Looking at the complexities. Network Analysis in Psychological Research. A focus on Bayesian Networks

In contemporary psychological research, network analysis has emerged as a powerful methodological paradigm, providing sophisticated tools for modeling complex relationships among measurable psychological constructs (Briganti et al., 2024). This framework represents a departure from conventional reductionist paradigms, embracing instead a comprehensive perspective that acknowledges the inherently complex and interdependent structure of psychological phenomena. This chapter starts by introducing the various network typologies and methodological applications currently used in psychological science and psychometrics (22.1, 22.2, 22.3). The focus then shifts specifically to Bayesian Networks (BNs), detailing their theoretical utility (23) and the algorithms used to learn them from empirical data (25). In Section 26, we demonstrate these concepts through a practical R implementation. Finally, we conclude with perspectives on the future of BNs in psychological research (27) and provide a comprehensive guide to software tools for structural and parameter learning (28).