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Analyzing h-index patterns through clustering among researchers in Sinaloa, Mexico

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  • Emiliano Teran

Abstract

This study evaluates the research performance of faculty at the Autonomous University of Sinaloa, Mexico, using the h-index to analyze disciplinary differences and institutional trends. The aim was to identify research productivity patterns and their implications for policy-making within international standards. A mixed-methods approach, including descriptive statistics, cluster analysis, and ordered logistic regression, analyzed data from 573 faculty members across eight research areas, focusing on the impact of individual and institutional factors on h-index variability. Significant disparities in h-index values were observed among disciplines (t = 4.27, P = .01). Area I (Physics, Mathematics, and Earth Sciences) showed the highest median h-index of 6, nearly threefold higher than Social Sciences (median of 3) and Humanities (median of 3) (F = 2.96, P = .01). Cluster analysis revealed a small group of highly productive researchers (h-index ∼ 100) disproportionately elevated institutional averages, masking areas with lower productivity. Regression confirmed significant effects of research area and seniority on h-index, while sex (t = 0.742, P = .45), region (t = 0.05, P = .95), and age (t = 1.01, P = .31) had no influence. The findings highlight the need for targeted policies to address disparities in research output and support underrepresented fields. The study also underscores the h-index’s limitations as a sole metric, advocating for complementary indicators to capture innovation and societal impact, aligning with OECD recommendations for equitable higher education.

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  • Emiliano Teran, 2025. "Analyzing h-index patterns through clustering among researchers in Sinaloa, Mexico," Research Evaluation, Oxford University Press, vol. 34, pages 1-020..
    • Handle: RePEc:oup:rseval:v:34:y:2025:i::p:rvaf020.
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    File URL: http://hdl.handle.net/10.1093/reseval/rvaf020
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