Data Visualization for Social Science Reflection

The introductory chapter of Data Visualization for Social Science by Kieran Healy highlights visual, statistical and physiological elements that affect poor form in data visualization. Healy defines these three pillars as aesthetic, substantive and perceptual, and “…while often found together, [they] are distinct from one another” (Healy, 2018).  Aesthetic missteps are frequently correlated to poor taste or visual complexity.

Substantive elements reflect how the data is presented. Cherry picking through the data or shifting the axis starting point can lead to misrepresenting the information. Healy builds a case study off of the infographic in the 2016 article by The New York Times, How Stable Are Democracies? ‘Warning Signs Are Flashing Red’”. What I found fascinating about the case study was that the infographic seems to represent if the respondents believe democracy is important (a Yes or No question), while the survey question was actually based off of a 10-point scale. The shortened axis dramatized the information and is a misleading representation of the data.

Perceptual elements relate to how a viewer processes and recognizes what they are looking at. Healy writes, “perception is not a simple matter of direct visual inputs producing straightforward mental representations of their content. Rather, our visual system is tuned to accomplish some tasks very well, and this comes at a cost in other ways” (Healy, 2018).

I agreed with Healy’s theory on aesthetic and substantive poor form in data visualization, but I found the perceptual elements challenging to grasp as many of them are beyond our conscious control. I would be curious to see how other classmates understood the perceptual elements and what are best practices to make data visualizations that limit perceptual missteps?

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