You’ve spent what feels like weeks and months checking your statistical analysis is right, twice as long inputting the data, and you finally understand the results. How should you present your findings? Keep reading to learn what questions you should be asking yourself.
Presenting your data
There are several ways to present your research findings; as a written paragraph, in a table, and as a graph. Examples of all three methods can be seen by looking at published, peer-reviewed journal articles. Remember presentation matters, don’t scare your audience with loads of numbers. Your aim is to take a mass of information and present it in a way that your audience will understand.
Is a graph appropriate?
You should first ask yourself if presenting the data in a graph is appropriate. Continue reading to learn more. You should present the relevant results to answer your research question. Include the statistical analysis conducted and briefly evaluate their significance. As a rule, continuous data such as frequency distribution suits a table or for visual impact box plots if they are answering your research question. Although general descriptive statistics cannot tell the whole story. In figure 1 the data for all four graphs have the same mean and standard deviation, but the relationships are very different.
Whilst a table of relationships can give an overview, presenting one or two scatter plots of key findings that answer your question will grab the reader’s attention. Do not use a figure just for the sake of it. If you can only graph one result, what would you choose? Consider what is the key research question or what is the take-home message of your study’s findings? You may have more than one figure but ask yourself these questions to ensure the visual presentation of your results gets your message across.
Which charts are best?
Pie charts show proportion (fig. 2), for example, percentage compliance with the standard. A pie chart would be inappropriate if, for example, it was being used to illustrate ‘type of treatment’ in cases where patients are likely to have had more than one treatment. In this instance, the pieces of the pie would add up to the total number of ‘treatments’ rather than the total number of patients, which could be misleading and is not very meaningful.
Bar or column charts (fig. 3) are generally used to show frequency, for example, time spent training in zone one, zone two, and zone three. Versions of bar charts (stacked or comparative) allow you to show more than one standard/question per chart, for example, time spent in each of the three training zones in different sports or participant groups (fig. 3).
Good charts should focus on getting your message across, rather than creating fancy and distracting images. Clutter should be avoided, and the charts clearly labelled. A chart might be considered unnecessary to illustrate the above data. It is always important to consider whether people need to see a graphical representation of the data, in some cases simply expressing the data as ‘x/n (y%)’ is sufficient. In a project with a lot of standards producing a chart for every single one may confuse rather than clarify the results. People may not remember which image is related to which standard.
Summary
Consider whether you need to represent your data graphically to get your message across. Consider which type of chart will deliver your message in the clearest way. Consider what information you should include in answering your audience’s questions. For example, sample size, percentages if displaying real values (or vice versa). Importantly, avoid clutter.
Going further
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