Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling

Rachel Grove*, Andrew Baillie, Carrie Allison, Simon BaronCohen, Rosa A. Hoekstra

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Background Autism research has previously focused on either identifying a latent dimension or searching for subgroups. Research assessing the concurrently categorical and dimensional nature of autism is needed. Aims To investigate the latent structure of autism and identify meaningful subgroups in a sample spanning the full spectrum of genetic vulnerability. Method Factor mixture models were applied to data on empathy, systemising and autistic traits from individuals on the autism spectrum, parents and general population controls. Results A twofactor threeclass model was identified, with two factors measuring empathy and systemising. Class one had high systemising and low empathy scores and primarily consisted of individuals with autism. Mainly comprising controls and parents, class three displayed high empathy scores and lower systemising scores, and class two showed balanced scores on both measures of systemising and empathy. Conclusions Autism is best understood as a dimensional construct, but meaningful subgroups can be identified based on empathy, systemising and autistic traits.

Original languageEnglish
Pages (from-to)400-406
Number of pages7
JournalBritish Journal of Psychiatry
Volume207
Issue number5
DOIs
Publication statusPublished - 1 Nov 2015

Fingerprint Dive into the research topics of 'Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling'. Together they form a unique fingerprint.

Cite this