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

TL;DR

This large-scale empirical study analyzes the skewness of citation impact data across disciplines and over time using percentile shares, revealing the influence of factors like journal impact factor and the benefits of normalization.

Contribution

The paper introduces the use of percentile shares for analyzing citation skewness and compares effects of bibliometric factors across disciplines and years.

Findings

Nearly half of citations are from top 10% papers in 2010.

Skewness varies significantly across disciplines, highest in humanities.

Journal impact factor shows the strongest correlation with citation impact.

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 year 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 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.