Social Research: Analyzing Data Misinterpretation Using Infographics

This essay explores the critical issue of data misinterpretation in social research, particularly when data is converted into visual formats like infographics. It highlights the potential risks associated with misinterpreting data, including flawed decision-making, unrealistic goal setting, negative outcomes, delayed responses to critical issues, financial wastage, and inaccurate revenue projections. The essay emphasizes the reliance of governments and businesses on accurate data for budgeting, resource allocation, and strategic planning, using the example of medical disease statistics. It further discusses how data misinterpretation can lead to over or under-reaction to pressing issues, particularly in healthcare, and can result in significant economic losses due to incorrect investment decisions and inaccurate profit margin analysis. The importance of using infographics effectively to prevent data misinterpretation and ensure informed decision-making is underscored. The article selected for analysis is Curtis, A. J., & Lee, W. A. A. (2010). Spatial patterns of diabetes related health problems for vulnerable populations in Los Angeles.

Running head: SOCIAL RESEARCH INFOGRAPHIC
1
Social Research Infographic
Student’s name
Institution affiliation

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Social Research Infographic
Article selected:
Curtis, A. J., & Lee, W. A. A. (2010). Spatial patterns of diabetes related health problems
for vulnerable populations in Los Angeles. International journal of health
geographics, 9(1), 43.
https://ij-healthgeographics.biomedcentral.com/articles/10.1186/1476-072X-9-43
Fig.1. WHO/World Health Organization, (2018). diabetes infographs as accessed from the WHO
website 2/8/2018
In most cases, data has often been prone to misinterpretation especially when it is being
converted to visuals from text (Curtis, & Lee, 2010). There are various risks that are associated

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to misinterpretation of data. These include wrong decisions, unrealistic goals and objectives,
more negative outcome, delay in reacting to serious issues, wastage of financial resources and
wrong revenue projections (Lankow, Ritchie, & Crooks, 2012).
Misinterpreting data leads to wrong solutions and decisions being made. For instance the
government depends on medical disease statistics to come up with strategies for curbing or
dealing with the problem (Greenland, 2011). For instance, before the state budgets for medical
response it relies on raw data on the prevalence of disease so that the right amounts of finances
are set to solve the issue. When the data is wrong, it means under-budgeting and thus the issue
continues disturbing the citizens (Visscher, Heitmann, Rissanen, Lahti-Koski, & Lissner, 2015).
It may also involve over-budgeting leaning to wasted resources. Wrong interpretation also leads
to overreaction or under-reaction based on the statistics. This may mean serious issues may not
be addressed in good time until it is too late when secondary effects have been evident (Foucher,
Combescure, Ashton‐Chess, & Giral, 2012). This is the case with contagious and deadly diseases
that cause death within short periods of time.
The other effect is economic-wise. Wrong interpretation of data causes wastage of
financial resources in businesses, especially through advertising and budget allocation. It may
affect stock investment decisions leading to losses (Jacobsen, Calvin, & Lobenhofer, 2009).
Stock sometimes relies on true market values of the elements being invested upon. Companies
invest when the stock has value and avoid investing when the contrary is the case. Investing at
the wrong time and low value of stock means losses to the investors and the business in general
(Ball, 2009).
Revenue projections are made using data. Misinterpreting information may lead to wrong
projections being made. Wrong interpretation of data in the business leads to unrealistic

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objectives and goals. This means that the business wastes time working for nothing (Obukhova,
Piskarev, Severov, Pazynina, Tuzikov, Navakouski, & Bovin, 2011). Profit margin analysis
depends entirely on data. When the data is wrong it means giving false financial stand of the
business at any given time. Wrong decisions are made from there leading to other negative
impacts on the business. In this case the use of infographics should be done well to avoid the
aspect of data misinterpretation

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References
Ball, P. (2009). Water: water—an enduring mystery. Nature, 452(7185), 291.
Curtis, A. J., & Lee, W. A. A. (2010). Spatial patterns of diabetes related health problems for
vulnerable populations in Los Angeles. International journal of health geographics, 9(1),
43.
Foucher, Y., Combescure, C., Ashton‐Chess, J., & Giral, M. (2012). Prognostic markers: data
misinterpretation often leads to overoptimistic conclusions. American Journal of
Transplantation, 12(4), 1060-1061.
Greenland, S. (2011). Null misinterpretation in statistical testing and its impact on health risk
assessment. Preventive medicine, 53(4-5), 225-228.
Jacobsen, L. B., Calvin, S. A., & Lobenhofer, E. K. (2009). Transcriptional effects of
transfection: the potential for misinterpretation of gene expression data generated from
transiently transfected cells. Biotechniques, 47(1), 617-624.
Lankow, J., Ritchie, J., & Crooks, R. (2012). Infographics: The power of visual storytelling. John
Wiley & Sons.
Obukhova, P., Piskarev, V., Severov, V., Pazynina, G., Tuzikov, A., Navakouski, M., ... &
Bovin, N. (2011). Profiling of serum antibodies with printed glycan array: room for data
misinterpretation. Glycoconjugate journal, 28(8-9), 501-505.
Visscher, T. L. S., Heitmann, B. L., Rissanen, A., Lahti-Koski, M., & Lissner, L. (2015). A break
in the obesity epidemic? Explained by biases or misinterpretation of the
data?. International Journal of Obesity, 39(2), 189.

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WHO/World Health Organization, (2018). diabetes infographs
http://www.who.int/mediacentre/infographic/diabetes/en/