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Correlation between Journal Impact Factor and Citation Performance: An experimental study

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  • Finardi, Ugo

Abstract

Since its introduction, the Journal Impact Factor has probably been the most extensively adopted bibliometric indicator. Notwithstanding its well-known strengths and limits, it is still widely misused as a tool for evaluation, well beyond the purposes it was intended for. In order to shed further light on its nature, the present work studies how the correlation between the Journal Impact Factor and the (time-weighed) article Mean Received Citations (intended as a measure of journal performance) has evolved through time. It focuses on a sample of hard sciences and social sciences journals from the 1999 to 2010 time period. Correlation coefficients (Pearson's Coefficients as well as Spearman's Coefficients and Kendall's τα) are calculated and then tested against several null hypotheses. The results show that in most cases Journal Impact Factors and their yearly variations do not display a strong correlation with citedness. Differences also exist among scientific areas.

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  • Finardi, Ugo, 2013. "Correlation between Journal Impact Factor and Citation Performance: An experimental study," Journal of Informetrics, Elsevier, vol. 7(2), pages 357-370.
    • Handle: RePEc:eee:infome:v:7:y:2013:i:2:p:357-370
    DOI: 10.1016/j.joi.2012.12.004
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    3. Guoliang Lyu & Ganwei Shi, 2019. "On an approach to boosting a journal’s citation potential," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1387-1409, September.
    4. Isabel Bernal, 2013. "Open Access and the Changing Landscape of Research Impact Indicators: New Roles for Repositories," Publications, MDPI, vol. 1(2), pages 1-22, July.
    5. Fabio Zagonari, 2019. "Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined," Publications, MDPI, vol. 7(2), pages 1-30, May.
    6. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2018. "Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 869-892, May.
    7. Campanario, Juan Miguel, 2014. "Analysis of the distribution of cited journals according to their positions in the h-core of citing journal listed in Journal Citation Reports," Journal of Informetrics, Elsevier, vol. 8(3), pages 534-545.
    8. Mike Thelwall, 2016. "Interpreting correlations between citation counts and other indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 337-347, July.
    9. Diego Raphael Amancio, 2015. "Comparing the topological properties of real and artificially generated scientific manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1763-1779, December.
    10. Wu, Dengsheng & Li, Jing & Lu, Xiaoli & Li, Jianping, 2018. "Journal editorship index for assessing the scholarly impact of academic institutions: An empirical analysis in the field of economics," Journal of Informetrics, Elsevier, vol. 12(2), pages 448-460.
    11. Lina M. Cortés & Andrés Mora-Valencia & Javier Perote, 2016. "The productivity of top researchers: a semi-nonparametric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 891-915, November.
    12. Yurij L. Katchanov & Yulia V. Markova, 2017. "The “space of physics journals”: topological structure and the Journal Impact Factor," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 313-333, October.
    13. Finardi, Ugo, 2014. "On the time evolution of received citations, in different scientific fields: An empirical study," Journal of Informetrics, Elsevier, vol. 8(1), pages 13-24.
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    15. Maximiano Ortiz-Pimentel & Carlos Molina & Guillermo Armando Ronda-Pupo, 2020. "Bibliometric assessment of papers on generations in management and business journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 445-469, October.

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