Article Details

Research Database: Article Details

Citation:  Dong, S., Fabian, E., & Luecking, R. G. (2015). Impact of school structural factors and student factors on employment outcomes for youth with disabilities in transition: A secondary data analysis. Rehabilitation Counseling Bulletin, 1-11.
Title:  Impact of school structural factors and student factors on employment outcomes for youth with disabilities in transition: A secondary data analysis
Authors:  Dong, S., Fabian, E., & Luecking, R. G.
Year:  2015
Journal/Publication:  Rehabilitation Counseling Bulletin
Publisher:  Hammill Institute on Disabilities
DOI:  https://doi.org/10.1177/0034355215595515
Full text:  http://journals.sagepub.com/doi/abs/10.1177/0034355215595515   
Peer-reviewed?  Yes
NIDILRR-funded?  Yes
Research design:  Database mining

Structured abstract:

Background:  Youth with disabilities experience high rates of unemployment as compared to their non disabled peers. A growing body of research has identified various individual, programmatic, and systemic factors associated with post school outcomes for youth with disabilities. A paid work experience is associated with post high school employment status. Research is limited about how school structural characteristics might influence employment outcomes for this group.
Purpose:  The purpose of this study was to examine school structural characteristics associated with employment outcomes for youth with disabilities. The research questions were to what extent do school structural factors contribute to getting a job for students with disabilities participating in a multi-state transition intervention and to what extent do school structural factors contribute to weekly job earnings for those students who secured jobs?
Study sample:  The sample was drawn from Bridges Program data set and included 3,289 students with disabilities from 121 high schools. The majority or 58.8% were male. Most 63.9% were African American. Students with learning disabilities were the largest group (73.1%). The remaining had other disabilities (20.2%) like chronic health impairments, orthopedic impairments, and other non specified; followed by emotional/behavioral disabilities (4.7%) or sensory impairments (2.0%).
Intervention:  There was no intervention.
Control or comparison condition:  There was no control or comparison condition.
Data collection and analysis:  Two dependent variables were collected from the Bridges data set. One was whether or not the student went to work, part or full time, during his tenure at the Bridges Program. About three quarters or (76.6%) of students secured a job. The second variable, mean weekly earnings, was $172.51; with an average of 22 hours worked per week. Eight student level prediction variables were collected: sex, ethnicity, receipt of SSI, prior paid work experience, certified significantly disabled by vocational rehabilitation's criteria, educational placement during school and Bridges project site. Type of disability was also available by categories: learning disabilities, emotional behavioral disability, sensory and others like orthopedic and other health impairments. Three school level variables were collected: percentage of minority students enrolled; combined free/reduced lunch percentage and student to teacher ratio. HLM was used in analysis and hierarchical generalized linear modeling was adopted for one variable. Regular HLM was use for the other dependent variable (weekly earnings). Prior to conducting the prediction model in HLM, the researchers conducted a fully unconditional model (FUM) which contained the dependent variable job placement outcomes or weekly earnings without student or school variables. Before the HLM analysis, chi square and regression analysis were conducted to understand data and variables to include in the HLM analysisl.
Findings:  The researchers found that the employment outcomes were related to student individual factors, rather an school structural factors, particularly prior paid work experience. Sex, disability, and prior work or paid work experience did influence whether or not the youth went to work and earned wages. The finding that school structural characteristics did not have a impact on employment was somewhat surprising.
Conclusions:  Youth with disabilities, especially low income minority youth, exhibit disparities in transition outcomes compared to non-minority more advantaged youth. Effective transition to work interventions can help improve post high school employment outcomes for youth with disabilities.

Disabilities served:  Autism / ASD
Blindness
Deafness
Dual sensory impairment
Hearing impairment
Learning disabilities
Orthopedic impairments
Speech or language impairment
Visual impairment
Populations served:  Gender: Female and Male
Transition-age youth (14 - 24)
Outcomes:  Employment acquisition