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Measuring the robustness of the journal h-index with respect to publication and citation values: A Bayesian sensitivity analysis

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Abstract

Braun, Glänzel, and Schubert (2006) recommended using the h-index as an alternative to the journal impact factor (IF) to qualify journals. In this paper, a Bayesian-based sensitivity analysis is performed with the aid of mathematical models to examine the behavior of the journal h-index to changes in the publication/citation counts of journals. Sensitivity of the h-index was most apparent for changes in the number of citations, revealing similar patterns of behavior for almost all models and independently to the field of research. In general, the h-index was found to be robust to changes in citations up to approximately the 25th percentile of the citation distribution, inflating its value afterwards.

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  • Malesios, C., 2016. "Measuring the robustness of the journal h-index with respect to publication and citation values: A Bayesian sensitivity analysis," Journal of Informetrics, Elsevier, vol. 10(3), pages 719-731.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:3:p:719-731
    DOI: 10.1016/j.joi.2016.03.002
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    1. Jean-Michel Courtault & Naïla Hayek, 2008. "On the Robustness of the h-index: a mathematical approach," Economics Bulletin, AccessEcon, vol. 3(78), pages 1-9.
    2. Chrisovalantis Malesios, 2015. "Some variations on the standard theoretical models for the h-index: A comparative analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2384-2388, November.
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    6. Rousseau, Ronald, 2007. "The influence of missing publications on the Hirsch index," Journal of Informetrics, Elsevier, vol. 1(1), pages 2-7.
    7. Fiorenzo Franceschini & Domenico Maisano & Luca Mastrogiacomo, 2013. "The effect of database dirty data on h-index calculation," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 1179-1188, June.
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    12. Bornmann, Lutz & Stefaner, Moritz & de Moya Anegón, Felix & Mutz, Rüdiger, 2016. "Excellence networks in science: A Web-based application based on Bayesian multilevel logistic regression (BMLR) for the identification of institutions collaborating successfully," Journal of Informetrics, Elsevier, vol. 10(1), pages 312-327.
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    1. Mingyang Wang & Shijia Jiao & Kah-Hin Chai & Guangsheng Chen, 2019. "Building journal’s long-term impact: using indicators detected from the sustained active articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 261-283, October.

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