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Bayesian network structure and predictability of autistic traits
Briganti,Giovanni Williams,Donald R. Mulder,Joris Linkowski,Paul
Briganti,Giovanni
Williams,Donald R.
Mulder,Joris
Linkowski,Paul
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.
Description
Date
2022
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Volume Title
Publisher
Research Projects
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Keywords
ASPERGER-SYNDROME, Autism, DISORDERS, FMRI, FUNCTIONING AUTISM, MEDIAL PREFRONTAL CORTEX, POPULATION, RELIABILITY, SPECTRUM QUOTIENT AQ, VERSION, network analysis, students
Citation
Briganti, G, Williams, D R, Mulder, J & Linkowski, P 2022, 'Bayesian network structure and predictability of autistic traits', Psychological Reports, vol. 125, no. 1, pp. 344-357. https://doi.org/10.1177/0033294120978159