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Sergio Cuevas

The University of Texas Rio Grande Valley



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Sandra Hansmann

The University of Texas Rio Grande Valley



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Hansapani Rodrigo

The University of Texas Rio Grande Valley



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Shawn Saladin

The University of Texas Rio Grande Valley



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Barbara Schoen

The University of Texas Rio Grande Valley



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337 – SPEED: Methodological Developments in Social Statistics, Part 1

The Impact of Vocational Rehabilitation Services and Demographic Factors Towards the Successful Employment Outcomes Among Hard-of-Hearing Individuals

Sponsor: Social Statistics Section
Keywords: Hard of hearing, Employment outcome, Vocational Rehabilitation Services, CHAID, logistic regression

Sergio Cuevas

The University of Texas Rio Grande Valley

Sandra Hansmann

The University of Texas Rio Grande Valley

Hansapani Rodrigo

The University of Texas Rio Grande Valley

Shawn Saladin

The University of Texas Rio Grande Valley

Barbara Schoen

The University of Texas Rio Grande Valley

This study explored the relationship between demographic variables (gender, race and ethnicity, age, level of education, and secondary disability), and the impact from 28 state-federal vocational rehabilitation (VR) services, on having a successful employment outcome among consumers who are hard-of-hearing. The U.S. Department of Education Rehabilitation Service Administration Case Service Report fiscal year 2014 data was used for the analysis. According to the Chi-square tests, individual impact from all the VR services and the demographic factors were significant except gender (p-value: 0.084). As per the optimal Chi-square Automatic Interaction Detector (CHAID) model, use of vocational rehabilitation services was the most influential factor (with the highest mean decrease in accuracy 0.264) which contributes for a positive outcome. A binary logistic regression analysis reveals that when a consumer who is hard-of hearing received on-the-job supports-supported employment as a VR service, the estimated odds of achieving a successful employment outcome is 6.39 times the estimated odds for a consumer who did not receive this service after controlling for other factors in the model.

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