Network analysis of multivariate data in psychological science

TLDR

Network analysis has been applied to identify and analyze patterns of statistical association in multivariate psychological data, representing variables as nodes and pairwise conditional associations as edges, enabling investigation of complex patterns and relationships. This Primer outlines the anatomy of network analysis techniques, reviews the current state of the art, discusses open problems, adoption in psychological research, and highlights limitations and future challenges. The authors describe how network analysis can be applied to cross‑sectional, repeated‑measures, and intensive longitudinal data, and discuss estimation and assessment methods for network robustness and replicability. Successful applications of network analysis across diverse research areas are highlighted. Borsboom et al.

Abstract

In recent years, network analysis has been applied to identify and analyse patterns of statistical association in multivariate psychological data. In these approaches, network nodes represent variables in a data set, and edges represent pairwise conditional associations between variables in the data, while conditioning on the remaining variables. This Primer provides an anatomy of these techniques, describes the current state of the art and discusses open problems. We identify relevant data structures in which network analysis may be applied: cross-sectional data, repeated measures and intensive longitudinal data. We then discuss the estimation of network structures in each of these cases, as well as assessment techniques to evaluate network robustness and replicability. Successful applications of the technique in different research areas are highlighted. Finally, we discuss limitations and challenges for future research. Network analysis allows the investigation of complex patterns and relationships by examining nodes and the edges connecting them. Borsboom et al. discuss the adoption of network analysis in psychological research.