Date created: | Last Updated:

Category: Project

Description: Network psychometrics is a new direction in psychological research that conceptualizes psychological constructs as systems of interacting variables. In network analysis, variables are represented as nodes and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for proper uncertainty quantification of the model and its parameters. Here, we outline a Bayesian approach to network analysis that offers three main benefits. In particular, applied researchers can use Bayesian methods to (1) determine structure uncertainty, (2) obtain evidence for edge inclusion and exclusion (i.e., distinguish conditional (in)dependence between variables), and (3) quantify parameter precision. The paper provides a conceptual introduction to Bayesian inference, describes how researchers can facilitate the three benefits for networks, and reviews the available R packages. In addition, we present two user-friendly software solutions: a new R package easybgm for fitting, extracting, and visualizing the Bayesian analysis of networks, and a graphical user interface implementation in JASP. The methodology is illustrated with a worked-out example of a network of personality traits and mental health.