poster highlights just the main point of the paper. Then, if visitors have questions, the author can informally discuss her/his findings.

To attract attention, poster presentations need to be nicely designed, or in the words of an advertising professor who schedules poster sessions at conferences, "be big, bold, and brief" ( Broyles , 2011). Large type (at least 18 pt.), graphics, tables, and photos are recommended.

A poster presentation session at a conference, by David Eppstein (Own work) [CC-BY-SA-3.0 ( www.creativecommons.org/licenses/by-sa/3.0 )], via Wikimedia Commons]

The Association for Education in Journalism and Mass Communication (AEJMC) has a  template for making an effective poster presentation . Many universities, copy shops, and Internet services also have large-scale printers, to print full-color research poster designs that can be rolled up and transported in a tube.

Judging Others' Research

After taking this course, you should have a basic knowledge of research methods. There will still be some things that may mystify you as a reader of other's research. For example, you may not be able to interpret the coefficients for statistical significance, or make sense of a complex structural equation. Some specialized vocabulary may still be difficult.

But, you should understand how to critically review research. For example, imagine you have been asked to do a blind (i.e., the author's identity is concealed) "peer review" of communication research for acceptance to a conference, or publication in an academic journal. For most  conferences  and  journals , submissions are made online, where editors can manage the flow and assign reviews to papers. The evaluations reviewers make are based on the same things that we have covered in this book. For example, the conference for the AEJMC ask reviewers to consider (on a five-point scale, from Excellent to Poor) a number of familiar research dimensions, including the paper's clarity of purpose, literature review, clarity of research method, appropriateness of research method, evidence presented clearly, evidence supportive of conclusions, general writing and organization, and the significance of the contribution to the field.

Beyond academia, it is likely you will more frequently apply the lessons of research methods as a critical consumer of news, politics, and everyday life. Just because some expert cites a number or presents a conclusion doesn't mean it's automatically true. John Allen Paulos, in his book  A Mathematician reads the newspaper , suggests some basic questions we can ask. "If statistics were presented, how were they obtained? How confident can we be of them? Were they derived from a random sample or from a collection of anecdotes? Does the correlation suggest a causal relationship, or is it merely a coincidence?" (1997, p. 201).

Through the study of research methods, we have begun to build a critical vocabulary and understanding to ask good questions when others present "knowledge." For example, if Candidate X won a straw poll in Iowa, does that mean she'll get her party's nomination? If Candidate Y wins an open primary in New Hampshire, does that mean he'll be the next president? If Candidate Z sheds a tear, does it matter what the context is, or whether that candidate is a man or a woman? What we learn in research methods about validity, reliability, sampling, variables, research participants, epistemology, grounded theory, and rhetoric, we can consider whether the "knowledge" that is presented in the news is a verifiable fact, a sound argument, or just conjecture.

American Psychological Association (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.

Broyles, S. (2011). "About poster sessions." AEJMC.  http://www.aejmc.org/home/2013/01/about-poster-sessions/ .

Faigley, L., George, D., Palchik, A., Selfe, C. (2004).  Picturing texts . New York: W.W. Norton & Company.

IBM (2011). Overview of Many Eyes.  http://www.research.ibm.com/social/projects_manyeyes.shtml .

McCandless, D. (2009).  The visual miscellaneum . New York: Collins Design.

Merskin, D. (2011). A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight. Journal of Communication Inquiry  35: 157-178. doi:10.1177/0196859911402992

Paulos, J. A. (1997).  A mathematician reads the newspaper . New York: Anchor.

Scott, J. (1996, May 18). Postmodern gravity deconstructed, slyly.  New York Times , http://www.nytimes.com/books/98/11/15/specials/sokal-text.html .

Sokal, A. (1996). Transgressing the boundaries: towards a transformative hermeneutics of quantum gravity.  Social Text  46/47, 217-252.

Tufte, E. R. (1990).  Envisioning information . Cheshire, CT: Graphics Press.

Tufte, E. R. (1983).  The visual display of quantitative information . Cheshire, CT: Graphics Press.

Turabian, Kate L. (2007).  A manual for writers of research papers, theses, and dissertations: Chicago style guide for students and researchers  (7th ed.). Chicago: University of Chicago Press.

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

By: Author Shrot Katewa

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

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

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

How to Create a Structure for your Qualitative Research Presentation?

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

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

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

But, what structure should one follow?

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

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

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

1. Objective of the Research

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

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

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

2. Key Parameters Considered for Measurement

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

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

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

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

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

3. Research Methodology Adopted

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

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

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

4. Research Outcomes (Presenting Descriptive Analysis)

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

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

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

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

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

5. Summary and Learnings from the Research

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

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

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

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

6. Inclusions and Exclusions (if any)

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

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

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

7. Conclusion of the Research

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

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

8. Questions

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

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

Tips for Effectively Presenting Qualitative Research Findings using PowerPoint

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

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

Tip 1 – Use Visuals

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

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

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

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

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

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

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

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

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

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

Tip 3 – Include Quotes of the Participants

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

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

When using quotes, keep these things in mind –

1. Use Quotes in their Unedited Form

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

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

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

2. How to Decide which Quotes to Consider?

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

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

3. Preserve Identity of the Participant

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

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

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

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

Tip 4 – Make your Presentation Visually Appealing and Engaging

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

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

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

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

4.1 Use a Simple Color Scheme

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

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

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

4.2 Make the Data Tables Simple and Visually Appealing

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

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

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

4.3 Use Graphs and Charts (wherever necessary)

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

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

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

4.4 Use Icons instead of Bullet Points

Consider the following example –

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

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

Tip 5 – Include the Outliers

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

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

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

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

Tip 6 – Take Inspiration from other Presentations

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

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

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

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

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

Tip 7 – Ask Others to Critic your Presentation

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

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

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

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

Final Thoughts

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

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

Presenting and evaluating qualitative research

Affiliation.

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

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

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

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

To read this content please select one of the options below:

Please note you do not have access to teaching notes, presenting findings from qualitative research: one size does not fit all.

The Production of Managerial Knowledge and Organizational Theory: New Approaches to Writing, Producing and Consuming Theory

ISBN : 978-1-78769-184-1 , eISBN : 978-1-78769-183-4

Publication date: 11 April 2019

In this chapter, the authors explore the state of our field in terms of ways to present qualitative findings. The authors analyze all articles based on qualitative research methods published in the Academy of Management Journal from 2010 to 2017 and supplement this by informally surveying colleagues about their “favorite” qualitative authors. As a result, the authors identify five ways of presenting qualitative findings in research articles. The authors suggest that each approach has advantages as well as limitations, and that the type of data and theorizing is an important consideration in determining the most appropriate approach for the presentation of findings. The authors hope that by identifying these approaches, they enrich the way authors, reviewers, and editors approach the presentation of qualitative findings.

• Qualitative research
• Interpretive research
• Findings presentation

Reay, T. , Zafar, A. , Monteiro, P. and Glaser, V. (2019), "Presenting Findings from Qualitative Research: One Size Does Not Fit All!", Zilber, T.B. , Amis, J.M. and Mair, J. (Ed.) The Production of Managerial Knowledge and Organizational Theory: New Approaches to Writing, Producing and Consuming Theory ( Research in the Sociology of Organizations, Vol. 59 ), Emerald Publishing Limited, Leeds, pp. 201-216. https://doi.org/10.1108/S0733-558X20190000059011

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• Open access
• Published: 06 May 2024

“We know what we should be eating, but we don’t always do that.” How and why people eat the way they do: a qualitative study with rural australians

• Nina Van Dyke   ORCID: orcid.org/0000-0002-8872-3451 1 ,
• Michael Murphy   ORCID: orcid.org/0000-0003-0434-4567 2 &
• Eric J. Drinkwater   ORCID: orcid.org/0000-0002-9594-9360 3  

BMC Public Health volume  24 , Article number:  1240 ( 2024 ) Cite this article

313 Accesses

Metrics details

There is evidence that most people are aware of the importance of healthy eating and have a broad understanding regarding types of food that enhance or detract from health. However, greater health literacy does not always result in healthier eating. Andreasen’s Social Marketing Model and Community-Based Social Marketing both posit that, in order to change health behaviours, it is crucial to understand reasons for current behaviours and perceived barriers and benefits to improved behaviours. Limited research has been conducted, however, that explores these issues with general populations. This study aimed to help address this gap in the evidence using a qualitative methodology.

Three group discussions were conducted with a total of 23 participants: (1) young women aged 18–24 with no children; (2) women aged 35–45 with primary school aged children; and (3) men aged 35–50 living with a partner and with pre- or primary school aged children. The discussions took place in a regional centre of Victoria, Australia. Transcriptions were thematically analysed using an inductive descriptive approach and with reference to a recent integrated framework of food choice that identified five key interrelated determinants: food– internal factors; food– external factors; personal-state factors; cognitive factors; and sociocultural factors.

We found that food choice was complex, with all five determinants evident from the discussions. However, the “Social environment” sub-category of “Food-external factors”, which included family, work, and social structures, and expectations (or perceived expectations) of family members, colleagues, friends, and others, was particularly prominent. Knowledge that one should practice healthy eating, which falls under the “Cognitive factor” category, while seen as an aspiration by most participants, was often viewed as unrealistic, trumped by the need and/or desire for convenience, a combination of Food-external factor: Social environment and Personal-state factor: Psychological components.

Conclusions

We found that decisions regarding what, when, and how much to eat are seen as heavily influenced by factors outside the control of the individual. It appears, therefore, that a key to improving people’s eating behaviours is to make it easy to eat more healthfully, or at least not much harder than eating poorly.

Peer Review reports

A plethora of recommendations exist regarding how people should eat to maintain better health [e.g., 1 , 2 , 3 ]. Moreover, there is evidence that most people have a reasonable awareness of connections between healthier foods and better health, and a broad understanding regarding types of food that enhance or detract from health [ 4 , 5 , 6 ]. However, greater health literacy does not always result in healthier eating [ 7 – 8 ].

Evidence suggests that public health and health-promotion interventions with a theoretical basis are more effective than those lacking such a foundation [ 9 , 10 , 11 ]. Andreasen’s Social Marketing Model [ 12 ] posits that a primary focus for behaviour change is on learning what people want and need rather than trying to persuade them to adopt particular behaviours or goals. Community-based social marketing sets out six steps necessary for enacting societal behavioural change; step two is to understand perceived barriers and benefits to develop interventions [ 13 ].

Limited research has been conducted, however, that explores how people in the general population eat and their perceptions regarding why they eat the way they do [ 14 – 15 ]. Although several recent papers have examined barriers to and enablers of healthier eating [e.g., 16 ], relatively few are from the perspective of the consumers themselves [e.g., 17 – 18 ] or are narrowly focused on particular types of healthy consumption [e.g., 19 ].

Healthy eating: knowing vs. doing

Food-based dietary guidelines are available for more than 90 countries globally. Although there is some variation across guidelines regarding particular foods, there is broad agreement to consume a variety of foods; consume some foods in higher proportion than others; consume fruits, vegetables, and legumes; and to limit sugar, fat, and salt [ 20 , 21 , 22 ].

There is mixed evidence regarding whether most people broadly understand what constitutes a healthy diet and believe they should try to eat healthily. A systematic review of the psychological literature on healthy diet, for example, found that the public has a “remarkably accurate” understanding of healthy nutrition and that this understanding reflects key dietary guidelines [ 23 ]. Focus groups with participants segmented by age and gender found that most participants were aware of the type of foods that contributed to a healthy diet and the importance of achieving a healthy balance within a diet [ 24 ]. Other studies, however, have found evidence of confusion and misperceptions amongst the general public. A cross-sectional survey of 1,097 adults aged 18–64 in Victoria, Australia and 135 professional dietitians, for example, found large discrepancies in which of various food items were considered healthy. Amongst women and those living in higher socio-economic areas, however, views were similar [ 25 ]. An earlier survey of Swiss consumers found that between 3% and 38% incorrectly answered procedural nutrition knowledge items. Again, this overall finding differed by sub-groups [ 26 ].

However, this knowledge does not necessarily result in healthy eating [ 27 ]. A systematic review of the relationship between nutrition knowledge and dietary intake found that the majority of studies reported significant, positive associations, but the relationship was weak ( r  < 0.5 ) and mostly involved slightly higher intake of fruits and vegetables. The authors also noted that study quality ranged widely and that most participants were female and with a tertiary education, with limited representation of individuals from lower socio-economic status background [ 28 ]. A qualitative study with adults in New Zealand reported “the impossible rightness of healthy eating”, meaning that the people in their study knew they should be eating healthfully, but simultaneously felt that this was very difficult or impossible to do [ 29 ]. A Canadian study argued that the concept of "food literacy" needed to extend beyond nutritional recommendations and cooking lessons to fostering connections between food, people, health, and the environment to bridge this gap between knowing and doing [ 30 ].

Theoretical frameworks

Andreasen’s Social Marketing Model [ 12 ] presents behaviour change as the dependent variable, influenced by four classes of independent variables: (1) the attractiveness of behavioural alternatives, (2) community pressures, (3) the cooperation of critical supporting agencies, and (4) marketing efforts. Of specific relevance to this study, Andreasen [ 12 ] posits that a primary focus for behaviour change is on learning what people want and need rather than trying to persuade them to adopt particular behaviours or goals.

Also relevant is Community-Based Social Marketing. Community-Based Social Marketing is based on six steps. Step one is to identify the target behaviour– in this case, unhealthy eating. Step two is to understand perceived barriers and benefits to develop interventions [ 13 ]. It is this second step that we focus on in this study.

• Food choice

Decisions regarding what food to eat, when, and in what quantity are “frequent, multifaceted, situational, dynamic, and complex” [ 31 ]. A recent review and analysis of existing models of food choice integrates key elements into a single framework (Fig.  1 ) [ 32 ]. In this framework, key determinants of general food choice were identified and categorised, including Food– internal factor (sensory and perceptual features), Food– external factor (information, social environment, physical environment), Personal– state factor (biological features and physiological needs, psychological components, habits and experiences), Cognitive factor (knowledge and skills, attitude, liking and preference, anticipated consequences, and personal identity), and Sociocultural factors (culture, economic variables, political elements). According to this framework, any attempt to shift choice must consider these interrelated factors.

Conceptual model of food choice. The lines in the figure indicate the interactions between different factors [ 32 ]

Literature on perceived barriers and enablers of healthy eating

Most of the recent evidence on perceived barriers to and enablers of healthy eating focuses on particular sub-populations, such as young people with obesity, shift workers, or people with Type 2 diabetes [ 33 , 34 , 35 , 36 , 37 ], and/or a particular type of diet, such as the Mediterranean Diet [ 38 – 39 ].

Studies examining more general populations tend to focus on younger people. A scoping review of barriers to and enablers of healthy eating for young adults in Western countries, for example, identified the following barriers: male apathy towards diet; unhealthy diet of friends and family; expected consumption of unhealthy foods in certain situations; relative low cost of unhealthy foods; lack of time to plan, shop, prepare, and cook healthy foods; lack of facilities to prepare, cook and store healthy foods; widespread presence of unhealthy foods; lack of knowledge and skills to plan, shop, prepare, and cook healthy foods; and lack of motivation to eat healthily (including risk-taking behaviour). Key enablers included: female interest in a healthy diet; healthy diet of friends and family; support/encouragement of friends and family to eat healthily; desire for improved health; desire for weight management; desire for improved self-esteem; desire for attractiveness to potential partners and others; possessing autonomous motivation to eat healthily and existence and use of self-regulatory skills [ 40 ]. A qualitative study of college students aged 18–24 at one university in Hawaii, U.S., of perceived barriers to and enablers of healthy eating found the largest barriers to be nutrition knowledge deficit, peer pressure, unsupportive institutional environment, and cost. The largest enablers were nutrition knowledge, parental influence, an institutional environment with consistent healthy offerings, and social media. It was noted that several of these factors served as barriers for some participants and enablers for others, such as nutrition knowledge, parental influence, and institutional environment [ 41 ]. Another qualitative study with college students at a U.S. college found that common barriers to healthy eating were time constraints, unhealthy snacking, convenience high-calorie food, stress, high prices of healthy food, and easy access to junk food. Conversely, enablers to healthy behaviour were improved food knowledge and education, meal planning, involvement in food preparation, and being physically active. Parental food behaviour and friends’ social pressure were considered to have both positive and negative influences on individual eating habits [ 42 ]. Much of this food choice literature identified the importance of social factors and social norms [ 43 – 44 ].

Limited research exists that explores why people in a general population eat the way they do and what, from their perspective, are the barriers and enablers to doing so. From a public health perspective, such evidence is crucial for developing population-level interventions or advocating for policy change. This study aimed to help address this gap in the evidence by using a qualitative methodology to explore the eating patterns and process by which eating decisions were made amongst a general population of non-metropolitan adults in Australia. A non-metropolitan sample was chosen for several reasons. First, Australians living in rural and remote areas experience higher rates of diet-related disease when compared to urban residents, including cardiovascular disease, type 2 diabetes, high blood pressure, chronic kidney disease, and obesity [ 45 – 46 ]. Second, access to healthy food is more challenging in rural and remote Australia due to further distances from urban centres and higher prices [ 47 – 48 ]. Third, Australians living in rural and remote areas experience greater socio-economic disadvantage than those living in urban areas [ 49 ], which makes healthy food relatively more unaffordable. Finally, most qualitative research in Australia tends to be conducted with people in metropolitan areas, with less known about people living in non-metropolitan locations.

This study is part of a larger, mixed-methods study examining eating behaviours. Data collection took place in 2010. A detailed discussion of the methodology employed for the qualitative component has been published previously in a paper examining what people think of intuitive eating [ 50 ]. Other papers published from this study include a quantitative investigation of the associations between intuitive eating and indicators of physical and mental health [ 51 ], a review of the literature on the relationship between intuitive eating and health indicators [ 52 ], and an experimental study testing whether the accuracy of self-reported height and weight in surveys could be improved by changes to the question wording [ 53 ].

Study design and participants

Three group discussions were conducted with a total of 23 participants: (1) young women aged 18–24 with no children; (2) women aged 35–45 with primary school aged children; and (3) men aged 35–50 living with a partner and with pre- or primary school aged children. These three group demographics were selected to target significant age and life-stages in which shifts in eating behaviours may occur [ 54 ]. The groups were conducted in Bendigo, a regional centre of Victoria, Australia, with participants recruited from Bendigo city and outlying areas.

Recruitment was conducted by a professional recruitment agency. Participants were paid AUD70. Participants were chosen such that at least two in each group had previously been on a weight loss diet and at least two had never been on a weight loss diet; at least three in each group were “over my most healthy weight”.

All focus groups were conducted in a hotel conference room facility in Bendigo and were recorded for the purposes of analysis. The groups began with a general discussion about food choices and approaches to eating, including discussion of the factors that influenced food choices. Topics included influences on eating decisions– what, when, how much; eating patterns– when, why, what; feelings around eating; enjoyment of food/eating; and the role that healthy eating played in their decisions around food.

Data analysis

With the permission of participants, all research sessions were recorded and transcribed. Transcriptions were thematically analysed using an inductive descriptive approach [ 55 – 56 ].

This study received ethics approval from the Charles Sturt University Human Research Ethics Committee (2010/144).

The conversations about what people ate in terms of choice of food and the amount consumed were contextualised within an appreciation of participants’ living and working situations. While it was beyond the scope of this study to provide a documentation of the diets of participants, some information was provided about specific food preferences. However, the main interest was on the factors that affected their food choices.

Across the groups, there was a general and consistent belief that what one ate was affected by a range of factors, and that as a consequence, none of these participants felt that they were entirely in control of their own diets. While some of these factors were personal, others were felt to be determined by family, work and other social structures.

Participants were clear that the term, “diet”, while most obviously associated with weight loss, can be used to refer to general eating patterns or specific kinds of approaches to eating. Hence, the term, “diet” will be used in this paper to refer to the usual or regular food and patterns of eating. When the topic is related to a specific kind of diet that is being pursued for a particular purpose, this is referred to as the specific kind of diet, and when the specific purpose is related to weight loss, we have referred to this as a “weight loss diet”.

As an inductive approach was used in the analysis, we did not endeavour to match identified themes to the factors presented in the Chen and Antonelli [ 32 ] model. Instead, we discuss how our findings align with this model in the Discussion section. Seven main themes were identified, most with several sub-themes. Main themes included taste and health considerations, family factors, work and workplaces, social factors, planning and preparation, meal patterns, and perceptions of own eating.

Taste and health considerations

Across the groups, participants commonly talked about foods that they liked or did not like and suggested that food tastes and preferences were a primary determinant of their diets. In each group, there was some discussion of eating according to what one feels like at the time. However, it was apparent that this approach tended to mean that people’s eating varied widely in terms of eating healthily or otherwise. While they might experience times when they simply felt like foods that they considered to be healthy, it was apparent that these cravings were not the norm, and that some were almost surprised at the idea of desiring salads or vegetables.

Some days you feel like eating cold meat and salad for tea, or some days you’ll just eat a whole loaf of garlic bread. (Women, 18–24)

Some noted that food preferences seem to go in phases.

I’ve just gone off those. (Women, 18–24)

Participants also commonly talked about health as a factor that would influence their diet, but that they tended to wax and wane in terms of their degree of commitment to maintaining a healthy diet. Even those who reported being quite focussed on health as a motivator felt that it was quite hard to consistently maintain a healthy diet, and that there would be times when they did not feel like making the effort. Underlying these thoughts was a belief that eating healthily was hard work, and certainly harder than eating for convenience.

Mine varies between wanting to be super detox, organic; as natural as possible to, um, I’m totally energy depleted, give me some carbs. So I will, like, live a contradictory diet by having regular meals that are semi-regular, so really, really good, and then just crash and you know you get into work and you come home and you haven’t had time for a proper lunch or you didn’t, you know, take the time to prepare it and they come home after school and… well, it annoys me because I want to be consistent basically, and I want to be role model for my kids as well. (Women, 35–45) Oh, I have had…I’ll have the healthy breakfast for you know a week or two and then I think, “Oh, I’m sick of that, I’ll just go for toast. You get a bit tired of being strong and healthy. (Women, 35–45)

Some mentioned specific health concerns, including particular diseases or even injuries that affected their capacity to prepare meals.

Oh, our eating habits are very erratic at the moment because I’m not cooking because of an injury, and my husband has to cook so if he’s late home from work, usually the kids have made something for themselves, like a chicken burger or a slice of bread, or a can of spaghetti or something like that. (Women, 35–45)

Within these discussions, it was apparent that participants’ knowledge about nutrition and health varied considerably, and that their level of knowledge did tend to affect food choices. Some participants talked about the idea of balance, and of making choices to ensure a balance of food over the day or week. For some, balance was also about compensating for other aspects of life and health, such as smoking or drinking or physical activity. Some of the men, in particular, talked about doing more activity to compensate for having eaten too much or consumed too much alcohol.

For me, like if I’ve eaten too much, one night I know I’ve got this exercise the next day, so I have to go to the gym or get up in the morning and do some physical activity. (Men, 35–50) Yeah to me I was the same, I used to smoke and I still drink every now and then you know, I’ll try to keep fit and I know if I eat too much, I’ve got to try and do some exercises to balance it out. (Men, 35–50) I do heaps of exercise because I love eating… I run so that I can eat. (Men, 35–50)

Family factors

Time and convenience.

Throughout the discussions, it was apparent that food choices were substantially affected by factors associated with time and convenience. Participants talked about having busy schedules (e.g., family, work, school, sports), and that these activities had an impact on both the choice and timing of food.

Convenience, especially in terms of the time available for food preparation, was a major factor in food choices. In this context, participants referred to take-away foods, frozen or pre-prepared foods, and meals that were quick to prepare as offering considerable advantage in terms of fitting in with their lifestyles. As noted later, these factors interacted with the time of the week, so that weekdays tended to be more hectic with less time available for food preparation, while weekends commonly afforded greater choice.

Household members

Across the groups, participants reported that the choice of food that they consumed at any particular time was not always entirely up to them. Rather, what they ate at any particular meal was commonly affected by where they were eating, who else they were eating with, and other people’s food preferences. This was especially an issue for people who lived with others, most obviously those who were parents and were catering for children and spouses, but also for those who lived in shared households. In this context, the household makeup was a primary determinant of food choices and approaches to eating. This included the mix of males and females in the household as well as the age of children.

That’s me: quick and easy. And I love the chance when I can actually get a recipe, get all the, um, ingredients and make it properly, but that doesn’t happen very often. It’s just usually what’s there and what’s quick. And what everyone will eat. (Women, 35–45) Oh, yes, that’s a big one for me of having four children and a couple of fussy buggers. You do tend to stick to the things that they will eat… spaghetti bol[ognese], four times a week. (Women, 35–45) You have to cater for different tastes in the household. (Women, 35–45) There’s nothing more heartbreaking… when you do go to a lot of effort and they won’t even try it. (Women, 35–45)

In this context, catering for teenage boys was raised as a specific issue. Parents of teenage boys reported that they were often primarily driven by a need to provide filling food, and this tended to mean a reliance on carbohydrate-based meals, such as rice or pasta. Some amongst the group of men also talked about the main motivator for food choices being about filling themselves up. They would choose foods that provided bulk so that they could feel full. Certainly amongst the men, and in the context of parents talking about their sons, there was a substantial focus on the need for food to be bulky and filling.

I usually choose my food for size, value for money and something that the boys will eat. Bigger is better. (Men, 35–50) Size, you know, steak, parma, my son will eat, you know, most things, money comes into it again, but bigger is better. (Men, 35–50) I’d rather go big than fancy. (Men, 35–50) For me I’ve always just, I eat until I’m completely full, if you are breathing and food isn’t coming into your mouth, because you’ve so full, then you are not full enough, so keep eating, that’s the kind of, my whole family is the same, none of them are overweight or fat. (Men, 35–50) Every second meal is probably pasta or rice [to fill up the kids]. (Women, 35–45)

Throughout these discussions, it was apparent that some of the women who were involved in preparing family meals tended to ignore their own preferences for the sake of catering for partners and children. They believed that it was not worth preparing a different meal for themselves, and so tended to eat whatever they were preparing for others. Several of the women commented that this meant that they did not eat as healthily as they would like to. When prompted, those in the group of mothers commented that they only really enjoyed some of their meals.

Whatever’s in the fridge or cupboard. If there’s salad I’ll have salad, but if we’ve got leftovers I’ll have that… whatever I can grab. (Women, 35–45) [I enjoy] half to three-quarters [of my meals] and the rest are a bit of a chore. (Women, 35–45) We’re just eating because you got to eat to keep going, but tea time is more of an enjoyable meal. And the snacks in between are usually enjoyable. (Women, 35–45) Well, it made me realise that probably maybe it’s more complicated in bringing up children, that I really ignored my own health for quite a long time. (Women, 35–45)

Interestingly, however, some of these same participants commented that when they did have the opportunity to choose meals that were not dependent on the preferences of others, such as when they were at home on their own during the day, they commonly chose foods that were convenient, and reported that they could not be bothered preparing for themselves. They reported that they would find something that they considered simple and easy to make (e.g., leftovers; toast; cheese and biscuits).

Yeah, there are days like that, I just grab one of those [Up & Go drinks]. Um, because I’m part-time sometimes I’ll be home at lunch time and I’ll say to myself in the morning, “Oh, I’ll eat when I go home. I’ll have a good meal when I go home", but what happens is that I stay on at school longer and I’ll come home at 2:00, 2:30/3:00 and then it’s like, “I’ll wait till the kids are home, we’ll just have afternoon…or I’ll come home carb crave, you know, deprived and just…just grab some, like Cruskets or Saladas or some rubbish, a bit of cheese". (Women, 35–45) I think if I didn’t have to cook for the kids I would eat differently but, then having said that, as we’ve been talking I thought you know I don’t make the effort at lunch time, I just go by routine, whatever, and…if I’m not enjoying it I’ll just eat it because it’s there rather than spend the time to make something I really like, like vegetables or a salad. A lot of basic things. (Women, 35–45)

Those who lived with children talked about the age of their children affecting both the kind of food they ate and when they ate. In particular, those with younger children tended to report that they tried to arrange meals around reasonably set timelines. They reasoned that this structure fit in best with other patterns of their children’s day-to-day activities, especially school, sports, and sleep. It was apparent that such set structures were less important for those with older children or without children.

Price and budgets

The cost of food was commonly mentioned as a determinant of food choices. This was especially the case for those with teenage boys, given the need to provide large amounts of food. Several of the family participants talked about buying food in bulk when it was cheap and commented that this would then govern their food choices for a period of time.

I buy cereal in boxes of twenty or thirty, so if Nutrigrain is on Special for $4 a box, I buy twenty or thirty… Vita Brits I went and brought, it was $2 a box or something for Vita Brits the other day, and $2 a box for Weet Bix somewhere else, so I actually had a whole car filled with two trolleys full of Vita Brits, Weet Bix, and I haven’t brought Nutrigrain in a while, we are down to about our last three boxes, we had about forty boxes in there the other day. (Men, 35–50) We’re looking at economy; we’ve all got children. You know, we’ve got to budget. (Women, 35–45)

Work and workplaces

Outside of the home, some noted that their lunch time food choices when they were at work depended on where they were, what was available, and who else they were eating with or purchasing for. Some commented that they were not always able to take lunch with them to work, and that this, combined with where they were working, determined what they could eat at lunch time. Some commented that they worked in areas with only limited choice and some reported that they would be on the road for work and what they ate depended on which town they were visiting at lunch time. In both of these situations, participants noted that it was especially difficult to make food choices that they believed were healthy, simply because the healthy options were not readily available. Some noted that at their workplaces, a group of workers would take it in turns to decide where they would go for lunch, and therefore the individual’s choice was dependent on what that one place had available that day.

Participants also commented that their workplace, type of work, and working hours determined when they could eat. Some experienced set working hours and had little flexibility to decide when they ate, with references being made to shift work, school hours, or retail businesses with defined customer service hours. Working hours were also regarded as one of the factors that determined whether breakfast was eaten and what was eaten at the time. Some participants talked about not feeling like eating as soon as they got up, preferring to wait until sometime later to have breakfast. However, some of these people also noted that the nature of their work meant that they were unable to eat at the time that they would prefer (e.g., teachers), and therefore that they would have to have something first thing in the morning so they could last through until lunch time.

Social factors

Location of eating.

Participants consistently pointed out that eating food that they had not prepared affected their choice of foods, from the perspective of both availability and desire. For example, when eating out, participants reported that they tended to have something they wouldn’t eat at home. They were more likely to have foods they considered to be treats. Some also commented that they would choose foods at these times that were restricted at home because others in the household did not like them. A specific example was food that was provided for free, which was typically at some kind of function. Free food meant different motivations for choice. Partly this was related to not being able to be as fussy as they would be if they were providing their own food or making their own choices. Partly it was related to going for the unusual, commonly more decadent, choice. In both of the above situations (eating out and free food), some participants talked about the idea of feeling like they had to eat all that they were served so as to not waste the opportunity or their money.

Most of the time if I’ve overeaten is when we go to the buffets, where it’s all you can eat sort of thing… so I try to avoid those sort of places, because I will overeat and I feel guilty and then I’ll go out for a walk before I go to bed and then I’ll punish myself the next day. (Men, 35–50)

Other factors related to location were discussed previously under the heading, ‘Work and workplaces’.

Social and physical activities

Participants talked about a range of activities that affected both choice and timing of food. A common factor was that of physical activity, and especially in the context of organised team sports. It was noted that these activities, especially if they were during the week, often overlapped with normal eating times, and therefore that meals would need to be rearranged around the activity. With respect to sports, participants also reported that they needed to consider the impact of their meal on their ability to take part in the sport, noting that they might not have sufficient energy to play a sport if they had not eaten, but that they could not eat too soon before being active. This commonly meant that meals on these evenings were either very early or very late, neither of which was regarded as ideal, but something that participants had no control over. It was also noted that physical activity could affect the type of food chosen, specifically that they would need to eat either to provide or replenish energy.

Some of those who were parents also noted that the sports activities of their children affected their own diet, in terms of both timing of meals and choice of food. Because families were reluctant to prepare more than one meal, the whole family had to fit around everyone else’s activities.

Well we have our set days where, like Wednesday nights we have to have Mackie cheese [macaroni cheese] and nuggets, because that’s what the boys want after their swimming lesson, and sometimes I have to go to the supermarket because I haven’t got any left in the fridge, so… pasta is a bit of a staple. (Men, 35–50) Wednesday is late because I’ve got touch football, so I don’t eat dinner before going to play, I don’t want to go on a full stomach, so lunch is always bigger on a Wednesday than any other day… I hate it because one of the touch footie games isn’t till seven thirty, I hate it, because normally eating at six, there is no way I can have tea beforehand, because I’m just going to run around and get sick, so you don’t get home till… eight thirty, quarter to nine, nine if they are running late, and… yeah, pretty much [McDonald’s] or homemade pizza… because you know they only take about eight minutes in the oven.(Women, 18–24) Well whether the boys are going to be home or we know they are going to be home or one of the daughters is playing sport or I’m playing sport, it varies. (Men, 35–50)

Participants talked about a range of other social activities, such as various groups and clubs, which affected when and what they ate. While these activities might not have had the same physiological impact on food preferences and choices as sports activities, they did similarly affect when meals were eaten, which in turn affected what was eaten. For example, some mentioned after work activities, which meant that they would not get a chance to eat until late, and by then the quickest and most convenient thing to do was to buy take-away food on the way home or eat pre-prepared frozen meals when they got home.

My partner plays pool on a Monday and Wednesday night, so we always have tea a lot earlier then and cook the simple things that don’t take as long, so he can have dinner before he goes rather than buying pub meals which cost more money.(Women, 18–24)

Planning and preparation

Throughout the research, it was apparent that different people had different approaches to planning and preparing meals. The approaches tended to depend on factors such as where they lived, how they shopped, and who and how many people they were shopping for. For example, some mentioned that they lived out of town and therefore that they tended to shop less frequently but buy more at a time. Some of those who reported having large families also mentioned that they would shop in bulk. Several of these participants talked about their food shopping being driven by pre-planned meals.

Yeah and as you drift through the town you stop at the supermarket and pick up the required… it’s a half hour drive in and out, so it creates that sense of planning. (Men, 35–50) For our family… my wife actually sits down each fortnight, because we get paid fortnightly, she works full time, I’m studying full time, and working part time, five kids, the budget is not extensive, so she actually sits down each fortnight and works out what we are going to eat for the fortnight, and then goes and gets all the set ingredients for those meals, and so there’s nothing above and beyond that, now and then there might be a treat thrown in or whatever, all the stuff for the school lunches and that sort of thing. So it’s basically dependent, the amount we eat is dependent on that. She works out ok we need so much to make a meal for seven people. (Men, 35–50)

Participants’ approach to planning was also driven by factors such as their work schedules. They reported that these factors meant that they had different amounts of time available on different days of the week, and therefore that the planning and food preparation process varied according to what was possible on each day.

Oh, well, my aspiration is that I eat more healthily and more natural foods but that’s quite often sabotaged by my planning. My husband probably does want to do that as well but, um, I find it’s often, “Oh, my goodness, I’ve got half an hour to make something and there’s nothing for them, there’s nothing in the fridge, so what are we going to have. So, occasionally it’s fish and chips instead or, um, yeah, just quickly putting something together which isn’t really what I’d want to do but if I’ve done more planning in advance then…(Women, 35–45)

It was also apparent that some participants simply preferred to have a set structure to their diet, and this meant set meals and set shopping patterns.

I guess going back to the getting groceries, I tend to map my weeks out from the Sunday, buy everything for the weekend and that’s it, but I stick to the same recipe every day, so usually lunch is a wrap with ham and a certain amount of grams of tomato and cucumber… it’s just easier to stick to.(Women, 18–24) I pretty much eat at the same time every day…. 9.30 breakfast, twelve lunch, six o’clock dinner. (Women, 18–24)

By contrast, others tended to be a bit more ad hoc in terms of planning, and therefore shopping. These participants reported that they would decide what to eat each day and might quickly visit the supermarket on the way home. It was apparent during these discussions that this approach was more likely in situations in which men were more involved in day-to-day food choices.

And depending on the timing of the day, what’s happened during the day and that sort of thing, what we feel like, necessarily on the day, will be dependent on… well [my wife] either sorts it out in the morning, or puts the slow cooker on or something like that… [depending on] you know who’s going where, that day, because she’s working, at the moment, she’s teaching up at the uni so she’s there till five o’clock most nights of the week… I’ve got subjects or classes, until four or five, I’ve got one on a Monday that finishes at seven, in the evening. (Men, 35–50)

Finally, participants varied in their attitudes regarding whether they liked to have food in the freezer that could be ready to thaw and prepare, or whether they preferred to buy and eat fresh food.

Meal patterns

Timing of meals.

As noted above, participants across these groups reported that their patterns of eating, in particular the time at which they ate, were commonly governed by factors that they felt were external and therefore that they had no control over. Some mentioned that they would eat in the morning because they needed something to get through the start of the day. Even if they did not feel hungry at this time, they were aware that they would feel hungry before there was another chance to eat. From this perspective, for some people and some meals, food was about fuel. They would stock up to prevent themselves running low later on, even if they did not really feel like eating at the time. As noted above, participants in each of the groups talked about the routines and structures of their day-to-day existence determining when they could eat, and that this affected what they would eat. To some extent, they did not feel that this was an ideal approach but felt that they had limited capacity to do otherwise. Hence, in some situations, timing of eating was based on the desire to prevent later hunger, rather than as a response to current hunger.

I think, I mostly eat because, well I’m hungry and you have to, rather than oh my god that’s fantastic, and I’d love to cook it and eat it and enjoy it, I think it’s just more of a…. (Men, 35–50) You’ve got to eat, it’s fuel. (Men, 35–50) Yeah, like breakfast I wouldn’t normally eat, well I don’t enjoy breakfast, but I eat because I know, come nine o’clock, ten o’clock I’m going to be hungry I’m going to be lethargic, so I’ll force Wheeties in or some toast or… I do enjoy food but I don’t deliberately go out because I enjoy the taste or the texture or whatever, it’s more, well you have to eat. (Men, 35–50) If I know I’m travelling and I have to skip lunch or something, I’ll probably have a bigger, breakfast than normal, but if I know I’m going to have access to lunch, then no problem, I’ll just have something to keep me, just to get me there, rather than, cook up the big pancakes and the bacon and eggs, you’ve got to taste nice, I’ll be just a couple of bits of toast just to keep the hunger away. (Men, 35–50)

Standard and variable meals

Participants were prompted to talk about which meals were standard and which were more variable. For most participants, breakfast, lunch, and dinner were each affected by different factors, as were weekday and weekend meals.

Weekday vs. Weekend

Across the groups, weekdays tended to involve more structure, and therefore the weekday meals also tended to involve more structure. This appeared to be most obviously true for those with younger (primary school age) children but was also the case for those with older children and those who did not have or live with children. In other words, the typical weekday involved a degree of externally imposed structure (e.g., working hours: travel times: sporting activities), and for those who lived with others, this was further impacted by the need to coordinate times. For some, food choices tended to be group choices rather than individual choices, especially during the week. By contrast, weekends tended to involve more flexibility of schedules, and as a consequence, more time could be spent in food preparation and decisions about meals were less time and convenience based.

I cook…during the week is when I have…we have set meals and then weekends when I don’t cook… [during the week] we have a meal together every night…at the moment they’re all young so no-one’s out doing things. Yeah, I’m cooking a meal every night, but on the weekend it’s more relaxed, it’s like, “get your own". (Women, 35–45)

Breakfast, lunch, and dinner

While there were some exceptions across these groups, breakfast tended to be a more standard and regular meal. To a large degree, this was because time was a major issue, as breakfast needed to be consumed at a set time and in a brief period of time, typically while the family was getting ready for the day’s activities. Interestingly, some participants suggested that they did not experience the same need for variety when it came to breakfast as they did with other meals, commenting that they were happy to have the same thing day after day. As noted above, weekend breakfasts were commonly quite different from weekday breakfasts, being more about choice, enjoyment, and variety than time and convenience. Weekend breakfasts also tended to be more of a family event than simply eating something before the day’s activities.

However, some participants in each of the groups reported that they did not always eat breakfast, typically feeling that it was too early to eat. Amongst this group, some reported having breakfast some days and not others. These people reported they would wake up and decide whether they felt hungry, and if so, what they felt like eating.

It was also common for some to talk about breakfast being a time when they were more in touch with what they felt like eating, or whether they felt like eating at all, although the breakfast choices tended to be quite narrow (e.g., toast: cereal: fruit). Similarly, some reported that they had two or more standard breakfasts, and that they would choose on the day what they “feel like".

I just wake up and whatever I feel like… like if I wake up hungry, then I’ll go and have some, if I feel like cereal, then I’ll have cereal… and if I do sport in the morning, then I usually have toast… I just feel like toast after a run. (Women, 18–24) It can range from cereal or toast in the morning, my wife makes her own sourdough, so we have that in the morning, which is really good… depends on the mood, because what happens, if the kids wake up, it’s cereal, and I’ll do three bowls at the same time, one, two, three… If everyone is still sleeping, I’ll make my toast and wrap it up and eat it on the way to work so… it just depends on how you feel. (Men, 35–50)

As discussed earlier, lunches tended to vary according to where people were and what they were doing. Convenience was also a key driver for lunch time choices. For those not working during the day, lunches were commonly leftovers from the night before or simple snacks. The mothers talked about not really putting aside time or food for lunch, and often skipping it or simply not getting around to it. If they were not at home, lunch would depend on where they were and what they were doing. For those who were working, there was also the issue of choice being affected by the group, as was previously documented.

Dinner was generally regarded as the most important meal of the day and was afforded more effort and planning. All of the factors discussed previously as influencing food choices tended to be applied to dinners. Most obviously, weekday dinners tended to follow somewhat more of a routine, while there was greater variation and potentially a broader choice on the weekends.

Perceptions of own eating

Participants were asked to comment on how they felt about their diets and their approach to eating. The typical response was to say that it was mostly okay but could be improved. There was a tendency for participants to comment that they ate too much of some foods that they perceived as not good foods, and/or not enough of other foods that they perceived as good foods. Interestingly though, participants commonly responded to these questions with a range of justifications for the shortcomings that they perceived in their diets. For example, some would claim that it was okay that they ate so much high fat foods because they did a lot of exercise; others would report that it was okay because they had a “good metabolism".

Yeah I’m pretty happy with mine [diet], I think I drink too much Coke, I’m really addicted to Coke, but apart from that I’m pretty happy with it. I really love my vegetables, so we eat a lot of vegies… maybe I do justify it, but I really do think that I eat alright. (Women, 18–24) I’m so lucky I’ve got a really good metabolism, and also people will be like, I’ve got a block of chocolate down to fifteen minutes, because if I’ve got a five-hour shift, I only get a few minutes, and they are like but that’s so bad for you, yeah but it’s like calcium… and then if I’m at uni and I want to be healthy, I’ll have like steamed dim sims instead of fried dim sims… so I can justify it all in my head, and I know that it’s not right.(Women, 18–24)

Amongst the younger women in particular, some felt that as long as they were happy with their weight, their diet was all right.

Yeah that’s right, I’ll go for a run, and I do exercise, I don’t put on weight, I don’t, but I do exercise, but I think I do justify my bad eating because I don’t put on weight. (Women, 18–24)

Participants were prompted to discuss whether they ever ate too much, and if so, in what circumstances. Generally, participants felt that they were aware when they were eating too much, but as with comments about their diets in general, they tended to have reasons for doing so that made it acceptable in the circumstances. Commonly, participants reported that when they went out for a meal they would clean their plates even if they were full. They reported that serving sizes tended to be large and that they did not want to leave food if they had paid for it. A specific example of this was the ‘All you can eat’ deals. In the context of these discussions, there was some awareness of the idea of stopping before you feel full, but it was apparent that the actual practice of this idea was less than the knowledge. In essence, participants experienced far more benefits to eating till they were full than disadvantages.

A [chicken parmigiana] and a steak and it’s huge, I’ll, because it’s there, I’ll just keep going until it’s finished… half way through I’ve probably had enough, I’ll be thinking I’m not hungry anymore, but I’ll just keep going. (Men, 35–50). And because you’ve paid for it. (Men, 35–50).

Overall, these findings support Sobal and Bisogni’s [ 31 ] contention that food choice is multifaceted, situational, dynamic, and complexx. However, some components of their model received more affirmation than others. A key overarching theme from the findings was the strong and pervasive impact of external forces, or at least the perception of these forces, on what and when food is eaten. Although taste and preferences for particular foods, as well as health considerations, were mentioned, often as competing considerations [ 57 ], most of the discussion was about the impact of outside forces on food choice. These included family, work, and social structures, and the expectations (or perceived expectations) of family members, colleagues, friends, and others. According to Chen and Antonelli’s [ 32 ] food choice framework, these largely fall into the category, Food-external factors and, in particular, the Social environment sub-category.

The knowledge that one should be practicing healthy eating, which falls under the Framework’s Cognitive factor category, while seen as an aspiration by most participants, was often viewed as unrealistic, trumped by the need and/or desire for convenience, which might be considered a combination of Food-external factor: Social environment and Personal-state factor: Psychological components, in the Framework. Mete et al. [ 58 ], in a qualitative study with adults aged 25–58, also concluded that healthy food choices were important but not a daily priority, and that healthy eating information was known but viewed as difficult to apply to everyday life. Other research has noted the importance of convenience in food choice [ 59 – 60 ]. Jabs et al. [ 61 ], for example, in a study with low-wage employed mothers, found that most expressed feelings of time scarcity and that, while they prioritised feeding their children, they also wanted to complete meals quickly to move on to other tasks. Bava et al. [ 62 ] found that, while the working women in their study said they would ideally choose healthier food, the reality of their lives demanded convenience in food provision to minimise time and cognitive effort.

Other categories and sub-categories of Chen and Antonelli’s [ 32 ] framework, while less discussed by participants, were mentioned. Dearth of food choices when travelling for work, for example, might be categorised under Food-external factor: Physical environment. Personal-state factor: Habits and experiences was demonstrated by discussions around eating the same breakfast every day [ 63 ]. Personal-state factor: Physiological needs came up in discussions around needing to eat even if one didn’t feel like it in order to not go hungry later in the day, or with men's and boys' needs to eat bulky food to fill up. Desires or cravings for less healthy foods (Food-internal factor) were also perceived as working against the ideal of healthy eating.

Although our study did not seek to explore gender or life stage differences in food choice, several tendencies were observed, which future research may want to further explore. In particular, the women with children discussed food choice largely in terms of what others in the family– i.e., their partner and children– liked and which fit in with their schedules. The men, on the other hand, all of whom had children, more often spoke of eating to fill themselves up, or ‘food as fuel.’ Newcome et al. [ 64 ], in a study with partnered men, concluded that men in families displayed unease at expressing enjoyment in food (‘Men downplayed their hedonic consumption’), and instead spoke about food as being largely functional as fuel for their bodies. If these gender and life stage differences prove to be robust, this may suggest quite different public health messaging targeted to women with children, men with children, and those without partners or children. Much of the literature on food choice focuses on women, who continue to be more involved with family food decisions than do their male partners [ 65 ], and thus more is known about women’s food choices.

The findings from this study suggest that public health efforts aimed at educating and encouraging individuals to eat more healthfully are, on their own, insufficient to significantly improve healthy eating at a population level. These public health efforts need to be delivered in conjunction with legislation that removes structural barriers to promote healthy eating.

The vast majority of our participants knew they should be eating more healthfully but felt largely unable to do so. Instead, some of these identified structural barriers must be addressed. In particular, improvements to the food environment are needed, particularly in rural areas where distances are greater [ 66 ]. Greater provision of quickly preparable, accessible, and reasonably priced food, for example, would assist with some of the time barriers. More workplaces could consider providing free and accessible fruit or other healthy snacks for their employees [ 67 ]. Children’s sporting facilities could ensure that healthy foods are available [ 68 ].

As with any study, this one has several limitations. First, the focus groups were conducted in 2010; since then, various changes have occurred in the food environment that are potentially relevant to food choice and the findings from this study. These include the rapid proliferation of online food delivery services. There is evidence, for example, that such services increase the geographic access to foods prepared away from home and that these foods tend not to meet healthy eating recommendations [ 69 ]. There has also been a significant increase in the production and promotion of convenience and ultra-processed foods over this time [ 70 ]. In addition, the marketing of fast food, beverage, and snack brands has expanded via social media [ 71 ], with evidence that digital food marketing and social media can influence food choices, preferences, and consumption [ 72 ]. Therefore, our findings should be interpreted within this context. Future studies are needed to determine the extent to which the various barriers and enablers to healthy eating identified in this study continue to hold.

Second, the findings of this study are based on only three groups of people with a total of 23 participants, all of whom live in or near a rural region in Victoria, Australia. However, one would assume that many of the discussions around personal, family, and workplace factors would translate beyond this specific group of people, and particularly to other people living in Western countries in non-metropolitan areas. A third limitation of this study is that neither actual dietary intake data nor measures of nutritional knowledge was collected from participants, which would have allowed comparison of what participants discussed against more objective data. However, the focus of this study was on understanding how people think about their eating behaviours and perceptions of motivations and barriers to eating more healthily, rather than on whether their self-reports are factually correct. Moreover, we know that food diary data is often inaccurate [ 73 – 74 ]. Fourth, a single researcher conducted the focus groups and analysed the data. However, with thematic analysis, coding quality is not dependent on multiple coders [ 75 ]. The results were discussed with the other co-authors and the first author also read the transcripts. All three authors agreed with the findings.

Despite a plethora of information regarding how people should eat, surprisingly little research explores how and why people eat the way they do– particularly in a general population. Based on findings from focus groups with a range of participants from a rural region of Victoria, Australia, we found that, although decisions regarding when, what, and how much to eat are determined in part by taste preferences and health considerations, they are heavily influenced by a host of other factors. Moreover, many of these factors exist outside the control of the individual, including other household members’ preferences, family activities, and workplace and time constraints, as well as convenience and price. It appears, therefore, that education alone will not solve the problem of unhealthy eating. People want to eat healthier, or at least know they should eat healthier, but it’s all just too hard. It would seem, then, that a key to improving people’s eating behaviours is to make it easy to eat more healthfully, or at least not much harder than eating poorly.

Data availability

De-identified transcripts will be considered by the corresponding author upon request.Due to the nature of the data (i.e.,dSAZX a small number of focus group participants from a single geographic area), it is very difficult to anonymize the data. In addition, the participants did not provide explicit consent for the transcripts to be shared publicly.

World Health Organization. Healthy diet. World Health Organization. Regional Office for the Eastern Mediterranean; 2019.

Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, Wilson MM, Reedy J. Update of the healthy eating index: HEI-2015. J Acad Nutr Dietetics. 2018;118(9):1591–602.

Article   Google Scholar  

Visseren FL, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, Benetos A, Biffi A, Boavida JM, Capodanno D, Cosyns B. 2021 ESC guidelines on cardiovascular disease prevention in clinical practice: developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies with the special contribution of the European Association of Preventive Cardiology (EAPC). Eur J Prev Cardiol. 2022;29(1):5–115.

Article   PubMed   Google Scholar  

Tiedje K, Wieland ML, Meiers SJ, Mohamed AA, Formea CM, Ridgeway JL, Asiedu GB, Boyum G, Weis JA, Nigon JA, Patten CA. A focus group study of healthy eating knowledge, practices, and barriers among adult and adolescent immigrants and refugees in the United States. Int J Behav Nutr Phys Activity. 2014;11(1):1–6.

Manickavasagan A, Al-Mahdouri AA, Al-Mufargi AM, Al-Souti A, Al-Mezeini AS, Essa MM. Healthy eating knowledge among college students in Muscat: a self reported survey. Pakistan J Nutr. 2014;13(7):397–403.

Carrillo E, Varela P, Fiszman S. Influence of nutritional knowledge on the use and interpretation of Spanish nutritional food labels. J Food Sci. 2012;77(1):H1–8.

Article   CAS   PubMed   Google Scholar  

Ross A, Bevans M, Brooks AT, Gibbons S, Wallen GR. Nurses and health-promoting behaviors: knowledge may not translate into self-care. AORN J. 2017;105(3):267–75.

Article   PubMed   PubMed Central   Google Scholar  

Ronto R, Ball L, Pendergast D, Harris N. Adolescents’ perspectives on food literacy and its impact on their dietary behaviours. Appetite. 2016;107:549–57.

Glanz K, Bishop DB. The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health. 2010;31:399–418.

Carins JE, Rundle-Thiele SR. Supporting healthy eating behavior through social marketing. Nutrition Science, Marketing Nutrition, Health claims, and Public Policy. Academic; 2023. pp. 231–41.

Harris JA, Carins J, Rundle-Thiele S. Can Social Cognitive Theory Influence Breakfast frequency in an institutional context: a qualitative study. Int J Environ Res Public Health. 2021;18(21):11270.

Andreasen AR. Marketing social change: changing behavior to promote health, social development, and the environment. San Francisco: Jossey-Bass; 1995.

Google Scholar  

McKenzie-Mohr D, Schultz PW. Choosing effective behavior change tools. Social Mark Q. 2014;20(1):35–46.

Bisogni CA, Connors M, Devine CM, Sobal J. Who we are and how we eat: a qualitative study of identities in food choice. J Nutr Educ Behav. 2002;34(3):128–39.

Monteleone E, Spinelli S, Dinnella C, Endrizzi I, Laureati M, Pagliarini E, Sinesio F, Gasperi F, Torri L, Aprea E, Bailetti LI. Exploring influences on food choice in a large population sample: the Italian taste project. Food Qual Prefer. 2017;59:123–40.

Ronto R, Saberi G, Carins J, Papier K, Fox E. Exploring young australians’ understanding of sustainable and healthy diets: a qualitative study. Public Health Nutr. 2022;25(10):2957–69.

Rose N, Reeve B, Charlton K. Barriers and enablers for healthy food systems and environments: the role of local governments. Curr Nutr Rep. 2022;11(1):82–93.

Rosewarne E, Chislett WK, McKenzie B, Mhurchu CN, Boelsen-Robinson T, Blake M, Webster J. Understanding enablers and barriers to the implementation of Nutrition standards in publicly funded institutions in Victoria. Nutrients. 2022;14(13):2628.

Godrich S, Kent K, Murray S, Auckland S, Lo J, Blekkenhorst L, Devine A. Australian consumer perceptions of regionally grown fruits and vegetables: Importance, enablers, and barriers. Int J Environ Res Public Health. 2020;17(1):63.

Herforth A, Arimond M, Álvarez-Sánchez C, Coates J, Christianson K, Muehlhoff E. A global review of food-based dietary guidelines. Adv Nutr. 2019;10(4):590–605.

Rong S, Liao Y, Zhou J, Yang W, Yang Y. Comparison of dietary guidelines among 96 countries worldwide. Trends Food Sci Technol. 2021;109:219–29.

Article   CAS   Google Scholar  

Fernandez ML, Raheem D, Ramos F, Carrascosa C, Saraiva A, Raposo A. Highlights of current dietary guidelines in five continents. Int J Environ Res Public Health. 2021;18(6):2814.

de Ridder D, Kroese F, Evers C, Adriaanse M, Gillebaart M. Healthy diet: Health impact, prevalence, correlates, and interventions. Psychol Health. 2017;32(8):907–41.

Chambers S, Lobb A, Butler LT, Traill WB. The influence of age and gender on food choice: a focus group exploration. Int J Consumer Stud. 2008;32(4):356–65.

Niven P, Morley B, Gascoyne C, Dixon H, McAleese A, Martin J, Wakefield M. Differences in healthiness perceptions of food and dietary patterns among the general public and nutrition experts: a cross-sectional online survey. Health Promotion J Australia. 2022;33(2):361–72.

Dickson-Spillmann M, Siegrist M. Consumers’ knowledge of healthy diets and its correlation with dietary behaviour. J Hum Nutr Dietetics. 2011;24(1):54–60.

Spronk I, Kullen C, Burdon C, O’Connor H. Relationship between nutrition knowledge and dietary intake. Br J Nutr. 2014;111(10):1713–26.

Guthrie J, Mancino L, Lin CT. Nudging consumers toward better food choices: Policy approaches to changing food consumption behaviors. Psychol Mark. 2015;32(5):501–11.

McDonald A, Braun V. Right, yet impossible? Constructions of healthy eating. SSM-Qualitative Res Health. 2022;2:100100.

Colatruglio S, Slater J. (2014). Food literacy: bridging the gap between food, nutrition and well-being. Sustainable well-being: Concepts, issues, and educational practices, 37–55.

Sobal J, Bisogni CA. Constructing food choice decisions. Ann Behav Med. 2009;38(suppl1):s37–46.

Chen PJ, Antonelli M. Conceptual models of food choice: influential factors related to foods, individual differences, and society. Foods. 2020;9(12):1898.

Brogan E, Rossiter C, Duffield C, Denney-Wilson E. Healthy eating and physical activity among new graduate nurses: a qualitative study of barriers and enablers during their first year of clinical practice. Collegian. 2021;28(5):489–97.

Kebbe M, Damanhoury S, Browne N, Dyson MP, McHugh TL, Ball GD. Barriers to and enablers of healthy lifestyle behaviours in adolescents with obesity: a scoping review and stakeholder consultation. Obes Rev. 2017;18(12):1439–53.

Kebbe M, Perez A, Buchholz A, McHugh TL, Scott SD, Richard C, Mohipp C, Dyson MP, Ball GD. Barriers and enablers for adopting lifestyle behavior changes in adolescents with obesity: a multi-centre, qualitative study. PLoS ONE. 2018;13(12):e0209219.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Nor NM, Shukri NM, Yassin NQ, Sidek S, Azahari N. Barriers and enablers to make lifestyle changes among type 2 diabetes patients: a review. Sains Malaysiana. 2019;48(7):1491–502.

Nicholls R, Perry L, Duffield C, Gallagher R, Pierce H. Barriers and facilitators to healthy eating for nurses in the workplace: an integrative review. J Adv Nurs. 2017;73(5):1051–65.

Scannell N, Villani A, Mantzioris E, Swanepoel L. Understanding the self-perceived barriers and enablers toward adopting a Mediterranean diet in Australia: an application of the theory of planned behaviour framework. Int J Environ Res Public Health. 2020;17(24):9321.

Mayr HL, Kelly JT, Macdonald GA, Russell AW, Hickman IJ. Clinician perspectives of barriers and enablers to implementing the Mediterranean dietary pattern in routine care for coronary heart disease and type 2 diabetes: a qualitative interview study. J Acad Nutr Dietetics. 2022;122(7):1263–82.

Munt AE, Partridge SR, Allman-Farinelli M. The barriers and enablers of healthy eating among young adults: a missing piece of the obesity puzzle: a scoping review. Obes Rev. 2017;18(1):1–7.

Amore L, Buchthal OV, Banna JC. Identifying perceived barriers and enablers of healthy eating in college students in Hawai’i: a qualitative study using focus groups. BMC Nutr. 2019;5(1):1–1.

Sogari G, Velez-Argumedo C, Gómez MI, Mora C. College students and eating habits: a study using an ecological model for healthy behavior. Nutrients. 2018;10(12):1823.

Herman CP, Polivy J, Pliner P, Vartanian LR. Social influences on eating. Volume 5. Cham: Springer; 2019 Sep.

Higgs S. Social norms and their influence on eating behaviours. Appetite. 2015;86:38–44.

Australian Institute of Health and Welfare. Rural and Remote Health. Available online: https://www.aihw.gov.au/reports/rural-remote-australians/rural-and-remote-health (accessed on 7 March 2024).

Alston L, Jacobs J, Allender S, Nichols M. A comparison of the modelled impacts on CVD mortality if attainment of public health recommendations was achieved in metropolitan and rural Australia. Public Health Nutr. 2020;23(2):339–47.

Moayyed H, Kelly B, Feng X, Flood V. Is living near healthier food stores associated with better food intake in regional Australia? Int J Environ Res Public Health. 2017;14(8):884.

Whelan J, Millar L, Bell C, Russell C, Grainger F, Allender S, Love P. You can’t find healthy food in the bush: poor accessibility, availability and adequacy of food in rural Australia. Int J Environ Res Public Health. 2018;15(10):2316.

National Rural Health Alliance. Poverty in rural and remote Australia. Available onlilne: https://www.ruralhealth.org.au/sites/default/files/publications/nrha-factsheet-povertynov2017.pdf (accessed 25 January 2024).

Van Dyke N, Murphy M, Drinkwater EJ. What do people think of intuitive eating? A qualitative exploration with rural australians. PLoS ONE. 2023;18(8):e0278979.

Van Dyke N, Drinkwater EJ. Intuitive eating is positively associated with indicators of physical and mental health among rural Australian adults. Aust J Rural Health. 2022;30(4):468–77.

Van Dyke N, Drinkwater EJ. Review article relationships between intuitive eating and health indicators: literature review. Public Health Nutr. 2014;17(8):1757–66.

Van Dyke N, Drinkwater EJ, Rachele JN. Improving the accuracy of self-reported height and weight in surveys: an experimental study. BMC Med Res Methodol. 2022;22(1):1–14.

Devine CM, Olson CM. Women’s dietary prevention motives: life stage influences. J Nutr Educ. 1991;23(6):269–74.

Thomas DR. A general inductive approach for analyzing qualitative evaluation data. Am J Evaluation. 2006;27(2):237–46.

Bingham AJ, Witkowsky P. Deductive and inductive approaches to qualitative data analysis. Analyzing and interpreting qualitative data: After the interview. 2021 Apr 8:133– 46. SAGE Publications.

Frank-Podlech S, Watson P, Verhoeven AA, Stegmaier S, Preissl H, de Wit S. Competing influences on healthy food choices: mindsetting versus contextual food cues. Appetite. 2021;166:105476.

Mete R, Curlewis J, Shield A, Murray K, Bacon R, Kellett J. Reframing healthy food choices: a content analysis of Australian healthy eating blogs. BMC Public Health. 2019;19(1):1–9.

Phan UT, Chambers IVE. Motivations for choosing various food groups based on individual foods. Appetite. 2016;105:204–11.

Aggarwal A, Rehm CD, Monsivais P, Drewnowski A. Importance of taste, nutrition, cost and convenience in relation to diet quality: evidence of nutrition resilience among US adults using National Health and Nutrition Examination Survey (NHANES) 2007–2010. Prev Med. 2016;90:184–92.

Jabs J, Devine CM, Bisogni CA, Farrell TJ, Jastran M, Wethington E. Trying to find the quickest way: employed mothers’ constructions of time for food. J Nutr Educ Behav. 2007;39(1):18–25.

Bava CM, Jaeger SR, Park J. Constraints upon food provisioning practices in ‘busy’women’s lives: Trade-offs which demand convenience. Appetite. 2008;50(2–3):486–98.

Jastran MM, Bisogni CA, Sobal J, Blake C, Devine CM. Eating routines. Embedded, value based, modifiable, and reflective. Appetite. 2009;52(1):127–36.

Newcombe MA, McCarthy MB, Cronin JM, McCarthy SN. Eat like a man. A social constructionist analysis of the role of food in men’s lives. Appetite. 2012;59(2):391–8.

Daminger A. The cognitive dimension of household labor. Am Sociol Rev. 2019;84(4):609–33.

Lenardson JD, Hansen AY, Hartley D. Rural and remote food environments and obesity. Curr Obes Rep. 2015;4:46–53.

Pescud M, Waterworth P, Shilton T, Teal R, Slevin T, Ledger M, Rosenberg M. A healthier workplace? Implementation of fruit boxes in the workplace. Health Educ J. 2016;75(7):843–54.

Kelly B, King L, Bauman AE, Baur LA, Macniven R, Chapman K, Smith BJ. Identifying important and feasible policies and actions for health at community sports clubs: a consensus-generating approach. J Sci Med Sport. 2014;17(1):61–6.

Brar K, Minaker LM. Geographic reach and nutritional quality of foods available from mobile online food delivery service applications: novel opportunities for retail food environment surveillance. BMC Public Health. 2021;21(1):1–11.

Baker P, Machado P, Santos T, Sievert K, Backholer K, Hadjikakou M, Lawrence M. (2020). Ultra-processed foods and the nutrition transition: global, regional and national trends, food systems transformations and political economy drivers. Obes Rev, 21(12), e13126.

Bragg MA, Pageot YK, Amico A, Miller AN, Gasbarre A, Rummo PE, Elbel B. (2020). Fast food, beverage, and snack brands on social media in the United States: an examination of marketing techniques utilized in 2000 brand posts. Pediatr Obes, 15(5), e12606.

Granheim SI, Løvhaug AL, Terragni L, Torheim LE, Thurston M. (2022). Mapping the digital food environment: a systematic scoping review. Obes Rev, 23(1), e13356.

Garden L, Clark H, Whybrow S, Stubbs RJ. Is misreporting of dietary intake by weighed food records or 24-hour recalls food specific? Eur J Clin Nutr. 2018;72(7):1026–34.

Saravia L, Moliterno P, Skapino E, Moreno LA, Food Diary. Food frequency questionnaire, and 24-Hour Dietary Recall. InBasic protocols in Foods and Nutrition 2022 Jun 8 (pp. 223–47). New York, NY: Springer US.

Braun V, Clarke V. Conceptual and design thinking for thematic analysis. Qualitative Psychol. 2022;9(1):3.

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Acknowledgements

We would like to thank the Social Research Centre for conducting the focus groups at cost. We would also like to acknowledge the focus group participants, who generously shared information and insights about themselves and their families.

This study was funded in part by a Research Development Fund from Charles Sturt University. In addition, The Social Research Centre provided an in lieu contribution of four hours per week of author Van Dyke’s time to work on this project.

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NV conceived the project and wrote the main manuscript text other than the Results section. MM conducted the analysis of data and wrote the Results section. All authors reviewed the manuscript.

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Van Dyke, N., Murphy, M. & Drinkwater, E.J. “We know what we should be eating, but we don’t always do that.” How and why people eat the way they do: a qualitative study with rural australians. BMC Public Health 24 , 1240 (2024). https://doi.org/10.1186/s12889-024-18432-x

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Drivers of district-level differences in outpatient antibiotic prescribing in Germany: a qualitative study with prescribers

• Benjamin Schüz 1 ,
• Oliver Scholle 2 ,
• Ulrike Haug 2 , 3 ,
• Roland Tillmann 4 &
• Christopher Jones 1 , 5  

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Previous studies have identified substantial regional variations in outpatient antibiotic prescribing in Germany, both in the paediatric and adult population. This indicates inappropriate antibiotic prescribing in some regions, which should be avoided to reduce antimicrobial resistance and potential side effects. The reasons for regional variations in outpatient antibiotic prescribing are not yet completely understood; socioeconomic and health care density differences between regions do not fully explain such differences. Here, we apply a behavioural perspective by adapting the Theoretical Domains Framework (TDF) to examine regional factors deemed relevant for outpatient antibiotic prescriptions by paediatricians and general practitioners.

Qualitative study with guideline-based telephone interviews of 40 prescribers (paediatricians and general practitioners) in outpatient settings from regions with high and low rates of antibiotic prescriptions, stratified by urbanity. TDF domains formed the basis of an interview guide to assess region-level resources and barriers to rational antibiotic prescription behaviour. Interviews lasted 30–61 min (M = 45 min). Thematic analysis was used to identify thematic clusters, and relationships between themes were explored through proximity estimation.

Both paediatricians and general practitioners in low-prescribing regions reported supporting contextual factors (in particular good collegial networks, good collaboration with laboratories) and social factors (collegial support and low patient demand for antibiotics) as important resources. In high-prescribing regions, poor coordination between in-patient and ambulatory health services, lack of region-level information on antimicrobial resistance, few professional development opportunities, and regional variations in patient expectations were identified as barriers to rational prescribing behaviour.

Conclusions

Interventions targeting professional development, better collaboration structures with laboratories and clearer and user-friendly guidelines could potentially support rational antibiotic prescribing behaviour. In addition, better networking and social support among physicians could support lower prescription rates.

Peer Review reports

Antimicrobial resistance is a major threat to global health systems [ 1 ]. Despite improvements in international surveillance programs [ 2 ], for example in the WHO European region in 2019 alone, around 541,000 deaths were associated with and 133,000 deaths were directly attributable to antimicrobial resistance [ 3 ]. One of the key drivers of antimicrobial resistance in humans is previous exposure to antibiotics [ 4 ]. To reduce the development of antimicrobial resistance, improving rational antibiotic prescription practices (i.e. avoiding unnecessary prescriptions) is crucial [ 5 , 6 ]. Most antibiotic prescriptions in outpatient settings in Europe are for respiratory and urinary tract infections [ 7 , 8 ]. In Germany, the setting of this study, most outpatient prescriptions for antibiotics are issued by general practitioners and paediatricians [ 8 , 9 ].

Germany consistently ranks among the European countries with the lowest community consumption of antibiotics, for example, in the 2021 surveillance report of the European Centre for Disease Prevention and Control [ 10 ], Germany has the 3rd lowest community consumption of antibiotics for systemic use. Still, there is considerable regional variation in outpatient prescription rates across regions in Germany [ 11 ]. International research suggests that such regional differences in outpatient prescriptions cannot be fully explained by regional differences in infectious disease prevalence [ 12 ]. Instead, socioeconomic, demographic and cultural differences have been highlighted as additional key determinants [ 13 ].

A recent small-area analysis based on health insurance claims data [ 11 ], breaking down differences between the 401 administrative districts in Germany, found up to 4-fold differences in outpatient prescription rates for children (between 188 and 710 age- and sex-standardized outpatient prescriptions per 1000 persons/year), and more than 2-fold differences in adults (between 300 and 693 prescriptions per 1000 persons/year). These substantial regional variations in prescription rates continue to raise concern about the appropriateness of antibiotic prescribing practices in Germany [ 8 ].

At the same time, reasons for the observed regional differences in outpatient antibiotic prescription rates are not fully understood. On the one hand, urban-rural differences in prescription patterns might be due to differences in health care access and socioeconomic differences in populations such as age or deprivation status [ 14 ]. Proximity to animal breeding or fattening farms has also been associated with variations in antibiotic prescriptions [ 15 ]. Further regional differences exist in the quality and accessibility of out-of-hours emergency primary care settings, which have both been associated with an increase in antibiotic prescriptions [ 16 ].

On the other hand, non-clinical factors such as demographic and socioeconomic differences [ 13 ], or differences in patient demand and prescription practices have been suggested to underlie regional variations [ 17 ], and the influence of patient demand on inappropriate antibiotic prescriptions is well documented [ 18 , 19 ]. Supporting small-area differences, calls have been made to take into account small-area regional factors in devising targeted interventions to support rational prescription practices [ 20 ].

Together, this suggests that a better understanding of the reasons underlying regional variations in outpatient antibiotic prescriptions is vital, especially for the development and implementation of better interventions to avoid inappropriate antibiotic prescriptions. The present study is based on a mixed methods research project commissioned by the German Federal Ministry of Health (SARA; “Studie zur Analyse der Regionalen Unterschiede bei der Antibiotika-Verordnung” [Study to analyse regional variations in antibiotic prescriptions]). Previous publications from this research project include the abovementioned small-area analysis of health insurance claims data [ 11 ] and a conference presentation containing some of the present data [ 21 ]. The current study focuses on the qualitative part of the project and reports results from interviews with prescribers in outpatient settings.

To this end, it builds on the patterns of regional differences identified in the previous quantitative study [ 11 ] to better understand the drivers of these regional differences in prescription behaviour based on perceptions of prescribers (general practitioners and paediatricians) in districts differing by outpatient antibiotic prescription rates.

In order to do so, an established framework of determinants of health care professional behaviours, the Theoretical Domains Framework (TDF; [ 22 , 23 ]) was used to guide qualitative interviews with prescribers.

The TDF is a psychological model developed for healthcare and behaviour change research and is based on comprehensive reviews of behavioural theories [ 22 , 24 ]. It comprises 14 key individual, social and contextual domains influencing human behaviour: knowledge, skills, social/professional role/identity, beliefs about capabilities, optimism, beliefs about consequences, reinforcement, intentions, goals, memory/attention/decision processes, environmental context/resources, social influences, emotion, and behavioural regulation. Both main effects of and interactions between domains are possible.

The TDF has been instrumental in examining individual determinants of antibiotic prescribing behaviour [ 25 , 26 , 27 , 28 ], and most studies show the domain of environmental context and resources to be influential for antibiotic prescriptions. However, which contextual aspects are particularly relevant is poorly understood to date.

The degree to which contextual resources and barriers as well as their interactions are specific to small-area districts and regions is vital to understand the observed variations in prescription rates and improve future intervention efforts. This study will therefore apply the TDF to understand differences in contextual determinants of antibiotic prescriptions and map these onto established small-area differences in paediatricians and general practitioners in Germany.

Participants and procedure

To identify region-level determinants of differences in outpatient antibiotic prescribing, semi-structured interviews were conducted with general practitioners and paediatricians working in outpatient settings. The protocol for this study was approved by the University of Bremen Ethics committee (AZ 2021-03).

Data collection materials

An interview guide (supplementary file 1 ) based on the Theoretical Domains Framework (TDF) [ 22 , 23 ] and previous studies using the TDF in antibiotic prescription contexts [ 25 , 28 ] was designed with input from a paediatrician (RT) and pharmacoepidemiologists (UH, OS) and was pilot-tested with GP representatives known to the researchers. The interview guide started with informing participants about the status of their district as high-or low-prescribing and subsequently asked an open question on prescribers’ ideas on reasons for this. Following this, we asked prescribers for their perceptions on regional levels of TDF domains relevant for prescribing antibiotics [ 25 , 28 ]; (i) knowledge, (ii) social support, (iii) environmental context and resources (and perceived differences to other districts), (iv) social and professional role, (v) social influences (patients), (vi) goals, (vii) beliefs about capabilities (patient expectation management), (viii) beliefs about consequences, (ix) optimism, (x) intentions, (xi) memory and attention processes.

Recruitment

We employed purposive sampling and stratified potential participants based on our previous quantitative analysis of regional differences in medical claims data of outpatient antimicrobial prescriptions in Germany [ 11 ]. Here, differences in prescriptions were compared between administrative districts (“Landkreise” or “kreisfreie Städte”; Nomenclature of Territorial Units for Statistics NUTS level-3 subdivision [ 29 ]).

In order to compare and contrast health care providers’ perspectives on regional differences, we selected, separately for paediatricians and GPs, 5 districts each that were within the 5% highest antibiotic prescription rates per 1,000 insured persons, and 5 districts that were within the 5% lowest antibiotic prescription rates. Within each district group, we further selected rural and urban districts (classification based on official regional statistics in Germany; [ 30 ]) to account for potential differences in settlement structure.

Contact information for paediatrician and GP practices in the respective districts were obtained through the regional representations of the respective medical councils, and were contacted through email and phone calls. Snowball recruitment was used during which participants recommended further colleagues within the respective districts, and a total of 1,444 contact attempts were made. Participants received €75 (approximately US$80) for their participation.

Prescribers who had expressed interest in the study were emailed a participant information sheet and were asked to suggest a date and time for a phone interview. Semi-structured telephone interviews were subsequently conducted by experienced female and male qualitative researchers (CJ, BS, PK), audio recorded and were transcribed verbatim. Interviews lasted a mean of 45 min (range 30–61 min) and started with an introduction, brief overview of the study goals, and verbal informed consent was obtained prior to interview commencement. The interviewed prescribers had no personal or professional connection to the researchers before the interviews.

Data analysis

Starting with the TDF domains in the interview guide, data analysis utilized an deductive approach and was based on thematic analysis [ 31 ]. Two researchers (CJ, BS) independently coded the material using MaxQDA data management software. Initial codes were reviewed between the two researchers, and saturation was achieved with both the paediatrician and GP interviews. All codes were mapped onto at least one of the TDF domains. Relationships between codes were examined looking at code overlaps in coded segments and analysing the relative proximity of coded segments in the transcribed text. The more frequently two codes appear in the same segment or in relative proximity, the more substantial overlaps between the codes are assumed. The relative positions of codes in this two-dimensional space were operationalized using multidimensional scaling implemented in MaxQDA. Here, a solution is estimated which replicates the distance between elements in the two-dimensional space between codes as well as possible relationships. Assigning of a code to a cluster of codes is estimated using the Unweighted Average Linkage method [ 32 ]. Disagreements were resolved through discussion between the researchers.

Results of the thematic analyses are presented separately for GPs and paediatricians.

Participants

A total of 40 interviews (17 paediatricians; 10 from high-prescription and 7 from low-prescription districts, 23 GPs; 10 from high-prescription and 13 from low-prescription districts) were conducted. Participants had between 1 and 35 years of experience in their current positions (mean 13.4 years, SD 9.9 years). Interviews lasted an average of 44.8 min (SD 7.1 min, range 30–61 min).

Paediatricians

TDF domains on region levels mentioned as influencing paediatricians’ prescribing behaviour (Fig.  1 ) included context and resources (86 mentions), social influences (56 mentions), knowledge (36 mentions), skills (22 mentions), social/professional role (15 mentions), beliefs about consequences (15 mentions), beliefs about capabilities (9 mentions), goals (9 mentions), behavioural regulation (6 mentions), optimism (3 mentions) and emotions (2 mentions).

TDF domains mentioned as barriers (red) or resources (blue) by paediatricians

Context and resources

Regional context and resources can affect prescribing behaviour through multiple, direct and indirect pathways, according to the participating paediatricians. The distinction between contextual (i.e., factors specific to the region) and composition effects (i.e., factors resulting from the composition of the population within a region; [ 33 ]) is particularly relevant.

Paediatricians mainly mentioned contextual factors, e.g., air pollution as a risk factor:

This area here is a former working-class area, air quality is poor, and this means we have more respiratory illnesses which are the most frequent reasons for antimicrobial prescriptions.

(A, paediatrician, urban area, high prescription rate)

Similar direct contextual effects are evident in the density of paediatricians:

…This means service provision for children in an emergency is limited, and they are rather seen by GPs. And the GPs are fantastic, […], but they don’t have our special training and might be a bit more anxious if they see a child with a high fever….

(B, paediatrician, rural area, high prescription rate)

This low density then results in overload of the paediatricians, which in turn can increase antimicrobial prescriptions:

I mean on a Monday in February I have seen about 200 children, or thereabouts. And then I can’t start discussing for ages, this just doesn’t work.

(C, paediatrician, rural area, high prescription rates)

Suboptimal transition from in-patient to out-patient care were also seen to increase antimicrobial prescriptions in districts with higher prescription rates:

…in the hospitals, they prescribe broad-spectrum antibiotics. And I have to say, after we have sat down together a year ago and have talked about outpatient antibiotic therapies, we had agreed on not prescribing some particular antibiotics. And now I see that these exact antibiotics are still being used in the hospital.

(D, paediatrician, rural area, high prescription rates)

Contextual effects however also can constitute resources for lower prescription rates, for example in high-quality laboratories and quick turnaround times:

This means we can get samples to them three or four times a day and are not dependent on pickups once a day like in the practices out there. This really is a resource I think .

(E, paediatrician, rural area, low prescription rates)

Social influences

Social influences have been mentioned frequently, both as social influences through patients and through other health care providers. In particular where patient characteristics are being discussed, such influences could also be classified as compositional context resources (see above). However, as most of the quotes illustrate, these compositional factors also contain social influences.

Social influences as factors affecting high prescription rates are mainly located on patient level, illustrated in the following quote referring to patients with Middle-Eastern migration history:

This is a totally different culture, also affecting ideas about illnesses. Their ideas are totally different, and antibiotics are seen as miracle drugs – they are over the moon if they can get an antibiotic.

(F, paediatrician, urban area, high prescription rates)

However, the demand by patients is also being attributed to context effects such as dominating agricultural influences:

I think that there are lots of expectations for antibiotics by patients. For example, I do have a mother who generally insists on getting an antibiotic for her child, and I wouldn’t prescribe it. And I tell you how she says it: ‘I also give this to my pigs, so it can’t be bad for my kids’. So I think that antibiotic practices in the farms around here, I think that this means they (antibiotics) are applied liberally and happily, and the parents have experience and want them for their kids as well.

At the same time, social influences are seen as malleable influences, in particular in combination with skills and knowledge which can then contribute to improvements in prescription practice:

It has become much better, yes. They (patients) now understand it, they have gotten used to it. And now we have, when the doctor says, you don’t need an antimicrobial, then more than half of them don’t go and see another doctor immediately and say ‘I need an antibiotic’.

(G, paediatrician, urban area, low prescription rates)

Knowledge included both information on current recommendations for antimicrobial prescribing, information on local resistance prevalence, information on local and personal prescription rates, and training content relevant to prescribing antimicrobials.

Participants from low-prescription districts mentioned knowledge on current recommendations as a resource and linked this knowledge to lower prescription rates within their districts:

We feel quite well informed. And everyone builds on that through individual research, further training and talking to colleagues. And I think, else we wouldn’t see these numbers.

(H, paediatrician, rural area, low prescription rates)

In contrast, paediatricians from high-prescription areas mentioned increased effort in obtaining relevant information:

[…] There is no information in the district, you always have to look after this yourself.

(I, paediatrician, rural area, high prescription rates).

In districts that had employed a paediatrician-initiated education programme (AnTiB; [ 34 ]), this programme was mentioned as an explicit resource:

We used to have this little informal guideline here in (city), which is also lying around in out-of-hours paediatric services and which every paediatrician here is likely to have in their practice. It is very useful and if you are doing emergency shifts, you pull it out of the drawer, look at the dosage and then prescribe.

(J, paediatrician, urban area, low prescription rate).

In contrast, the lack of specific knowledge in paediatric emergency services is seen as a barrier to effective prescribing:

We live in one of the areas with the most children in Germany, and, you can’t make this stuff up, we don’t have a paediatric out-of-hours service. This means out-of-hours is staffed by colleagues, e.g., urologists who have no clue, who start googling first – and then quickly prescribe an antibiotic.

Skills as mentioned by the paediatricians include both discipline-specific and generic skills such as language skills or interpersonal skills.

Lack of specific treatment skills are mentioned as barriers to lower prescription rates by paediatricians in high-prescribing districts:

Perhaps the experience that as a urologist, you might not have that much experience with these really high fever temperatures in toddlers under two years.

(K, paediatrician, rural area, high prescription rate).

Similarly, a lack of language skills both on the side of the prescribers and patients is being seen as a barrier, both to non-prescribing and to instructing parents to monitor their children’s health:

… there is such a large language barrier which prevents you from explaining what the parents have to look out for, what are the signs of deteriorations, when do they need to come back, well, that this is a problem overall.

(L, paediatrician, urban area, high prescription rate)

Social and professional role

Social and professional role are mainly seen as a resource for low prescription rates. The main effects are seen to be indirect, via social norms and better professional networks. In some areas, this professional role is a relevant part of paediatricians’ identity which is used to be a role model to other paediatricians.

I think there are these lighthouse or role model practices here, the bigger ones. And they do this on purpose, to set standards and blaze a trail, and the younger colleagues or others then orient themselves on them.

(M, paediatrician, urban area, low prescription rates)

In addition, the social influence through networks is being seen as strengthened through social and professional roles and identity:

So we do have quite a number of colleagues who are really well connected. They always participate in our quality groups, participate very reliably, and have good contact amongst themselves.

Beliefs about consequences

Beliefs about consequences tend to be related to contextual and environmental resources or barriers as well as regional outcomes. A particularly strong motive seems to be using antibiotics to prevent potential risks.

Paediatricians from districts with high prescription rates discuss avoiding consequences in particular with regards to patient overload:

My personal record in winter was 209 children a day. […] I have briefly checked them and then prescribed an antibiotic, because even if most of it is viral, you have children with whooping cough and I tend to be generous, because the hospitals are full of pneumonia.

Paediatricians from districts with low prescription rates on the other hand discuss low beliefs about negative consequences such as patients changing doctors due to low competition pressure:

So we don’t really have a competitive mindset here, because changes from one paediatrician to the other are really, really rare.

Interestingly, beliefs about consequences in terms of developing resistant microbes differ between paediatricians from low- and high-prescribing districts. Whereas those from high-prescribing districts argue that the responsibility for resistances is mainly located in the agricultural sector:

I think that resistant microbes develop if the farms in the area use lots of antibiotics […] So the kids who have MRSA here, they are all from farms. So they didn’t get MRSA because we gave them antibiotics but because the farms at home use lots of antibiotics.

(C, paediatrician, rural area, high prescription rates),

Those from low-prescription districts tend to attribute resistance development to health care professional behaviour:

The less antibiotics one prescribes, and if this happens everywhere, then we can expect, that the development of resistances will be less bad than elsewhere.

Beliefs about competences

Beliefs about competences mainly revolved around perceptions of competence to influence local resistance developments and largely mirror those exemplified in the beliefs about consequences section.

Both paediatricians from low- and high-prescribing districts explicitly mentioned goals to prescribe less antimicrobials, and mention that these goals are also shared by colleagues in the respective districts. Differences exist in the context within goals are mentioned – paediatricians from low-prescription districts mention the goal of lower prescriptions as part of a combinations of goals (e.g., optimal therapy or limiting resistance development), paediatricians from high-prescription districts concentrate on potentially more relevant goals than lower prescription rates:

…I think I can speak for most of my colleagues here, one tries to prescribe as little as possible. But if they really all read the reports, do they change their prescription behaviour, I doubt that. There are quite some other problems here that need solving as well.

Behavioural regulation

Behavioural regulation had only six mentions, but these were mainly together with contextual factors in districts with high prescription prevalence to highlight that contextual factors can pose barriers which also affect the low likelihood to change through impeding behavioural regulation:

And I think that these are basically deeply rooted, historic, ritualized prescription patterns, which then manifest regionally such that it is really difficult to change this.

General Practitioners (GPs)

TDF domains on district level that affected GP prescribing behaviour (Fig.  2 ) included context and resources (159 mentions), social influence (60 mentions), knowledge (41 mentions), beliefs about consequences (29 mentions), social/professional role (16 mentions), skills (16 mentions), goals (6 mentions), and behavioural regulation (4 mentions).

TDF domains mentioned as barriers (red) or resources (blue) by GPs

Similar to the paediatric participants, GPs reported on a range of regional contextual factors that influenced prescribing behaviour. These can also be differentiated along contextual and compositional factors [ 33 ].

A combination of contextual (main industry in the region) and compositional (migrant workers in the main industry) is a good example for these influences:

With the (migrant) workers in the meat industry, we do have a lot of people who might have potentially problems in dental hygiene, infections due to cuts for example. This happens a lot, and then increases the prescription of (antimicrobials).

(N, GP, rural area, high prescription rates).

GPs also report on regional differences in the influence of pharmaceutical representatives in their practices. For example, a GP from a low-prescription rural district mentioned that their local quality circles “will not invite pharmaceutical representatives if possible”.

Social influence

Social influences differ between districts, according to GP participants, and similar to paediatricians, these influences come through colleagues and patients.

One example for a local social influence could be long established GPs who influence local quality circles:

…as a young and newly arrived doctor, I quit going to the quality circles because the old guard was so present and influenced communication, work and thinking about practices. However, we do have now a new generation of GPs and things change.

(O, GP, rural area, low prescription rates)

Patient-level influences are also perceived to differ between districts, with some of the differences in expectations to be prescribed antibiotics being attributed to cultural factors:

There is a group of patients who are really eager to get antibiotics and who are incredibly demanding. Germans from the former Soviet Republics, and we do have many of them in this district. For them, it (not being prescribed antibiotics) is not a real therapy, even if it is viral….

(P, GP, rural area, high prescription rates)

Similar to cultural factors, the age distribution in a district is perceived to affect prescription, with more older adults in a district being associated with higher antibiotic demand.

Similar to this influence on higher prescriptions, specific regional social influences are also perceived as being influential for low prescription rates:

I mean, (city) is a very special city. It’s an administrative centre, a big university city, so I think there are a lot of people with a relatively high educational attainment, relatively little industry and I guess it’s also related to the fact that people have a bit of a different attitude. .

(Q, GP, urban area, low prescription rates)

Similar to the results in paediatricians, knowledge about current recommendations, information on local resistance, and training content relevant to prescribing antimicrobials were seen as relevant resources. One particular additional factor was that in one of the participating districts, the local university was seen as influential for particularly rational prescribing behaviour:

I think that this is due to the fact that here in (city) there are many doctors who have studied in (city). And I remember from my studies that antibiotic prescriptions were an important topic, and that in microbiology et cetera we were always being reminded that one does not just prescribe antibiotics but needs to justify this really well.

(R, GP, rural area, low prescription rates)

At the same time, similar to the paediatricians, a lack of knowledge in out-of-hours services is seen as a relevant factor for high prescription rates:

But there are many colleagues working in the out-of-hours primary care and doing GP tasks who have for example an anesthesia background, or something else from the hospital, they don’t know it any better.

(N, GP, rural area, high prescription rates)

These knowledge factors interact with resources and barriers on context level.

Similar to paediatricians, beliefs about consequences include beliefs about having to avoid liabilities, which are often mentioned in combination with structural and contextual factors:

And if something does go wrong, and that’s always a problem in outpatient settings, you are the one who screwed it up. That’s what all the colleagues are afraid of. So the fear of making a mistake and not prescribing the antibiotic is always bigger than the fear of damaging something with the antibiotic.

(S, GP, rural area, high prescription rates)

Losing patients to other practices in situations with strong competition was a strong belief about consequences in districts with high prescriptions:

You can say, No I am not going to prescribe this, but then you lose the patient, they are just going somewhere else.

(T, GP, urban area, high prescription rates).

At the same time, a lack of such perceived consequences has been perceived as a resource for lower prescriptions:

…at least we don’t have to bow to patient demands too much. It is very different here compared to (city) where I was before, in the inner city, where there was a lot of competition due to too many GPs. You are much more likely to give in to irrational demands then.

(U, GP, rural area, low prescription rates)

Social / professional role

Specific regional ideas on the professional roles are perceived to influence prescription behaviour, in particular in combination with specific aspects of rurality that could affect the composition of the local GP structure:

I just see what kind of colleagues – to say it cautiously – are coming to this region, who take over old practices or establish new ones. They are not necessarily the most committed doctors.

(V, GP, rural area, high prescription rates)

In districts with low prescription rates, skills were mainly being mentioned with regards to interpersonal skills regarding expectation management with patients, which were perceived to be higher in the respective districts:

…in fact, skills training in multiple areas. General communication skills, difficult patients, bad prognosis, diagnosis, or making the patients understand why a particular therapy is indicated – these are all key skills and have always been emphasized during our studies.

Similar to paediatricians, GPs from both low- and high-prescription districts mention the goal of low prescription rates, and assume that their colleagues in the district have similar goals. GPs in low-prescription districts mention this goal as part of multiple goals (ideal therapy, avoid resistance) in low-prescription districts, GPs in high-prescription districts mention this goal as having lower priority compared to competing demands.

Similar to the paediatricians, a lack of behavioural regulation in combination with contextual measures such as relatively old GPs in the district was seen as a risk factor for higher prescriptions:

Prescription behaviour by older colleagues plays a role I think. You can see this when you look at the age structure of the GPs here. They tend to prescribe antibiotics quickly whenever there are respiratory infections.

(W, GP, rural area, high prescription rates)

This study examined prescribers’ perceptions of region-specific drivers of outpatient antibiotic prescriptions. We conducted 40 interviews in districts stratified by antibiotic prescription rates, and mapped these perceptions on dimensions of the Theoretical Domains Framework [ 22 , 23 ]. A total of 11 domains were identified, and these served as, partially interacting, barriers against and resources for low antibiotic prescription rates. Most barriers and facilitators were similar between paediatricians and GPs. However, while GPs mentioned the age and workforce structure in districts as additional barrier, paediatricians emphasized a lack of skills and knowledge of GP colleagues treating young children as a barrier in districts with only few paediatricians.

We could link differences in the perception of TDF domains and their interactions to differences in prescribing behaviour in the districts to identify overarching barriers and resources for appropriate prescription practices.

Overarching barriers to low prescription rates

Both paediatric and GPs mentioned a lack of knowledge on district-level resistance developments as particular barrier to rational prescribing. This knowledge factor overlaps with a lack of contextual and environmental resources which could provide this information such as routine information flows between laboratories and health care providers.

Similarly, a lack of collaboration and coordination of knowledge in out-of-hours services was perceived to be associated with higher prescription rates – partly also due to a perception to avoid liabilities if prescribing antibiotics.

Lower health care provider density as contextual factor has been associated with higher prescription rates in previous international studies [ 35 , 36 ]. In the present study, lower prescriber density has only indirectly been associated with higher prescription rates – in the cases where lower density correlates with suboptimal emergency services prescription guidelines [ 16 ].

Participants also associated specific regional industries in rural districts (pig farming and meat factories) with higher patient demands due to either antibiotic practices in farming [ 15 ] or an increased demand for antibiotics by migrant workers and due to cuts in meat factories.

Social influences included culture-specific expectations about the effectiveness of antibiotics leading to higher patient demand for antibiotics, which together with time pressure from high patient load increased pressure on prescribers during consultations. This finding replicates findings from other European studies on antibiotic prescribing behaviour [ 18 , 19 ].

Overarching resources for low prescription rates

Overarching resources for low prescription rates that were mentioned by both paediatricians and GPs included environmental context and resources . Here in particular existing local networks supporting quality control were perceived as supportive of appropriate prescribing, both through the provision of information, best-practice examples and social norms. This replicates an earlier study suggesting that well-functioning local or regional primary care networks in Germany are associated with more appropriate antibiotic prescribing [ 37 ]. In addition, laboratories routinely providing information on local resistance data were perceived as resources for rational prescribing, which is in line with previous studies in Germany outlining the lack of local resistance information as a barrier to appropriate prescribing [ 38 ] and, similarly, showing that practitioners perceive information on local resistance as beneficial [ 39 ]. Low local population demand for antibiotics was also perceived as resource, as participants reported this to positively impact their prescription practice.

Implications

Most barriers and resources to rational outpatient prescribing in this study were contextual factors. However, contextual factors such as the local population, the main local branches of industry or (at least in Germany), or the free choice of practitioners to open practices anywhere within a district are not directly modifiable. This means that interventions should in particular target local collaboration structures and the availability of locally adapted guidelines.

If collaborations between local medical councils and laboratories can be improved to routinely provide local antimicrobial resistance data to prescribers, this information can readily be included into the prescription decision-making process [ 39 ]. In particular since both German and international studies [ 40 ] show that there is substantial variation in the degree to which individual practices take local resistance data into consideration, routine approaches are warranted. Germany has implemented a standardized surveillance program for multiresistant microbes such as MSRA [ 2 ], but the degree to which these surveillance findings are broken down locally and are available to practices varies considerably between districts, suggesting policies to standardise practice. If these findings are then included into local prescription guidelines such as the AnTiB guidelines [ 34 ], local prescription practices can be improved.

Routine antibiotic stewardship programmes that support paediatric and general practices could also help facilitating such closer collaborations and in turn build on some of the networking aspects mentioned as resources in the interviews. At the moment, antibiotic stewardship programs for outpatient settings in Germany are supported through national professional and scientific associations and are eligible for training credits, but implementation depends on local initiatives [ 5 ]. National policies to mandate such programmes would help to reduce the current regional disparities in antibiotic prescription practices, and the current antibiotic strategy of the German government DART 2030 [ 41 ] plans to explore compulsory training.

In terms of knowledge resources, participants mentioned that easy-to-use recommendations for emergency practice services are an important resource in particular if there is no paediatric emergency service, and children are seen by non-paediatrician practitioners. In Germany, initiatives such as Antibiotic Therapy in Bielefeld (AnTiB; [ 34 ]) provide such guidelines, but a systems-wide implementation of easy-to-follow guidelines such as e.g., NICE guidelines for upper respiratory tract infections [ 42 ] is currently lacking and would likely improve prescription practices in Germany.

Patient information such as leaflets might lead to increased patient knowledge about the role of antibiotics in managing infections and lower patient demand [ 43 ] without increased reconsultations [ 44 ]. At the same time, the role of involving audiences in the design of such leaflets and ensuring their understandability is crucial [ 45 ].

Strengths and limitations

A particular strength of the study lies in using the TDF to examine district-level differences in prescription behaviour, which allowed us to identify and interpret the impact of the factors mentioned by GPs and paediatricians. This deductive approach allowed mapping key themes on an established framework, which can in turn be used to determine and develop potential intervention applications. Our study complements previous work applying the TDF to understand antibiotic prescribing behaviour [ 25 ] by extending the perspective of the TDF on individual determinants onto characteristics of the district.

At the same time, the perceptions of participants regarding district-level TDF-based characteristics are subjective perceptions and do not necessarily correspond to the actual level of resources and barriers in the districts. Compared to face-to-face interviews, telephone interviews miss out on nonverbal information, but have allowed us to accommodate prescribers’ schedules. Due to the self-report nature of interviews, demand characteristics might affect responses such that participants exaggerate or downplay relevant factors.

Saturation in that no new codes emerged was achieved in all study cells (defined by practitioner group, urban/rural practice site and prescription rates) apart from GPs from high-prescribing urban areas, where only one interview could be realised. It is thus possible that additional interviews could have provided additional barriers and resources.

Substantial district-level differences in outpatient antibiotic prescriptions in paediatric and general practices can be mapped on differences in prescriber perceptions of district-level barriers and resources to rational prescribing. Given the regional variation in underlying reasons for inappropriate prescribing of antibiotics, similar qualitative studies in all districts in Germany with high prescription rates could be a promising approach to design targeted interventions. According to the results of interviews conducted in this study, routine provision of local antibiotic resistance data, better and clearer guidelines for paediatric patients in ambulatory emergency services, patient information and a wider implementation of standardised antibiotic stewardship programs could be promising targets for interventions.

Data availability

The qualitative data collected for this study was de-identified before analysis. Consent was not obtained to use or publish individual-level data from the participants and therefore may not be shared publicly. The de-identified (German) data can be obtained from the corresponding author upon reasonable request.

Change history

09 may 2024.

A typesetting mistake in the figure formatting in the HTML version of the article was corrected.

World Health Organisation. Antimicrobial resistance. https://www.who.int/health-topics/antimicrobial-resistance . Accessed 6 Oct 2023.

Baede VO, David MZ, Andrasevic AT, Blanc DS, Borg M, Brennan G, et al. MRSA surveillance programmes worldwide: moving towards a harmonised international approach. Int J Antimicrob Agents. 2022;59:106538.

Article   CAS   PubMed   Google Scholar  

European Antimicrobial Resistance Collaborators. The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis. Lancet Public Health. 2022;7:e897–913.

Article   Google Scholar  

Chatterjee A, Modarai M, Naylor NR, Boyd SE, Atun R, Barlow J, et al. Quantifying drivers of antibiotic resistance in humans: a systematic review. Lancet Infect Dis. 2018;18:e368–78.

Kern WV. Rationale Antibiotikaverordnung in Der Humanmedizin. Bundesgesundheitsbl. 2018;61:580–8.

O’Neill J. Tackling drug-resistant infections globally: final report and recommendations. 2016. https://amr-review.org/sites/default/files/160525_Final%20paper_with%20cover.pdf . Accessed 6 Oct 2023.

Goossens H, Ferech M, Stichele RV, Elseviers M. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet. 2005;365:579–87.

Article   PubMed   Google Scholar  

Zweigner J, Meyer E, Gastmeier P, Schwab F. Rate of antibiotic prescriptions in German outpatient care - are the guidelines followed or are they still exceeded? GMS Hyg Infect Control. 2018;13:Doc04.

PubMed   PubMed Central   Google Scholar  

Poss-Doering R, Kronsteiner D, Kamradt M, Andres E, Kaufmann-Kolle P, Wensing M, et al. Antibiotic prescribing for acute, non-complicated infections in primary care in Germany: baseline assessment in the cluster randomized trial ARena. BMC Infect Dis. 2021;21:877.

Article   PubMed   PubMed Central   Google Scholar  

European Centre for Disease Prevention and Control. Antimicrobial consumption in the EU/EEA (ESAC-Net) - Annual Epidemiological Report for 2021. Stockholm: European Centre for Disease Prevention and Control; 2022.

Google Scholar  

Scholle O, Asendorf M, Buck C, Grill S, Jones C, Kollhorst B et al. Regional variations in Outpatient Antibiotic Prescribing in Germany: a small area analysis based on Claims Data. Antibiotics. 2022;11.

Zhang Y, Steinman MA, Kaplan CM. Geographic Variation in Outpatient Antibiotic Prescribing among older adults. Arch Intern Med. 2012;172:1465–71.

Filippini M, Masiero G, Moschetti K. Socioeconomic determinants of regional differences in outpatient antibiotic consumption: evidence from Switzerland. Health Policy. 2006;78:77–92.

Devine P, O’Kane M, Bucholc M. Trends, Variation, and factors influencing antibiotic prescribing: a longitudinal study in primary care using a Multilevel Modelling Approach. Antibiot (Basel). 2021;11:17.

van der Roof I, Oude Boerrigter L, Wielders CCH, Smit LAM. Use of antibiotics among residents living close to Poultry or Goat farms: a nationwide analysis in the Netherlands. Antibiot (Basel). 2021;10:1346.

Article   CAS   Google Scholar  

Huibers L, Vestergaard CH, Keizer E, Bech BH, Bro F, Christensen MB. Variation of GP antibiotic prescribing tendency for contacts with out-of-hours primary care in Denmark - a cross-sectional register-based study. Scand J Prim Health Care. 2022;40:227–36.

Bizune D, Tsay S, Palms D, King L, Bartoces M, Link-Gelles R, et al. Regional Variation in Outpatient Antibiotic Prescribing for Acute Respiratory Tract infections in a commercially insured Population, United States, 2017. Open Forum Infect Dis. 2023;10:ofac584.

Sijbom M, Büchner FL, Saadah NH, Numans ME, de Boer MGJ. Determinants of inappropriate antibiotic prescription in primary care in developed countries with general practitioners as gatekeepers: a systematic review and construction of a framework. BMJ Open. 2023;13:e065006.

Lescure DLA, van Oorschot W, van der Brouwer R, Tjon-A-Tsien AML, Bonnema IV, et al. Providing antibiotics to immigrants: a qualitative study of general practitioners’ and pharmacists’ experiences. BMC Prim Care. 2022;23:100.

Szymczak JE, Linder JA. Cultural Variation in Antibiotic Prescribing: have Regional differences had their day? Open Forum Infect Dis. 2023;10:ofad025.

Schüz B, Jones C, Scholle O, Haug U. Regional variations in antibiotic prescribing in Germany: understanding differences through an adapted theoretical domains Framework. Health psychology for all: Equity, Inclusiveness and Transformation. Bremen, Germany: European Health Psychology Society; 2023. p. 280.

Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;7:37.

Atkins L, Francis J, Islam R, O’Connor D, Patey A, Ivers N, et al. A guide to using the theoretical domains Framework of behaviour change to investigate implementation problems. Implement Sci. 2017;12:77.

Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A, et al. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care. 2005;14:26–33.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Courtenay M, Rowbotham S, Lim R, Peters S, Yates K, Chater A. Examining influences on antibiotic prescribing by nurse and pharmacist prescribers: a qualitative study using the theoretical domains Framework and COM-B. BMJ Open. 2019;9:e029177.

Bursey K, Hall A, Pike A, Etchegary H, Aubrey-Bassler K, Patey AM, et al. Physician-reported barriers to using evidence-based antibiotic prescription guidelines in primary care: protocol for a systematic review and synthesis of qualitative studies using the theoretical domains Framework. BMJ Open. 2022;12:e066681.

Talkhan H, Stewart D, McIntosh T, Ziglam H, Abdulrouf PV, Al-Hail M, et al. Investigating clinicians’ determinants of antimicrobial prescribing behaviour using the theoretical domains Framework. J Hosp Infect. 2022;122:72–83.

Sargent L, McCullough A, Del Mar C, Lowe J. Using theory to explore facilitators and barriers to delayed prescribing in Australia: a qualitative study using the theoretical domains Framework and the Behaviour Change Wheel. BMC Fam Pract. 2017;18:20.

Commission Regulation (EU). 2016/2066 of 21 November 2016 amending the annexes to Regulation (EC) No 1059/2003 of the European Parliament and of the Council on the establishment of a common classification of territorial units for statistics (NUTS). 2016.

Laufende Raumbeobachtung - Raumabgrenzungen. BBSR. https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html . Accessed 2 Oct 2023.

Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Res Psychol. 2006;3:77–101.

Kuckartz U, Rädiker S. Analyzing qualitative data with MAXQDA: text, Audio, and video. Cham: Springer International Publishing; 2019.

Book   Google Scholar  

Leyland AH, Groenewegen PP, Context. Composition and how their influences vary. In: Leyland AH, Groenewegen PP, editors. Multilevel Modelling for Public Health and Health Services Research: Health in Context. Cham: Springer International Publishing; 2020. pp. 107–22.

Chapter   Google Scholar  

Bornemann R, Tillmann R. [Antibiotic therapy in Bielefeld (AnTiB)-a local project for the promotion of rational antibiotic prescribing in the outpatient paediatric sector]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2019;62:952–9.

Stedman M, Lunt M, Davies M, Fulton-McAlister E, Hussain A, van Staa T, et al. Controlling antibiotic usage—A national analysis of General Practitioner/Family Doctor practices links overall antibiotic levels to demography, geography, comorbidity factors with local discretionary prescribing choices. Int J Clin Pract. 2020;74:e13515.

Simon M, Thilly N, Pereira O, Pulcini C. Factors associated with the appropriateness of antibiotics prescribed in French general practice: a cross-sectional study using reimbursement databases. Clinical Microbiology and Infection. 2022;28:609.e1-609.e6.

Poss-Doering R, Kamradt M, Glassen K, Andres E, Kaufmann-Kolle P, Wensing M. Promoting rational antibiotic prescribing for non-complicated infections: understanding social influence in primary care networks in Germany. BMC Fam Pract. 2020;21:51.

Neugebauer M, Ebert M, Vogelmann R. [Lack of information and provision of information at the workplace as potential reasons for inappropriate antibiotic therapy in Germany]. Z Evid Fortbild Qual Gesundhwes. 2019;144–145:35–41.

Petruschke I, Stichling K, Greser A, Gagyor I, Bleidorn J. [The general practitioner perspective of a multimodal intervention for the adequate use of antibiotics in urinary tract infection - a qualitative interview study]. Z Evid Fortbild Qual Gesundhwes. 2022;170:1–6.

McKay R, Law M, McGrail K, Balshaw R, Reyes R, Patrick DM. What can we learn by examining variations in the use of urine culture in the management of acute cystitis? A retrospective cohort study with linked administrative data in British Columbia, Canada, 2005–2011. PLoS One. 2019;14:e0213534.

Bundesministerium für Gesundheit. DART 2030 - Deutsche Antibiotika-Resistenzstrategie. https://www.bundesgesundheitsministerium.de/themen/praevention/antibiotika-resistenzen/dart-2030 . Accessed 1 Mar 2024.

Kim NN, Marikar D. Antibiotic prescribing for upper respiratory tract infections: NICE guidelines. Archives Disease Child - Educ Pract. 2020;105:104–6.

de Bont EGPM, Alink M, Falkenberg FCJ, Dinant G-J, Cals JWL. Patient information leaflets to reduce antibiotic use and reconsultation rates in general practice: a systematic review. BMJ Open. 2015;5:e007612.

O’Sullivan JW, Harvey RT, Glasziou PP, McCullough A. Written information for patients (or parents of child patients) to reduce the use of antibiotics for acute upper respiratory tract infections in primary care. Cochrane Database Syst Rev. 2016;11:CD011360.

PubMed   Google Scholar  

Biezen R, Ciavarella S, Manski-Nankervis J-A, Monaghan T, Buising K. Addressing Antimicrobial stewardship in primary care-developing patient information sheets using Co-design Methodology. Antibiot (Basel). 2023;12:458.

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Acknowledgements

The support of Paula Kinzel during data assessment is gratefully acknowledged.

Open Access funding enabled and organized by Projekt DEAL.

The SARA project—on which this publication is based—was commissioned by the Federal Ministry of Health (grant number ZMVI1-2519FSB115).

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Praxis für Kinder- und Jugendmedizin Roland Tillmann, Ärztenetz Bielefeld, Bielefeld, Germany

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Contributions

BS conceived of the study, analysed and interpreted data and wrote the first draft of the manuscript. OS contributed to design of the study, interpretation of data. UH contributed to acquisition, concept and design of the study as well as interpretation of data. RT contributed to design and acquisition of the study as well as interpretation of data. CJ contributed to design and concept of the study as well as assessment, analysis and interpretation of the data. All authors critically reviewed the manuscript.

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Correspondence to Benjamin Schüz .

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Ethics approval was obtained through the University of Bremen ethics committee (AZ 2021-03). All methods and procedures were performed in accordance with the relevant guidelines. All participants provided informed consent including before being interviewed for this study.

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Competing interests

OS and UH are working at an independent, non-profit research institute, the Leibniz Institute for Prevention Research and Epidemiology – BIPS. Unrelated to this study, BIPS occasionally conducts studies financed by the pharmaceutical industry. These are post-authorization safety studies (PASS) requested by health authorities. The design and conduct of these studies as well as the interpretation and publication are not influenced by the pharmaceutical industry. The study presented was not funded by the pharmaceutical industry. The Federal Ministry of Health specified the research question and the main content of the study concept and regularly participated in discussions on the implementation of the study. The authors were independent in the specific design, execution, interpretation, and writing of the study. The Federal Ministry of Health has authorized the publication of the results. BS, RT and CJ declare no conflicts of interest.

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Schüz, B., Scholle, O., Haug, U. et al. Drivers of district-level differences in outpatient antibiotic prescribing in Germany: a qualitative study with prescribers. BMC Health Serv Res 24 , 589 (2024). https://doi.org/10.1186/s12913-024-11059-z

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Received : 09 November 2023

Accepted : 29 April 2024

Published : 06 May 2024

DOI : https://doi.org/10.1186/s12913-024-11059-z

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Insights in Veterinary Pharmacology and Toxicology: 2023

Blebbistatin as a Novel Antiviral Agent Targeting Equid Herpesvirus Type 8 Provisionally Accepted

• 1 Liaocheng University, China
• 2 Liaocheng Research Institute of Donkey High-efficiency Breeding and Ecological Feeding, Liaocheng University, China
• 3 Liaocheng University, China

The final, formatted version of the article will be published soon.

Equid herpesvirus type 8 (EqHV-8) poses a significant threat to equine health, leading to miscarriages and respiratory diseases in horses and donkeys, and results in substantial economic losses in the donkey industry. Currently, there are no effective drugs or vaccines available for EqHV-8 infection control. In this study, we investigated the in vitro and in vivo antiviral efficacy of Blebbistatin, a myosin II ATPase inhibitor, against EqHV-8. Our results demonstrated that Blebbistatin significantly inhibited EqHV-8 infection in Rabbit kidney (RK-13) and Madin-Darby Bovine Kidney (MDBK) cells in a concentration-dependent manner. Notably, Blebbistatin was found to disrupt EqHV-8 infection at the entry stage by modulating myosin II ATPase activity. Moreover, in vivo experiments revealed that Blebbistatin effectively reduced EqHV-8 replication and mitigated lung pathology in a mouse model.Collectively, these findings suggest that Blebbistatin holds considerable potential as an antiviral agent for the control of EqHV-8 infection, presenting a novel approach to addressing this veterinary challenge.

Keywords: EqHV-8, Blebbistatin, myosin II ATPase inhibitor, Antiviral activity, animal model

Received: 23 Feb 2024; Accepted: 13 May 2024.

Copyright: © 2024 Li, Cui, Li, Li, Li, Chen, Khan, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Muhammad Z. Khan, Liaocheng University, Shangdong, China Prof. Changfa Wang, Liaocheng Research Institute of Donkey High-efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng, 252000, Shandong Province, China Prof. Tongtong Wang, Liaocheng University, Shangdong, China

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