The focus of this study was to explore the impact of challenging behaviors on Applied Behaviors Analysis treatment in Autism Spectrum Disorder. The prevalence of ASD is on the rise, so it is important that we understand how patients are responding to treatment. In this study, we cluster patients (N=854) based on their eight observed challenging behaviors using k-means, a machine learning algorithm, and then perform a multiple linear regression analysis to find significant differences between average exemplars mastered. The goal of this study was to expand the research in the area of ABA treatment for ASD and to help provide more insight helpful for creating personalized therapeutic interventions with maximum efficacy, minimum time and minimum cost for individuals.
| Identifer | oai:union.ndltd.org:chapman.edu/oai:digitalcommons.chapman.edu:cads_theses-1003 |
| Date | 29 May 2019 |
| Creators | Hoag, Juliana |
| Publisher | Chapman University Digital Commons |
| Source Sets | Chapman University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Computational and Data Sciences (MS) Theses |
| Rights | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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