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Understanding Interaction Models: Improving Empirical Analyses

Published online by Cambridge University Press:  04 January 2017

Thomas Brambor ,
William Roberts Clark  and
Matt Golder
Thomas Brambor
Affiliation:
New York University, Department of Politics, 726 Broadway, 7th Floor, New York, NY 10003. e-mail: thomas.brambor@nyu.edu
William Roberts Clark
Affiliation:
University of Michigan, Center for Political Studies, ISR 4202 Box 1248, 426 Thompson Street, Ann Arbor, MI 48106–1248. e-mail: wrclark@umich.edu
Matt Golder*
Affiliation:
Florida State University, Department of Political Science, 531 Bellamy Building, Tallahassee, FL 32306-2230
*
e-mail: matt.golder@nyu.edu (corresponding author)
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Abstract

Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist.

Type
Research Article
Information
Political Analysis , Volume 14 , Issue 1 , Winter 2006 , pp. 63 - 82
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: Our thanks go to Nathaniel Beck, Fred Boehmke, Michael Gilligan, Sona Nadenichek Golder, Jonathan Nagler, and two anonymous reviewers for their extremely useful comments on this paper. We also thank the research assistants at Political Analysis—Jeronimo Cortina, Tse-hsin Chen, and Seung Jin Jang—for kindly double-checking the results from our literature survey. Finally, we are grateful to those authors who have provided us with their data. To accompany this paper, we have constructed a Web page at http://homepages.nyu.edu/~mrg217/interaction.html that is devoted to multiplicative interaction models. On this page, you will find (i) the data and computer code necessary to replicate the analyses conducted here, (ii) information relating to marginal effects and standard errors in interaction models, (iii) STATA code for producing figures illustrating marginal effects and confidence intervals for a variety of continuous and limited dependent variable models, and (iv) detailed results from our literature survey. STATA 8 was the statistical package used in this study.

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