Bayesian Network Structure and Predictability of Autistic Traits. Issue 1 (February 2022)

• Record Type: Journal Article
• Authors: Briganti, Giovanni; Williams, Donald R.; Mulder, Joris; Linkowski, Paul
• Is Part Of: Psychological reports. Volume 125:Issue 1(2022)
• Page Start: 344
• Page End: 357
• Publication Date: 2022-02
• Subjects: Autism -- network analysis -- students; Psychology -- Periodicals; Psychiatry; Psychology; Psychologie -- Périodiques; Psychology; 150.5
• DOI: 10.1177/0033294120978159
• Languages: English
• ISSNs: 0033-2941

Abstract

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.