Understanding Interaction Models: Improving Empirical Analyses
2005/05/18 by Thomas Brambor, William Roberts Clark, William R. Clark +1 · 63 citations
Business, Management and Accounting · Social Sciences · #Electoral Systems and Political Participation #Policy Transfer and Learning #Political Influence and Corporate Strategies
paper · doi:10.1093/pan/mpi014
openalex publication_date 2005/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
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.
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