Date of Award

Spring 5-29-2019

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computational and Data Sciences

First Advisor

Erik J. Linstead, Ph.D.

Second Advisor

Elizabeth Stevens, Ph.D.

Third Advisor

Dennis Dixon, Ph.D.

Abstract

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.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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