Network analysis conceptualizes psychopathology as systems of symptoms that interact and influence each other. It is hypothesized that network analysis can identify core symptoms relevant to the diagnosis and treatment of the disorder. We applied network analysis to avoidant personality disorder DSM-5 diagnostic criteria to identify such symptoms in a non-clinical and clinical sample (N = 718, N = 354). We estimated the networks as unregularized Ising models by fitting a log-linear model to each sample. Further on, we examined centrality indices, network stability, and normalized accuracy to determine which nodes are more central amongst avoidant personality disorder diagnostic criteria. “Fear of criticism and rejection” and “Certainty of being liked” emerged as the most central nodes in both networks. Symptom “Inferiority” had the lowest centrality levels. Results are discussed in terms of implications for the conceptualization of avoidant personality disorder and similarities with other studies that focused on DSM-5 criteria.

Dataset for: Marian, Ș., Sava, F. A., & Dindelegan, C. (2022). A network analysis of DSM-5 avoidant personality disorder diagnostic criteria. Personality and Individual Differences, 188, 111454. https://doi.org/10.1016/j.paid.2021.111454