Resources for the CDO training, Summer 2021.
Here are a few resources to get you started.
There has been a lot of discussions in the news lately that cite some pretty large figures. Whether we're talking about an infrastructure plan [https://www.whitehouse.gov/briefing-room/statements-releases/2021/06/24/fact-sheet-president-biden-announces-support-for-the-bipartisan-infrastructure-framework/]($1.2 trillion), the cost of the war in Afghanistan [https://watson.brown.edu/costsofwar/figures/2021/human-and-budgetary-costs-date-us-war-afghanistan-2001-2021]($2.261 trillion), or legislative actions related to COVID [https://www.covidmoneytracker.org/]($5.9 trillion), these numbers can seem impossibly difficult to fathom given their sheer size.
For example - what you picture in your mind when you think of a thousand dollars? A million? A billion? A trillion dollars? Visualizing data can help us add context and comparison while better understanding the differences between the sheer size of these numbers by comparing them to things we might understand a bit better.
In preparation for our upcoming module, read this article:
Norton, Michael I., and Dan Ariely. “Building a Better America-One Wealth Quintile at a Time.” Perspectives on Psychological Science: A Journal of the Association for Psychological Science 6, no. 1 (January 2011): 9–12. [https://doi.org/10.1177/1745691610393524](Link to paper)
Next, view this data visualization (try to make it all the way through if you can...)
“Wealth, Shown to Scale.” Accessed May 5, 2020. https://mkorostoff.github.io/1-pixel-wealth/
Does the visualization change your understanding of wealth and how you think about income disparity? We'll discuss your observations during our class.
Another area we'll talk about is receiving feedback on our own data visualizations. It's an under-appreciated mechanism to learn how effective we are at using data to tell stories. We do however see direct user feedback in the news all the time when data are used to explain a point or tell a story that maybe isn't so well supported by the data itself. As an example, [https://www.bloomberg.com/opinion/articles/2019-03-28/living-on-500-000-a-year-in-income-can-seem-hard](see this article from Bloomberg). Review the associated infographics referenced, which can be found linked in the article. Next, see this data story depicting the [https://www.vox.com/2016/5/23/11704246/wealth-inequality-cartoon](changing economic classes in the United States). And, as you're reading the articles notice how the design choices are used to support the intended narrative. Some use pictures of people, others use particular colors that convey yet more information, or sometimes emotions. Different visualization types (like the Alluvial Diagram for the $100K budget) show movement and flow. All of these elements combine to influence us as readers even more, and are sometimes designed to influence our interpretation of the data and the story. Pay attention to how you feel as you review the data visualizations - does a graphic make you sad? Angry? Interested to learn more? Pay attention to those elements that are leading you to feel or respond a certain way and see if you can figure out why.
Narratives can be formed around any dataset, and the same dataset can be used to tell different stories. But, if we tell the wrong story with our dataset we might break the trust with our readers. If this happens we run the risk of damaging the reliability and trustworthiness of our work in the future. We see this play out in how people tend to distrust news coming from highly-partisan sources or politicians. We're used to facts, stats and numbers being distorted to serve a purpose.
As you go about your daily lives in the coming week, be on the lookout for data that tells a story, but maybe - just maybe - by design isn't actually telling the whole story, or the story that the data actually supports. If you run across any good examples feel free to share them during our class using the chat function.