Testing the Latent Structure of the Autism Spectrum Quotient in a Sub-clinical Sample of University Students Using...

Area of Science:

• Psychology
• Psychometrics
• Quantitative Psychology

Background:

• Autism Spectrum Quotient (AQ) is widely used to assess autistic traits.
• Population heterogeneity in autism spectrum disorder (ASD) is well-established at clinical levels.
• Understanding sub-clinical heterogeneity is crucial for comprehensive trait assessment.

Purpose of the Study:

• To investigate the latent structure of the Autism-Spectrum Quotient (AQ) using factor mixture models (FMMs).
• To identify distinct subgroups (latent classes) within a non-clinical undergraduate population based on AQ scores.
• To explore the interplay between latent classes and common factors within these subgroups.

Main Methods:

• Utilized factor mixture models (FMMs), integrating latent-class and common-factor approaches.
• Analyzed data from 633 undergraduate students using the 50-item Autism-Spectrum Quotient (AQ).
• Examined population heterogeneity by identifying distinct latent classes and their factor structures.

Main Results:

• Findings indicated the presence of either two or six distinct latent classes within the sample.
• Each latent class exhibited unique response profiles across the 50 AQ items.
• Within each identified class, individuals showed further differentiation based on five latent factors.

Conclusions:

• The study demonstrates significant phenotypical heterogeneity at the sub-clinical level of autism traits.
• Factor mixture models effectively capture complex population structures in psychometric data.
• Results highlight the importance of considering nuanced subgroup differences in AQ research and interpretation.