Understanding Interaction Models: Improving Empirical Analyses

TLDR

Multiplicative interaction models are widely used in political science to capture how relationships between inputs and outcomes vary with institutional context and strategic interaction. The authors propose a simple checklist of dos and don’ts for using multiplicative interaction models to improve empirical analyses. A survey of top journals from 1998–2002 shows frequent inferential errors in these models, with only 10 % of articles adhering to the recommended checklist.

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.