Underground mining is a high-risk industry with a history of frequent accidents and deaths. The purpose of this study is to identify cognitive and psychomotor factors that may predict, and ultimately be used to prevent injuries. More specifically, I tested the extent to which the Raven's Progressive Matrices, a measure of cognitive ability, and the Vienna Test System, a measure of psychomotor ability, predicted injury - It was hypothesized that the Raven's scores would explain additional unique variance beyond the psychomotor scores alone. The results show that the Raven's scores were significantly predictive of Serious Injuries when analyzed in isolation, however, the scores did not explain unique variance when analyzed with other psychomotor variables. Models were established for predicting injuries across three injury levels (Dressing Case, Lost Time, and Serious Injury). Expected increases in accuracy of predicting were identified and translated into expected cost savings for the organization studied.
First Committee Member
Second Committee Member
Date of Degree
Master of Arts (MA)
Social and Behavioral Sciences
Aguilera-Vanderheyden, Rachel, "Selection System Prediction Of Safety: A Step Toward Zero Accidents In South African Mining" (2013). All Theses, Dissertations, and Other Capstone Projects. 145.
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