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Successful vocational outcomes: A multilevel analysis of self-employment through U.S. vocational rehabilitation agencies

机译:成功的职业成果:通过美国职业康复机构进行的自营职业的多层次分析

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This study examined self-employment within the context of U.S. vocational rehabilitation (VR) to identify significant predictors of successful self-employment case closure, how these predictors changed over time, and whether there were differences in the likelihood of successful self-employment closure across states. To answer the research questions, five fiscal years (FYs) of RSA 911 data from 2003 to 2007, constituting more than a million cases, were analyzed using a two-level Hierarchical Linear Modeling (HLM). These years were selected because they occurred between the two most recent economic recessions. Statistically significant (p<0.001) predictors of successful self-employment closure across the FYs were: gender, ethnicity, cost of VR services, education attainment, and public supports. The only difference occurred in FY 2004, when significant-disability status was also a significant predictor. Among the significant predictors, ethnicity had the largest effect, followed by education attainment and gender. States were significantly different in their likelihood of successful self-employment closures. Analyses of additional, more recent years of RSA data using HLM with other predictors are warranted to draw more definitive conclusions and develop substantive theoretical explanations. Limitations and implications of this study for researchers and VR agencies are discussed in conclusion.
机译:这项研究在美国职业康复(VR)的背景下研究了自谋职业,以确定成功完成自谋职业的案例的重要预测因素,这些预测因素如何随着时间而变化,以及跨行业成功自谋职业的可能性是否存在差异状态。为了回答研究问题,我们使用两级层次线性模型(HLM)对2003年至2007年的五个会计年度(FY)的RSA 911数据进行了分析,构成了超过一百万个案例。选择这些年份是因为它们发生在最近两次经济衰退之间。在整个财政年度中,成功完成自雇工作的统计上显着(p <0.001)的预测指标是:性别,种族,虚拟现实服务的成本,受教育程度和公共支持。唯一的区别发生在2004财年,当时严重残疾状态也是一个重要的预测指标。在重要的预测因素中,种族影响最大,其次是教育程度和性别。各国成功关闭自营职业的可能性差异很大。使用HLM和其他预测因子对RSA数据进行分析,可以得出更明确的结论并提供实质性的理论解释。最后讨论了这项研究对研究人员和VR机构的局限性和意义。

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