Akademik Veri Yönetim Sistemi
Social Sciences and Humanities Open, cilt.12, 2025 (Scopus)
The increasing complexity and competitiveness of global higher education necessitate the development of new approaches to rank and differentiate universities, even in relatively specialized fields such as Statistics and Operational Research. This study focuses on the top 250 institutions in this field and proposes a comprehensive ranking analysis framework. First, the impact ratios of basic performance variables, including academic dignity, employer dignity, citation counts, and H-index, are calculated using p,q-Quasirung Orthopair Fuzzy Sets (p,q-QOFSs) operators. Subsequently, universities are classified according to their performance using the K-means clustering method. Following this stage, the clustered universities are evaluated through the ALWAS (Aczel-Alsina Weighted Assessment) methodology to obtain a transparent and reliable ranking. The validity of the presented method was tested with sensitivity analysis. Two performance-based clusters were identified: one with 78 top-tier and another with 172 lower-ranked universities. Reputation indicators explained 54 % of the variance, research-oriented metrics 46 %. The ALWAS results were consistent with QS, placing MIT, Stanford, and Harvard at the top. By employing advanced multi-criteria decision-making (MCDM) techniques, this study contributes to the growing body of literature on university rankings and serves as a strategic resource for institutions seeking to enhance their international visibility and competitiveness. The proposed framework offers valuable insights for university administrators, policymakers, and prospective students into the key determinants of excellence in the fields of Statistics and Operations Research.