Best Practices for Estimating, Interpreting, and Presenting Nonlinear Interaction Effects

Sociological Science, 2019

470 citations recorded by the source corpus.

Many effects of interest to sociologists are nonlinear. Additionally, many effects of interest are interaction effects—that is, the effect of one independent variable is contingent on the level of another independent variable. The proper way to estimate, interpret, and present these two types of effects individually are well known. However, many analyses that combine these two—that is, tests of interaction when the effects of interest are nonlinear—are not properly interpreted or tested. The consequences of approaching nonlinear interaction effects the way one would approach a linear interaction effect are severe and can often result in incorrect conclusions. I cover both nonlinear effects in the context of linear regression, and—most thoroughly—nonlinear effects in models for categorical outcomes (focusing on binary logit/probit). My goal in this article is to synthesize an evolving methodological literature and to provide straightforward advice and techniques to estimate,interpret, and present nonlinear interaction effects.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1d34f6bf0c33674a8df5c8a17857ade2dbed196b

Citations in the corpus

• Enhancing Rigor in Quantitative Entrepreneurship Research (2019, unknown): “This can be fruitfully combined with plotting the marginal effects graphically, which is particularly relevant and informative when interpreting interaction effects in nonlinear models (Greene, 2012, p. 733-741; Hoetker, 2007; Mize, 2019; Wulff, 2015).”