By Jacob Coverstone, FACEHP, Managing Director, Neurovations Education
Alliance National Learning Competency 3.1
Use evaluation and outcomes data to assess and determine:
- the educational outcomes/results of the activities/ interventions on participants’ attitudes, knowledge levels, skills, performance and/or patient outcomes,
- unmet learning needs, and
- the quality and success of activities/interventions
If you’re a data geek, you probably admire and hate William Playfair. But, on the off chance you’re not obsessed with obscure histories of data analysis, I’ll explain.
William Playfair (1759–1823) was the first person to portray data visually when that information did not represent time or location. He invented the line, area and bar charts1. Like drawstrings on garbage bags, it’s almost strange to think something as ubiquitous as the bar chart had to be invented, but it did — and William Playfair did it.
In 1801, Playfair invented the pie chart2, published in The Commercial and Political Atlas (see below), and its use has remained largely unchanged to this day. (That’s bad.)
Pie, Oh My
What was innovative 200 years ago is hated today. And why is that?
As Stephen Few, author of “Now You See it: Simple Visualization Techniques for Quantitative Analysis,” puts it:
Here sits the friendly pie chart:
Its slices are upturned into an inviting smile. Its simple charm is beloved by all but a few, welcomed almost everywhere; familiar and rarely threatening. Of all the graphs that play major roles in the lexicon of quantitative communication, however, the pie chart is by far the least effective. Its colorful voice is often heard, but rarely understood. It mumbles when it talks.3
The controversy can be reduced to a simple principle: Your chart is bad if you have to explain it. Pie charts work best to explain comparative parts of a whole, but they’re rarely used that way. Even when they are, perception of the pie is frequently impeded with 3-D effects, shading, shadowing or tilting the chart visually on the page — presenting the reader not with a pie, but a sliced-up egg of sorts.
Why? If your data isn’t interesting, you don’t need to jazz it up; you need better data.
The biggest problem with pie charts was alluded to by Stephen Few: They’re poorly understood. We visualize data to make it better understood. Picking the wrong chart or graph is antithetical to this principle. Therefore, pie charts are rarely the right choice because they’re comparably more difficult to understand. Adding visual clutter and effects to your pies only makes it worse.
Caroline Ziemkiewicz, a postdoctoral researcher at Brown University’s Visualization Research Lab, spent a lot of time researching perceptive accuracy of visualization. Along with Robert Kosara at UNC Charlotte, Ziemkiewicz compared how accurately viewers could assess values represented in four common charts: pies, doughnuts, staked bar and square pies4.
When the work was complete, a new king of the culinary-named visualization emerged (see above).
Square Pie Charts
You can hate the name and still love the chart. I think it’s stupid whenever a pizzeria or national chain introduces “square” pizza, as if pepperoni, cheese and a good crust requires innovation. But what fails with food works well with data.
Ziemkiewicz and Kosara demonstrated that square pies have both greater accuracy and less variation of interpretation than pies, doughnuts or bar charts — and these results have been replicated.
If that wasn’t enough reason to switch, Kosara assessed reader confidence with their ability to estimate an accurate value. Once again, square pies decimated the competition with far more readers reporting a “high” degree of confidence.
Ultimately, square pie charts are more accurate, less confusing and lead to fewer subjective interpretations than bars, round pies or doughnuts. If, like me, you hate the name “square pie,” draw a 10x10 grid on it and call it a waffle.
Follow any these links for how-to guides teaching waffle-chart creating in your chosen platform:
- Playfair, William, 1759-1823. The Commercial and Political Atlas and Statistical Breviary. New York: Cambridge University Press
- Playfair, William. The Statistical Breviary: Shewing, on a Principle Entirely New, the Resources of Every State and Kingdom in Europe ... to Which Is Added, a Similar Exhibition of the Ruling Powers of Hindoostan. London: For J. Wallis, 1801
- Few, Stephen, “Save the Pies for Desert.” Visual Business Intelligence Newsletter, El Dorado Hills, CA: Perceptual Edge, 2007
- Kosara, Robert, and Caroline Ziemkiewicz. “Do Mechanical Turks dream of square pie charts?” Proceedings of the 3rd BELIV’10 Workshop: Beyond time and errors: novel evaluation methods for Information Visualization. ACM, 2010.