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Political Analysis Advance Access originally published online on May 18, 2005
Political Analysis 2006 14(1):63-82; doi:10.1093/pan/mpi014
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© The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org

Understanding Interaction Models: Improving Empirical Analyses

Thomas Brambor

New York University, Department of Politics, 726 Broadway, 7th Floor, New York, NY 10003

William Roberts Clark

University of Michigan, Center for Political Studies, ISR 4202 Box 1248, 426 Thompson Street, Ann Arbor, MI 48106-1248

Matt Golder

Florida State University, Department of Political Science, 531 Bellamy Building, Tallahassee, FL 32306-2230

e-mail: thomas.brambor{at}nyu.edu
e-mail: wrclark{at}umich.edu
e-mail: matt.golder{at}nyu.edu (corresponding author)

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.


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