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Quantitative Analysis of Heterogeneity in Academic Achievement of Children With Autism
Author(s) -
Lang Chen,
Daniel A. Abrams,
Miriam RosenbergLee,
Teresa Iuculano,
Holly N. Wakeman,
Sandhya Prathap,
Tianwen Chen,
Vinod Me
Publication year - 2018
Publication title -
clinical psychological science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.74
H-Index - 47
eISSN - 2167-7034
pISSN - 2167-7026
DOI - 10.1177/2167702618809353
Subject(s) - psychology , autism spectrum disorder , autism , cognition , reading (process) , academic skills , cognitive psychology , developmental psychology , population , cognitive skill , intervention (counseling) , clinical psychology , mathematics education , linguistics , philosophy , psychiatry , demography , neuroscience , sociology
Autism spectrum disorders (ASD) represent a quintessential example of a clinical population with diverse symptom presentations and marked variation in cognitive abilities. However, the extent literature lacks rigorous quantitative procedures for characterizing heterogeneity of cognitive abilities in these individuals. Here we employ novel clustering and cross-validation procedures to investigate the stability of heterogeneous patterns of cognitive abilities in reading and math in a relatively large sample (N=114) of children with ASD and matched controls (N=96). Our analysis revealed a unique profile of heterogeneity in ASD, consisting of a low-achieving subgroup with poor math skills compared to reading, and a high-achieving subgroup who showed superior math skills compared to reading. Verbal and central executive working memory skills further differentiated these subgroups. Findings provide insights into distinct profiles of academic achievement in children with ASD, with implications for educational practice and intervention, and provide a novel framework for quantifying heterogeneity in the disorder.

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