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dc.contributor.advisorBurns, Gary
dc.contributor.authorKumar, Lavanya Shravan
dc.date.accessioned2020-05-21T04:37:54Z
dc.date.available2020-05-21T04:37:54Z
dc.date.created2020-05
dc.date.issued2020-05
dc.date.submittedMay 2020
dc.identifier.urihttp://hdl.handle.net/11141/3126
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2020.en_US
dc.description.abstractWorkplace 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.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCC BY 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectSafetyen_US
dc.subjectRisken_US
dc.subjectOccupationen_US
dc.subjectAccidentsen_US
dc.subjectInjuryen_US
dc.subjectIllnessen_US
dc.subjectO*NETen_US
dc.subjectMachine learningen_US
dc.titleDeterminants of Safety Outcomes in Organizations: Exploring O*NET Data to Predict Occupational Accident Ratesen_US
dc.typeThesisen_US
dc.date.updated2020-05-11T14:26:18Z
thesis.degree.nameMaster of Science in Industrial-Organizational Psychologyen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineIndustrial/Organizational Psychologyen_US
thesis.degree.departmentPsychologyen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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