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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Hoag, Juliana"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Erik J. Linstead, Ph.D.","Elizabeth Stevens, Ph.D.","Dennis Dixon, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-05-29T07:00:00Z","date_published":"2019-05-29T07:00:00Z","updated_at":"2026-07-24T01:38:00Z","subjects":["Autism","Challenging Behaviors","Treatment","Machine Learning","Other Psychiatry and Psychology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_theses/4","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Erik J. Linstead, Ph.D.","Elizabeth Stevens, Ph.D.","Dennis Dixon, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Hoag, Juliana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Autism","Challenging Behaviors","Treatment","Machine Learning","Other Psychiatry and Psychology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_theses/4"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:source","label":"Dc Source","values":["J. Hoag, \"Exploring the impact of challenging behaviors on treatment efficacy in Autism Spectrum Disorder,\" M. S. thesis, Chapman University, Orange, CA, 2019. https://doi.org/10.36837/chapman.000077"]},{"key":"dc:title","label":"Title","values":["Exploring the Impact of Challenging Behaviors on Treatment Efficacy in Autism Spectrum Disorder"]}]}],"canonical_facts":{"dc:contributor":["Erik J. Linstead, Ph.D.","Elizabeth Stevens, Ph.D.","Dennis Dixon, Ph.D."],"dc:creator":["Hoag, Juliana"],"dc:description.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.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_theses/4"],"dc:source":["J. Hoag, \"Exploring the impact of challenging behaviors on treatment efficacy in Autism Spectrum Disorder,\" M. S. thesis, Chapman University, Orange, CA, 2019. https://doi.org/10.36837/chapman.000077"],"dc:subject":["Autism","Challenging Behaviors","Treatment","Machine Learning","Other Psychiatry and Psychology"],"dc:title":["Exploring the Impact of Challenging Behaviors on Treatment Efficacy in Autism Spectrum Disorder"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:38:00Z"}