how to present your data in 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.

Top tips for presenting your data according to research

What do we really know about how to present complex data in ways that are easy to understand and have impacts that might help address complex issues such as climate change? Dr Lucy Richardson explores some of the useful tips provided by data visualisation and communication research that can help you effectively communicate complex information.

This article is part of the ISC’s Transform21 series, which features resources from our network of scientists and change-makers to help inform the urgent transformations needed to achieve climate and biodiversity goals.

Over the last year or so, many people across the world have become used to seeing charts and graphs with COVID-19 statistics in their news feeds, but all charts are not created equal when it comes to effectively communicating a key message.

Researchers have been examining how different aspects of data presentation influence audiences for many years. They have looked at the issue from diverse angles such as which components are viewed in what order and why, and whether text, graphs or maps are more engaging and easily understood. These diverse research questions have been addressed using a wide variety of methods ranging from tracking audience eye movements to surveys and social media polls. From this collection of research, we have gained valuable insights that can help make data visuals more effective communication tools.

A useful framework to think about when designing data visualisations follows the broad process of audience interaction with the presented information: (a) first the audience perceives the information (b) then they think about the information, and (c) then some sort of change or impact occurs due to those thoughts.

Perceiving the information (Perception)

Assuming that your data visualisation is presented to your target audience in a time and place where they are likely to see it, your audience needs to be able to perceive and differentiate each of the key components of your visualisation in order to discern its meaning.

Perception tends to happen in sequence, following a visual hierarchy of attention based on the following characteristics of any object (including maps and graphs): size, colour, contrast, alignment, repetition, proximity, whitespace, and texture and styles. Within each of these elements are further sub-hierarchies. For example, people tend to notice large elements before smaller ones, and bright colours before muted ones. Similarly, dramatic contrasting components are noticed more than those with less contrast.

The effect of these hierarchical elements can be impacted by perception challenges and should be carefully considered to ensure that they promote your message rather than confusing or distracting your audience. There are a range of different perception challenges that can impact on the effectiveness of data visualisations, but did you know there are actually seven different forms of colour blindness ? You can even run your data visualisation through a colour blindness simulator to see how it might be viewed by someone with these challenges.

Thinking about the information (Cognition)

When your audience thinks about and derives meaning from information they perceive, this is known as cognitive processing. It includes thinking, knowing, remembering, judging, and problem-solving; any number of which may be used when processing information associated with visualised data.

Some things you can do to help encourage the desired interpretation of meaning from your data visualisation include providing chart titles that are the main message rather than just a description of the content. A title such as ‘Higher amounts of green vegetation in cities is associated with lower summer temperatures’ is much more effective at guiding meaning-making than titling the same chart as ‘Green vegetation and temperature in Australian cities’.

Some topic areas that may require data visualisations can also have underlying psycho-social (psychological, social and/or political) factors that should be considered. This is particularly the case for climate change, a heavily politicised issue that is quite polarising in some countries. When presenting data relating to climate change, some valuable tips include:

• Avoid catastrophic messaging that can cause people to shut down as a coping response to their fear.
• Include solutions-based information can help counteract fear by promoting a sense that climate change can be addressed.
• Provide locally relevant information where possible, as this will resonate more strongly. People are naturally most interested in what happens in their local area.
• Where possible, consider if there are other ways to cover the issue without mentioning ‘climate change’ if communicating to audiences who may not accept current scientific evidence of its existence and urgency. This is easier for messages relating to adapting to changes in climate than mitigation, as there are often diverse benefits beyond climate change that can be used to frame adaptation information.

It’s also important to recognize that people are generally more likely to remember meaning than detail. This means that people are more likely to remember a trend—such as it’s getting ‘worse’ or ‘better’, ‘increasing’ or ‘decreasing’—but may not remember the specific amount or rate of that increase or decrease.

Changes effected (Impact)

There are a range of possible impacts that might arise from audiences viewing your data visualisation. These could be changes in thought (for example, awareness, understanding, attitudes or concern), or changes in behaviour (for example, information seeking, discussion with others, or even adoption of climate-friendly behaviours). The likelihood of change being effected due to your data visualisation will be enhanced by ensuring your messages are clear and relevant, where clarity will come from effectively addressing perception and cognition considerations and relevance will come from appropriate message framing and consideration of psycho-social factors. Knowing the kind of change you want to achieve will be critical in determining how best to integrate these various factors into your work.

Alternative formats

While most people wishing to present complex scientific data tend to think of charts, graphs, maps, and infographics, it is also possible to present information for perception by other senses such as through sound. Some researchers have been testing data sonification as an alternative to visual data representation. Sonification takes each data point and applies a mix of sound elements that can allow trends to be distinguished—for example, pitch, volume, and choice of instrument—to provide an audio representation of the information. NASA has done this so that people can ‘listen’ to the Milky Way Galaxy , and researchers at the Monash University Climate Change Communication Research Hub have sonified cyclone Debbie ’s movements around Australia in 2017.

A free best practice guide has been developed based on a review of data visualisation research. Hopefully, it will help you decide how you can best present your data for effective perception, cognition and impact. You can access the Best practice data visualisation: Guidelines and case study on the Monash Climate Change Communication Research Hub website .

Lucy Richardson

Dr Lucy Richardson is based at the Monash Climate Change Communication Research Hub, Monash University, on the lands of the Kulin Nations, Melbourne, Australia, and a member of the  Commonwealth Futures Climate Research Cohort  established by The Association of Commonwealth Universities and the British Council to support 26 rising-star researchers to bring local knowledge to a global stage in the lead-up to COP26.

The header image was created by NASA’s Scientific Visualization Studio to support a series of talks from NASA scientists for COP26. It is a still from a video that shows the atmosphere in three dimensions and highlights the accumulation of CO 2  during a single calendar year. You can watch the visualisation and find out more about the data on which it’s based here .

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World Climate Research Programme launches a Lighthouse Activity on Climate Intervention Research

Kigali Declaration: Climate science for a sustainable future for all

Climate inequality: The stark realities and the road to equitable solutions

Biodiversity data is distorted by past inequities. Scientists are wrestling to get a clearer picture.

Early-career researchers' insights on climate

Science reading list from the International Science Council

Readers' choice: our most shared science blogs this year

Seizing the momentum: a year of climate science advocacy

Large Ocean States and climate change: a plea for adaptation

COP28 agreement: embracing the urgency of the scientific consensus?

Fostering tomorrow’s science: the ISC's engagements with Early and Mid-Career Researchers in 2023

"What's holding us back?": how economists and social scientists might hold the key to climate action

Podcast with Cory Doctorow: Science Fiction and the Future of Science: Leveraging Digital Advancements for the Future

"Beyond weathering the storm": rethinking climate resilience

For science-based decision-making on the climate emergency: 10 new insights in climate science

COP28: The International Science Council's engagement in climate science

Scientific communication: Join the Ethnografilm festival for a filmmaking experience in Paris

publications

Policy brief: global sea-level rise, the costs of shifting scenarios: why the ipcc should maintain consistent vocabulary in climate assessments.

New ISC policy brief: A call for a formal scientific voice in the global fight against plastic pollution

Converging and interdependent crises are amplifying the impacts of one another with often devastating consequences

Reframing Trust in Science for Multilateral Policy: Insights from the Science Journalists Forum

One Planet Polar Summit, scientists striving to bridge the science-policy gap for urgent action: "every tenth of a degree Celsius matters"

Science Fiction and the Future of Science

Call for nomination of experts to draft an outline of the IPCC Special Report on Climate Change and Cities - deadline 15 November

Kigali declaration pledges to bridge climate injustice

The human dimension of disaster risk reduction: social sciences and climate adaptation 

From monsoon joy to fear: a climate crisis awakening

Global solidarity for climate justice: perspectives from an early-career researcher

One world, one climate: a planetary call to action

Transdisciplinary research for sustainability solutions in urban Africa

Cultivating a proactive approach to crises: first meeting of the UNEP/ISC foresight expert panel

Clearing the air: is ammonia our key to cleaner skies?

Unearthing sustainability: soil science for the SDGs

Helping newsrooms get disaster risk ready, an International Science Council and World Editors Forum collaboration

Our Future Depends on Us: Antarctic climate change and the environment

Unveiling the health benefits of forests and trees

How do we talk about science and uncertainty?

From Authoritarian Threats to Funding Disparities: Key Challenges in Global Science

Frontiers Planet Prize, second edition: celebrating the world’s most innovative sustainability scientists by 1 November

Protecting the ocean: 5 essential reads on invasive species, overfishing and other threats to sea life

The Frontiers Planet Prize unveils its champions

ISC's nomination track ensures transdisciplinary and global representation in UNEP's Scientific Advisory Group for GEO-7 Assessment

UN 2023 Water Conference carries new engagements towards realizing SDG6 and future avenues for a decade of action

IPCC report: the world must cut emissions and urgently adapt to the new climate realities

ISC-BBC StoryWorks partnership ends on a high note, delivering some of the highest engagement for the BBC

Youth perspectives on climate ‘COP’ negotiations and ways to get involved 

Public participation in scientific programmes: Citizen science for biodiversity  

The ISC and UNEP to cooperate on advancing the use of science in environmental policy and decision-making

Science in Times of Crisis Episode 3 - The Fallout of Conflict: The Arctic and Outer Space

Innovative approaches to storytelling in science celebrated at Women in Science Film Festival in Cape Town

Ten key messages for the Convention on Biological Diversity

COP27 ends with commitment on financial support

There are 8 years left to meet the UN Sustainable Development Goals, but is it enough time?

Special interview series on COP 27- Interview with Nick Perkins about climate change and science communication

People on the front lines of climate change must be included in climate action

Are we in a new era of climate adaptation implementation? The role of regional governments in facilitating local action

In the face of climate threats, we cannot afford not to act

The Role of Integrated Science in Understanding the Earth-Human System

In the face of extreme weather events, coordinated global action to address climate change is needed at COP27

Perspectives from the ISC network on expectations for the COP

Professor Carlos Lopes on why Africa needs to stick to renewables despite the temptation of gas

How can scientists make a difference at COP27?

Call for contributions for the Sustainability Research & Innovation Congress (SRI 2023)

Frontiers Planet Prize: Science for a Sustainable Planet

The scientific community can support the call for decisive action at cop27.

Stories of transformations to sustainability

Happy birthday to the Montreal Protocol – the most successful environmental treaty of all time?

Risk of passing multiple climate tipping points escalates above 1.5°C global warming

World Youth Skills Day 2022: From Resilience to Fearlessness 

WorldFAIR: Global cooperation on FAIR data policy and practice – Kick-Off Meeting introduces major new initiative to advance implementation of the FAIR data principles

Six Takeaways on Science Communication from our Talk Back Better Webinar Series

What's on the horizon for scientific data services? The latest from the World Data System

Implementing FAIR data principles – what’s behind the acronym?

WorldFAIR: Global cooperation on FAIR data policy and practice

A letter to our fellow citizens of Earth

The Biggest Carbon Sink of All

Policy Brief: Harnessing data to accelerate the transition from disaster response to recovery

CODATA and ISC celebrate Metrology in the Digital Era on World Metrology Day

Managed retreat from areas threatened by floods can catalyse positive social transformations

‘Now or never’ to limit warming to 1.5°C, according to latest IPCC report

How Science Affects my Everyday Life as a Fourteen-Year-Old

Joint Statement of Intent on the Digital Transformation in the International Scientific and Quality Infrastructure

The African Open Science Platform begins to take shape

Video tutorials on science ethics and science communication

Women Leading on Equitable and Inclusive Solutions to address the Climate Emergency: Webinar

Principles and Structures of Science Advice: An outline

The window for climate action to avoid dangerous systemic risks is narrowing, warns latest IPCC report

The transformative potential of managed retreat in the face of rising sea levels

The seven warmest years on record were the last seven

The Paris Agreement is working as intended, but we’ve still got a long way to go

An early career perspective on the science-policy interface in the decade of climate action following COP26

Holiday binge-watching: Science edition

Our most popular stories from 2021

Global Risks Perceptions Report 2021 released

Staying below 1.5°c: what are the chances.

Picturing the future of complex, cascading climate risks

If universities want to hit climate targets, they should use their land for carbon offsetting

Emerging Climate Risks and what will it take to limit global warming to 2.0°C?

What Antarctica can teach us about global climate change

How to teach energy transition and climate in business schools

Convening the scientific knowledge required to boost climate action

Major scientific assessment of the Amazon region issues urgent call to end deforestation and avert tipping points

Urban Health and Wellbeing in the Anthropocene

Compelling stories, curious science: #UnlockingScience launched

Building resilience in a climate challenged world.

Climate change projections for Pakistan: the need for sustainable solutions to protect its people and biodiversity

Four considerations for accelerating progress on climate change at the science-policy interface

Ten New Insights in Climate Science 2021 report highlights critical research and policy implications for addressing the climate crisis

Ahead of COP26, Ekanem I. Braide shares her perspective on the priorities for action and the role of science

Climate risk assessment gaps: seamless integration of weather and climate information for community resilience

Increasing the participation of women in the climate change debate, including as leaders, is essential for a carbon-zero future

Deepening interactions between science and policy on the way to COP26: What role for science publishers?

Predicting the climate of the next decade

Global citizens and scientists on how to achieve a thriving net zero future

Big Earth Data Advances Science and Engineering for SDGs

Call for emergency action to limit global temperature increases, restore biodiversity, and protect health

Professor Karen O'Brien: People are the most powerful solution to climate change

Climate justice and the decarbonization of shipping

A call for reconceptualizing carbon pricing policies with both eyes open

Coastal communities in the Arctic rely on structural measures to adapt to climate change, but should they?

Enabling positive tipping points towards global sustainability in uncertain times

Solar Geoengineering at a Standstill?

Scientific advances underpinning latest IPCC report demonstrate need for rapid action

Deep and sustained emissions reductions required to head off rapid climate change affecting all regions of the world

The carbon skyscraper

What we are reading

The mental health burden of climate change is growing – now it’s time to act

Climate finance - a sticking point for the COP26?

Call for papers: Discussion meeting on statistical aspects of climate change

World Youth Skills Day 2021: resilience and creativity

Tell me a story – why climate change communication needs to embrace our childlike curiosity

Climate change solutions in focus ahead of COP26

Four insights on collaborating at scale to advance climate adaptation

We're in the midst of a global wake-up call

A Global Survey of Science Offers Hope and Challenging Lessons

Can zero emissions and economic growth go together? Yes, but conditions apply

Climate explained: why is the Arctic warming faster than other parts of the world?

Why water-driven migration and displacement must be part of the climate agenda

COP26 Climate Action Champion Nigel Topping on creating an 'ambition loop' for bolder pathways to change

Climate scientists: concept of net zero is a dangerous trap

Building a climate future requires a regional approach

Designing responsible policy pathways for a zero-carbon transition

Earth Day 2021: we need bold, creative, innovative solutions

Strengthening the links between science and society for action on climate change in France

Target high-carbon emitters to accelerate green transition, say leading experts on behavioural change

What would a 3°C warmer world mean for Australia?

Citizen scientists: perhaps without a degree but certainly making a difference

The new climate change activism is emotional, and it’s a good thing

ISC Science Communications Network

Top ten insights in climate science from the past year

Mary Robinson - No Time to Spare for the Paris Climate Promise

Global change research and the COVID-19 pandemic

Children’s comic book series introduces solar-terrestrial physics

Working together: Future Earth and WCRP announce partnership to jointly address major societal challenges

Global Carbon Budget 2020 Finds Record Drop in Emissions

What are the climate breakthroughs in 2020?

Taking the temperature of the Paris Agreement: perspectives from our community

The “How” of transformation

Project Syndicate talks with Mary Robinson on climate change and her new podcast

Taking stock of progress on global change: what to expect from the unep global assessments synthesis report.

Ozone hole over Antarctica ‘largest’ and ‘deepest’ it has been in years

Redefining business as usual for scientific publishing

Learning from COVID-19 and building more resilient food systems

Global Science TV: Arctic ice keeps shrinking. Here’s what that means for all of us

The COVID-19 Pandemic Illustrates the Need for Open Science

World’s first high-level science initiative dedicated to the survival of the Amazon

Statistical thinking as an essential skill for reading the news

Global Science TV: Why can't we deal with climate change as urgently as COVID-19?

Why can’t we deal with climate change as urgently as COVID-19?

Earth day 50th anniversary calls for climate action.

A Data Ecosystem to Defeat COVID-19

Making data work for cross-domain grand challenges

Tackling Climate Change with COVID-19 Urgency - by ISC Patron, Mary Robinson and ISC President, Daya Reddy

Four major international data organizations join forces to optimize the research data ecosystem, launching a COVID-19 appeal as their first joint action

Call for Expressions of Interest to Host the World Data System International Programme Office (Partial Submissions Allowed)

Join the first ‘Transformation Talks’ webinar to explore how research communication can be transformative

TROP ICSU - Climate Change Education Across the Globe

A rapidly-evolving new normal: Pep Canadell comments on Australia’s Fires

ISC joins WCRP in celebrating its 40th year of international climate science

COP25: Time for action

Call for nominations of experts to serve on the Editorial Board of the IPCC Emission Factor Database

Why we need a UN charter

World Data System Data Stewardship Award 2019

Achieving Risk Reduction Across Sendai, Paris And The SDGs

Disaster Loss Data In Monitoring The Implementation Of The Sendai Framework

The political challenge of achieving transformations to 1.5ºC – the role of social justice

A vision for the African Open Science Platform

COP24 side event on The CitiesIPCC Research and Action Agenda for effective urban responses to climate change

International data week gets underway in Gaborone, Botswana

Transforming southern African cities in a changing climate – Q and A with Alice McClure from the University of Cape Town

Review of the World Climate Research Programme (WCRP)

Ten essentials for research that responds to the climate challenge

Vacancy: executive director of the icsu world data system (wds) (re-advertised).

Vacancy: Communications Intern for the LIRA 2030 programme

News from LIRA2030: Seedbeds of Transformation conference, South Africa

World data system workshop held in rio de janeiro.

ICSU World Data System International Technology Office to open in Canada

Pavel Kabat appointed WMO Chief Scientist and Research Director

Why the IPCC's upcoming 1.5°C report offers an unexpected glimpse of hope

The IPCC at 30: Is the 1.5°C Special Report a turning point?

The origins of the IPCC: How the world woke up to climate change

The state of biodiversity in the regions: What to expect from the IPBES in 2018

Highlights of 2017

Why 2018 is a big year for global environmental assessments

Heide Hackmann recieves award for science diplomacy

IAMAS urges United States to continue support of Earth Observation systems

Cop23 side event on climate change- when and where will habitability limits be reached.

Largest Ever Science Gathering in the Middle East for World Science Forum 2017

Belmont Forum announces Mustapha Mokrane as new Co-Lead of Open Data Initiative

Future of science: voices from our partners.

Future of science: Voices from our partners

Call for scientific and non-academic reviewers for lira 2030, early career scientists gather for lira trans-disciplinary workshop in uganda.

Focus on Interactions: Second Nexus Conference Announced for 2018

International Council for Science calls on United States to support international efforts to combat dangerous climate change

ICSU President to receive International Meterological Prize from WMO

ICSU at the U.N. Ocean Conference

Science Plan on Global Environmental Change – ICSU Regional Office for Africa

Committee on data (codata).

World Data System (WDS)

Climate Change

Future Earth

"Open Data in a Big Data World" accord passes 120 endorsements

World Climate Research Programme (WCRP)

NZ Government thanks IRDR and CODATA groups for their help following 2016 Kaikoura earthquake

Making a case for science at the United Nations

ICSU Unions receive award to launch multi-year initiatives in science outreach and education

New commentary published: climate research must sharpen its view, african open science platform to boost the impact of open data for science and society, icsu co-organizes side event at cop22 on urgent questions in climate research.

Future Earth-PROVIA-IPCC risks and solutions workshop: livestream available

Call for nominations - early career scientists at habitat iii, open data in a big data world, world data system marks fifth anniversary of international programme office, advisory note: science communication (2010/2016), leading science groups urge global accord on open data in a big data world, 30 years pioneering collaboration on global change research: igbp closes down dec 2015, the international council for science and climate change.

Twelve things we've learned on the Road to Paris

Science International to agree international accord on open data

New scientific committee and chair appointed for icsu world data system, landmark scientifc data conference ends with strong support of data sharing for sustainability, future earth 2025 vision.

Future Earth Strategic Research Agenda 2014

Open access to scientific data and literature and the assessment of research by metrics

Icsu-endorsed initiative sustainable deltas 2015 launches in rotterdam.

International Council for Science endorses open access to scientific record; cautions against misuse of metrics

Celebrating 30 years of global change research, call for proposals: transformations to sustainability.

Review of CODATA, the Committee on Data for Science and Technology

Future earth initial design report: executive summary.

Annual global carbon emissions set to reach record 36 billion tonnes in 2013

World social science report 2013: changing global environments.

Scientists meet at the UN for Expert Group Meeting on Sustainable Development Goals

Black carbon report from igbp project generates significant media coverage, ad-hoc strategic coordinating committee on information and data (sccid report), how to describe nanomaterials – an icsu workshop in paris, the international council for science pledges support for scientists in the l’aquila case, icsu-unesco regional science and technology workshops for rio+20, climate research in the next decade of earth system research, icsu's new world data system opens new international progamme office in tokyo, international programme office of icsu’s new world data system opened, icsu foresight analysis peer-reviewed, workshop on the description of nanomaterials, advisory note on access to shared data to reduce global inequality, rio+20 policy briefs released by the gec programmes, advisory note on sharing scientific data, with a focus on developing countries, icsu releases statement on the controversy around the 4th ipcc assessment, polar year comes to a close, a vision for earth system research: have your say, polar research reveals new evidence of global environmental change, upcoming release of new evidence about change in the polar regions, review of the international geosphere-biosphere programme, review of the world climate research programme (2009), ipy polar day focusing above the polar regions, international science community agrees on first steps to establish a global virtual library for scientific data, icsu launches new programme to understand the human impact on earth’s life-support systems, international council for science (icsu) launches major research programme on natural disasters, ipy polar day focusing on people, ipy polar land and life day, report from the ad hoc strategic committee on information and data, ipy day focusing on changing earth, ipy day focusing on ice sheets, ipy presents sea ice day, global launch of international polar year (ipy) 2007-2008, co2 rise heightens concern over vulnerability of polar regions, icsu hosts conference on hazards and disasters, icsu and climate science (2006), at pivotal event in china, the international council for science releases new strategy to strengthen international science for the benefit of society, international experts call for new approach to ensure challenges to data access and management don’t slow scientific progress, icsu pursues new initiative that challenges science to do more to prevent natural disasters, socioeconomic data in relation to the integrated global observing strategy partnership igos-p (2004), priority area assessment on scientific data and information, cern announces major conference on the information society, icsu launches an agenda for action in advance of the world summit on the information society, science in the information society: policy issues for scientific information (2003), science in the information society: optimizing knowledge (2003), science in the information society: decision making and governance (2003), science in the information society: universal access to scientific knowledge (2003), icsu/codata launch online forum for world summit on the information society.

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• Disclaimer: The translations are automatically generated by Google Translate and may contain errors. The ISC is not liable for any damage or issues that may arise from these translations. You can provide your feedback by emailing us at [email protected] Note: Please note that the ‘Science Summit at the UN General Assembly’ in September 2023 is not an event of the International Science Council. The International Science Council has no association with this event nor with the organizer of the event.

Privacy Overview

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

• Introduction

How do you present qualitative data?

Data visualization.

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

Table of contents

• Transparency and rigor

Navigate to other guide parts:

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

• Presenting qualitative data

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

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

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

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

Preparing the data

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

Coding the data

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

Uncovering insights

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

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

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

ATLAS.ti helps you make sense of your data

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

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

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

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

Importance of data visualization

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

Prerequisites for generating data visualizations

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

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

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

Requirements for research writing

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

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

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

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

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

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

Transparency

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

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

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

The process of research publication

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

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

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

Guide your research to publication with ATLAS.ti

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

Part 1: The Basics

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

Part 2: Handling Qualitative Data

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

Part 3: Presenting Qualitative Data

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

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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Skills for Learning : Research Skills

Data analysis is an ongoing process that should occur throughout your research project. Suitable data-analysis methods must be selected when you write your research proposal. The nature of your data (i.e. quantitative or qualitative) will be influenced by your research design and purpose. The data will also influence the analysis methods selected.

We run interactive workshops to help you develop skills related to doing research, such as data analysis, writing literature reviews and preparing for dissertations. Find out more on the Skills for Learning Workshops page.

We have online academic skills modules within MyBeckett for all levels of university study. These modules will help your academic development and support your success at LBU. You can work through the modules at your own pace, revisiting them as required. Find out more from our FAQ What academic skills modules are available?

Quantitative data analysis

Broadly speaking, 'statistics' refers to methods, tools and techniques used to collect, organise and interpret data. The goal of statistics is to gain understanding from data. Therefore, you need to know how to:

• Produce data – for example, by handing out a questionnaire or doing an experiment.
• Organise, summarise, present and analyse data.
• Draw valid conclusions from findings.

There are a number of statistical methods you can use to analyse data. Choosing an appropriate statistical method should follow naturally, however, from your research design. Therefore, you should think about data analysis at the early stages of your study design. You may need to consult a statistician for help with this.

Tips for working with statistical data

• Plan so that the data you get has a good chance of successfully tackling the research problem. This will involve reading literature on your subject, as well as on what makes a good study.
• To reach useful conclusions, you need to reduce uncertainties or 'noise'. Thus, you will need a sufficiently large data sample. A large sample will improve precision. However, this must be balanced against the 'costs' (time and money) of collection.
• Consider the logistics. Will there be problems in obtaining sufficient high-quality data? Think about accuracy, trustworthiness and completeness.
• Statistics are based on random samples. Consider whether your sample will be suited to this sort of analysis. Might there be biases to think about?
• How will you deal with missing values (any data that is not recorded for some reason)? These can result from gaps in a record or whole records being missed out.
• When analysing data, start by looking at each variable separately. Conduct initial/exploratory data analysis using graphical displays. Do this before looking at variables in conjunction or anything more complicated. This process can help locate errors in the data and also gives you a 'feel' for the data.
• Look out for patterns of 'missingness'. They are likely to alert you if there’s a problem. If the 'missingness' is not random, then it will have an impact on the results.
• Be vigilant and think through what you are doing at all times. Think critically. Statistics are not just mathematical tricks that a computer sorts out. Rather, analysing statistical data is a process that the human mind must interpret!

Top tips! Try inventing or generating the sort of data you might get and see if you can analyse it. Make sure that your process works before gathering actual data. Think what the output of an analytic procedure will look like before doing it for real.

(Note: it is actually difficult to generate realistic data. There are fraud-detection methods in place to identify data that has been fabricated. So, remember to get rid of your practice data before analysing the real stuff!)

Statistical software packages

Software packages can be used to analyse and present data. The most widely used ones are SPSS and NVivo.

SPSS is a statistical-analysis and data-management package for quantitative data analysis. Click on ‘ How do I install SPSS? ’ to learn how to download SPSS to your personal device. SPSS can perform a wide variety of statistical procedures. Some examples are:

• Data management (i.e. creating subsets of data or transforming data).
• Summarising, describing or presenting data (i.e. mean, median and frequency).
• Looking at the distribution of data (i.e. standard deviation).
• Comparing groups for significant differences using parametric (i.e. t-test) and non-parametric (i.e. Chi-square) tests.
• Identifying significant relationships between variables (i.e. correlation).

NVivo can be used for qualitative data analysis. It is suitable for use with a wide range of methodologies. Click on ‘ How do I access NVivo ’ to learn how to download NVivo to your personal device. NVivo supports grounded theory, survey data, case studies, focus groups, phenomenology, field research and action research.

• Process data such as interview transcripts, literature or media extracts, and historical documents.
• Code data on screen and explore all coding and documents interactively.
• Rearrange, restructure, extend and edit text, coding and coding relationships.
• Search imported text for words, phrases or patterns, and automatically code the results.

Qualitative data analysis

Miles and Huberman (1994) point out that there are diverse approaches to qualitative research and analysis. They suggest, however, that it is possible to identify 'a fairly classic set of analytic moves arranged in sequence'. This involves:

• Affixing codes to a set of field notes drawn from observation or interviews.
• Noting reflections or other remarks in the margins.
• Sorting/sifting through these materials to identify: a) similar phrases, relationships between variables, patterns and themes and b) distinct differences between subgroups and common sequences.
• Isolating these patterns/processes and commonalties/differences. Then, taking them out to the field in the next wave of data collection.
• Highlighting generalisations and relating them to your original research themes.
• Taking the generalisations and analysing them in relation to theoretical perspectives.

        (Miles and Huberman, 1994.)

Patterns and generalisations are usually arrived at through a process of analytic induction (see above points 5 and 6). Qualitative analysis rarely involves statistical analysis of relationships between variables. Qualitative analysis aims to gain in-depth understanding of concepts, opinions or experiences.

Presenting information

There are a number of different ways of presenting and communicating information. The particular format you use is dependent upon the type of data generated from the methods you have employed.

Here are some appropriate ways of presenting information for different types of data:

Bar charts: These   may be useful for comparing relative sizes. However, they tend to use a large amount of ink to display a relatively small amount of information. Consider a simple line chart as an alternative.

Pie charts: These have the benefit of indicating that the data must add up to 100%. However, they make it difficult for viewers to distinguish relative sizes, especially if two slices have a difference of less than 10%.

Other examples of presenting data in graphical form include line charts and  scatter plots .

Qualitative data is more likely to be presented in text form. For example, using quotations from interviews or field diaries.

• Plan ahead, thinking carefully about how you will analyse and present your data.
• Think through possible restrictions to resources you may encounter and plan accordingly.
• Find out about the different IT packages available for analysing your data and select the most appropriate.
• If necessary, allow time to attend an introductory course on a particular computer package. You can book SPSS and NVivo workshops via MyHub .
• Code your data appropriately, assigning conceptual or numerical codes as suitable.
• Organise your data so it can be analysed and presented easily.
• Choose the most suitable way of presenting your information, according to the type of data collected. This will allow your information to be understood and interpreted better.

Primary, secondary and tertiary sources

Information sources are sometimes categorised as primary, secondary or tertiary sources depending on whether or not they are ‘original’ materials or data. For some research projects, you may need to use primary sources as well as secondary or tertiary sources. However the distinction between primary and secondary sources is not always clear and depends on the context. For example, a newspaper article might usually be categorised as a secondary source. But it could also be regarded as a primary source if it were an article giving a first-hand account of a historical event written close to the time it occurred.

• Primary sources
• Secondary sources
• Tertiary sources
• Grey literature

Primary sources are original sources of information that provide first-hand accounts of what is being experienced or researched. They enable you to get as close to the actual event or research as possible. They are useful for getting the most contemporary information about a topic.

Examples include diary entries, newspaper articles, census data, journal articles with original reports of research, letters, email or other correspondence, original manuscripts and archives, interviews, research data and reports, statistics, autobiographies, exhibitions, films, and artists' writings.

Some information will be available on an Open Access basis, freely accessible online. However, many academic sources are paywalled, and you may need to login as a Leeds Beckett student to access them. Where Leeds Beckett does not have access to a source, you can use our  Request It! Service .

Secondary sources interpret, evaluate or analyse primary sources. They're useful for providing background information on a topic, or for looking back at an event from a current perspective. The majority of your literature searching will probably be done to find secondary sources on your topic.

Examples include journal articles which review or interpret original findings, popular magazine articles commenting on more serious research, textbooks and biographies.

The term tertiary sources isn't used a great deal. There's overlap between what might be considered a secondary source and a tertiary source. One definition is that a tertiary source brings together secondary sources.

Examples include almanacs, fact books, bibliographies, dictionaries and encyclopaedias, directories, indexes and abstracts. They can be useful for introductory information or an overview of a topic in the early stages of research.

Depending on your subject of study, grey literature may be another source you need to use. Grey literature includes technical or research reports, theses and dissertations, conference papers, government documents, white papers, and so on.

Artificial intelligence tools

Before using any generative artificial intelligence or paraphrasing tools in your assessments, you should check if this is permitted on your course.

If their use is permitted on your course, you must  acknowledge any use of generative artificial intelligence tools  such as ChatGPT or paraphrasing tools (e.g., Grammarly, Quillbot, etc.), even if you have only used them to generate ideas for your assessments or for proofreading.

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• v.74(8); 2010 Oct 11

Presenting and Evaluating Qualitative Research

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.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

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How to Present Data and Statistics in Your Research Paper: Language Matters 

Statistics is an inexact science as it is based on probabilities rather than certainties. However, the language used to present data and statistics in your thesis or research paper needs to be accurate to avoid misunderstandings when your work is read by others. If the written descriptions of your data and statistics are not clear and accurate, experienced researchers may lose confidence in your entire study and dismiss your results, no matter how compelling they may be. 

The presentation of data in research and effective communication of statistical results requires writers to be very careful in their word choices. You must be confident that you understand the analysis you performed and the meaning of the results to really know how to present the data and statistics in your research paper effectively. Here are some terms and concepts that are often misused and may be confusing to early career researchers. 

Averages, the measures of the central tendency of a dataset, can be calculated in several different ways. The word “average” in non-scholarly writings typically refers to the arithmetic mean. However, the median and mode are two other frequently used measures. In your research paper, it is critical to state exactly what measure you are using. Therefore, don’t report an average but a mean, median, or mode. 

Percentages

Percentages are commonly used in presentations of data in research. They can indicate concentrations, probabilities, or comparisons, and they are frequently used to report changes in values. For example, the annual crime rate increased by 25%. However, unless you have a basis for this number, it’s difficult to judge the meaningfulness of this increase 1 . Did the number of crimes increase from 4 incidents to 5 or from 4,000 incidents to 5,000? Be sure to include enough information for the reader to understand the context.  

In addition, when used for comparison, make sure your comparison is complete. For instance, if the temperature was 17% higher in 2022, be sure to include that it was 17% higher than the temperature in 2017. 

Descriptive vs. inferential statistics

Descriptive statistics deal with populations, while inferential statistics deal with samples. A population is a group of objects or measurements that includes all possible instances, and a sample is a subset of that population. For example, you measure the mass of all the 1.1 kg jars of peanut butter at your favorite grocery store and report the mean and standard deviation. These are descriptive statistics for this population of peanut butter jars. However, if you then say that this is the mean of all such jars of peanut butter produced, you are engaging in inferential statistics because you now have measured only a sample of jars. You are inferring a characteristic of a population based on a sample. Inferential statistics are usually reported with a margin of error or confidence interval, such as 1.1 ± .02 kg. 

A hypothesis is a testable statement about the relationship between two or more groups or variables that forms the basis of the scientific method. The appropriate language around the topic of hypotheses and hypothesis testing can be confusing for even seasoned researchers. 

The alternative hypothesis is generally the researcher’s prediction for the study, and the null hypothesis is the negation of the alternative hypothesis. The aim of the study is to find evidence to reject the null hypothesis, which supports the truth of the alternative hypothesis. 

When writing up the results of your hypothesis test, it is important to understand exactly what the results mean. Remember, hypothesis testing can never “prove” anything – it merely provides evidence for either rejecting or not rejecting the null hypothesis. Also, be careful that you don’t overgeneralize the meaning of the results. Just because you find evidence that the null hypothesis can be rejected in this case does not mean the same is true under all conditions. 

Tips for effectively presenting statistics in academic writing

Presenting your data and statistical results can be very challenging. For researchers without extensive experience or statistical training, writing this part of the study report can be especially daunting. Here are some things to keep in mind when presenting your data and statistical results 1 . 

• If you don’t completely understand a statistical procedure, do not attempt to write it up without guidance from an expert. This is the most important thing you can do. 
• Keep your audience in mind. When you present your data and statistical results, think about how familiar your readers may be with the analysis and include the amount of detail needed for them to be comfortable 2 .  
• Use tables and graphics to illustrate your results more clearly and make your writing more understandable. 

We hope the points above help answer the question of how to present data and statistics in your research paper correctly. All the best! 

• The University of North Carolina at Chapel Hill Writing Center. Statistics. https://writingcenter.unc.edu/tips-and-tools/statistics/ [Accessed October 10, 2022] 
• Purdue University Online Writing Lab. Writing with statistics. https://owl.purdue.edu/owl/research_and_citation/using_research/writing_with_statistics/index.html [Accessed October 10, 2022] 

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A Beginner’s Guide to Owning and Presenting your Research

By Liam Hines

Photo by Sewupari Studio for Noun Project

Summer has officially come to an end, and with its departure a new semester dawns. And with a new semester come ripe opportunities, scholarly resolutions, and motivating little white lies. I myself have lain in bed the night before the start of classes committed to such delusions as “I will attend my 9:30 lectures” and “I will start my p-sets the day they are assigned.” However, you may find that the most wishful fantasies are served to you; those that have spent their summer UROPing will be familiar with promising speculation of future events: “Great work, we can turn this into a paper.”

Like the little pre-semester promises we make to ourselves, these plans to publish can be more wishful thinking than substance. And for good reason: justifications for not publishing can be quite substantial. While your portion of a project may be completed, there can be other much larger pieces on much larger timeframes. Professors have many projects, and while your project may be your top priority it may be low on their list to finalize for publication. And unfortunately, even if your project is complete, concerns such as public availability of intellectual property may make it impossible for you to publish independently.

But graduate school applications are looming, career fairs are just around the corner, and your resume needs updating. And boy, wouldn’t this research experience look much better if it included a publication with your name on it? You would be 100% correct in your diagnosis, but if you are waiting around for someone else to publish your research, you may have the wrong remedy in mind.

Student Research is a Hot Commodity (for the right audience)

While your professor may be after prestigious publications, you can set your sights a little lower. What niche in your field does your research apply to? Are there smaller journals that would be receptive to your work? What about journals published by professional societies? Even better, what about conferences or journals that specifically publish student work?

These avenues for publication not only exist but encourage student contributions. The American Nuclear Society welcomes student submissions to its Annual Conference and even has a separate student-only conference every Spring. MIT hosts the aptly-named Undergraduate Research Technology Conference every fall to showcase the type of research you have performed! Is your professor presenting at a conference this semester? Chances are, that same conference has a student competition. Does your department have an Undergraduate Research festival? There is a presentation opportunity. Even presenting at your professor’s group meeting can give you a chance to practice presenting your research in a professional setting. While they may not be career-defining publications, presenting your research will develop the skills that you will need to acquire these much-sought-after resume line-items.

Now that you have a venue for publication, mission accomplished, right? Well, possibly. But you may run into the same problem publishing your work that your professor ran into. Your research is a small component in a much larger project—a project that is most likely not close to completion. Additionally, your goal was to present your own research, not to swoop in and beat your professor to publish this larger project. So, what exactly can you present?

Focus Your Presentation on Your Research

It can be daunting to try to present your work separate from the larger project it is part of. Your work may seem important in reaching specific project goals, but appears, at first glance, to make little sense by itself. These are reasonable feelings to have, as you have probably only ever heard your work explained as part of a larger project. However, with a little creativity, you may find your work has its own legs to stand on. While I cannot offer a one-size-fits-all solution here, I can offer a few guiding questions that can help recontextualize your research into a standalone project.

Your best bet is to start from the actual data you collected: What did you measure? And how? Did you use an industry standard measurement technique? Did you process and analyze the measured data using any new methods? While these methods may have been developed in the specific context of your project, they may have broad merits applicable to other areas of research.

Or try this: Is this technique applicable in different operating conditions (higher temperatures, lower pressures, etc.) than traditional methods? Even if your research has not produced a revolutionary outcome, a new, simpler path to the same destination is worth presenting in its own right. If there was nothing new to be gained from your research, chances are your project would have never made it past a proposal. By asking the right questions, you can tease out what implications your research has and why it should be presented on its own.

These questions are not meant to be rhetorical. Ask your professor. If your summer project could have been a literature search, it would have been. The person who secured funding for you to perform this research very likely knows why that research had to take place. While it may be true that your advisor has a very good reason not to have your work published, do not let a small possibility of failure dissuade you from publishing your work! You are much more likely to find support in putting together an abstract and finding the journal or conference most receptive to your work.

No research outcome is as rewarding as a publication with your name on it. Trust me when I tell you it feels even better to be your own first author. So why wait around for someone else to publish your work? You may just find yourself a few new items on your resume and a little self-empowerment along the way.

Liam Hines received his undergraduate degree from MIT NSE, and is now a graduate student in Prof. Koroush Shirvan’s group. He is also an NSE Communication Fellow.

Published September 27, 2023

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How to Present Your Research (Guidelines and Tips)

Published on 01 Feb 2023

Presenting at a conference can be stressful, but can lead to many opportunities, which is why coming prepared is super beneficial.

The internet is full to the brim with tips for making a good presentation. From what you wear to how you stand to good slide design, there’s no shortage of advice to make any old presentation come to life. 

But, not all presentations are created equal. Research presentations, in particular, are unique. 

Communicating complex concepts to an audience with a varied range of awareness about your research topic can be tricky. A lack of guidance and preparation can ruin your chance to share important information with a conference community. This could mean lost opportunities in collaboration or funding or lost confidence in yourself and your work.

So, we’ve put together a list of tips with research presentations in mind. Here’s our top to-do’s when preparing to present your research.

Take every research presentation opportunity

The worst thing you could do for your research is to not present it at all. As intimidating as it can be to get up in front of an audience, you shouldn’t let that stop you from seizing a good opportunity to share your work with a wider community.

These contestants from the Vitae Three Minute Thesis Competition have some great advice to share on taking every possible chance to talk about your research. 

Double-check your research presentation guidelines

Before you get started on your presentation, double-check if you’ve been given guidelines for it. 

If you don’t have specific guidelines for the context of your presentation, we’ve put together a general outline to help you get started. It’s made with the assumption of a 10-15 minute presentation time. So, if you have longer to present, you can always extend important sections or talk longer on certain slides:

• Title Slide (1 slide) - This is a placeholder to give some visual interest and display the topic until your presentation begins.
• Short Introduction (2-3 slides) - This is where you pique the interest of your audience and establish the key questions your presentation covers. Give context to your study with a brief review of the literature (focus on key points, not a full review). If your study relates to any particularly relevant issues, mention it here to increase the audience's interest in the topic.
• Hypothesis (1 slide) - Clearly state your hypothesis.
• Description of Methods (2-3 slides) - Clearly, but briefly, summarize your study design including a clear description of the study population, the sample size and any instruments or manipulations to gather the data.
• Results and Data Interpretation (2-4 slides) - Illustrate your results through simple tables, graphs, and images. Remind the audience of your hypothesis and discuss your interpretation of the data/results.
• Conclusion (2-3 slides) - Further interpret your results. If you had any sources of error or difficulties with your methods, discuss them here and address how they could be (or were) improved. Discuss your findings as part of the bigger picture and connect them to potential further outcomes or areas of study.
• Closing (1 slide) - If anyone supported your research with guidance, awards, or funding, be sure to recognize their contribution. If your presentation includes a Q&A session, open the floor to questions.

Plan for about one minute for each slide of information that you have. Be sure that you don’t cram your slides with text (stick to bullet points and images to emphasize key points).

And, if you’re looking for more inspiration to help you in scripting an oral research presentation. University of Virginia has a helpful oral presentation outline script .

A PhD Student working on an upcoming oral presentation.

Put yourself in your listeners shoes

As mentioned in the intro, research presentations are unique because they deal with specialized topics and complicated concepts. There’s a good chance that a large section of your audience won’t have the same understanding of your topic area as you do. So, do your best to understand where your listeners are at and adapt your language/definitions to that.

There’s an increasing awareness around the importance of scientific communication. Comms experts have even started giving TED Talks on how to bridge the gap between science and the public (check out Talk Nerdy to Me ). A general communication tip is to find out what sort of audience will listen to your talk. Then, beware of using jargon and acronyms unless you're 100% certain that your audience knows what they mean. 

On the other end of the spectrum, you don’t want to underestimate your audience. Giving too much background or spending ages summarizing old work to a group of experts in the field would be a waste of valuable presentation time (and would put you at risk of losing your audience's interest). 

Finally, if you can, practice your presentation on someone with a similar level of topic knowledge to the audience you’ll be presenting to.

Use scientific storytelling in your presentation

In scenarios where it’s appropriate, crafting a story allows you to break free from the often rigid tone of scientific communications. It helps your brain hit the refresh button and observe your findings from a new perspective. Plus, it can be a lot of fun to do!

If you have a chance to use scientific storytelling in your presentation, take full advantage of it. The best way to weave a story for your audience into a presentation is by setting the scene during your introduction. As you set the context of your research, set the context of your story/example at the same time. Continue drawing those parallels as you present. Then, deliver the main message of the story (or the “Aha!”) moment during your presentation’s conclusion.

If delivered well, a good story will keep your audience on the edge of their seats and glued to your entire presentation.

Emphasize the “Why” (not the “How”) of your research

Along the same lines as using storytelling, it’s important to think of WHY your audience should care about your work. Find ways to connect your research to valuable outcomes in society. Take your individual points on each slide and bring things back to the bigger picture. Constantly remind your listeners how it’s all connected and why that’s important.

One helpful way to get in this mindset is to look back to the moment before you became an expert on your topic. What got you interested? What was the reason for asking your research question? And, what motivated you to power through all the hard work to come? Then, looking forward, think about what key takeaways were most interesting or surprised you the most. How can these be applied to impact positive change in your research field or the wider community?

Be picky about what you include

It’s tempting to discuss all the small details of your methods or findings. Instead, focus on the most important information and takeaways that you think your audience will connect with. Decide on these takeaways before you script your presentation so that you can set the scene properly and provide only the information that has an added value.

When it comes to choosing data to display in your presentation slides, keep it simple. Wherever possible, use visuals to communicate your findings as opposed to large tables filled with numbers. This article by Richard Chambers has some great tips on using visuals in your slides and graphs.

Hide your complex tables and data in additional slides

With the above tip in mind: Just because you don’t include data and tables in your main presentation slides, doesn’t mean you can’t keep them handy for reference. If there’s a Q&A session after your presentation (or if you’ll be sharing your slides to view on-demand after) one great trick is to include additional slides/materials after your closing slide. You can keep these in your metaphorical “back pocket” to refer to if a specific question is asked about a data set or method. They’re also handy for people viewing your presentation slides later that might want to do a deeper dive into your methods/results.

However, just because you have these extra slides doesn’t mean you shouldn’t make the effort to make that information more accessible. A research conference platform like Fourwaves allows presenters to attach supplementary materials (figures, posters, slides, videos and more) that conference participants can access anytime.

Leave your audience with (a few) questions

Curiosity is a good thing. Whether you have a Q&A session or not, you should want to leave your audience with a few key questions. The most important one:

“Where can I find out more?”

Obviously, it’s important to answer basic questions about your research context, hypothesis, methods, results, and interpretation. If you answer these while focusing on the “Why?” and weaving a good story, you’ll be setting the stage for an engaging Q&A session and/or some great discussions in the halls after your presentation. Just be sure that you have further links or materials ready to provide to those who are curious. 

Conclusion: The true expert in your research presentation

Throughout the entire process of scripting, creating your slides, and presenting, it’s important to remember that no one knows your research better than you do. If you’re nervous, remind yourself that the people who come to listen to your presentation are most likely there due to a genuine interest in your work. The pressure isn’t to connect with an uninterested audience - it’s to make your research more accessible and relevant for an already curious audience.

Finally, to practice what we preached in our last tip: If you’re looking to learn more about preparing for a research presentation, check out our articles on how to dress for a scientific conference and general conference presentation tips .

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How to present your data in quantitative research lecture (CC-BY, 2020)

CC BY is the correct license for this work. Ignore the ones in the slides: use this materials as you like, with attribution. (KW, 02-2020)

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