Authors - Mashrur Wasek, Prishita Taksalee, Faisal Quader Abstract - University rankings influence student enrollment decisions, institutional funding, and strategic planning, yet diagnostic analyses often conflate correlation with actionable drivers; for example, by including prior-year rank as a predictor, which exploits persistence rather than identifying genuine drivers of rank change. This study presents a reproducible, leakage-safe framework for diagnosing rank determinants using a 31-year panel (1996–2026) of 3,785 university-year observations across 191 U.S. institutions, constructed from College Scorecard administrative data and OpenAlex bibliometrics. The approach explicitly distinguishes diagnostic modeling—estimating expected rank from contemporaneous institutional attributes—from predictive modeling, which exploits temporal persistence. The framework integrates dual methodological lenses: a U.S. News–style observable composite and a Times Higher Education (THE)–style research/teaching proxy framework. Driver importance stability is quantified through year-by-year coefficient analysis, revealing that retention, graduation rate, and selectivity exhibit the highest magnitudes while Pell share, admission rate, and several secondary resource related variables show substantial temporal drift. Case studies of MIT, University of Maryland–College Park, and George Washington University demonstrate how residual analysis and feature decomposition translate into institution-specific improvement priorities. The framework provides a transparent, reproducible blueprint for understanding rank movements and identifying high-impact interventions.
