The Miller Prize

On behalf of this year's Miller Prize committee (myself, Alexander Theodoridis, Patrick Brandt, and Jeff Gill), I’m delighted to announce the winner of the Society for Political Methodology’s 2020 Miller Prize for the best paper published in Political Analysis. This year the prize goes to the article "How Much Should We Trust Estimates from Multiplicative Interaction Models: Simple Tools to Improve Empirical Practice," by Jens Hainmueller, Jonathan Mummolo, and Yiqing Xu. Multiple nominators praised the article's importance, the breadth of its rigor, and its utility for a wide range of practitioners. Please join us in congratulating the authors for this excellent piece of scholarship. The abstract is pasted below.

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.