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  • English

Ten Guidelines for Better Tables

Published online by Cambridge University Press:  30 July 2020

Jonathan A. Schwabish
Jonathan A. Schwabish*
Affiliation:
The Urban Institute 500 L’Enfant Plaza, SW Washington, DC20024
*
e-mail: jschwabish@urban.org
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Abstract

Tables are a unique form of visualizing data because, unlike many charts, they are not usually intended to give a quick, visual representation of data. Instead, tables are useful when you want to show the exact values of your data or estimates. They are not the best solution if you want to show a lot of data or if you want to show the data in a compact space, but a well-designed table can help your reader find specific numbers and discover patterns and outliers. In this article, I present 10 guidelines for creating better, more effective tables; I then model these lessons by redesigning six tables from articles previously published in the Journal of Benefit-Cost Analysis.

Type
Invited Paper
Information
Journal of Benefit-Cost Analysis , Volume 11 , Issue 2 , Summer 2020 , pp. 151 - 178
Copyright
© Society for Benefit-Cost Analysis, 2020

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References

Blomquist, Glenn C., Coomes, Paul A., Jepsen, Christopher, Koford, Brandon C., and Troske, Kenneth R.. 2014. “Estimating the Social Value of Higher Education: Willingness to Pay for Community and Technical Colleges.” Journal of Benefit-Cost Analysis, 5 (1): 3–41.CrossRefGoogle Scholar
Cherdarchuk, Joey. 2014. “Clear off the Table.” Dark Horse Analytics (blog), (March 27). https://www.darkhorseanalytics.com/blog/clear-off-the-table.Google Scholar
Correll, Michael., and Gleicher, Michael. 2014. “Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error.” IEEE Transactions on Visualization and Computer Graphics, 20 (12): 2142–2151.CrossRefGoogle Scholar
Few, Stephen. 2004. Show Me the Numbers. Oakland, CA: Analytics Press.Google Scholar
Fraas, Art., and Egorenkov, Alex. 2018. “Retrospective Analyses Are Hard: A Cautionary Tale from EPA’s Air Toxics Regulations.” Journal of Benefit-Cost Analysis, 9 (2): 247–284.CrossRefGoogle Scholar
Hammitt, James K., and Robinson, Lisa A.. 2011. “The Income Elasticity of the Value per Statistical Life: Transferring Estimates between High and Low Income Populations.” Journal of Benefit-Cost Analysis, 2 (1): 1–29.CrossRefGoogle Scholar
Hansen, Wallace R. (ed). 1991. Suggestions to Authors of the Reports of the United States Geological Survey. Washington, DC: US Department of the Interior, US Geological Survey.Google Scholar
Hullman, Jessica, Resnick, Paul, and Adar, Eytan. 2015. “Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences about Reliability of Variable Ordering.” PloS one, 10 (11): e0142444.CrossRefGoogle ScholarPubMed
Kniesner, Thomas J., and Rustamov, Galib. 2018. “Differential and Distributional Effects of Energy Efficiency Surveys: Evidence from Electricity Consumption.” Journal of Benefit-Cost Analysis, 9 (3): 375–406.CrossRefGoogle Scholar
Masterman, Clayton J., and Kip Viscusi, W.. 2018. “The Income Elasticity of Global Values of a Statistical Life: Stated Preference Evidence.” Journal of Benefit-Cost Analysis, 9 (3): 407–434.CrossRefGoogle Scholar
Schwabish, Jonathan. 2016. Better Presentations: A Guide for Scholars, Researchers, and Wonks. New York: Columbia University Press.CrossRefGoogle Scholar
Schwabish, Jonathan. 2020a. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. New York: Columbia University Press.Google Scholar
Schwabish, Jonathan. 2020b. Elevate the Debate: A Multilayered Approach to Communicating Your Research. Hoboken, NJ: John Wiley & Sons.Google Scholar
Sunstein, Cass R. 2019. “Rear Visibility and Some Unresolved Problems for Economic Analysis (With Notes on Experience Goods).” Journal of Benefit-Cost Analysis, 10 (3): 317–350.CrossRefGoogle Scholar
Viscusi, W Kip. 2018. “Best Estimate Selection Bias in the Value of a Statistical Life.” Journal of Benefit-Cost Analysis, 9 (2): 205–246.CrossRefGoogle Scholar