Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning

<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>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year

2020

Author dc:creator

• Hyde, Kayleigh

Contributors dc:contributor

• Dr. Erik Linstead
• Dr. Amy-Jane Griffiths
• Dr. Elizabeth Stevens

• Machine Learning
• Data Science
• Autism Spectrum Disorders
• Employment
• Other Computer Sciences

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/5

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1005