<b>qgraph</b>: Network Visualizations of Relationships in Psychometric Data

TLDR

The authors introduce the qgraph R package, an interface for visualizing psychometric data as networks, and demonstrate its use on NEO‑PI‑R personality questionnaire data. qgraph visualizes matrices (e.g., correlations, covariances, factor loadings, regression parameters, p‑values) as networks with nodes for variables and edge widths proportional to magnitude, and can automatically plot exploratory and confirmatory factor analysis results from sem and lavaan. Applying qgraph to NEO‑PI‑R data illustrates its ability to generate interpretable network visualizations of personality questionnaire results.

Abstract

We present the qgraph package for R, which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can be represented in this fashion, for example matrices that contain (implied) covariances, factor loadings, regression parameters and p values. qgraph can also be used as a psychometric tool, as it performs exploratory and confirmatory factor analysis, using sem and lavaan; the output of these packages is automatically visualized in qgraph, which may aid the interpretation of results. In this article, we introduce qgraph by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.