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Comparison of the effect of mean-based method and z-score for field normalization of citations at the level of Web of Science subject categories

Author

Listed:
  • Zhihui Zhang

    (Shanghai Jiao Tong University)

  • Ying Cheng

    (Shanghai Jiao Tong University)

  • Nian Cai Liu

    (Shanghai Jiao Tong University)

Abstract

Field normalization is a necessary step in a fair cross-field comparison of citation impact. In practice, mean-based method (m-score) is the most popular method for field normalization. However, considering that mean-based method only utilizes the central tendency of citation distribution in the normalization procedure and dispersion is also a significant characteristic, an open and important issue is whether alternative normalization methods which take both central tendency and variability into account perform better than mean-based method. With the aim of collapsing citation distributions of different fields into a universal distribution, this study compares the normalization effect of m-score and z-score based on 236 Web of Science (WoS) subject categories. The results show that both m-score and z-score have remarkable normalization effect as compared with raw citations, but neither of them can realize the ideal goal of “universality of citation distributions”. The results also suggest that m-score is generally preferable to z-score. The essential cause that m-score has an edge over z-score as a whole has a direct relationship with the characteristics of skewed citation distributions in which case m-score is more applicable than z-score.

Suggested Citation

  • Zhihui Zhang & Ying Cheng & Nian Cai Liu, 2014. "Comparison of the effect of mean-based method and z-score for field normalization of citations at the level of Web of Science subject categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1679-1693, December.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:3:d:10.1007_s11192-014-1294-7
    DOI: 10.1007/s11192-014-1294-7
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    7. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    8. Gerson Pech & Catarina Delgado, 2020. "Percentile and stochastic-based approach to the comparison of the number of citations of articles indexed in different bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 223-252, April.
    9. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.

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