vix.ing · top · new · best · stats

Interpreting Interaction Effects in Generalized Linear Models of Nonlinear Probabilities and Counts

2021/02/01 by Connor J. McCabe, Connor McCabe, Max A. Halvorson +3 · 97 citations
Mathematics · Psychology · #Advanced Causal Inference Techniques #Computer science #Econometrics #Generalized additive model #Generalized linear mixed model #Generalized linear model #Interaction #Linear model #Marginal model #Mathematics #Mental Health Research Topics #Nonlinear system #Regression analysis #Statistical Methods and Bayesian Inference #Statistics #Variable (mathematics) #Variables

paper · open access · doi:10.1080/00273171.2020.1868966

published in Multivariate Behavioral Research 57(2-3), 243-263 (Taylor & Francis)

openalex publication_date 2021/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Psychology research frequently involves the study of probabilities and counts. These are typically analyzed using generalized linear models (GLMs), which can produce these quantities via nonlinear transformation of model parameters. Interactions are central within many research applications of these models. To date, typical practice in evaluating interactions for probabilities or counts extends directly from linear approaches, in which evidence of an interaction effect is supported by using the product term coefficient between variables of interest. However, unlike linear models, interaction effects in GLMs describing probabilities and counts are not equal to product terms between predictor variables. Instead, interactions may be functions of the predictors of a model, requiring nontraditional approaches for interpreting these effects accurately. Here, we define interactions as change in a marginal effect of one variable as a function of change in another variable, and describe the use of partial derivatives and discrete differences for quantifying these effects. Using guidelines and simulated examples, we then use these approaches to describe how interaction effects should be estimated and interpreted for GLMs on probability and count scales. We conclude with an example using the Adolescent Brain Cognitive Development Study demonstrating how to correctly evaluate interaction effects in a logistic model.

Citations

Cited by

Related