The structure of the Autism Diagnostic Interview-Revised: diagnostic and phenotypic implications

Area of Science:

• Psychiatry
• Developmental Psychology
• Biostatistics

Background:

• Pervasive developmental disorders (PDD) diagnosis benefits from multivariate statistics.
• The Autism Diagnostic Interview-Revised (ADI-R) is a key PDD assessment tool.
• Investigating the ADI-R's factor structure and its relation to other functioning measures is crucial.

Purpose of the Study:

• To examine the factor structure of the Autism Diagnostic Interview-Revised (ADI-R).
• To assess the convergence of ADI-R scores with measures of adaptive, language, and intellectual functioning.
• To explore the utility of statistical models in refining PDD nosology.

Main Methods:

• Confirmatory and exploratory factor analyses (CFA, EFA) were performed on ADI-R scores from 1,861 individuals (ages 4-18) with PDD.
• Analyses were stratified by verbal status (verbal vs. nonverbal) and item set (algorithm vs. all items).
• ADI-R scores were correlated with adaptive, language, and intellectual functioning measures.

Main Results:

• Confirmatory factor analyses indicated that two- and three-factor solutions were superior to a one-factor solution.
• Exploratory factor analyses and internal consistency measures supported a two-factor model: social/communication behaviors and restricted/repetitive behaviors.
• Functioning measures correlated with ADI-R scores in verbal children but not in nonverbal children.

Conclusions:

• Autism symptomatology can be statistically represented by a two-domain model.
• Specific symptoms identified by this model may be valuable for genetic linkage analyses.
• The findings support the implications of a two-factor model for understanding and researching autism spectrum disorder.