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Cluster analysis of social competence profiles of autistic children, using the parent report version of the Multidimensional Social Competence Scale (MSCS)

Resource type
Thesis type
(Thesis) M.A.
Date created
2022-06-27
Authors/Contributors
Author (aut): Mount, Vivienne
Abstract
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.
Document
Extent
93 pages.
Identifier
etd22082
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor (ths): Iarocci, Grace
Language
English
Member of collection
Download file Size
etd22082.pdf 2.38 MB

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