There is marked variability in the presentation of core diagnostic features across autistic individuals. This poses a significant obstacle for research design and interpretability, as well as for understanding clinical outcomes. Cluster Analysis (CA) has been used to divide large autistic populations into smaller, more homogeneous subgroups on selected criteria. Social Competence (SC) is a promising variable for identifying autistic subtypes within the domain of social functioning. The present study aimed to extract homogenous SC-based subgroups from a sample of 78 autistic youth (13 females) aged 6-13 (mean = 9.8, SD = 1.75) of average intellectual functioning. Five CAs were conducted on select subsets of participant's Multidimensional Social Competence Scale (MSCS) profiles, and One-way Analyses of Variance (ANOVAs) were conducted to examine the MSCS-related variance within and between clusters. Between-cluster differences in Social Responsiveness Scale, 2nd Edition (SRS-2) scores, IQ, age, and gender were also examined. One cluster solution resulted in a "low", "medium" and "high" scoring cluster, while the remaining cluster solutions resulted in more complex SC profiles. Results show that heterogeneous autistic populations can successfully be divided into more socially homogeneous subgroups using CA techniques. The continued conceptualization of homogeneous autistic subgroups may be used to advance the development of individualized interventions, and further our understanding of between-group differences within autism.
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Thesis advisor: Iarocci, Grace
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