Evaluating Modern Academic Research Performance and Quality using Quantitative Approaches and Statistical Indices

Abstract This study evaluates modern academic research performance and quality using quantitative approaches and statistical indices within a multi-disciplinary bibliometric framework. Adopting a cross-sectional design, the study analysed secondary bibliometric data for 120 academic researchers drawn equally from four disciplinary groupings: Science, Technology, Engineering, and Mathematics (STEM);SocialSciences; Business/Economics; and Humanities, over a five-year period (2018–2023) from Scopus and institutional repositories. Research performance was operationalized using a normalised outcome measure, while key predictors included publication count, citation count, h-index, collaboration index, degree of collaboration, and a composite productivity, impact, index and integrating collaboration dimensions. Descriptive analysis revealed substantial disciplinary variation, with STEM disciplines consistently exhibiting higher research performance, while hierarchical regression analysis demonstrated that the composite index explains substantially more variance in performance outcomes than single-indicator models. One-way analysis of variance (ANOVA) further revealed statistically significant differences in research performance across disciplines, with post hoc tests indicating that STEM disciplines significantly outperform Humanities. Collectively, the findings establish that research performance is bothdiscipline-sensitive and multidimensional, providing empirical support for composite, field-normalised evaluation bibliometric frameworks, while cautioning against the uniform application of single bibliometric indicators across heterogeneous academic domains and contributes to contemporary debates on robust and context-aware research evaluation. output, citation impact, and collaboration intensity relative to other fields while Humanities exhibited comparatively lower quantitative indicators. Pearson correlation analysis indicated strong and statistically significant positive associations among productivity, citation-based impact, and collaboration measures, indicating that these indices represent interrelated dimensions of scholarly performance. Multiple regression results confirmed that individual bibliometric indicators significantly predict research