A Tutorial on Regularized Partial Correlation Networks

TL;DR

This tutorial explains how to estimate regularized partial correlation networks for psychological data, highlighting methods, practical implementation in R, and considerations for data characteristics and sample size.

Contribution

It provides a comprehensive guide on estimating and interpreting regularized partial correlation networks in psychology, including practical steps and troubleshooting.

Findings

Demonstrated the application on PTSD data

Discussed hyperparameter effects on network estimation

Provided a checklist for common estimation problems

Abstract

Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on post-traumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks.