Date of Award

5-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Psychology

First Advisor

Gary Burns

Second Advisor

Patrick Converse

Third Advisor

Debasis Mitra

Fourth Advisor

Lisa Steelman

Abstract

Workplace safety is of utmost importance given the regular occurrence of both fatal and nonfatal occupational injuries all around the world. Although research in this area is hugely prevalent, it is focused mainly on safety climate and lacks an integrated approach when examining predictors of safety outcomes. The development of an occupational risk factor that predicts safety outcomes will aid in understanding the relative importance of different factors that contribute to safety and help organizations target their safety programs and interventions efficiently. The present study is an exploratory analysis utilizing publicly available O*NET data (work activities, work context features, and worker characteristics) to predict annual occupational injury and illness incident rates (nonfatal) published by the U.S. Bureau of Labor Statistics. The use of statistical learning methods (LASSO, random forest, and gradient boosting) for analysis using Python also helped compare results to those obtained by past research utilizing traditional statistical methods. Findings indicate that the O*NET descriptors related to work, work context, and to a lesser extent worker characteristics were indeed significant in predicting nonfatal occupational injury/incident rates. The amount of variance explained in the outcome by the predictors varied from 27.8% (gradient boosting) to 33.1% (random forest) with 19 unique predictors across the three machine learning methods. This study adds to the literature surrounding person and situation-based antecedents to workplace safety, presents a huge step toward the development of a cross-occupational risk factor, and has several other implications for research and practice.

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