Autism Spectrum Disorders (ASD) are a class of neurodevelopmental disorders which usually present with difficulties in social interactions, verbal and nonverbal forms of communication, repetitive behaviors, and restricted interests. Employment rates of young adults with ASD is a national concern, and research suggests that young adults with “high functioning” ASD experience significant difficulty in transitioning to work. One of the goals of this study was to identify the barriers associated with these individuals’ transition into the world of work. A classification tree analysis was used with a sample of 236 caregivers of individuals with ASD or the individuals themselves, who completed an online survey. The analysis identified key factors in predicting successful employment for individuals 21 years and under as well as for those over 21 years old. While there are several guides that describe the Americans with Disabilities Act (ADA) requirements for employers looking to hire those with disabilities, the academic literature describing actual current employer programming to support employees with disabilities is scarce. With the ultimate goal of understanding shortcomings in employment practices that can be improved through a combination of educational programs and changes to corporate culture, this study also utilizes K-Means clustering and a classification decision tree to explore the policies and practices of 285 employers with regard to ASD.
","abstract_html":"<p>Autism Spectrum Disorders (ASD) are a class of neurodevelopmental disorders which usually present with difficulties in social interactions, verbal and nonverbal forms of communication, repetitive behaviors, and restricted interests. Employment rates of young adults with ASD is a national concern, and research suggests that young adults with “high functioning” ASD experience significant difficulty in transitioning to work. One of the goals of this study was to identify the barriers associated with these individuals’ transition into the world of work. A classification tree analysis was used with a sample of 236 caregivers of individuals with ASD or the individuals themselves, who completed an online survey. The analysis identified key factors in predicting successful employment for individuals 21 years and under as well as for those over 21 years old. While there are several guides that describe the Americans with Disabilities Act (ADA) requirements for employers looking to hire those with disabilities, the academic literature describing actual current employer programming to support employees with disabilities is scarce. With the ultimate goal of understanding shortcomings in employment practices that can be improved through a combination of educational programs and changes to corporate culture, this study also utilizes K-Means clustering and a classification decision tree to explore the policies and practices of 285 employers with regard to ASD.</p>","abstract_has_math":false,"creators":["Hyde, Kayleigh"],"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. Amy-Jane Griffiths","Dr. Elizabeth Stevens"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-01T08:00:00Z","date_published":"2020-01-01T08:00:00Z","updated_at":"2026-07-24T01:38:09Z","subjects":["Machine Learning","Data Science","Autism Spectrum Disorders","Employment","Other Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/5","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. Amy-Jane Griffiths","Dr. Elizabeth Stevens"]},{"key":"dc:creator","label":"Author","values":["Hyde, Kayleigh"]}]},{"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","Data Science","Autism Spectrum Disorders","Employment","Other Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/5"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Autism Spectrum Disorders (ASD) are a class of neurodevelopmental disorders which usually present with difficulties in social interactions, verbal and nonverbal forms of communication, repetitive behaviors, and restricted interests. Employment rates of young adults with ASD is a national concern, and research suggests that young adults with “high functioning” ASD experience significant difficulty in transitioning to work. One of the goals of this study was to identify the barriers associated with these individuals’ transition into the world of work. A classification tree analysis was used with a sample of 236 caregivers of individuals with ASD or the individuals themselves, who completed an online survey. The analysis identified key factors in predicting successful employment for individuals 21 years and under as well as for those over 21 years old. While there are several guides that describe the Americans with Disabilities Act (ADA) requirements for employers looking to hire those with disabilities, the academic literature describing actual current employer programming to support employees with disabilities is scarce. With the ultimate goal of understanding shortcomings in employment practices that can be improved through a combination of educational programs and changes to corporate culture, this study also utilizes K-Means clustering and a classification decision tree to explore the policies and practices of 285 employers with regard to ASD.
"]},{"key":"dc:source","label":"Dc Source","values":["K. Hyde, \"Exploring the employment landscape for individuals with Autism Spectrum Disorders using supervised and unsupervised machine learning\", Ph.D. dissertation, Chapman University, Orange, CA, Year. https://doi.org/10.36837/chapman.000116"]},{"key":"dc:title","label":"Title","values":["Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning"]}]}],"canonical_facts":{"dc:contributor":["Dr. Erik Linstead","Dr. Amy-Jane Griffiths","Dr. Elizabeth Stevens"],"dc:creator":["Hyde, Kayleigh"],"dc:description.abstract":["Autism Spectrum Disorders (ASD) are a class of neurodevelopmental disorders which usually present with difficulties in social interactions, verbal and nonverbal forms of communication, repetitive behaviors, and restricted interests. Employment rates of young adults with ASD is a national concern, and research suggests that young adults with “high functioning” ASD experience significant difficulty in transitioning to work. One of the goals of this study was to identify the barriers associated with these individuals’ transition into the world of work. A classification tree analysis was used with a sample of 236 caregivers of individuals with ASD or the individuals themselves, who completed an online survey. The analysis identified key factors in predicting successful employment for individuals 21 years and under as well as for those over 21 years old. While there are several guides that describe the Americans with Disabilities Act (ADA) requirements for employers looking to hire those with disabilities, the academic literature describing actual current employer programming to support employees with disabilities is scarce. With the ultimate goal of understanding shortcomings in employment practices that can be improved through a combination of educational programs and changes to corporate culture, this study also utilizes K-Means clustering and a classification decision tree to explore the policies and practices of 285 employers with regard to ASD.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/5"],"dc:source":["K. Hyde, \"Exploring the employment landscape for individuals with Autism Spectrum Disorders using supervised and unsupervised machine learning\", Ph.D. dissertation, Chapman University, Orange, CA, Year. https://doi.org/10.36837/chapman.000116"],"dc:subject":["Machine Learning","Data Science","Autism Spectrum Disorders","Employment","Other Computer Sciences"],"dc:title":["Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning"],"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"}