Testing Group Differences in Network Models using Bayesian Model Selection: A Tutorial

Network models are increasingly used to model multivariate data and many research questions are about whether networks differ across groups. This tutorial provides an accessible introduction to Bayesian model-selection for testing group differences in networks, as implemented in the R package bgms. This approach provides posterior inclusion probabilities and Bayes factors for each group difference, allowing researchers to distinguish evidence for a difference from evidence for equality. We focus the tutorial on group differences in ordinal network models, since the vast majority of data in cross-sectional research are measured on ordinal scales. We explain the model parameters, prior choices, missing-data handling, convergence checks, checks of model fit, and the interpretation of posterior summaries of group differences. The workflow is illustrated with a reproducible comparison of problematic pornography use symptom networks in women and men.