Analysis of determining factors in the QS world university ranking and their evolution over time

Abstract

University rankings serve as essential instruments for informing students regarding the quality of higher education institutions. This study analyzes the factor structure of the QS World University Ranking using principal component analysis to identify the primary factors underlying variability in university rankings during the period 2023-2025. The research was motivated by the exponential increase in higher education enrollment, which rose from 19% to 38% between 2000 and 2018, and the increasing significance of rankings as instruments for transparency and institutional comparison. The methodology comprised descriptive analysis, correlation assessment, reliability testing (Cronbach’s α = 0.838 and McDonald’s ω = 0.852), and exploratory factor analysis conducted across more than 1,400 universities. Components were extracted using the eigenvalue criterion and explained variance, followed by a criteria refinement process that eliminated variables with low factor loadings. The analysis was conducted using RStudio and Python. The results identified two main components that were consistently present across the three years analyzed: Reputation, which integrates academic reputation, employability, and research productivity, and International Diversity, associated with the proportion of international students and faculty. Following methodological refinement, these factors accounted for between 70% and 72.8% of the total variance, demonstrating remarkable temporal stability in the factor structure. These findings provide a clear understanding of the variables associated with university excellence and offer valuable insights for students, university administrators, and higher education researchers. Furthermore, the longitudinal consistency of the model reinforces its validity and applicability in future evaluations of global university rankings.