Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR).
Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.
Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational.
","abstract_html":"<p>Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR).</p> <p>Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.</p> <p>Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational.</p>","abstract_has_math":false,"creators":["Rosenfield, Natalia"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Dr. Erik Linstead","Dr. Adrian Vajiac","Dr. Dave Frederick"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-01T07:00:00Z","date_published":"2020-08-01T07:00:00Z","updated_at":"2026-07-24T01:38:09Z","subjects":["machine learning","autism","autism spectrum disorder","eating disorders","anorexia","body image","Data Science","Psychology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/14","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Erik Linstead","Dr. Adrian Vajiac","Dr. Dave Frederick"]},{"key":"dc:creator","label":"Author","values":["Rosenfield, Natalia"]}]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["machine learning","autism","autism spectrum disorder","eating disorders","anorexia","body image","Data Science","Psychology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/14"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR).
Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.
Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational.
"]},{"key":"dc:source","label":"Dc Source","values":["N.S. Rosenfield, \"Gaining computational insight into psychological data: Applications of machine learning with eating disorders and autism spectrum disorder\", Ph.D. dissertation, Chapman University, Orange, CA, 2020. https://doi.org/10.36837/chapman.000182"]},{"key":"dc:title","label":"Title","values":["Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder"]}]}],"canonical_facts":{"dc:contributor":["Dr. Erik Linstead","Dr. Adrian Vajiac","Dr. Dave Frederick"],"dc:creator":["Rosenfield, Natalia"],"dc:description.abstract":["Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR).
Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.
Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/14"],"dc:source":["N.S. Rosenfield, \"Gaining computational insight into psychological data: Applications of machine learning with eating disorders and autism spectrum disorder\", Ph.D. dissertation, Chapman University, Orange, CA, 2020. https://doi.org/10.36837/chapman.000182"],"dc:subject":["machine learning","autism","autism spectrum disorder","eating disorders","anorexia","body image","Data Science","Psychology"],"dc:title":["Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:09Z"}