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Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on Web of Science data

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  • Bornmann, Lutz
  • Leydesdorff, Loet

Abstract

Using percentile shares, one can visualize and analyze the skewness in bibliometric data across disciplines and over time. The resulting figures can be intuitively interpreted and are more suitable for detailed analysis of the effects of independent and control variables on distributions than regression analysis. We show this by using percentile shares to analyze so-called “factors influencing citation impact” (FICs; e.g., the impact factor of the publishing journal) across years and disciplines. All articles (n=2,961,789) covered by WoS in 1990 (n=637,301), 2000 (n=919,485), and 2010 (n=1,405,003) are used. In 2010, nearly half of the citation impact is accounted for by the 10% most-frequently cited papers; the skewness is largest in the humanities (68.5% in the top-10% layer) and lowest in agricultural sciences (40.6%). The comparison of the effects of the different FICs (the number of cited references, number of authors, number of pages, and JIF) on citation impact shows that the JIF has indeed the strongest correlations with the citation scores. However, the correlation between FICs and citation impact is lower, if citations are normalized instead of using raw citation counts.

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  • Bornmann, Lutz & Leydesdorff, Loet, 2017. "Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on Web of Science data," Journal of Informetrics, Elsevier, vol. 11(1), pages 164-175.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:1:p:164-175
    DOI: 10.1016/j.joi.2016.12.001
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    4. Lutz Bornmann & Adam Y. Ye & Fred Y. Ye, 2018. "Identifying “hot papers” and papers with “delayed recognition” in large-scale datasets by using dynamically normalized citation impact scores," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 655-674, August.
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    8. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.
    9. Juan Xie & Kaile Gong & Ying Cheng & Qing Ke, 2019. "The correlation between paper length and citations: a meta-analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 763-786, March.
    10. Heng Huang & Donghua Zhu & Xuefeng Wang, 2022. "Evaluating scientific impact of publications: combining citation polarity and purpose," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5257-5281, September.
    11. Yaxue Ma & Zhichao Ba & Yuxiang Zhao & Jin Mao & Gang Li, 2021. "Understanding and predicting the dissemination of scientific papers on social media: a two-step simultaneous equation modeling–artificial neural network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7051-7085, August.
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    20. Lanu Kim & Jason H. Portenoy & Jevin D. West & Katherine W. Stovel, 2020. "Scientific journals still matter in the era of academic search engines and preprint archives," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1218-1226, October.

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