Measurement equivalence of the autism symptom phenotype in children and youth

Abstract

Background: The Autism Diagnostic Interview-Revised (ADI-R) is a gold standard assessment of Autism Spectrum Disorder (ASD) symptoms and behaviours. A key underlying assumption of studies using the ADI-R is that it measures the same phenotypic constructs across different populations (i.e., males/females, younger/older, verbal/nonverbal). The objectives of this study were to evaluate alternative measurement models for the autism symptom phenotype based on the ADI-R algorithm items and to examine the measurement equivalence of the most parsimonious and best fitting model across subgroups of interest.

Methods: Data came from the Autism Genome Project consortium and consisted of 3,628 children aged 4-18 years (84.2% boys and 75% verbal). Twenty-eight algorithm items applicable to both verbal and nonverbal participants were used in the analysis. Measurement equivalence of the autism phenotype was examined using categorical confirmatory factor analysis.

Results: A second-order model resembling the proposed DSM-5 two-factor structure of the phenotype showed good overall fit, but not for all the subgroups. The autism symptom phenotype was best indexed by the first-order, six-factor measurement model proposed by Liu et al. (2011). This model was well fitting and measurement equivalent across subgroups of participants (age, verbal ability and sex).

Conclusions: The autism symptom phenotype is adequately characterized by a six-factor measurement model; this model appears to be measurement equivalent across subgroups of children and youth with ASD that differ in age, sex and verbal ability. The two-factor model provides equally good fit for the sample as a whole, but comparison of these two dimensions between subgroups that might differ in terms of age, sex or verbal ability is challenged by lack of measurement equivalence.

Keywords: Autism Diagnostic Interview-Revised; autism spectrum disorder; confirmatory factor analysis; measurement equivalence; structural equation modelling.