Estimating Group Differences in Network Models using Moderation Analysis

Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications the goal is to compare such network models across groups. In this paper I introduce a method to estimate differences in network models across groups that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups within a single model, and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.