In spite of all the efforts in different industries to reduce the number of undesirable accidents, a lot of events always threaten industrial societies. These events often cause huge damages to the environment, facilities and even in some cases, fatalities and disabilities for people. Therefore, it is important to analyse the numerous variables associated with industrial accident considerations and the inter play among these variables. This paper adopts a novel combination of two statistical methods to analyse the various hazards factors in the most important sector of the economy. In this analysis, critical hazards variables were identified and classified based on HSE standards OSHA. Kendall’s Coefficient of Concordance (KCC ) and Principal Component Analysis (PCA) was employed to analyse the set of data generated from respondents and summarize them into a number of factors that would promote occupational safety and health in industries. KCC was used to analyze data matrix generated by thirteen Judges who were requested to rank the thirty two identified hazards variables in merit order of sequentiality scaled with 5-point Rensis Likert’s attitudinal scale and administered to 22 respondents where only 13 were retrieved. The PCA aided by StatistiXL software package was proficient in achieving parsimony in factor reduction from thirty-two variables to mere five factors. The results obtained by KCC suggested that the judges ranking of the thirty two variables were consistent with index of concordance computed as W = 0.958. The result by PCA indicates that five factors creatively labelled: Work World Culture, Ground Rule Matters, Safety Considerations, Work Condition and Perception of Safety represent the principal factors that influence the industrial safety. It is hoped that this would help to unwrapped the deeper meanings associated with multi-dimensional factors in industrial safety.
Principal Component Analysis Hazards Industrial accident Concordance Hazards, Industrial accident, Concordance
Primary Language | English |
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Subjects | Engineering |
Journal Section | Research Articles |
Authors | |
Publication Date | January 31, 2022 |
Acceptance Date | December 24, 2021 |
Published in Issue | Year 2022 Volume: 4 Issue: 1 |
This work is licensed under a Creative Commons Attribution 4.0 International License