Discussion: Displaying Data
The visual display of data in research is essential to help in the easy representation and interpretation of large data (Midway, 2020). Public health experts participate in research, and hence their ability to represent data visually is essential. This post aims to create two figures, a pie chart, and a bar graph, and discuss the pros and cons of the figures.
Figure: Pie chart representation of accommodation.
A pie chart is a circular and numerical representation of usually nominal data (In & Lee, 2017). The above pie chart represents three variables: owning a home, renting a home, and others. The good thing about the pie chart above is that the statistical data is easy to view and analyze. On the other hand, the chart can only be interpreted by an individual who knows the interpretation of pie charts; naming the slices within the chart will have been easier for non-statisticians to interpret.
Figure: simple bar graph representation of highest degree level count.
A bar graph is a vertical or horizontal representation of categorical data (Whitaker & Jacobbe, 2017). The bar graph above is clear to view and presents a clear message of the highest degree levels of a selected group of people. The above figure also summarizes the entire degree levels of the selected individuals in a visual display. Comparisons are made easy with the visual display above. The simplicity of the graph ensures an easy and quick explanation of the data. On the other hand, the data can only be understood by literates who have basic knowledge above graphs. The information is prone to bias in the interpretation stages.
In conclusion, visual data representation is vital for an easy and straightforward overview of large and complex information (Eberhard, 2021). Despite the advantages of representing data with a figure, there may still be some form of disadvantages. The public health expert should understand the disadvantages of the various graphs.
References
Eberhard, K. (2021). The effects of visualization on judgment and decision-making: a systematic literature review. Management Review Quarterly, 1-48. https://link.springer.com/article/10.1007/s11301-021-00235-8
In, J., & Lee, S. (2017). Statistical data presentation. Korean journal of anesthesiology, 70(3), 267.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5453888/
Midway, S. R. (2020). Principles of effective data visualization. Patterns, 100141.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733875/
Whitaker, D., & Jacobbe, T. (2017). Students’ understanding of bar graphs and histograms: Results from the LOCUS assessments. Journal of Statistics Education, 25(2), 90-102. https://www.tandfonline.com/doi/full/10.1080/10691898.2017.1321974
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