Statistical Analysis of the Predictive Power of University Rankings on Job Performance

Abstract

In this paper, we investigate the impact of university ranking (UR) on employees’ job performance (JP) within the context of Saudi Arabia. A quantitative research design was employed, involving students and faculty graduates from universities with varying ranking levels who possessed labor market experience. Data were collected using a structured questionnaire administered via Google Forms, with responses recorded on a seven-point Likert scale. The collected data were analyzed using ADANCO software, applying a range of statistical techniques to ensure measurement accuracy. Reliability and validity were assessed using Cronbach’s alpha, convergent validity, and discriminant validity (Fornell-Larcker criterion). Additionally, the coefficient of determination (R² and adjusted R²) and path coefficients were examined to evaluate the model’s predictive strength. The findings indicate that job performance is positively and significantly influenced by university ranking. Key attributes of university ranking—namely teaching quality, academic reputation, and student satisfaction—were found to be significant predictors of job performance dimensions, including knowledge, motivation, and leadership. Although the study is not exhaustive, it provides practical empirical evidence on the predictive capacity of university ranking, an area that has been underexplored in prior literature.

Keywords: University Ranking, Job Performance, Coefficient of Determination, Convergent Validity, Path Coefficient