Abstract
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.
Advisor
Daniel Sachau
Committee Member
Kristie Campana
Committee Member
Kathleen Dale
Date of Degree
2013
Language
english
Document Type
Thesis
Degree
Master of Arts (MA)
Department
Psychology
College
Social and Behavioral Sciences
Recommended Citation
Aguilera-Vanderheyden, R. (2013). Selection System Prediction Of Safety: A Step Toward Zero Accidents In South African Mining [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/145/
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