Drivers of Hiring Discrimination for Individuals with Disabilities

McMahon, Brian T.; Hurley, Jessica; Chan, Fong; Rumrill, Philip D.; Roessler, Richard T.

doi:10.1007/s10926-008-9136-1

Cited by 11 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As noted previously, the use of classification trees are increasingly being used in rehabilitation and disability research to better understand factors that influence eligibility, services, and outcomes (F. Chan et al, 2005Chan et al, , 2006Kolakowsky-Hayner, 2010;McMahon et al, 2008;West et al, 2008). To our knowledge, this study is the first use of this analysis method using the NLTS2 data.…”

Section: Discussionmentioning

confidence: 99%

“…Chan, Wong, Rosenthal, Kundu, and Dutta (2005) and Kolakowsky-Hayner (2010) utilized classification trees in assessing the characteristics that influence eligibility for the VR program. Classification trees were used by McMahon, Hurley, Chan, Rumrill, and Roessler (2008) and West, Campbell, McMahon, and Davis (2008) to model discrimination claims under the Americans with Disabilities Act using the national claims database maintained by the Equal Employment Opportunity Commission (EEOC).…”

mentioning

confidence: 99%

See 1 more Smart Citation

An Evaluation of Risk Factors Related to Employment Outcomes for Youth With Disabilities

Sima

Wehman

Chan

et al. 2014

Career Development and Transition for Exceptional Individuals

Self Cite

View full text Add to dashboard Cite

This study explores non-modifiable risk factors associated with poor post-school competitive employment outcomes for students with disabilities. A classification tree analysis was used with a sample of 2,900 students who were in the second National Longitudinal Transition Study–2 (NLTS2) up to 6 years following school exit to identify groups of youth with poor employment outcomes based on characteristics that are unable to be modified through an intervention. Post-school employment was related to the disability characteristics of the student (disability type, ability to understand what is being said, general health status), family characteristics (yearly income, community setting), and youth characteristics (race).The classification tree analysis identified four groups of students who were at greatest risk for poor outcomes. Future research will be directed toward modifiable variables that mitigate risks for those groups that may potentially lead to school- and home-based interventions.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

An Evaluation of Risk Factors Related to Employment Outcomes for Youth With Disabilities

Sima

Wehman

Chan

et al. 2014

Career Development and Transition for Exceptional Individuals

Self Cite

View full text Add to dashboard Cite

show abstract

“…To our knowledge, this study is the first use of this analysis method to further understand employment barriers and facilitators from the perspective of caregivers of autistic individuals. Those conducting disability research are increasingly using classification trees to better understand the factors that influence eligibility, services, and outcomes (Chan et al, 2006;Kolakowsky-Hayner, 2010;Hurley et al, 2008). The classification tree methodology highlights key variables that pertain to helping to prepare individuals for post-secondary life, and, in turn, improved employment outcomes.…”

Section: Discussionmentioning

confidence: 99%

Enhancing employment outcomes for autistic youth: Using machine learning to identify strategies for success

Griffiths

Hurley‐Hanson

Giannantonio

et al. 2023

JVR

View full text Add to dashboard Cite

BACKGROUND: The employment rates of autistic young adults continue to be significantly lower than that of their neurotypical peers. OBJECTIVE: Researchers in this study sought to identify the barriers and facilitators associated with these individuals’ transition into the workforce to better understand how educators and stakeholders can support students’ post-secondary career plans. METHODS: Investigators used a classification tree analysis with a sample of 236 caregivers of autistic individuals or the individuals themselves, who completed an online survey. RESULTS: The analysis identified critical factors in predicting successful employment for respondents 21 years and under and those over 21 years old. These factors included: difficulties in the job search process, challenges with relationships at work, resources used, job maintenance, motivation to work, and the application process. CONCLUSION: These findings represent the first use of machine learning to identify pivotal points on the path to employment for autistic individuals. This information will better prepare school-based professionals and other stakeholders to support their students in attaining and maintaining employment, a critical aspect of achieving fulfillment and independence. Future research should consider the perspectives of other stakeholders, including employers, and apply the findings to the development of interventions.

show abstract

“…The study assesses the selected work incentive and suggests a possible methodology for its impact valuation (on employment outcomes) employing CHAID analysis, i.e., the same method applied by us. By applying the same procedure in the same labour market, the methods of discrimination against the employment of jobseekers with disabilities were investigated in the study by [27]. This type of jobseeker represents a part of the labour force that constitutes a specific part of the total unemployed population in each economy; it is also characteristic for Slovakia.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Ensemble Modelling for Predicting Unemployment Duration

Gabrikova,

Svabova,

Kramarova

2023

Applied Sciences

View full text Add to dashboard Cite

Predictions of the unemployment duration of the economically active population play a crucial assisting role for policymakers and employment agencies in the well-organised allocation of resources (tied to solving problems of the unemployed, whether on the labour supply or demand side) and providing targeted support to jobseekers in their job search. This study aimed to develop an ensemble model that can serve as a reliable tool for predicting unemployment duration among jobseekers in Slovakia. The ensemble model was developed using real data from the database of jobseekers (those registered as unemployed and actively searching for a job through the Local Labour Office, Social Affairs, and Family) using the stacking method, incorporating predictions from three individual models: CART, CHAID, and discriminant analysis. The final meta-model was created using logistic regression and indicates an overall accuracy of the prediction of unemployment duration of almost 78%. This model demonstrated high accuracy and precision in identifying jobseekers at risk of long-term unemployment exceeding 12 months. The presented model, working with real data of a robust nature, represents an operational tool that can be used to check the functionality of the current labour market policy and to solve the problem of long-term unemployed individuals in Slovakia, as well as in the creation of future government measures aimed at solving the problem of unemployment. The measures from the state are financed from budget funds, and by applying the appropriate model, it is possible to arrive at the rationalization of the financing of these measures, or to specifically determine the means intended to solve the problem of long-term unemployment in Slovakia (this, together with the regional disproportion of unemployment, is considered one of the most prominent problems in the labour market in Slovakia). The model also has the potential to be adapted in other economies, taking into account country-specific conditions and variables, which is possible due to the data-mining approach used.

show abstract

Drivers of Hiring Discrimination for Individuals with Disabilities

Abstract: Providers of training and technical assistance regarding hiring and disability may be able to adjust their services accordingly on the basis of findings such as these.

Cited by 11 publications

References 4 publications

An Evaluation of Risk Factors Related to Employment Outcomes for Youth With Disabilities

An Evaluation of Risk Factors Related to Employment Outcomes for Youth With Disabilities

Enhancing employment outcomes for autistic youth: Using machine learning to identify strategies for success

Machine Learning Ensemble Modelling for Predicting Unemployment Duration

Contact Info

Product

Resources

About