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

Political analysis, 2019-04, Vol.27 (2), p.163-192 [Peer Reviewed Journal]

Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. ;The Author(s) 2018 ;2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. This article is published under (http://creativecommons.org/licenses/by-nc-sa/4.0/) (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1047-1987 ;EISSN: 1476-4989 ;DOI: 10.1017/pan.2018.46

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  • Title:
    How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice
  • Author: Hainmueller, Jens ; Mummolo, Jonathan ; Xu, Yiqing
  • Subjects: Best practice ; Economic models ; Estimation ; Hypotheses ; Political science ; Regression analysis ; Researchers ; Social sciences
  • Is Part Of: Political analysis, 2019-04, Vol.27 (2), p.163-192
  • Description: 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.
  • Publisher: New York, USA: Cambridge University Press
  • Language: English
  • Identifier: ISSN: 1047-1987
    EISSN: 1476-4989
    DOI: 10.1017/pan.2018.46
  • Source: ProQuest Databases
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