what is concise visual presentation in quantitative research

• Open access
• Published: 25 August 2021

Effect of different visual presentations on the comprehension of prognostic information: a systematic review

• Eman Abukmail 1 ,
• Mina Bakhit 1 ,
• Chris Del Mar 1 &
• Tammy Hoffmann 1  

BMC Medical Informatics and Decision Making volume  21 , Article number:  249 ( 2021 ) Cite this article

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Understanding prognostic information can help patients know what may happen to their health over time and make informed decisions. However, communicating prognostic information well can be challenging.

To conduct a systematic review to identify and synthesize research that has evaluated visual presentations that communicate quantitative prognostic information to patients or the public.

Data sources

MEDLINE, EMBASE, CINAHL, PsycINFO , ERIC and the Cochrane Central Register of Controlled Trials (CENTRAL) (from inception to December 2020), and forward and backward citation search.

Study selection

Two authors independently screened search results and assessed eligibility. To be eligible, studies required a quantitative design and comparison of at least one visual presentation with another presentation of quantitative prognostic information. The primary outcome was comprehension of the presented information. Secondary outcomes were preferences for or satisfaction with the presentations viewed, and behavioral intentions.

Data extraction

Two authors independently assessed risk of bias and extracted data.

Data synthesis

Eleven studies (all randomized trials) were identified. We grouped studies according to the presentation type evaluated. Bar graph versus pictograph (3 studies): no difference in comprehension between the groups. Survival vs mortality curves (2 studies): no difference in one study; higher comprehension in survival curve group in another study. Tabular format versus pictograph (4 studies): 2 studies reported similar comprehension between groups; 2 found higher comprehension in pictograph groups. Tabular versus free text (3 studies): 2 studies found no difference between groups; 1 found higher comprehension in a tabular group.

Limitations

Heterogeneity in the visual presentations and outcome measures, precluding meta-analysis.

Conclusions

No visual presentation appears to be consistently superior to communicate quantitative prognostic information.

Peer Review reports

Introduction

Shared decision making is a bidirectional communication process in which clinicians and patients collaborate on making a health decision and discuss the available options (based on the best available evidence), the benefits and harms of each option, and the patient’s values, preferences, and circumstances [ 1 , 2 ]. As part of making decisions about the prevention or management of a health condition, patients need to know about more than just treatment options; they also need to know about the prognosis of the condition, with and without treatment.

Communication of prognostic information is essential as it helps patients to know what may happen to their health over time, to make appropriate preparations, and to make informed decisions about whether to intervene and if so, how. If prognostic information is not communicated adequately, patients may have inaccurate expectations about the likely course of their illness [ 3 , 4 , 5 ]. Poor communication of prognostic information can also make patients anxious, confused, and damage the relationship between clinician and patient [ 6 , 7 ].

Prognosis communication can be complex and challenging for clinicians and patients. Clinicians sometimes try to avoid or delay this kind of communication, while patients often wait for their clinicians to initiate the process [ 8 ]. As with the communication of treatment information, a contributor to the challenge of communicating prognostic information well is the difficulty that clinicians and patients can have understanding relevant quantitative information [ 9 , 10 , 11 ].

To facilitate discussion about prognosis, clinicians may use visual means (such as a graph) to present the quantitative information. Although there is a large body of synthesized evidence on how to communicate the quantitative benefits and harms of treatments, we are unaware of any synthesis about the various visual presentations that can be used to communicate quantitative prognostic information.

The protocol of this systematic review is registered at (CRD42020192564) and can be found in the Open Science Framework osf.io/ze26g.

This systematic review aimed to identify and synthesize research that has evaluated visual presentations that communicate quantitative prognostic information to patients or the public.

Information sources

We searched for studies in six databases: MEDLINE, EMBASE, CINAHL, ERIC, PsycINFO, and the Cochrane Central Register of Controlled Trials (CENTRAL), each from date of inception till December 2020. We used a tailored search strategy for each database (see Additional file 1 ). Forward and backward citation analysis of the included studies was performed using Web of Science.

Eligibility criteria

Study types and participants.

We included only studies with a quantitative design that looked at the prognosis of any health condition (real condition, hypothetical scenario, or fictitious condition). The only participation restriction was the exclusion of health professionals or health professional students. Studies of mixed populations (e.g. health professionals and patients) were eligible if data were reported separately for the eligible group. There was no limitation on study setting or publication language.

Interventions

Studies were eligible if they compared a visual presentation (e.g. a graph, words and numbers displayed in tabular format) with at least one another type of presentation (e.g. another type of graph, or free text) to display quantitative prognostic information and included a specified time frame (e.g. over the next 5 years) or time point (e.g. at 1 year). Prognostic information was defined as information about the likelihood of any future outcome in patients with a given health condition including those who received no treatment (natural history) or those who did.

Our primary outcome was comprehension of the presented information that was assessed using questions that required a quantitative answer (e.g. likelihood or duration of the outcome). For this reason, only some of the questions asked were eligible. Studies/data that assessed comprehension with questions requiring qualitative responses were ineligible. Secondary outcomes included: preferences for any of the presentations evaluated; satisfaction with the presentation; and behavioral intentions relevant to the information presented (e.g. intention to be screened).

Two authors (EA and MB) independently screened the titles and abstracts, and then the full text of potentially included studies. Discrepancies were resolved through consultation with the other two senior authors (CDM and TH).

Data extraction and risk of bias assessment

Data were extracted into a custom-designed spreadsheet. The studies’ characteristics (e.g., study settings, sampling methods, study design) and participants’ characteristics (e.g., age, sex, educational level, health literacy level, numeracy level, and the health condition studied). Intervention details (including type of presentation (e.g., bar graph), the presented information, who delivered the information, how, where and when the information was delivered), outcome details (including the eligible outcomes, how they were measured and at what timepoints) and result details (including number of responses analysed, follow up rate, results of eligible studies) are tabulated in the Additional file 1 and show the data extracted. Two authors (EA and MB) independently extracted relevant data and assessed risk of bias of included studies using the Cochrane Risk of Bias tool for randomized trials—version 2 (RoB2) [ 12 ]. Any discrepancies either in data extraction or risk of bias assessment were resolved by consulting the other two senior authors (CDM and TH).

Due to heterogeneity of the primary outcome measures (comprehension) and visual presentations used, we were unable to conduct a meta-analysis and therefore report the results narratively. The percentage of participants who answered each eligible question correctly are reported separately for each question and we calculated the average percentage correct across the eligible questions in each study. In studies that did not report the percentage correct for an individual question, we extracted the overall percentage correct for all comprehension questions as reported in the studies.

Modifications from the protocol

After reviewing the articles generated from our search, we added explicit exclusion criteria that were not detailed in our protocol. Studies that compared alternative statistical formats (e.g. relative risk reduction vs. absolute risk reduction) and studies that compared the framing (i.e. positive or negative) of health information were excluded as they have been previously synthesized [ 13 , 14 ]. Also excluded were studies that only compared methods of wording free text for conveying prognostic information. The eligibility of the primary outcome measure was also clarified to include questions that required participants to perform a quantitative calculation and then select the answer from categorical or dichotomous response options. The search strategy (see Additional file 1 ) was slightly modified by adding more MeSH terms (e.g., Comprehension, Knowledge, Data Display, Communication, Perception, “Decision Making, Shared”) at the request of reviewers during the peer-review process. This resulted in no additional eligible studies.

Our search identified 5648 articles across the databases and 614 articles identified through forward and backward citation analysis of the included studies (6262 in total), 5133 remained after duplicates were removed. After the full-text screening, we identified 9 articles: in 2 of these, 2 separate studies were reported, resulting in 11 eligible studies [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ] (Fig.  1 ).

PRISMA flow chart of systematic search and selection

Characteristics of included studies

All 11 included studies were randomized trials. Seven studies were conducted in the United States, 3 in Germany, and 1 in the United Kingdom. The total number of participants was 9737 (mean 885, range 120 to 2305). All participants were adults; 4 studies included only men, 3 studies included only women, 3 included both, and 1 did not report this. Eight studies were conducted online and 3 face-to-face. Health information in the interventions related to the prognosis of cancer (9 studies), middle ear infection (1 study), and multiple sclerosis (1 study). Details of the included studies are presented in the Table of characteristics (see Additional file 1 ).

Risk of bias assessment

The overall assessment of the risk of bias of the 11 studies was judged at “some concerns” level. All included articles were judged to have “some concerns” for at least one domain of risk of bias. Ten studies had “some concerns” for the selection of reported results and six had “some concerns” for their randomization process. (Fig.  2 a, b).

Risk of bias assessment of the included studies. a : overall, b : individual studies

Visual presentations evaluated

Details of the visual presentations evaluated are provided in the Additional file 1 (Table of interventions). Studies were grouped into 4 categories according to the presentation type they used: 6 studies compared a graphic format to another graphic format: 3 studies compared a bar graph to a pictograph/icon array, 2 studies compared survival curves to mortality curves, and 1 study compared two variations of pictographs (the last is covered in “other comparisons”. Four studies compared a graphic format to a tabular format (also known as a ‘fact box’), Three studies compared a tabular format to free text. (Fig.  3 ).

Interventions compared in the included studies. 1 A third group received both survival and mortality curve. 2 Eight interventions with variation of each format type were tested (3 pictographs, 2 bar graphs, 1 line graph, 2 pie graphs). 3 Four interventions: 2 static (bar graph, pictograph) and 2 animated (bar graph, pictograph). 4 Survival curves were delivered either in 5y or 15y worth data and the same for mortality curves. 5 Four interventions were tested (4-options pictograph, 4-options bar graph, 2-options pictograph, 2-options bar graph). 6 Two pictographs; a graph with survival only outcome versus a graph with multiple outcomes. 7 The three formats were embedded in fact box format, two studies were reported one face to face and one online. 8 Two conditions were tested in this study; only one contained prognostic information (middle ear infection) and was included. 9 Two studies were reported (breast cancer screening, female participants) and (prostate cancer screening, male participants). Each study tested 3 interventions (pictograph, tabular, free text). X refers to a study group

Primary outcome: comprehension

Graph versus graph (6 studies), bar graph versus pictograph (i.e. icon array) (3 studies).

Three studies compared bar graphs to pictographs (see Fig.  4 a). There was no statistically significant difference in comprehension between these types of graphs [ 17 , 18 , 21 ]. One of the studies enrolled 420 men and randomized them to 1 of 8 graph variations communicating the likelihood of recurrence of prostate cancer over 3-time points (including: 3 pictographs and 2 bar graphs variations). It found that 89% of participants who viewed pictographs (regardless of the variation shown) answered comprehension questions correctly compared to 87% of those who viewed bar graphs (regardless of the variation shown) [ 17 ].

Comprehension results of visual presentation comparisons ( a - d ). Graphs are different in each study (see Table of interventions in the Additional file 1 ). Questions are different in each study, for exact wording (see Table of outcomes in the Additional file 1 ). Kasper 2017 has 1 eligible question. Petrova 2015-1 and 2 reported the overall comprehension per format

In a study on breast cancer prognosis, 1619 participants were randomized to 1 of 4 graph variations (2 pictographs, 2 bar graphs), 51% of participants who viewed pictographs (regardless of the variation) answered comprehension questions correctly compared to 42% who viewed bar graphs (regardless of the variation) [ 21 ]. In a study with 682 people with multiple sclerosis, participants were randomized to 1 of 4 graph variations (2 pictographs, 2 bar graphs). The question about prognosis was answered correctly by 91% of those who viewed the pictograph, compared to 64% who viewed the bar graph [ 18 ].

Survival curves versus mortality curves (2 studies)

Two studies measured comprehension after presenting participants with either survival curves or mortality curves (Fig.  4 b). A study on the communication of breast cancer prognosis analyzed responses of 1461 participants and found that there was no difference in comprehension regardless of whether a survival or mortality curve was presented [ 22 ]. A study of 451 participants, using a colon cancer scenario, found that those who viewed survival curves scored significantly better than those who viewed mortality curves, with an average correct difference of 13%. A group of participants in the same study who were shown both survival and mortality curves performed slightly better than the group who only saw mortality curves, but the difference was not significant [ 15 ].

Graph versus tabular format (i.e. fact box) format (4 studies)

Four studies compared pictographs to a tabular format (Fig.  4 c). Two studies (reported in the same article, one conducted face-to-face and one online) used three groups to compare a tabular format alone, to a tabular format plus a single pictograph (that showed both benefits and harms), to a tabular format plus two pictographs (one showing benefits and one showing harms). In both studies, the authors reported that the interventions had a similar effect in facilitating comprehension. An average of 74% of participants who viewed the tabular format in the face-to-face study (75% in the online study) compared to 80% who viewed the pictographs (results from both pictograph formats combined) (60% in the online study) correctly answered comprehension questions [ 20 ].

Another article reported two studies: a study communicating prostate cancer screening information for male participants and a study communicating breast cancer screening for female participants. The study about prostate cancer found that the use of a pictograph format significantly improved comprehension ( P  = 0.003) compared to a tabular format, with 72% and 61% of participants answered correctly, respectively. Similar results were reported in the breast cancer study, with 62% of those who viewed the pictograph were able to answer the questions correctly compared to 58% who viewed the tabular format [ 19 ].

Tabular format versus text (3 studies)

Three studies compared a tabular format to a free text format (Fig.  4 d). Two studies (reported in the same article) found no significant difference between using a tabular format and free text to communicate prostate cancer prognosis to male participants and prognosis of breast cancer to female participants. In the prostate cancer study, 64% of participants who viewed the text format compared to 61% who viewed the tabular format answered the questions correctly. In the breast cancer study, 61% of those who viewed the text format answered correctly compared to 60% of those who viewed tabular format [ 19 ]. A study communicating prognosis of acute middle ear infection with and without antibiotics found that participants who saw a tabular format scored significantly higher on comprehension questions than those who saw a free text format (85% vs 72% correctly answering) [ 16 ].

Other comparisons

Static formats were better understood than animated formats when displayed online in a German web-based study of 682 people with multiple sclerosis [ 18 ]. Displays that contained less information were generally better understood than those with more as found in two studies conducted by the same author [ 21 , 23 ]. One of these found that pictographs with information about the outcome of two treatment alternatives, compared to those with four, were significantly better understood [ 21 ]. In the other study, pictographs that presented data for only one outcome (survival only; the number of women alive after 10 years who had treatment) were significantly better understood than pictographs that presented information about multiple outcomes (survival, mortality due to cancer, mortality due to all causes) [ 23 ].

Secondary outcomes

Format preference (1 study).

In the study that randomized participants to 1 of 8 formats (3 pictographs, 2 bar graphs, 1 line graph, 2 pie graphs), participants preferred the bar graph and thought they would understand it better than other formats. The pictograph was rated the lowest on both preference and expected understanding, regardless of the format they were randomized to. Overall, there was no statistically significant difference between graph preference and comprehension in this study [ 17 ].

Satisfaction (2 studies)

In a study that compared 4 visual presentations (2 bar graph variations, 2 pictograph variations), both the pictograph formats (4-options pictograph and 2-options pictograph) received statistically significantly higher satisfaction scores compared to the 4-options bar graph. However, even though participants were significantly more satisfied with the 2-options bar graph compared to the 4-options bar graph, the satisfaction scores were not as high as for the pictograph formats [ 21 ]. In another study, a pictograph that contained only survival data had significantly higher satisfaction scores than a pictograph showing multiple outcomes [ 23 ]. Participants of the study involving middle ear infection prognostic information reported being more engaged with the tabular format compared to the free text format [ 16 ].

Behavioral intentions (4 studies)

Participants in the communication of breast cancer prognosis study who viewed the survival-only pictograph were statistically significant less likely to say that they would have both chemotherapy and hormonal therapy compared to those who viewed the multiple outcome pictograph ( P  = 0.04) [ 23 ]. In a 3-arm study that used colon cancer information to compare survival curves, mortality curves, and both curves, participants who viewed survival curves were more likely to choose a preventive colectomy than an annual exam compared to the other two groups [ 15 ]. In the study of providing middle ear infection prognostic information, the format did not affect participants’ recommendation to a family member to use antibiotics [ 16 ]. Two studies on the communication of prostate cancer prognosis screening outcomes found no association between the type of presentation and the intention to be screened for prostate cancer at any measurement time point in two studies (one conducted online, one conducted face to face) [ 20 ].

Discussion and conclusion

Our main finding is that from the existing studies there does not appear to be a single type of visual presentation that is consistently superior over another for improving the comprehension of quantitative prognostic information for members of the public. In the few studies that examined this, simpler formats (such as one outcome instead of multiple, and fewer intervention options presented at one time) were generally better understood and achieved higher levels of satisfaction. The impact of various types of visual presentations on behavioral intention is inconsistent.

Many primary studies and reviews [ 9 , 10 , 13 , 14 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], have investigated various methods of communicating treatment benefits and harms. While there are similarities between the communication of treatment benefits and harms and the communication of prognosis information, the extent to which methods identified as superior for communicating treatment quantitative information are also suitable for facilitating the comprehension of prognosis information is unknown. Similar to the findings of a review of methods of communicating quantitative treatment information [ 9 ], we found little difference between bar graphs and pictographs in facilitating comprehension and that there is no superior single method for conveying quantitative information.

Research on the comprehension of information from a survival curve with different variations found that comprehension was generally good across each variation [ 32 ]. Our review found inconsistent findings of the comparative effect of survival and mortality curves for the comprehension of prognosis information. However, we only identified two studies that had examined this. Details of the chosen curve, such as the complexity of information and the time frame used in the curve, maybe as important as the type of curve [ 33 , 34 , 35 , 36 , 37 ].

The strengths of our systematic review arise from the rigorous method of systematically identifying, screening, and reviewing the relevant literature. Our search was not limited by language; however, studies that do not have an English language title or abstract in the databases might have been missed. Although we searched six databases and conducted citation analysis of the included studies, we may have missed eligible studies. A meta-analysis was precluded due to heterogeneity of the included studies as they used many variations of visual presentations and comprehension was assessed with different measures. Most of the included studies were conducted online, using hypothetical scenarios with participants who did not have the condition being presented. Studies involving participants with the condition of interest may generate different impacts on comprehension, satisfaction, and decisions.

Few primary studies have compared the effectiveness of different visual presentations on the comprehension of quantitative prognostic information and more are needed. Most of the existing studies used cancer scenarios and so future research that explores other conditions would address this research gap, as would head-to-head studies that compare the different presentations. As the superiority of any single visual presentation was not established in this review, visual presentations should be co-designed and piloted with the target population before widespread use.

From the existing research, there is inconsistency about the superiority of a particular visual presentation to use when discussing quantitative prognostic information with patients. Any of the existing visual presentations that were identified in this review may be suitable to use to aid comprehension.

Practice implication

Any of the visual presentations identified may be suitable to aid clinicians in discussing prognostic information with patients or their carers. More primary research is needed to identify how patients or the general public understand the prognostic information. Piloting any newly developed tool to communicate prognosis with the target population is highly recommended.

Availability of data and materials

The protocol of this review is registered at PROSPERO (CRD42020192564) and can be found in the Open Science Framework osf.io/ze26g. More information is provided in the Additional file 1 . The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Justin Clark for his consultation and valuable advice in the development of the search strategy.

No specific funding was received for this systematic review; however, the first author is supported on a PhD scholarship which is funded by the Centre for Research Excellence in Minimising Antibiotic Resistance in the Community (CRE-MARC), funded by the National Health and Medical Research Council (NHMRC), Australia (Reference Number: 1153299). CDM and TH are chief investigators of CREMARC and MB is employed as a postdoctoral research fellow on this grant.

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EA, TH, CDM conceived the study, EA developed the search strategy with consultation from TH, CDM and information specialist Justin Clark. EA and MB screened, assessed the eligibility, and assessed the quality of the included studies with consultation from TH and CDM. EA analysed the data and created the figures with consultation from TH and CDM. EA is responsible for the data management and storage. EA drafted the manuscript, and all authors reviewed the manuscript and approved the final version for submission.

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Supplementary Information

Additional file 1.

: the additional file includes: Search strategy, Table of characteristics, Table of interventions,Table of outcomes, and Table of excluded studies.

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Abukmail, E., Bakhit, M., Del Mar, C. et al. Effect of different visual presentations on the comprehension of prognostic information: a systematic review. BMC Med Inform Decis Mak 21 , 249 (2021). https://doi.org/10.1186/s12911-021-01612-9

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DOI : https://doi.org/10.1186/s12911-021-01612-9

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Effective Visual Communication for the Quantitative Scientist

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• 1 Biostatistical Sciences and Pharmacometrics, Novartis Pharma AG, Basel, Switzerland.
• 2 Biostatistical Sciences and Pharmacometrics, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA.
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Effective visual communication is a core competency for pharmacometricians, statisticians, and, more generally, any quantitative scientist. It is essential in every step of a quantitative workflow, from scoping to execution and communicating results and conclusions. With this competency, we can better understand data and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. The goal of this tutorial is to convey this competency. We posit three laws of effective visual communication for the quantitative scientist: have a clear purpose, show the data clearly, and make the message obvious. A concise "Cheat Sheet," available on https://graphicsprinciples.github.io, distills more granular recommendations for everyday practical use. Finally, these laws and recommendations are illustrated in four case studies.

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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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Presentation of Quantitative Data

References • Health information and basic medical statistics: Park’s Textbook of PSM, 23rd ed. 2016 • Methods in Biostatistics: B.K. Mahajan, Jaypee Brothers Medical Publishers • Informative Presentation of Tables, Graphs and Statistics: University of Reading, Statistical Services Centre. Biometrics Advisory and Support Service to DFID, March 2000 • Making Data Meaningful, A guide to presenting statistics, UNITED NATIONS, Geneva, 2009

The importance of visual presentation of research results

Jul 18, 2017 | Knowledge exhange | 0 comments

This poster, produced by Esther Mc Sween-Cadieux  in collaboration with Christian Dagenais and Valéry Ridde, was presented at the 2017 Canadian Knowledge Mobilization Forum , which was held on May 17-18 in Ottawa-Gatineau. The poster discusses the importance of visual presentation of research results and received the second place of the Poster, visual art & design awards.

Although making search results accessible and understandable is not sufficient to ensure their use, adaptation and presentation of information still has a role to play in the motivation to read a document And subsequent retention of information. Knowledge from the field of information design should further guide researchers’ communication tools (eg web platforms, policy notes, poster presentations, etc.) in order to make them cognitively more attractive! The objective is to draw inspiration from the concepts of information design and graphic design in order to present scientific information in such a way that it is understood effectively.

In order to validate (or not) the importance of the presentation format of an information to be retained by the reader, the poster served as a test during the congress. Using an interactive display, the public could discover step by step the same information but presented differently. Give it a try hereunder…

Click on the poster to enlarge or download it (pdf)

Thus, from the same content and the same research results, three different types of products (bilingual) are developed:

• A “classical” format: type of poster usually presented at scientific symposium
• An “intermediate” format: less textual content and some visual elements
• An “infographic” format: limited textual content and emphasis on the sequence of presentation of information

So, what do you remember according to the format of presentation?

To download the posters separately, click on the thumbnails below:

You can also find below a small visual presentation giving you 7 practical tips when designing a poster.

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Research Infographics: Advantages and Tips to Create Impactful Visuals

Human beings are visual creatures, and this is where research infographics come into play. According to an interesting study, 90% of the information transmitted to the brain is visual. 1 In fact, humans are capable of processing visuals 60,000 times faster than text. 2 This is why the use of research infographics is an important and popular way to communicate complex scientific information in a simple, easy-to-understand way. Using appropriate visuals when writing your manuscript is a great way to enhance your research effectiveness and reach, ensuring it is read and understood by a wider audience. In this article, we will explore the importance of using research infographics, how it can help improve your article, and how to present data in research infographics effectively.

Table of Contents

What is a research infographic?

Infographics are visual representations of data, information, or knowledge that can help researchers and authors to communicate complex concepts in a way that is simple, engaging, and effective. By using research infographics, researchers can ensure that readers are able to understand and retain key messages faster and more efficiently. The clear, concise representation of data can reinforce your arguments, provide context, highlight the significance of your findings, and adding credibility to your work. This was confirmed by a study undertaken by Cornell University, which found that if a scientific claim is presented in simple text or numerical values, 68% of people will believe that the information is accurate and truthful. But if you add a simple visual or infographic to the claim, the number rises to 97%! 3

Advantages of using research infographics

Research infographics leverage the power of visual representation to convey complex data and ideas in a concise and engaging manner. Here’s why they are an excellent addition to your research article:

  Helps simplify complex scientific processes and concepts

Many academic writers find it difficult to convey complex scientific concepts and processes, which is where visuals work best. Breaking down concepts into easily digestible research infographics allows readers to quickly grasp the key takeaways of your research, driving greater impact than using just hard statistics. A good example here would be the simple visualization of complex scientific processes to show how certain results were achieved.

Enhances comprehension and retention of data

By combining text and visuals, research infographics cater to different learning styles, enhancing the overall understanding and retention of your research findings. For example, a line graph of increasing levels of stress in academia can help visualize the growth of the phenomenon more easily.

Grabs the readers’ attention and interest quickly

In text-heavy research papers, research infographics stand out and capture readers’ attention by conveying relevant information quickly, compelling them to delve deeper into your article. One impactful research infographic example is emphasizing the effect of drastic climate change through well-designed visuals that convey the speed at which our icebergs are melting.

Broadens the audience reach of your research

Visual tools like research infographics employ colors, fonts, shapes, and symbols to highlight and convey the most relevant findings of your research, while also clearly defining its implications. This helps to transcend language barriers, making your research more accessible to a global audience, including those with diverse language backgrounds. For example, using graphs and charts to visualize trends, correlations, and patterns in your data work better than text to convey the significance of your findings.

How to create effective research infographics

Creating impactful research infographics doesn’t have to be daunting. Follow these simple steps to craft compelling visuals for your research article:

• Define the purpose and audience: Before you start creating your research infographic, identify who it is meant for and what it is meant to convey. This will determine the type of infographic, the information to be included, and dictate the final design.
• Gather and organize the data: Next, organize and arrange the information in a logical and coherent manner. Ensure a clear flow that guides readers through your research findings without confusion.
• Choose a suitable format: Select visual elements and formats (timelines, maps, charts, images, graphs, etc.) that best represent your data and complement your research narrative effectively.
• Design the infographic: Avoid information overload and stick to a clean minimalistic design layout that highlights the core message. Be careful not to overwhelm of distract the audience with fancy fonts, random icons, and irrelevant images. Use colors to enhance the visual appeal and emphasize key points.
• Cite sources correctly: Accurately cite the sources used at the end of the research infographic or within the infographic itself. By giving credit to original authors you can help avoid any plagiarism.

Mistakes to avoid when creating research infographics

While research infographic can be a valuable addition to your research article, beware of these common mistakes.

• Misleading representation: Ensure your visual representations accurately reflect the data and take care to avoid any manipulation that may mislead readers.
• Inadequate labeling: Provide clear and concise labels for each element in your research infographic, ensuring easy comprehension.
• Overcrowding data: Avoid cramming too much information into a single research infographic or it could leave the reader confused. Instead, create multiple infographics to maintain clarity.
• Ignoring accessibility: Make your research infographics accessible to readers across the globe by providing alternative text descriptions for images and graphics.

Clearly, the use of visuals and infographics in academic writing can be powerful tools for conveying complex concepts and data as it allows us to process different types of patterns more easily than text and therefore, they must be an important addition to papers and manuscripts submitted by researchers.

References:

• Alexis, C. 29 Incredible Stats that Prove the Power of Visual Marketing. Movable Ink, October 2022. Available at https://movableink.com/blog/29-incredible-stats-that-prove-the-power-of-visual-marketing
• Words Of Wisdom: Using Data Visualization For Data Storytelling, CSpring Blog. Available at https://cspring.com/data-visualization-for-data-storytelling/
• S. Fotis Jr. The Power of Data Visualization. Aegis IT Research website, May 2020. Available at https://aegisresearch.eu/the-power-of-data-visualization/

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Effect of different visual presentations on the comprehension of prognostic information: a systematic review

• Institute for Evidence-Based Healthcare
• Faculty of Health Sciences & Medicine

Research output : Contribution to journal › Article › Research › peer-review

BACKGROUND: Understanding prognostic information can help patients know what may happen to their health over time and make informed decisions. However, communicating prognostic information well can be challenging.

PURPOSE: To conduct a systematic review to identify and synthesize research that has evaluated visual presentations that communicate quantitative prognostic information to patients or the public.

DATA SOURCES: MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC and the Cochrane Central Register of Controlled Trials (CENTRAL) (from inception to December 2020), and forward and backward citation search.

STUDY SELECTION: Two authors independently screened search results and assessed eligibility. To be eligible, studies required a quantitative design and comparison of at least one visual presentation with another presentation of quantitative prognostic information. The primary outcome was comprehension of the presented information. Secondary outcomes were preferences for or satisfaction with the presentations viewed, and behavioral intentions.

DATA EXTRACTION: Two authors independently assessed risk of bias and extracted data.

DATA SYNTHESIS: Eleven studies (all randomized trials) were identified. We grouped studies according to the presentation type evaluated. Bar graph versus pictograph (3 studies): no difference in comprehension between the groups. Survival vs mortality curves (2 studies): no difference in one study; higher comprehension in survival curve group in another study. Tabular format versus pictograph (4 studies): 2 studies reported similar comprehension between groups; 2 found higher comprehension in pictograph groups. Tabular versus free text (3 studies): 2 studies found no difference between groups; 1 found higher comprehension in a tabular group.

LIMITATIONS: Heterogeneity in the visual presentations and outcome measures, precluding meta-analysis.

CONCLUSIONS: No visual presentation appears to be consistently superior to communicate quantitative prognostic information.

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

• 10.1186/s12911-021-01612-9 Licence: CC BY
• Effect of different visual presentations on the comprehension of prognostic information: a systematic review Final published version, 1.98 MB Licence: CC BY

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• comprehension Social Sciences 100%
• Systematic Reviews Medicine & Life Sciences 95%
• Health Engineering & Materials Science 94%
• Group Social Sciences 38%
• Survival Medicine & Life Sciences 23%
• Intention Medicine & Life Sciences 19%
• Information Storage and Retrieval Medicine & Life Sciences 19%
• MEDLINE Medicine & Life Sciences 18%

T1 - Effect of different visual presentations on the comprehension of prognostic information: a systematic review

AU - Abukmail, Eman

AU - Bakhit, Mina

AU - Del Mar, Chris

AU - Hoffmann, Tammy

N1 - © 2021. The Author(s). Funding Information: No specific funding was received for this systematic review; however, the first author is supported on a PhD scholarship which is funded by the Centre for Research Excellence in Minimising Antibiotic Resistance in the Community (CRE-MARC), funded by the National Health and Medical Research Council (NHMRC), Australia (Reference Number: 1153299). CDM and TH are chief investigators of CREMARC and MB is employed as a postdoctoral research fellow on this grant. Publisher Copyright: © 2021, The Author(s).

PY - 2021/8/25

Y1 - 2021/8/25

N2 - BACKGROUND: Understanding prognostic information can help patients know what may happen to their health over time and make informed decisions. However, communicating prognostic information well can be challenging.PURPOSE: To conduct a systematic review to identify and synthesize research that has evaluated visual presentations that communicate quantitative prognostic information to patients or the public.DATA SOURCES: MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC and the Cochrane Central Register of Controlled Trials (CENTRAL) (from inception to December 2020), and forward and backward citation search.STUDY SELECTION: Two authors independently screened search results and assessed eligibility. To be eligible, studies required a quantitative design and comparison of at least one visual presentation with another presentation of quantitative prognostic information. The primary outcome was comprehension of the presented information. Secondary outcomes were preferences for or satisfaction with the presentations viewed, and behavioral intentions.DATA EXTRACTION: Two authors independently assessed risk of bias and extracted data.DATA SYNTHESIS: Eleven studies (all randomized trials) were identified. We grouped studies according to the presentation type evaluated. Bar graph versus pictograph (3 studies): no difference in comprehension between the groups. Survival vs mortality curves (2 studies): no difference in one study; higher comprehension in survival curve group in another study. Tabular format versus pictograph (4 studies): 2 studies reported similar comprehension between groups; 2 found higher comprehension in pictograph groups. Tabular versus free text (3 studies): 2 studies found no difference between groups; 1 found higher comprehension in a tabular group.LIMITATIONS: Heterogeneity in the visual presentations and outcome measures, precluding meta-analysis.CONCLUSIONS: No visual presentation appears to be consistently superior to communicate quantitative prognostic information.

AB - BACKGROUND: Understanding prognostic information can help patients know what may happen to their health over time and make informed decisions. However, communicating prognostic information well can be challenging.PURPOSE: To conduct a systematic review to identify and synthesize research that has evaluated visual presentations that communicate quantitative prognostic information to patients or the public.DATA SOURCES: MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC and the Cochrane Central Register of Controlled Trials (CENTRAL) (from inception to December 2020), and forward and backward citation search.STUDY SELECTION: Two authors independently screened search results and assessed eligibility. To be eligible, studies required a quantitative design and comparison of at least one visual presentation with another presentation of quantitative prognostic information. The primary outcome was comprehension of the presented information. Secondary outcomes were preferences for or satisfaction with the presentations viewed, and behavioral intentions.DATA EXTRACTION: Two authors independently assessed risk of bias and extracted data.DATA SYNTHESIS: Eleven studies (all randomized trials) were identified. We grouped studies according to the presentation type evaluated. Bar graph versus pictograph (3 studies): no difference in comprehension between the groups. Survival vs mortality curves (2 studies): no difference in one study; higher comprehension in survival curve group in another study. Tabular format versus pictograph (4 studies): 2 studies reported similar comprehension between groups; 2 found higher comprehension in pictograph groups. Tabular versus free text (3 studies): 2 studies found no difference between groups; 1 found higher comprehension in a tabular group.LIMITATIONS: Heterogeneity in the visual presentations and outcome measures, precluding meta-analysis.CONCLUSIONS: No visual presentation appears to be consistently superior to communicate quantitative prognostic information.

UR - http://www.scopus.com/inward/record.url?scp=85113373196&partnerID=8YFLogxK

U2 - 10.1186/s12911-021-01612-9

DO - 10.1186/s12911-021-01612-9

M3 - Article

C2 - 34433455

SN - 1472-6947

JO - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

How to develop a graphical framework to chart your research

Graphic representations or frameworks can be powerful tools to explain research processes and outcomes. David Waller explains how researchers can develop effective visual models to chart their work

David Waller

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While undertaking a study, researchers can uncover insights, connections and findings that are extremely valuable to anyone likely to read their eventual paper. Thus, it is important for the researcher to clearly present and explain the ideas and potential relationships. One important way of presenting findings and relationships is by developing a graphical conceptual framework.

A graphical conceptual framework is a visual model that assists readers by illustrating how concepts, constructs, themes or processes work. It is an image designed to help the viewer understand how various factors interrelate and affect outcomes, such as a chart, graph or map.

These are commonly used in research to show outcomes but also to create, develop, test, support and criticise various ideas and models. The use of a conceptual framework can vary depending on whether it is being used for qualitative or quantitative research.

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There are many forms that a graphical conceptual framework can take, which can depend on the topic, the type of research or findings, and what can best present the story.

Below are examples of frameworks based on qualitative and quantitative research.

As shown by the table below, in qualitative research the conceptual framework is developed at the end of the study to illustrate the factors or issues presented in the qualitative data. It is designed to assist in theory building and the visual understanding of the exploratory findings. It can also be used to develop a framework in preparation for testing the proposition using quantitative research.

In quantitative research a conceptual framework can be used to synthesise the literature and theoretical concepts at the beginning of the study to present a model that will be tested in the statistical analysis of the research.

It is important to understand that the role of a conceptual framework differs depending on the type of research that is being undertaken.

So how should you go about creating a conceptual framework? After undertaking some studies where I have developed conceptual frameworks, here is a simple model based on “Six Rs”: Review, Reflect, Relationships, Reflect, Review, and Repeat.

Process for developing conceptual frameworks:

Review: literature/themes/theory.

Reflect: what are the main concepts/issues?

Relationships: what are their relationships?

Reflect: does the diagram represent it sufficiently?

Review: check it with theory, colleagues, stakeholders, etc.

Repeat: review and revise it to see if something better occurs.

This is not an easy process. It is important to begin by reviewing what has been presented in previous studies in the literature or in practice. This provides a solid background to the proposed model as it can show how it relates to accepted theoretical concepts or practical examples, and helps make sure that it is grounded in logical sense.

It can start with pen and paper, but after reviewing you should reflect to consider if the proposed framework takes into account the main concepts and issues, and the potential relationships that have been presented on the topic in previous works.

It may take a few versions before you are happy with the final framework, so it is worth continuing to reflect on the model and review its worth by reassessing it to determine if the model is consistent with the literature and theories. It can also be useful to discuss the idea with  colleagues or to present preliminary ideas at a conference or workshop –  be open to changes.

Even after you come up with a potential model it is good to repeat the process to review the framework and be prepared to revise it as this can help in refining the model. Over time you may develop a number of models with each one superseding the previous one.

A concern is that some students hold on to the framework they first thought of and worry that developing or changing it will be seen as a weakness in their research. However, a revised and refined model can be an important factor in justifying the value of the research.

Plenty of possibilities and theoretical topics could be considered to enhance the model. Whether it ultimately supports the theoretical constructs of the research will be dependent on what occurs when it is tested.  As social psychologist, Kurt Lewin, famously said “ There's nothing so practical as good theory ”.

The final result after doing your reviewing and reflecting should be a clear graphical presentation that will help the reader understand what the research is about as well as where it is heading.

It doesn’t need to be complex. A simple diagram or table can clarify the nature of a process and help in its analysis, which can be important for the researcher when communicating to their audience. As the saying goes: “ A picture is worth 1000 words ”. The same goes for a good conceptual framework, when explaining a research process or findings.

David Waller is an associate professor at the University of Technology Sydney .

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Excel Data Analysis pp 19–54 Cite as

Presentation of Quantitative Data

• Hector Guerrero 2  
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We often think of data as being strictly numerical values, and in business, those values are often stated in terms of dollars. Although data in the form of dollars are ubiquitous, it is quite easy to imagine other numerical units: percentages, counts in categories, units of sales, etc. This chapter, and Chap. 3 , discusses how we can best use Excel’s graphics capabilities to effectively present quantitative data ( ratio and interval ), whether it is in dollars or some other quantitative measure, to inform and influence an audience. In Chaps. 4 and 5 we will acknowledge that not all data are numerical by focusing on qualitative ( categorical/nominal or ordinal ) data. The process of data gathering often produces a combination of data types, and throughout our discussions it will be impossible to ignore this fact: quantitative and qualitative data often occur together.

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Graphically Presenting Quantitative Relationships: Elements of Effective Posters

Sharing research in a public forum is an important (perhaps the most important) component of doing research. Poster sessions are increasingly used in political science as a vehicle for sharing research, yet we often do not spend much time with students focusing on how best to use the poster format to communicate the message of our research. This workshop asks students to read several chapters from a text that focuses on graphical display of data, to consider poster presentations that they have seen, to think about what worked well, (or didn't work well) and why. This leads to reflection on the goals of a poster and the best way those goals can be achieved. After writing about their reflections, the class discusses the process using some examples of good and bad techniques.

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Preparing and Presenting Effective Research Posters

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APPENDIX A.2. Comparison of Research Papers, Presentations, and Posters—Contents.

Posters are a common way to present results of a statistical analysis, program evaluation, or other project at professional conferences. Often, researchers fail to recognize the unique nature of the format, which is a hybrid of a published paper and an oral presentation. This methods note demonstrates how to design research posters to convey study objectives, methods, findings, and implications effectively to varied professional audiences.

A review of existing literature on research communication and poster design is used to identify and demonstrate important considerations for poster content and layout. Guidelines on how to write about statistical methods, results, and statistical significance are illustrated with samples of ineffective writing annotated to point out weaknesses, accompanied by concrete examples and explanations of improved presentation. A comparison of the content and format of papers, speeches, and posters is also provided.

Each component of a research poster about a quantitative analysis should be adapted to the audience and format, with complex statistical results translated into simplified charts, tables, and bulleted text to convey findings as part of a clear, focused story line.

Conclusions

Effective research posters should be designed around two or three key findings with accompanying handouts and narrative description to supply additional technical detail and encourage dialog with poster viewers.

An assortment of posters is a common way to present research results to viewers at a professional conference. Too often, however, researchers treat posters as poor cousins to oral presentations or published papers, failing to recognize the opportunity to convey their findings while interacting with individual viewers. By neglecting to adapt detailed paragraphs and statistical tables into text bullets and charts, they make it harder for their audience to quickly grasp the key points of the poster. By simply posting pages from the paper, they risk having people merely skim their work while standing in the conference hall. By failing to devise narrative descriptions of their poster, they overlook the chance to learn from conversations with their audience.

Even researchers who adapt their paper into a well-designed poster often forget to address the range of substantive and statistical training of their viewers. This step is essential for those presenting to nonresearchers but also pertains when addressing interdisciplinary research audiences. Studies of policymakers ( DiFranza and the Staff of the Advocacy Institute 1996 ; Sorian and Baugh 2002 ) have demonstrated the importance of making it readily apparent how research findings apply to real-world issues rather than imposing on readers to translate statistical findings themselves.

This methods note is intended to help researchers avoid such pitfalls as they create posters for professional conferences. The first section describes objectives of research posters. The second shows how to describe statistical results to viewers with varied levels of statistical training, and the third provides guidelines on the contents and organization of the poster. Later sections address how to prepare a narrative and handouts to accompany a research poster. Because researchers often present the same results as published research papers, spoken conference presentations, and posters, Appendix A compares similarities and differences in the content, format, and audience interaction of these three modes of presenting research results. Although the focus of this note is on presentation of quantitative research results, many of the guidelines about how to prepare and present posters apply equally well to qualitative studies.

WHAT IS A RESEARCH POSTER?

Preparing a poster involves not only creating pages to be mounted in a conference hall, but also writing an associated narrative and handouts, and anticipating the questions you are likely to encounter during the session. Each of these elements should be adapted to the audience, which may include people with different levels of familiarity with your topic and methods ( Nelson et al. 2002 ; Beilenson 2004 ). For example, the annual meeting of the American Public Health Association draws academics who conduct complex statistical analyses along with practitioners, program planners, policymakers, and journalists who typically do not.

Posters are a hybrid form—more detailed than a speech but less than a paper, more interactive than either ( Appendix A ). In a speech, you (the presenter) determine the focus of the presentation, but in a poster session, the viewers drive that focus. Different people will ask about different facets of your research. Some might do policy work or research on a similar topic or with related data or methods. Others will have ideas about how to apply or extend your work, raising new questions or suggesting different contrasts, ways of classifying data, or presenting results. Beilenson (2004) describes the experience of giving a poster as a dialogue between you and your viewers.

By the end of an active poster session, you may have learned as much from your viewers as they have from you, especially if the topic, methods, or audience are new to you. For instance, at David Snowdon's first poster presentation on educational attainment and longevity using data from The Nun Study, another researcher returned several times to talk with Snowdon, eventually suggesting that he extend his research to focus on Alzheimer's disease, which led to an important new direction in his research ( Snowdon 2001 ). In addition, presenting a poster provides excellent practice in explaining quickly and clearly why your project is important and what your findings mean—a useful skill to apply when revising a speech or paper on the same topic.

WRITING FOR A VARIED PROFESSIONAL AUDIENCE

Audiences at professional conferences vary considerably in their substantive and methodological backgrounds. Some will be experts on your topic but not your methods, some will be experts on your methods but not your topic, and most will fall somewhere in between. In addition, advances in research methods imply that even researchers who received cutting-edge methodological training 10 or 20 years ago might not be conversant with the latest approaches. As you design your poster, provide enough background on both the topic and the methods to convey the purpose, findings, and implications of your research to the expected range of readers.

Telling a Simple, Clear Story

Write so your audience can understand why your work is of interest to them, providing them with a clear take-home message that they can grasp in the few minutes they will spend at your poster. Experts in communications and poster design recommend planning your poster around two to three key points that you want your audience to walk away with, then designing the title, charts, and text to emphasize those points ( Briscoe 1996 ; Nelson et al. 2002 ; Beilenson 2004 ). Start by introducing the two or three key questions you have decided will be the focus of your poster, and then provide a brief overview of data and methods before presenting the evidence to answer those questions. Close with a summary of your findings and their implications for research and policy.

A 2001 survey of government policymakers showed that they prefer summaries of research to be written so they can immediately see how the findings relate to issues currently facing their constituencies, without wading through a formal research paper ( Sorian and Baugh 2002 ). Complaints that surfaced about many research reports included that they were “too long, dense, or detailed,” or “too theoretical, technical, or jargony.” On average, respondents said they read only about a quarter of the research material they receive for detail, skim about half of it, and never get to the rest.

To ensure that your poster is one viewers will read, understand, and remember, present your analyses to match the issues and questions of concern to them, rather than making readers translate your statistical results to fit their interests ( DiFranza and the Staff of the Advocacy Institute 1996 ; Nelson et al. 2002 ). Often, their questions will affect how you code your data, specify your model, or design your intervention and evaluation, so plan ahead by familiarizing yourself with your audience's interests and likely applications of your study findings. In an academic journal article, you might report parameter estimates and standard errors for each independent variable in your regression model. In the poster version, emphasize findings for specific program design features, demographic, or geographic groups, using straightforward means of presenting effect size and statistical significance; see “Describing Numeric Patterns and Contrasts” and “Presenting Statistical Test Results” below.

The following sections offer guidelines on how to present statistical findings on posters, accompanied by examples of “poor” and “better” descriptions—samples of ineffective writing annotated to point out weaknesses, accompanied by concrete examples and explanations of improved presentation. These ideas are illustrated with results from a multilevel analysis of disenrollment from the State Children's Health Insurance Program (SCHIP; Phillips et al. 2004 ). I chose that paper to show how to prepare a poster about a sophisticated quantitative analysis of a topic of interest to HSR readers, and because I was a collaborator in that study, which was presented in the three formats compared here—as a paper, a speech, and a poster.

Explaining Statistical Methods

Beilenson (2004) and Briscoe (1996) suggest keeping your description of data and methods brief, providing enough information for viewers to follow the story line and evaluate your approach. Avoid cluttering the poster with too much technical detail or obscuring key findings with excessive jargon. For readers interested in additional methodological information, provide a handout and a citation to the pertinent research paper.

As you write about statistical methods or other technical issues, relate them to the specific concepts you study. Provide synonyms for technical and statistical terminology, remembering that many conferences of interest to policy researchers draw people from a range of disciplines. Even with a quantitatively sophisticated audience, don't assume that people will know the equivalent vocabulary used in other fields. A few years ago, the journal Medical Care published an article whose sole purpose was to compare statistical terminology across various disciplines involved in health services research so that people could understand one another ( Maciejewski et al. 2002 ). After you define the term you plan to use, mention the synonyms from the various fields represented in your audience.

Consider whether acronyms are necessary on your poster. Avoid them if they are not familiar to the field or would be used only once or twice on your poster. If you use acronyms, spell them out at first usage, even those that are common in health services research such as “HEDIS®”(Health Plan Employer Data and Information Set) or “HLM”(hierarchical linear model).

Poor: “We use logistic regression and a discrete-time hazards specification to assess relative hazards of SCHIP disenrollment, with plan level as our key independent variable.” Comment: Terms like “discrete-time hazards specification” may be confusing to readers without training in those methods, which are relatively new on the scene. Also the meaning of “SCHIP” or “plan level” may be unfamiliar to some readers unless defined earlier on the poster.

Better: “Chances of disenrollment from the State Children's Health Insurance Program (SCHIP) vary by amount of time enrolled, so we used hazards models (also known as event history analysis or survival analysis) to correct for those differences when estimating disenrollment patterns for SCHIP plans for different income levels.” Comment: This version clarifies the terms and concepts, naming the statistical method and its synonyms, and providing a sense of why this type of analysis is needed.

To explain a statistical method or assumption, paraphrase technical terms and illustrate how the analytic approach applies to your particular research question and data:

Poor : “The data structure can be formulated as a two-level hierarchical linear model, with families (the level-1 unit of analysis) nested within counties (the level-2 unit of analysis).” Comment: Although this description would be fine for readers used to working with this type of statistical model, those who aren't conversant with those methods may be confused by terminology such as “level-1” and “unit of analysis.”

Better: “The data have a hierarchical (or multilevel) structure, with families clustered within counties.” Comment: By replacing “nested” with the more familiar “clustered,” identifying the specific concepts for the two levels of analysis, and mentioning that “hierarchical” and “multilevel” refer to the same type of analytic structure, this description relates the generic class of statistical model to this particular study.

Presenting Results with Charts

Charts are often the preferred way to convey numeric patterns, quickly revealing the relative sizes of groups, comparative levels of some outcome, or directions of trends ( Briscoe 1996 ; Tufte 2001 ; Nelson et al. 2002 ). As Beilenson puts it, “let your figures do the talking,” reducing the need for long text descriptions or complex tables with lots of tiny numbers. For example, create a pie chart to present sample composition, use a simple bar chart to show how the dependent variable varies across subgroups, or use line charts or clustered bar charts to illustrate the net effects of nonlinear specifications or interactions among independent variables ( Miller 2005 ). Charts that include confidence intervals around point estimates are a quick and effective way to present effect size, direction, and statistical significance. For multivariate analyses, consider presenting only the results for the main variables of interest, listing the other variables in the model in a footnote and including complex statistical tables in a handout.

Provide each chart with a title (in large type) that explains the topic of that chart. A rhetorical question or summary of the main finding can be very effective. Accompany each chart with a few annotations that succinctly describe the patterns in that chart. Although each chart page should be self-explanatory, be judicious: Tufte (2001) cautions against encumbering your charts with too much “nondata ink”—excessive labeling or superfluous features such as arrows and labels on individual data points. Strive for a balance between guiding your readers through the findings and maintaining a clean, uncluttered poster. Use chart types that are familiar to your expected audience. Finally, remember that you can flesh out descriptions of charts and tables in your script rather than including all the details on the poster itself; see “Narrative to Accompany a Poster.”

Describing Numeric Patterns and Contrasts

As you describe patterns or numeric contrasts, whether from simple calculations or complex statistical models, explain both the direction and magnitude of the association. Incorporate the concepts under study and the units of measurement rather than simply reporting coefficients (β's) ( Friedman 1990 ; Miller 2005 ).

Poor: “Number of enrolled children in the family is correlated with disenrollment.” Comment: Neither the direction nor the size of the association is apparent.

Poor [version #2]: “The log-hazard of disenrollment for one-child families was 0.316.” Comment: Most readers find it easier to assess the size and direction from hazards ratios (a form of relative risk) instead of log-hazards (log-relative risks, the β's from a hazards model).

Better: “Families with only one child enrolled in the program were about 1.4 times as likely as larger families to disenroll.” Comment: This version explains the association between number of children and disenrollment without requiring viewers to exponentiate the log-hazard in their heads to assess the size and direction of that association. It also explicitly identifies the group against which one-child families are compared in the model.

Presenting Statistical Test Results

On your poster, use an approach to presenting statistical significance that keeps the focus on your results, not on the arithmetic needed to conduct inferential statistical tests. Replace standard errors or test statistics with confidence intervals, p- values, or symbols, or use formatting such as boldface, italics, or a contrasting color to denote statistically significant findings ( Davis 1997 ; Miller 2005 ). Include the detailed statistical results in handouts for later perusal.

To illustrate these recommendations, Figures 1 and ​ and2 2 demonstrate how to divide results from a complex, multilevel model across several poster pages, using charts and bullets in lieu of the detailed statistical table from the scientific paper ( Table 1 ; Phillips et al. 2004 ). Following experts' advice to focus on one or two key points, these charts emphasize the findings from the final model (Model 5) rather than also discussing each of the fixed- and random-effects specifications from the paper.

Presenting Complex Statistical Results Graphically

Text Summary of Additional Statistical Results

Multilevel Discrete-Time Hazards Models of Disenrollment from SCHIP, New Jersey, January 1998–April 2000

Source : Phillips et al. (2004) .

SCHIP, State Children's Health Insurance Program; LRH, log relative-hazard; SE, standard error.

Figure 1 uses a chart (also from the paper) to present the net effects of a complicated set of interactions between two family-level traits (race and SCHIP plan) and a cross-level interaction between race of the family and county physician racial composition. The title is a rhetorical question that identifies the issue addressed in the chart, and the annotations explain the pattern. The chart version substantially reduces the amount of time viewers need to understand the main take-home point, averting the need to mentally sum and exponentiate several coefficients from the table.

Figure 2 uses bulleted text to summarize other key results from the model, translating log-relative hazards into hazards ratios and interpreting them with minimal reliance on jargon. The results for family race, SCHIP plan, and county physician racial composition are not repeated in Figure 2 , averting the common problem of interpreting main effect coefficients and interaction coefficients without reference to one another.

Alternatively, replace the text summary shown in Figure 2 with Table 2 —a simplified version of Table 1 which presents only the results for Model 5, replaces log-relative hazards with hazards ratios, reports associated confidence intervals in lieu of standard errors, and uses boldface to denote statistical significance. (On a color slide, use a contrasting color in lieu of bold.)

Relative Risks of SCHIP Disenrollment for Other * Family and County Characteristics, New Jersey, January 1998–April 2000

Statistically significant associations are shown in bold.

Based on hierarchical linear model controlling for months enrolled, months-squared, race, SCHIP plan, county physician racial composition, and all variables shown here. Scaled deviance =30,895. Random effects estimate for between-county variance =0.005 (standard error =0.006). SCHIP, State Children's Health Insurance Program; 95% CI, 95% confidence interval.

CONTENTS AND ORGANIZATION OF A POSTER

Research posters are organized like scientific papers, with separate pages devoted to the objectives and background, data and methods, results, and conclusions ( Briscoe 1996 ). Readers view the posters at their own pace and at close range; thus you can include more detail than in slides for a speech (see Appendix A for a detailed comparison of content and format of papers, speeches, and posters). Don't simply post pages from the scientific paper, which are far too text-heavy for a poster. Adapt them, replacing long paragraphs and complex tables with bulleted text, charts, and simple tables ( Briscoe 1996 ; Beilenson 2004 ). Fink (1995) provides useful guidelines for writing text bullets to convey research results. Use presentation software such as PowerPoint to create your pages or adapt them from related slides, facilitating good page layout with generous type size, bullets, and page titles. Such software also makes it easy to create matching handouts (see “Handouts”).

The “W's” (who, what, when, where, why) are an effective way to organize the elements of a poster.

• In the introductory section, describe what you are studying, why it is important, and how your analysis will add to the existing literature in the field.
• In the data and methods section of a statistical analysis, list when, where, who, and how the data were collected, how many cases were involved, and how the data were analyzed. For other types of interventions or program evaluations, list who, when, where, and how many, along with how the project was implemented and assessed.
• In the results section, present what you found.
• In the conclusion, return to what you found and how it can be used to inform programs or policies related to the issue.

Number and Layout of Pages

To determine how many pages you have to work with, find out the dimensions of your assigned space. A 4′ × 8′ bulletin board accommodates the equivalent of about twenty 8.5″ × 11″ pages, but be selective—no poster can capture the full detail of a large series of multivariate models. A trifold presentation board (3′ high by 4′ wide) will hold roughly a dozen pages, organized into three panels ( Appendix B ). Breaking the arrangement into vertical sections allows viewers to read each section standing in one place while following the conventions of reading left-to-right and top-to-bottom ( Briscoe 1996 ).

• At the top of the poster, put an informative title in a large, readable type size. On a 4′ × 8′ bulletin board, there should also be room for an institutional logo.

Suggested Layout for a 4′ × 8′ poster.

• In the left-hand panel, set the stage for the research question, conveying why the topic is of policy interest, summarizing major empirical or theoretical work on related topics, and stating your hypotheses or project aims, and explaining how your work fills in gaps in previous analyses.
• In the middle panel, briefly describe your data source, variables, and methods, then present results in tables or charts accompanied by text annotations. Diagrams, maps, and photographs are very effective for conveying issues difficult to capture succinctly in words ( Miller 2005 ), and to help readers envision the context. A schematic diagram of relationships among variables can be useful for illustrating causal order. Likewise, a diagram can be a succinct way to convey timing of different components of a longitudinal study or the nested structure of a multilevel dataset.
• In the right-hand panel, summarize your findings and relate them back to the research question or project aims, discuss strengths and limitations of your approach, identify research, practice, or policy implications, and suggest directions for future research.

Figure 3 (adapted from Beilenson 2004 ) shows a suggested layout for a 4′ × 8′ bulletin board, designed to be created using software such as Pagemaker that generates a single-sheet presentation; Appendix C shows a complete poster version of the Phillips et al. (2004) multilevel analysis of SCHIP disenrollment. If hardware or budget constraints preclude making a single-sheet poster, a similar configuration can be created using standard 8.5″ × 11″ pages in place of the individual tables, charts, or blocks of text shown in Figure 3 .

Find out well in advance how the posters are to be mounted so you can bring the appropriate supplies. If the room is set up for table-top presentations, tri-fold poster boards are essential because you won't have anything to attach a flat poster board or pages to. If you have been assigned a bulletin board, bring push-pins or a staple gun.

Regardless of whether you will be mounting your poster at the conference or ahead of time, plan how the pages are to be arranged. Experiment with different page arrangements on a table marked with the dimensions of your overall poster. Once you have a final layout, number the backs of the pages or draw a rough sketch to work from as you arrange the pages on the board. If you must pin pages to a bulletin board at the conference venue, allow ample time to make them level and evenly spaced.

Other Design Considerations

A few other issues to keep in mind as you design your poster. Write a short, specific title that fits in large type size on the title banner of your poster. The title will be potential readers' first glimpse of your poster, so make it inviting and easy to read from a distance—at least 40-point type, ideally larger. Beilenson (2004) advises embedding your key finding in the title so viewers don't have to dig through the abstract or concluding page to understand the purpose and conclusions of your work. A caution: If you report a numeric finding in your title, keep in mind that readers may latch onto it as a “factoid” to summarize your conclusions, so select and phrase it carefully ( McDonough 2000 ).

Use at least 14-point type for the body of the poster text. As Briscoe (1996) points out, “many in your audience have reached the bifocal age” and all of them will read your poster while standing, hence long paragraphs in small type will not be appreciated! Make judicious use of color. Use a clear, white, or pastel for the background, with black or another dark color for most text, and a bright, contrasting shade to emphasize key points or to identify statistically significant results ( Davis 1997 ).

NARRATIVE TO ACCOMPANY A POSTER

Prepare a brief oral synopsis of the purpose, findings, and implications of your work to say to interested parties as they pause to read your poster. Keep it short—a few sentences that highlight what you are studying, a couple of key findings, and why they are important. Design your overview as a “sound byte” that captures your main points in a succinct and compelling fashion ( Beilenson 2004 ). After hearing your introduction, listeners will either nod and move along or comment on some aspect of your work that intrigues them. You can then tailor additional discussion to individual listeners, adjusting the focus and amount of detail to suit their interests. Gesture at the relevant pages as you make each point, stating the purpose of each chart or table and explaining its layout before describing the numeric findings; see Miller (2005) for guidelines on how to explain tables and charts to a live audience. Briscoe (1996) points out that these mini-scripts are opportunities for you to fill in details of your story line, allowing you to keep the pages themselves simple and uncluttered.

Prepare short answers to likely questions about various aspects of your work, such as why it is important from a policy or research perspective, or descriptions of data, methods, and specific results. Think of these as little modules from an overall speech—concise descriptions of particular elements of your study that you can choose among in response to questions that arise. Beilenson (2004) also recommends developing a few questions to ask your viewers, inquiring about their reactions to your findings, ideas for additional questions, or names of others working on the topic.

Practice your poster presentation in front of a test audience acquainted with the interests and statistical proficiency of your expected viewers. Ideally, your critic should not be too familiar with your work: A fresh set of eyes and ears is more likely to identify potential points of confusion than someone who is jaded from working closely with the material while writing the paper or drafting the poster ( Beilenson 2004 ). Ask your reviewer to identify elements that are unclear, flag jargon to be paraphrased or defined, and recommend changes to improve clarity ( Miller 2005 ). Have them critique your oral presentation as well as the contents and layout of the poster.

Prepare handouts to distribute to interested viewers. These can be produced from slides created in presentation software, printed several to a page along with a cover page containing the abstract and your contact information. Or package an executive summary or abstract with a few key tables or charts. Handouts provide access to the more detailed literature review, data and methods, full set of results, and citations without requiring viewers to read all of that information from the poster ( Beilenson 2004 ; Miller 2005 ). Although you also can bring copies of the complete paper, it is easier on both you and your viewers if you collect business cards or addresses and mail the paper later.

The quality and effectiveness of research posters at professional conferences is often compromised by authors' failure to take into account the unique nature of such presentations. One common error is posting numerous statistical tables and long paragraphs from a research paper—an approach that overwhelms viewers with too much detail for this type of format and presumes familiarity with advanced statistical techniques. Following recommendations from the literature on research communication and poster design, this paper shows how to focus each poster on a few key points, using charts and text bullets to convey results as part of a clear, straightforward story line, and supplementing with handouts and an oral overview.

Another frequent mistake is treating posters as a one-way means of communication. Unlike published papers, poster sessions are live presentations; unlike speeches, they allow for extended conversation with viewers. This note explains how to create an oral synopsis of the project, short modular descriptions of poster elements, and questions to encourage dialog. By following these guidelines, researchers can substantially improve their conference posters as vehicles to disseminate findings to varied research and policy audiences.

CHECKLIST FOR PREPARING AND PRESENTING AN EFFECTIVE RESEARCH POSTERS

• Design poster to focus on two or three key points.
• Adapt materials to suit expected viewers' knowledge of your topic and methods.
• Design questions to meet their interests and expected applications of your work.
• Paraphrase descriptions of complex statistical methods.
• Spell out acronyms if used.
• Replace large detailed tables with charts or small, simplified tables.
• Accompany tables or charts with bulleted annotations of major findings.
• Describe direction and magnitude of associations.
• Use confidence intervals, p -values, symbols, or formatting to denote statistical significance.

Layout and Format

• Organize the poster into background, data and methods, results, and study implications.
• Divide the material into vertical sections on the poster.
• Use at least 14-point type in the body of your poster, at least 40-point for the title.

Narrative Description

• Rehearse a three to four sentence overview of your research objectives and main findings.
• Summary of key studies and gaps in existing literature
• Data and methods
• Each table, chart, or set of bulleted results
• Research, policy, and practice implications
• Solicit their input on your findings
• Develop additional questions for later analysis
• Identify other researchers in the field
• Prepare handouts to distribute to interested viewers.
• Print slides from presentation software, several to a page.
• Or package an executive summary or abstract with a few key tables or charts.
• Include an abstract and contact information.

Acknowledgments

I would like to thank Ellen Idler, Julie Phillips, Deborah Carr, Diane (Deedee) Davis, and two anonymous reviewers for helpful comments on earlier drafts of this work.

Supplementary Material

The following supplementary material for this article is available online:

APPENDIX A.1. Comparison of Research Papers, Presentations, and Posters—Materials and Audience Interaction.

Suggested Layout for a Tri-Fold Presentation Board.

Example Research Poster of Phillips et al. 2004 Study.

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Chapter 20. Presentations

Introduction.

If a tree falls in a forest, and no one is around to hear it, does it make a sound? If a qualitative study is conducted, but it is not presented (in words or text), did it really happen? Perhaps not. Findings from qualitative research are inextricably tied up with the way those findings are presented. These presentations do not always need to be in writing, but they need to happen. Think of ethnographies, for example, and their thick descriptions of a particular culture. Witnessing a culture, taking fieldnotes, talking to people—none of those things in and of themselves convey the culture. Or think about an interview-based phenomenological study. Boxes of interview transcripts might be interesting to read through, but they are not a completed study without the intervention of hours of analysis and careful selection of exemplary quotes to illustrate key themes and final arguments and theories. And unlike much quantitative research in the social sciences, where the final write-up neatly reports the results of analyses, the way the “write-up” happens is an integral part of the analysis in qualitative research. Once again, we come back to the messiness and stubborn unlinearity of qualitative research. From the very beginning, when designing the study, imagining the form of its ultimate presentation is helpful.

Because qualitative researchers are motivated by understanding and conveying meaning, effective communication is not only an essential skill but a fundamental facet of the entire research project. Ethnographers must be able to convey a certain sense of verisimilitude, the appearance of true reality. Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them. And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important. This chapter will address how to organize various kinds of presentations for different audiences so that your results can be appreciated and understood.

In the world of academic science, social or otherwise, the primary audience for a study’s results is usually the academic community, and the primary venue for communicating to this audience is the academic journal. Journal articles are typically fifteen to thirty pages in length (8,000 to 12,000 words). Although qualitative researchers often write and publish journal articles—indeed, there are several journals dedicated entirely to qualitative research [1] —the best writing by qualitative researchers often shows up in books. This is because books, running from 80,000 to 150,000 words in length, allow the researcher to develop the material fully. You have probably read some of these in various courses you have taken, not realizing what they are. I have used examples of such books throughout this text, beginning with the three profiles in the introductory chapter. In some instances, the chapters in these books began as articles in academic journals (another indication that the journal article format somewhat limits what can be said about the study overall).

While the article and the book are “final” products of qualitative research, there are actually a few other presentation formats that are used along the way. At the very beginning of a research study, it is often important to have a written research proposal not just to clarify to yourself what you will be doing and when but also to justify your research to an outside agency, such as an institutional review board (IRB; see chapter 12), or to a potential funder, which might be your home institution, a government funder (such as the National Science Foundation, or NSF), or a private foundation (such as the Gates Foundation). As you get your research underway, opportunities will arise to present preliminary findings to audiences, usually through presentations at academic conferences. These presentations can provide important feedback as you complete your analyses. Finally, if you are completing a degree and looking to find an academic job, you will be asked to provide a “job talk,” usually about your research. These job talks are similar to conference presentations but can run significantly longer.

All the presentations mentioned so far are (mostly) for academic audiences. But qualitative research is also unique in that many of its practitioners don’t want to confine their presentation only to other academics. Qualitative researchers who study particular contexts or cultures might want to report back to the people and places they observed. Those working in the critical tradition might want to raise awareness of a particular issue to as large an audience as possible. Many others simply want everyday, nonacademic people to read their work, because they think it is interesting and important. To reach a wide audience, the final product can look like almost anything—it can be a poem, a blog, a podcast, even a science fiction short story. And if you are very lucky, it can even be a national or international bestseller.

In this chapter, we are going to stick with the more basic quotidian presentations—the academic paper / research proposal, the conference slideshow presentation / job talk, and the conference poster. We’ll also spend a bit of time on incorporating universal design into your presentations and how to create some especially attractive and impactful visual displays.

Researcher Note

What is the best piece of advice you’ve ever been given about conducting qualitative research?

The best advice I’ve received came from my adviser, Alford Young Jr. He told me to find the “Jessi Streib” answer to my research question, not the “Pierre Bourdieu” answer to my research question. In other words, don’t just say how a famous theorist would answer your question; say something original, something coming from you.

—Jessi Streib, author of The Power of the Past and Privilege Lost 

Writing about Your Research

The journal article and the research proposal.

Although the research proposal is written before you have actually done your research and the article is written after all data collection and analysis is complete, there are actually many similarities between the two in terms of organization and purpose. The final article will (probably—depends on how much the research question and focus have shifted during the research itself) incorporate a great deal of what was included in a preliminary research proposal. The average lengths of both a proposal and an article are quite similar, with the “front sections” of the article abbreviated to make space for the findings, discussion of findings, and conclusion.

Figure 20.1 shows one model for what to include in an article or research proposal, comparing the elements of each with a default word count for each section. Please note that you will want to follow whatever specific guidelines you have been provided by the venue you are submitting the article/proposal to: the IRB, the NSF, the Journal of Qualitative Research . In fact, I encourage you to adapt the default model as needed by swapping out expected word counts for each section and adding or varying the sections to match expectations for your particular publication venue. [2]

You will notice a few things about the default model guidelines. First, while half of the proposal is spent discussing the research design, this section is shortened (but still included) for the article. There are a few elements that only show up in the proposal (e.g., the limitations section is in the introductory section here—it will be more fully developed in the conclusory section in the article). Obviously, you don’t have findings in the proposal, so this is an entirely new section for the article. Note that the article does not include a data management plan or a timeline—two aspects that most proposals require.

It might be helpful to find and maintain examples of successfully written sections that you can use as models for your own writing. I have included a few of these throughout the textbook and have included a few more at the end of this chapter.

Make an Argument

Some qualitative researchers, particularly those engaged in deep ethnographic research, focus their attention primarily if not exclusively on describing the data. They might even eschew the notion that they should make an “argument” about the data, preferring instead to use thick descriptions to convey interpretations. Bracketing the contrast between interpretation and argument for the moment, most readers will expect you to provide an argument about your data, and this argument will be in answer to whatever research question you eventually articulate (remember, research questions are allowed to shift as you get further into data collection and analysis). It can be frustrating to read a well-developed study with clear and elegant descriptions and no argument. The argument is the point of the research, and if you do not have one, 99 percent of the time, you are not finished with your analysis. Calarco ( 2020 ) suggests you imagine a pyramid, with all of your data forming the basis and all of your findings forming the middle section; the top/point of the pyramid is your argument, “what the patterns in your data tell us about how the world works or ought to work” ( 181 ).

The academic community to which you belong will be looking for an argument that relates to or develops theory. This is the theoretical generalizability promise of qualitative research. An academic audience will want to know how your findings relate to previous findings, theories, and concepts (the literature review; see chapter 9). It is thus vitally important that you go back to your literature review (or develop a new one) and draw those connections in your discussion and/or conclusion. When writing to other audiences, you will still want an argument, although it may not be written as a theoretical one. What do I mean by that? Even if you are not referring to previous literature or developing new theories or adapting older ones, a simple description of your findings is like dumping a lot of leaves in the lap of your audience. They still deserve to know about the shape of the forest. Maybe provide them a road map through it. Do this by telling a clear and cogent story about the data. What is the primary theme, and why is it important? What is the point of your research? [3]

A beautifully written piece of research based on participant observation [and/or] interviews brings people to life, and helps the reader understand the challenges people face. You are trying to use vivid, detailed and compelling words to help the reader really understand the lives of the people you studied. And you are trying to connect the lived experiences of these people to a broader conceptual point—so that the reader can understand why it matters. ( Lareau 2021:259 )

Do not hide your argument. Make it the focal point of your introductory section, and repeat it as often as needed to ensure the reader remembers it. I am always impressed when I see researchers do this well (see, e.g., Zelizer 1996 ).

Here are a few other suggestions for writing your article: Be brief. Do not overwhelm the reader with too many words; make every word count. Academics are particularly prone to “overwriting” as a way of demonstrating proficiency. Don’t. When writing your methods section, think about it as a “recipe for your work” that allows other researchers to replicate if they so wish ( Calarco 2020:186 ). Convey all the necessary information clearly, succinctly, and accurately. No more, no less. [4] Do not try to write from “beginning to end” in that order. Certain sections, like the introductory section, may be the last ones you write. I find the methods section the easiest, so I often begin there. Calarco ( 2020 ) begins with an outline of the analysis and results section and then works backward from there to outline the contribution she is making, then the full introduction that serves as a road map for the writing of all sections. She leaves the abstract for the very end. Find what order best works for you.

Presenting at Conferences and Job Talks

Students and faculty are primarily called upon to publicly present their research in two distinct contexts—the academic conference and the “job talk.” By convention, conference presentations usually run about fifteen minutes and, at least in sociology and other social sciences, rely primarily on the use of a slideshow (PowerPoint Presentation or PPT) presentation. You are usually one of three or four presenters scheduled on the same “panel,” so it is an important point of etiquette to ensure that your presentation falls within the allotted time and does not crowd into that of the other presenters. Job talks, on the other hand, conventionally require a forty- to forty-five-minute presentation with a fifteen- to twenty-minute question and answer (Q&A) session following it. You are the only person presenting, so if you run over your allotted time, it means less time for the Q&A, which can disturb some audience members who have been waiting for a chance to ask you something. It is sometimes possible to incorporate questions during your presentation, which allows you to take the entire hour, but you might end up shorting your presentation this way if the questions are numerous. It’s best for beginners to stick to the “ask me at the end” format (unless there is a simple clarifying question that can easily be addressed and makes the presentation run more smoothly, as in the case where you simply forgot to include information on the number of interviews you conducted).

For slideshows, you should allot two or even three minutes for each slide, never less than one minute. And those slides should be clear, concise, and limited. Most of what you say should not be on those slides at all. The slides are simply the main points or a clear image of what you are speaking about. Include bulleted points (words, short phrases), not full sentences. The exception is illustrative quotations from transcripts or fieldnotes. In those cases, keep to one illustrative quote per slide, and if it is long, bold or otherwise, highlight the words or passages that are most important for the audience to notice. [5]

Figure 20.2 provides a possible model for sections to include in either a conference presentation or a job talk, with approximate times and approximate numbers of slides. Note the importance (in amount of time spent) of both the research design and the findings/results sections, both of which have been helpfully starred for you. Although you don’t want to short any of the sections, these two sections are the heart of your presentation.

Fig 20.2. Suggested Slideshow Times and Number of Slides

Should you write out your script to read along with your presentation? I have seen this work well, as it prevents presenters from straying off topic and keeps them to the time allotted. On the other hand, these presentations can seem stiff and wooden. Personally, although I have a general script in advance, I like to speak a little more informally and engagingly with each slide, sometimes making connections with previous panelists if I am at a conference. This means I have to pay attention to the time, and I sometimes end up breezing through one section more quickly than I would like. Whatever approach you take, practice in advance. Many times. With an audience. Ask for feedback, and pay attention to any presentation issues that arise (e.g., Do you speak too fast? Are you hard to hear? Do you stumble over a particular word or name?).

Even though there are rules and guidelines for what to include, you will still want to make your presentation as engaging as possible in the little amount of time you have. Calarco ( 2020:274 ) recommends trying one of three story structures to frame your presentation: (1) the uncertain explanation , where you introduce a phenomenon that has not yet been fully explained and then describe how your research is tackling this; (2) the uncertain outcome , where you introduce a phenomenon where the consequences have been unclear and then you reveal those consequences with your research; and (3) the evocative example , where you start with some interesting example from your research (a quote from the interview transcripts, for example) or the real world and then explain how that example illustrates the larger patterns you found in your research. Notice that each of these is a framing story. Framing stories are essential regardless of format!

A Word on Universal Design

Please consider accessibility issues during your presentation, and incorporate elements of universal design into your slideshow. The basic idea behind universal design in presentations is that to the greatest extent possible, all people should be able to view, hear, or otherwise take in your presentation without needing special individual adaptations. If you can make your presentation accessible to people with visual impairment or hearing loss, why not do so? For example, one in twelve men is color-blind, unable to differentiate between certain colors, red/green being the most common problem. So if you design a graphic that relies on red and green bars, some of your audience members may not be able to properly identify which bar means what. Simple contrasts of black and white are much more likely to be visible to all members of your audience. There are many other elements of good universal design, but the basic foundation of all of them is that you consider how to make your presentation as accessible as possible at the outset. For example, include captions whenever possible, both as descriptions on slides and as images on slides and for any audio or video clips you are including; keep font sizes large enough to read from the back of the room; and face the audience when you are.

Poster Design

Undergraduate students who present at conferences are often encouraged to present at “poster sessions.” This usually means setting up a poster version of your research in a large hall or convention space at a set period of time—ninety minutes is common. Your poster will be one of dozens, and conference-goers will wander through the space, stopping intermittently at posters that attract them. Those who stop by might ask you questions about your research, and you are expected to be able to talk intelligently for two or three minutes. It’s a fairly easy way to practice presenting at conferences, which is why so many organizations hold these special poster sessions.

A good poster design will be immediately attractive to passersby and clearly and succinctly describe your research methods, findings, and conclusions. Some students have simply shrunk down their research papers to manageable sizes and then pasted them on a poster, all twelve to fifteen pages of them. Don’t do that! Here are some better suggestions: State the main conclusion of your research in large bold print at the top of your poster, on brightly colored (contrasting) paper, and paste in a QR code that links to your full paper online ( Calarco 2020:280 ). Use the rest of the poster board to provide a couple of highlights and details of the study. For an interview-based study, for example, you will want to put in some details about your sample (including number of interviews) and setting and then perhaps one or two key quotes, also distinguished by contrasting color background.

Incorporating Visual Design in Your Presentations

In addition to ensuring that your presentation is accessible to as large an audience as possible, you also want to think about how to display your data in general, particularly how to use charts and graphs and figures. [6] The first piece of advice is, use them! As the saying goes, a picture is worth a thousand words. If you can cut to the chase with a visually stunning display, do so. But there are visual displays that are stunning, and then there are the tired, hard-to-see visual displays that predominate at conferences. You can do better than most presenters by simply paying attention here and committing yourself to a good design. As with model section passages, keep a file of visual displays that work as models for your own presentations. Find a good guidebook to presenting data effectively (Evergreen 2018 , 2019 ; Schwabisch 2021) , and refer to it often.

Let me make a few suggestions here to get you started. First, test every visual display on a friend or colleague to find out how quickly they can understand the point you are trying to convey. As with reading passages aloud to ensure that your writing works, showing someone your display is the quickest way to find out if it works. Second, put the point in the title of the display! When writing for an academic journal, there will be specific conventions of what to include in the title (full description including methods of analysis, sample, dates), but in a public presentation, there are no limiting rules. So you are free to write as your title “Working-Class College Students Are Three Times as Likely as Their Peers to Drop Out of College,” if that is the point of the graphic display. It certainly helps the communicative aspect. Third, use the themes available to you in Excel for creating graphic displays, but alter them to better fit your needs . Consider adding dark borders to bars and columns, for example, so that they appear crisper for your audience. Include data callouts and labels, and enlarge them so they are clearly visible. When duplicative or otherwise unnecessary, drop distracting gridlines and labels on the y-axis (the vertical one). Don’t go crazy adding different fonts, however—keep things simple and clear. Sans serif fonts (those without the little hooks on the ends of letters) read better from a distance. Try to use the same color scheme throughout, even if this means manually changing the colors of bars and columns. For example, when reporting on working-class college students, I use blue bars, while I reserve green bars for wealthy students and yellow bars for students in the middle. I repeat these colors throughout my presentations and incorporate different colors when talking about other items or factors. You can also try using simple grayscale throughout, with pops of color to indicate a bar or column or line that is of the most interest. These are just some suggestions. The point is to take presentation seriously and to pay attention to visual displays you are using to ensure they effectively communicate what you want them to communicate. I’ve included a data visualization checklist from Evergreen ( 2018 ) here.

Ethics of Presentation and Reliability

Until now, all the data you have collected have been yours alone. Once you present the data, however, you are sharing sometimes very intimate information about people with a broader public. You will find yourself balancing between protecting the privacy of those you’ve interviewed and observed and needing to demonstrate the reliability of the study. The more information you provide to your audience, the more they can understand and appreciate what you have found, but this also may pose risks to your participants. There is no one correct way to go about finding the right balance. As always, you have a duty to consider what you are doing and must make some hard decisions.

The most obvious place we see this paradox emerge is when you mask your data to protect the privacy of your participants. It is standard practice to provide pseudonyms, for example. It is such standard practice that you should always assume you are being given a pseudonym when reading a book or article based on qualitative research. When I was a graduate student, I tried to find information on how best to construct pseudonyms but found little guidance. There are some ethical issues here, I think. [7] Do you create a name that has the same kind of resonance as the original name? If the person goes by a nickname, should you use a nickname as a pseudonym? What about names that are ethnically marked (as in, almost all of them)? Is there something unethical about reracializing a person? (Yes!) In her study of adolescent subcultures, Wilkins ( 2008 ) noted, “Because many of the goths used creative, alternative names rather than their given names, I did my best to reproduce the spirit of their chosen names” ( 24 ).

Your reader or audience will want to know all the details about your participants so that they can gauge both your credibility and the reliability of your findings. But how many details are too many? What if you change the name but otherwise retain all the personal pieces of information about where they grew up, and how old they were when they got married, and how many children they have, and whether they made a splash in the news cycle that time they were stalked by their ex-boyfriend? At some point, those details are going to tip over into the zone of potential unmasking. When you are doing research at one particular field site that may be easily ascertained (as when you interview college students, probably at the institution at which you are a student yourself), it is even more important to be wary of providing too many details. You also need to think that your participants might read what you have written, know things about the site or the population from which you drew your interviews, and figure out whom you are talking about. This can all get very messy if you don’t do more than simply pseudonymize the people you interviewed or observed.

There are some ways to do this. One, you can design a study with all of these risks in mind. That might mean choosing to conduct interviews or observations at multiple sites so that no one person can be easily identified. Another is to alter some basic details about your participants to protect their identity or to refuse to provide all the information when selecting quotes . Let’s say you have an interviewee named “Anna” (a pseudonym), and she is a twenty-four-year-old Latina studying to be an engineer. You want to use a quote from Anna about racial discrimination in her graduate program. Instead of attributing the quote to Anna (whom your reader knows, because you’ve already told them, is a twenty-four-year-old Latina studying engineering), you might simply attribute the quote to “Latina student in STEM.” Taking this a step further, you might leave the quote unattributed, providing a list of quotes about racial discrimination by “various students.”

The problem with masking all the identifiers, of course, is that you lose some of the analytical heft of those attributes. If it mattered that Anna was twenty-four (not thirty-four) and that she was a Latina and that she was studying engineering, taking out any of those aspects of her identity might weaken your analysis. This is one of those “hard choices” you will be called on to make! A rather radical and controversial solution to this dilemma is to create composite characters , characters based on the reality of the interviews but fully masked because they are not identifiable with any one person. My students are often very queasy about this when I explain it to them. The more positivistic your approach and the more you see individuals rather than social relationships/structure as the “object” of your study, the more employing composites will seem like a really bad idea. But composites “allow researchers to present complex, situated accounts from individuals” without disclosing personal identities ( Willis 2019 ), and they can be effective ways of presenting theory narratively ( Hurst 2019 ). Ironically, composites permit you more latitude when including “dirty laundry” or stories that could harm individuals if their identities became known. Rather than squeezing out details that could identify a participant, the identities are permanently removed from the details. Great difficulty remains, however, in clearly explaining the theoretical use of composites to your audience and providing sufficient information on the reliability of the underlying data.

There are a host of other ethical issues that emerge as you write and present your data. This is where being reflective throughout the process will help. How and what you share of what you have learned will depend on the social relationships you have built, the audiences you are writing or speaking to, and the underlying animating goals of your study. Be conscious about all of your decisions, and then be able to explain them fully, both to yourself and to those who ask.

Our research is often close to us. As a Black woman who is a first-generation college student and a professional with a poverty/working-class origin, each of these pieces of my identity creates nuances in how I engage in my research, including how I share it out. Because of this, it’s important for us to have people in our lives who we trust who can help us, particularly, when we are trying to share our findings. As researchers, we have been steeped in our work, so we know all the details and nuances. Sometimes we take this for granted, and we might not have shared those nuances in conversation or writing or taken some of this information for granted. As I share my research with trusted friends and colleagues, I pay attention to the questions they ask me or the feedback they give when we talk or when they read drafts.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

Final Comments: Preparing for Being Challenged

Once you put your work out there, you must be ready to be challenged. Science is a collective enterprise and depends on a healthy give and take among researchers. This can be both novel and difficult as you get started, but the more you understand the importance of these challenges, the easier it will be to develop the kind of thick skin necessary for success in academia. Scientists’ authority rests on both the inherent strength of their findings and their ability to convince other scientists of the reliability and validity and value of those findings. So be prepared to be challenged, and recognize this as simply another important aspect of conducting research!

Considering what challenges might be made as you design and conduct your study will help you when you get to the writing and presentation stage. Address probable challenges in your final article, and have a planned response to probable questions in a conference presentation or job talk. The following is a list of common challenges of qualitative research and how you might best address them:

• Questions about generalizability . Although qualitative research is not statistically generalizable (and be prepared to explain why), qualitative research is theoretically generalizable. Discuss why your findings here might tell us something about related phenomena or contexts.
• Questions about reliability . You probably took steps to ensure the reliability of your findings. Discuss them! This includes explaining the use and value of multiple data sources and defending your sampling and case selections. It also means being transparent about your own position as researcher and explaining steps you took to ensure that what you were seeing was really there.
• Questions about replicability. Although qualitative research cannot strictly be replicated because the circumstances and contexts will necessarily be different (if only because the point in time is different), you should be able to provide as much detail as possible about how the study was conducted so that another researcher could attempt to confirm or disconfirm your findings. Also, be very clear about the limitations of your study, as this allows other researchers insight into what future research might be warranted.

None of this is easy, of course. Writing beautifully and presenting clearly and cogently require skill and practice. If you take anything from this chapter, it is to remember that presentation is an important and essential part of the research process and to allocate time for this as you plan your research.

Data Visualization Checklist for Slideshow (PPT) Presentations

Adapted from Evergreen ( 2018 )

Text checklist

• Short catchy, descriptive titles (e.g., “Working-class students are three times as likely to drop out of college”) summarize the point of the visual display
• Subtitled and annotations provide additional information (e.g., “note: male students also more likely to drop out”)
• Text size is hierarchical and readable (titles are largest; axes labels smallest, which should be at least 20points)
• Text is horizontal. Audience members cannot read vertical text!
• All data labeled directly and clearly: get rid of those “legends” and embed the data in your graphic display
• Labels are used sparingly; avoid redundancy (e.g., do not include both a number axis and a number label)

Arrangement checklist

• Proportions are accurate; bar charts should always start at zero; don’t mislead the audience!
• Data are intentionally ordered (e.g., by frequency counts). Do not leave ragged alphabetized bar graphs!
• Axis intervals are equidistant: spaces between axis intervals should be the same unit
• Graph is two-dimensional. Three-dimensional and “bevelled” displays are confusing
• There is no unwanted decoration (especially the kind that comes automatically through the PPT “theme”). This wastes your space and confuses.

Color checklist

• There is an intentional color scheme (do not use default theme)
• Color is used to identify key patterns (e.g., highlight one bar in red against six others in greyscale if this is the bar you want the audience to notice)
• Color is still legible when printed in black and white
• Color is legible for people with color blindness (do not use red/green or yellow/blue combinations)
• There is sufficient contrast between text and background (black text on white background works best; be careful of white on dark!)

Lines checklist

• Be wary of using gridlines; if you do, mute them (grey, not black)
• Allow graph to bleed into surroundings (don’t use border lines)
• Remove axis lines unless absolutely necessary (better to label directly)

Overall design checklist

• The display highlights a significant finding or conclusion that your audience can ‘”see” relatively quickly
• The type of graph (e.g., bar chart, pie chart, line graph) is appropriate for the data. Avoid pie charts with more than three slices!
• Graph has appropriate level of precision; if you don’t need decimal places
• All the chart elements work together to reinforce the main message

Universal Design Checklist for Slideshow (PPT) Presentations

• Include both verbal and written descriptions (e.g., captions on slides); consider providing a hand-out to accompany the presentation
• Microphone available (ask audience in back if they can clearly hear)
• Face audience; allow people to read your lips
• Turn on captions when presenting audio or video clips
• Adjust light settings for visibility
• Speak slowly and clearly; practice articulation; don’t mutter or speak under your breath (even if you have something humorous to say – say it loud!)
• Use Black/White contrasts for easy visibility; or use color contrasts that are real contrasts (do not rely on people being able to differentiate red from green, for example)
• Use easy to read font styles and avoid too small font sizes: think about what an audience member in the back row will be able to see and read.
• Keep your slides simple: do not overclutter them; if you are including quotes from your interviews, take short evocative snippets only, and bold key words and passages. You should also read aloud each passage, preferably with feeling!

Supplement: Models of Written Sections for Future Reference

Data collection section example.

Interviews were semi structured, lasted between one and three hours, and took place at a location chosen by the interviewee. Discussions centered on four general topics: (1) knowledge of their parent’s immigration experiences; (2) relationship with their parents; (3) understanding of family labor, including language-brokering experiences; and (4) experiences with school and peers, including any future life plans. While conducting interviews, I paid close attention to respondents’ nonverbal cues, as well as their use of metaphors and jokes. I conducted interviews until I reached a point of saturation, as indicated by encountering repeated themes in new interviews (Glaser and Strauss 1967). Interviews were audio recorded, transcribed with each interviewee’s permission, and conducted in accordance with IRB protocols. Minors received permission from their parents before participation in the interview. ( Kwon 2022:1832 )

Justification of Case Selection / Sample Description Section Example

Looking at one profession within one organization and in one geographic area does impose limitations on the generalizability of our findings. However, it also has advantages. We eliminate the problem of interorganizational heterogeneity. If multiple organizations are studied simultaneously, it can make it difficult to discern the mechanisms that contribute to racial inequalities. Even with a single occupation there is considerable heterogeneity, which may make understanding how organizational structure impacts worker outcomes difficult. By using the case of one group of professionals in one religious denomination in one geographic region of the United States, we clarify how individuals’ perceptions and experiences of occupational inequality unfold in relation to a variety of observed and unobserved occupational and contextual factors that might be obscured in a larger-scale study. Focusing on a specific group of professionals allows us to explore and identify ways that formal organizational rules combine with informal processes to contribute to the persistence of racial inequality. ( Eagle and Mueller 2022:1510–1511 )

Ethics Section Example

I asked everyone who was willing to sit for a formal interview to speak only for themselves and offered each of them a prepaid Visa Card worth $25–40. I also offered everyone the opportunity to keep the card and erase the tape completely at any time they were dissatisfied with the interview in any way. No one asked for the tape to be erased; rather, people remarked on the interview being a really good experience because they felt heard. Each interview was professionally transcribed and for the most part the excerpts are literal transcriptions. In a few places, the excerpts have been edited to reduce colloquial features of speech (e.g., you know, like, um) and some recursive elements common to spoken language. A few excerpts were placed into standard English for clarity. I made this choice for the benefit of readers who might otherwise find the insights and ideas harder to parse in the original. However, I have to acknowledge this as an act of class-based violence. I tried to keep the original phrasing whenever possible. ( Pascale 2021:235 )

Further Readings

Calarco, Jessica McCrory. 2020. A Field Guide to Grad School: Uncovering the Hidden Curriculum . Princeton, NJ: Princeton University Press. Don’t let the unassuming title mislead you—there is a wealth of helpful information on writing and presenting data included here in a highly accessible manner. Every graduate student should have a copy of this book.

Edwards, Mark. 2012. Writing in Sociology . Thousand Oaks, CA: SAGE. An excellent guide to writing and presenting sociological research by an Oregon State University professor. Geared toward undergraduates and useful for writing about either quantitative or qualitative research or both.

Evergreen, Stephanie D. H. 2018. Presenting Data Effectively: Communicating Your Findings for Maximum Impact . Thousand Oaks, CA: SAGE. This is one of my very favorite books, and I recommend it highly for everyone who wants their presentations and publications to communicate more effectively than the boring black-and-white, ragged-edge tables and figures academics are used to seeing.

Evergreen, Stephanie D. H. 2019. Effective Data Visualization 2 . Thousand Oaks, CA: SAGE. This is an advanced primer for presenting clean and clear data using graphs, tables, color, font, and so on. Start with Evergreen (2018), and if you graduate from that text, move on to this one.

Schwabisch, Jonathan. 2021. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks . New York: Columbia University Press. Where Evergreen’s (2018, 2019) focus is on how to make the best visual displays possible for effective communication, this book is specifically geared toward visual displays of academic data, both quantitative and qualitative. If you want to know when it is appropriate to use a pie chart instead of a stacked bar chart, this is the reference to use.

• Some examples: Qualitative Inquiry , Qualitative Research , American Journal of Qualitative Research , Ethnography , Journal of Ethnographic and Qualitative Research , Qualitative Report , Qualitative Sociology , and Qualitative Studies . ↵
• This is something I do with every article I write: using Excel, I write each element of the expected article in a separate row, with one column for “expected word count” and another column for “actual word count.” I fill in the actual word count as I write. I add a third column for “comments to myself”—how things are progressing, what I still need to do, and so on. I then use the “sum” function below each of the first two columns to keep a running count of my progress relative to the final word count. ↵
• And this is true, I would argue, even when your primary goal is to leave space for the voices of those who don’t usually get a chance to be part of the conversation. You will still want to put those voices in some kind of choir, with a clear direction (song) to be sung. The worst thing you can do is overwhelm your audience with random quotes or long passages with no key to understanding them. Yes, a lot of metaphors—qualitative researchers love metaphors! ↵
• To take Calarco’s recipe analogy further, do not write like those food bloggers who spend more time discussing the color of their kitchen or the experiences they had at the market than they do the actual cooking; similarly, do not write recipes that omit crucial details like the amount of flour or the size of the baking pan used or the temperature of the oven. ↵
• The exception is the “compare and contrast” of two or more quotes, but use caution here. None of the quotes should be very long at all (a sentence or two each). ↵
• Although this section is geared toward presentations, many of the suggestions could also be useful when writing about your data. Don’t be afraid to use charts and graphs and figures when writing your proposal, article, thesis, or dissertation. At the very least, you should incorporate a tabular display of the participants, sites, or documents used. ↵
• I was so puzzled by these kinds of questions that I wrote one of my very first articles on it ( Hurst 2008 ). ↵

The visual presentation of data or information through graphics such as charts, graphs, plots, infographics, maps, and animation.  Recall the best documentary you ever viewed, and there were probably excellent examples of good data visualization there (for me, this was An Inconvenient Truth , Al Gore’s film about climate change).  Good data visualization allows more effective communication of findings of research, particularly in public presentations (e.g., slideshows).

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.