How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice

Jens Hainmueller, Jonathan Mummolo, Yiqing Xu

Research output: Contribution to journalArticlepeer-review

464 Scopus citations

Abstract

Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.

Original languageEnglish (US)
Pages (from-to)163-192
Number of pages30
JournalPolitical Analysis
Volume27
Issue number2
DOIs
StatePublished - Apr 1 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

Keywords

  • interaction models
  • linear regression
  • local regression
  • marginal effects
  • misspecification

Fingerprint

Dive into the research topics of 'How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice'. Together they form a unique fingerprint.

Cite this