Structural Equation Modeling Modeling (SEM) of Occupational Accidents Size Based on Risk Management Factors; A Field Study in Process Industries

Abstract

Background: Due to high risks for occupational accidents, process industries are one of the most dangerous industries around the world. Accidents' size in these industries are influenced by combination of different factors. Objectives: The present study aimed at analyzing and modeling occupational accidents' size and investigating the role of different risk management factors on accidents' size in process industries. Methods: This analytical study was carried out on accidents in ten process industries, including petrochemicals, refineries, and chemical industries during eight years (2008 to 2015). Studied data were included variables and factors of risk management systems and information about human injuring accidents. Data analysis and modeling were done based on feature selection by Pearson χ 2 coefficient and structural equation modeling (SEM) approach using statistical software of IBM SPSS AMOS v22.0. Results: Lost working days (LWD) as index of accident size was estimated 197.42 ± 111.06 days. Results of feature selection and SEM approach showed that LWD was affected by different factors such as safety and health (S and H) training, risk management, and risk control, and its indicator variables (P < 0.05). Conclusions: The findings implied that structural equation modeling is a reliable and applicable accidents analysis method. Furthermore, the results should be considered to prevent and reduce occupational accidents' size in process industries.

Association of Non-Organizational Factors and Occupational Accidents: A Field Study based on Structural Equation Modeling

“…Therefore, since several independent variables exist that should be examined for their effect on dependent variables, structural equation modeling is necessary. Also, the goodness of fit of the models extracted and inferred from structural equation modeling using general indices, including χ2/df (2-3) and RMSEA (0.05-0-08), and comparative indices, including GFI, CFI, NFI, and NNFI or TLI (0.95-1.0), is evaluated [16,17].…”

Incidence investigation of accidents in chemical industries: A comprehensive study based on factor analysis

“…Also, the group contribution of coherent indicator variables in latent factors can be evaluated. 2,8,18 Furthermore, investigating the relationships between latent factors aided the researchers in designing and implementing a comprehensive, rational, and applicable conceptual model for this study. For example, the appropriate.…”

“…The effect of each latent group (and its indicator variables) can be determined using multiple regression analysis. Also, the group contribution of coherent indicator variables in latent factors can be evaluated 2,8,18 . Furthermore, investigating the relationships between latent factors aided the researchers in designing and implementing a comprehensive, rational, and applicable conceptual model for this study.…”