Applying Logistic Regression Models to Predict Faculty Satisfaction at the University of Benghazi: An Applied Quantitative Study - Naeema Ali Abdelnabi (Journal of Scientific and Human Dimensions, 2026)
This study aimed to apply logistic regression models to predict the level of job satisfaction among faculty members at University of Benghazi , Suluq branch, the colleges targeted in the study [College of Arts and Sciences – College of Agriculture – College of Economics], by analyzing a set of demographic, occupational, institutional, and contextual factors affecting academic satisfaction. The study adopted a quantitative analytical approach, and data were collected using a questionnaire consisting of 13 items distributed across four main dimensions. A random sample of (116) faculty members from the University of Benghazi, Suluq branch, was selected, based on a population of 165 faculty members at the branch, and using the (Krejcie & Morgan ,1970) table to determine the sample size. SPSS v28 was used for data analysis through descriptive statistics, reliability tests, the Kolmogorov–Smirnov Test, and logistic regression models. The findings revealed a high overall level of job satisfaction among the participants, despite the existence of challenges related to limited research resources and technical infrastructure. The results also indicated that years of experience were the most influential variable in predicting satisfaction levels, whereas gender and age did not show statistically significant effects. Furthermore, the binary logistic regression model demonstrated good predictive effectiveness (Nagelkerke R² = 0.487, Classification Accuracy = 78.8%, Hosmer-Lemeshow p = 0.624), Indicating adequate model fit to the observed data. The study concluded that improving the academic work environment and enhancing administrative support and research resources are essential for increasing faculty satisfaction and improving the quality of higher education in Libyan universities.
Keywords: Logistic Regression; Job Satisfaction; Faculty Members; University of Benghazi; Higher Education; Statistical Analysis