Guides: Posters: Content development

Selecting content for your poster

The task instructions and rubric are your guide for organising the content. Despite discipline specific elements, many posters include the following sections:

Title	Capture what you are presenting and help viewers understand the topic quickly. Choose an engaging title to encourage your audience to take a closer look. Caution: If you report numerical findings in your title, keep in mind that your viewers may consider it as a “factoid”, so select it carefully.
Authors	Include who developed the poster, who was involved in the research project, and your contact information, for example, your email or social media handles, so the viewers can reach you.
Introduction (Background & gaps in the literature)	Outline the current literature, any gaps in the research literature and the problem you hoped to solve.
Research aim	What did your research set out to do? Include your research questions/hypotheses that you hoped to answer.
Designs and methods	Your methodology, approach, materials. What did you do? In the data and statistical analysis, list when, where, who, and how the data were collected, how many cases were involved, and how the data were analysed. For interventions or evaluations, list who, when, where, and how many, along with how the project was implemented and assessed.
Results (Outcomes/ Key Findings)	What are the outcomes produced by your research? Key findings of experiments and research questions. Make it simple to understand by using graphs, charts, tables, and figures to communicate results. Make sure the results answer the questions you posed in the aims.
Discussion/Conclusions	Your interpretation of the results. How do your results fit in the literature/ previous findings? Justification/explanation of the results. Conclusions you have drawn from the results. Implication for future research. Limitations of your studies.

Tips:

Contents and sections of your poster are specific to your task descriptions. You may be asked to be more detailed or modify headings to suit. Check with your coordinator if you are in doubt.
Sometimes, it is logical to separate discussion and conclusions. You may even want to add future directions or recommendations.

For science disciplines, posters often adapt the IMRAD structure (Introduction, Methods, Results And Discussion). Although not included in the name, these posters also feature a Conclusion. Find out more about the IMRAD scientific report writing here.

Scientific report writing (UniSC)
This resource will help you plan and write each section of your scientific report.

Presenting with graphs and charts

There are several questions to ask when displaying data:

Who am I presenting my data to? Context is important. Are the audience academics, HDR candidates, professional staff, lay persons or all of these?
What message am I trying to convey?
Am I using the right kind of chart or table?

Figure 1. A graphic shows the differences between a bar chart and a pie chart. Figure adapted from Duarte, N. (2014, April 16). The quick and dirty on data visualization. Harvard Business Review.

Think about the relationships that you want to present in your data.
Comparison of data values
Comparision/composition of whole data
Relationships between data
Correlation or distribution of data

Figure 2: A visual showing different types of charts that demonstrate the relationships in your data. Figure adapted from A Well Defined & Well Designed Dashboard Guide.

Do my visuals accurately reflect the numbers?

Figure 3: A visual displaying a side by side bar chart, presenting yearly revenue per region. Figure adapted from Duarte, N. (2014, April 16). The quick and dirty on data visualization. Harvard Business Review.

Looking for help to select the right data visualisation for your poster? Check out the additional resources section:

Presenting numeric patterns and contrasts

When describing patterns or numeric contrasts, regardless of the level of statistical complexity of the analysis, it is important to explain the direction and magnitude of the association, concepts under study and units of measurements rather than reporting coefficients (Miller, 2013).
Example:

Text quality	Example text	Comments
Poor	“Number of enrolled children in the family is correlated with disenrollment.”	Neither the direction nor the size of the association is apparent.
Poor	“The log-hazard of disenrollment for one-child families was 0.316.”	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).
Better	“Families with only one child enrolled in the program were about 1.4 times as likely as larger families to disenroll.”	This version explains the association between number of children and disenrollment without requiring viewers to calculate the log-hazard themselves to assess the size and direction of the association. It also explicitly identifies the group against which one-child families are compared in the model.

Presenting statistical results

Avoid focusing on the arithmetic needed to conduct the statistical tests.
Use confidence intervals, p-values, boldface, italics or a contrasting colour to denote significant findings.
Avoid excessive use of jargons.

Example:

Figure 4: A visual showing side by side bar chat, depicting odd ratios of disenrolment from NJ KidCare by race of family and percentage of physicians. Figure adapted from Phillips, J. A., Miller, J. E., Cantor, J. C., & Gaboda, D. (2004). Context or Composition: What Explains Variation in SCHIP Disenrollment? Health Services Research, 39(4p1), 865–886. https://doi.org/10.1111/j.1475-6773.2004.00262.x

Additional resources

Looking for help to select the right data visualisation for your poster? Check out these resources:

From data to viz (From Data to Viz)
From Data to Viz leads you to the most appropriate graph for your data. It links to the code to build it and lists common caveats you should avoid.
Graph selection matrix (Perceptual Edge)
How to choose the right data visualization (Atlassian)
Data visualizations are a vital component of a data analysis, as they have the capability of summarizing large amounts of data efficiently in a graphical format. There are many chart types available, each with its own strengths and use cases. One of the trickiest parts of the analysis process is choosing the right way to represent your data using one of these visualizations.
A periodic table of visualisation methods (Visual Literacy)

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