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Perspectives on Politics (2007), 5 : 755-771 Cambridge University Press
Copyright © 2007 American Political Science Association
doi:10.1017/S1537592707072209
Published online by Cambridge University Press 28 Nov 2007
Perspectives on Politics (2007), 5:4:755-771 Cambridge University Press
Copyright © 2007 American Political Science Association
doi:10.1017/S1537592707072209

Using Graphs Instead of Tables in Political Science


Jonathan P.  Kastellec  a1 and Eduardo L.  Leoni  a2
a1 Columbia University, E-mail: jpk2004@columbia.edu
a2 Columbia University, E-mail: eleoni@hmdc.harvard.edu

Article author query
kastellec jp   [Google Scholar
leoni el   [Google Scholar
 

Abstract

When political scientists present empirical results, they are much more likely to use tables than graphs, despite the fact that graphs greatly increases the clarity of presentation and makes it easier for a reader to understand the data being used and to draw clear and correct inferences. Using a sample of leading journals, we document this tendency and suggest reasons why researchers prefer tables. We argue that the extra work required in producing graphs is rewarded by greatly enhanced presentation and communication of empirical results. We illustrate their benefits by turning several published tables into graphs, including tables that present descriptive data and regression results. We show that regression graphs emphasize point estimates and confidence intervals and that they can successfully present the results of regression models. A move away from tables towards graphs would improve the discipline's communicative output and make empirical findings more accessible to every type of audience. a b



Footnotes

a Jonathan P. Kastellec (jpk2004@columbia.edu) and Eduardo L. Leoni (eleoni@hmdc.harvard.edu) are Doctoral Candidates in Political Science at Columbia University. The authors' names appear in alphabetical order. They would like to thank Andrew Gelman, Rebecca Weitz-Shapiro, Gary King, David Epstein, Jeff Gill, Piero Stanig, and three anonymous reviewers for helpful comments and suggestions. We also thank Noah Kaplan, David Park, and Travis Ridout for generously making their data publicly available. Eduardo Leoni is grateful for support from the Harvard MIT Data Center, where he was a fellow while working on this project.

b We have created a web site, http://tables2graphs.com, that contains complete replication code for all the graphs that appear in this article, as well as additional graphs that we did not present due to space limitations.



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