1995
DOI: 10.1177/001440299506100506
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Predicting Employment for Students Who Leave Special Education High School Programs

Abstract: A hierarchical regression analysis featuring 35 community, family, student, and school program characteristics, entered in a controlled order, was used to assess prediction of employment. Results showed that personal characteristics (especially being male and having living skills and academic skills) dominated the prediction of postschool employment. Family income, our proxy for socioeconomic status, also contributed significantly to this prediction. School programs had minimal effect on postschool employment … Show more

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Cited by 69 publications
(81 citation statements)
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“…In linear regression models any joint prediction (mulitcolinearity) is assigned to the earlier block, so that variables in the earlier block function as control variables (covariates) for later blocks (Heal and Rusch, 1995).…”
Section: Discussionmentioning
confidence: 99%
“…In linear regression models any joint prediction (mulitcolinearity) is assigned to the earlier block, so that variables in the earlier block function as control variables (covariates) for later blocks (Heal and Rusch, 1995).…”
Section: Discussionmentioning
confidence: 99%
“…These factors place women, racial/ethnic minorities, and individuals with disabilities at particularly high risk for career choice problems. Relatedly, the best predictors of postschool employment for students with disabilities have been found to be personal and family characteristics (Heal & Rusch, 1995).…”
mentioning
confidence: 99%
“…Research indicates students who participated in occupational coursework had higher postsecondary education engagement (Halpern, Yovanoff, Doren, & Benz, 1995) and postsecondary employment (Heal & Rusch, 1995) than students who received no instruction in occupational courses.…”
Section: Occupational Coursesmentioning
confidence: 99%