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

Lutz Bornmann () and Loet Leydesdorff

Journal of Informetrics, 2017, vol. 11, issue 1, 164-175

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.

Keywords: Citation impact; Factors influencing citations; Percentile shares; Impact factors; Normalization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:11:y:2017:i:1:p:164-175

DOI: 10.1016/j.joi.2016.12.001

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