Showing 2 results for Factor Analysis
Ahmad Ghorbani, Ahmad Soltanzadeh,
Volume 3, Issue 1 (1-2019)
Abstract
Background: With the development of technology and its increased use, potential dangers in industrial environments has increased. The purpose of this study was to evaluate the attitudes of the Fajr Institute's Health & Safety Executive (HSE) personnel toward safety. Method: The study population consisted of HSE staff (n: 39). Data gathering tool in this descriptive-analytical study was a researcher-developed, 30-item questionnaire with acceptable validity (1.9) and reliability (0.87). Data were analyzed using the SPSS 19. Samples were selected by census sampling and the entire study population was studied. Statistical methods used were mean, variance, standard deviation, t-test, Pearson's correlation coefficient, and factor analysis. Result: The average age of the participants was 28.6 years, 56.5% of them were single and 43.5% married. The average work experience of the participants was 5.2 years. Bachelor's degree (54%) and Master's degree (23%) were the most and least frequent academic degrees, respectively. The Pearson correlation coefficients showed age and work experience were not correlated with safety attitudes. The t-test results showed there was a significant difference between the viewpoints of single and married workers and attitude variable (P<0.01).The t-test results also showed there was no significant difference between education level and attitude. Conclusion: According to the factor analysis results, items were classified into four categories: management factors, educational factors, communication factors, and regulatory factors.
Heidar Mohammadi, Hamidreza Heidari, Shahram Arsang Jang, Mona Ghafourian , Ahmad Soltanzadeh,
Volume 4, Issue 4 (10-2020)
Abstract
Background: Investigating the influence of various proactive factors on reactive indices in the chemical industries can result in providing preventive and control measures in these industries. This study was designed and conducted to measure the relationship between reactive and proactive safety indices in the chemical industry. Methods: This cross-sectional study was conducted in 2018 in 12 chemical industries. The study data were associated with a period of 5 years (2013-2017). Study data has been analyzed based on factor analysis using analytical software IBM SPSS AMOS v. 22.0. χ2 / df, RMSEA, CFI, NFI, and NNFA (TLI) indices were used to evaluate the model's goodness fit in this study. Results: The mean reactive indices of recurrence coefficient and accident severity in this study was 14.15(18.32) and 182.112(10.50) days, respectively. The exploratory factor analysis results determined that 16 indicator variables were categorized into 4 groups of proactive indices, including safety training, risk management, control of unsafe situations, and unsafe acts. Analyzing the confirmatory factor additionally confirmed that there is a significant relationship between the two groups of reactive and proactive indices in this study(P <0.05), and the goodness of model fit was also recognized appropriate (RMSEA = 0.055). Conclusion: This study's findings approved that the proactive indices affect the incidence and severity of accidents as safety reactive indices in the chemical industries. Also, the risk management proactive index and insecurity conditions were more effective than other indices.
