IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v101y2014i3d10.1007_s11192-014-1294-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1294-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-014-1294-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pedro Albarrán & Juan A. Crespo & Ignacio Ortuño & Javier Ruiz-Castillo, 2011. "The skewness of science in 219 sub-fields and a number of aggregates," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 385-397, August.
    2. Filippo Radicchi & Claudio Castellano, 2012. "A Reverse Engineering Approach to the Suppression of Citation Biases Reveals Universal Properties of Citation Distributions," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    3. Lundberg, Jonas, 2007. "Lifting the crown—citation z-score," Journal of Informetrics, Elsevier, vol. 1(2), pages 145-154.
    4. Gregory John Lee, 2010. "Assessing publication performance of research units: extensions through operational research and economic techniques," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 717-734, September.
    5. Eva Lillquist & Sheldon Green, 2010. "The discipline dependence of citation statistics," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 749-762, September.
    6. Opthof, Tobias & Leydesdorff, Loet, 2010. "Caveats for the journal and field normalizations in the CWTS (“Leiden”) evaluations of research performance," Journal of Informetrics, Elsevier, vol. 4(3), pages 423-430.
    7. R E de Bruin & A Kint & M Luwel & H F Moed, 1993. "A study of research evaluation and planning: the University of Ghent," Research Evaluation, Oxford University Press, vol. 3(1), pages 25-41, April.
    8. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2012. "How important is choice of the scaling factor in standardizing citations?," Journal of Informetrics, Elsevier, vol. 6(4), pages 645-654.
    9. Li, Yunrong & Radicchi, Filippo & Castellano, Claudio & Ruiz-Castillo, Javier, 2013. "Quantitative evaluation of alternative field normalization procedures," Journal of Informetrics, Elsevier, vol. 7(3), pages 746-755.
    10. Ludo Waltman & Nees Jan van Eck & Anthony F. J. van Raan, 2012. "Universality of citation distributions revisited," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 72-77, January.
    11. van Raan, Anthony F.J. & van Leeuwen, Thed N. & Visser, Martijn S. & van Eck, Nees Jan & Waltman, Ludo, 2010. "Rivals for the crown: Reply to Opthof and Leydesdorff," Journal of Informetrics, Elsevier, vol. 4(3), pages 431-435.
    12. Anton J. Nederhof, 2006. "Bibliometric monitoring of research performance in the Social Sciences and the Humanities: A Review," Scientometrics, Springer;Akadémiai Kiadó, vol. 66(1), pages 81-100, January.
    13. Wolfgang Glänzel & Bart Thijs & András Schubert & Koenraad Debackere, 2009. "Subfield-specific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(1), pages 165-188, January.
    14. Vinkler, Péter, 2012. "The case of scientometricians with the “absolute relative” impact indicator," Journal of Informetrics, Elsevier, vol. 6(2), pages 254-264.
    15. Per O. Seglen, 1992. "The skewness of science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 43(9), pages 628-638, October.
    16. Larivière, Vincent & Gingras, Yves, 2011. "Averages of ratios vs. ratios of averages: An empirical analysis of four levels of aggregation," Journal of Informetrics, Elsevier, vol. 5(3), pages 392-399.
    17. J Sylvan Katz, 2000. "Scale-independent indicators and research evaluation," Science and Public Policy, Oxford University Press, vol. 27(1), pages 23-36, February.
    18. Ludo Waltman & Nees Jan van Eck & Anthony F. J. van Raan, 2012. "Universality of citation distributions revisited," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 72-77, January.
    19. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2012. "Revisiting the scaling of citations for research assessment," Journal of Informetrics, Elsevier, vol. 6(4), pages 470-479.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Xing, 2024. "The misuse of the nonlinear field normalization method: Nonlinear field normalization citation counts at the paper level should not be added or averaged," Journal of Informetrics, Elsevier, vol. 18(3).
    2. Tolga Yuret, 2018. "Author-weighted impact factor and reference return ratio: can we attain more equality among fields?," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 2097-2111, September.
    3. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Grilli, Leonardo, 2015. "Funnel plots for visualizing uncertainty in the research performance of institutions," Journal of Informetrics, Elsevier, vol. 9(4), pages 954-961.
    4. Roshni Das, 2023. "Does public service motivation predict performance in public sector organizations? A longitudinal science mapping study," Management Review Quarterly, Springer, vol. 73(3), pages 1237-1271, September.
    5. Giovanni Abramo & Corrado Costa & Ciriaco Andrea D’Angelo, 2015. "A multivariate stochastic model to assess research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1755-1772, February.
    6. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    7. 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.
    8. Zhihui Zhang & Ying Cheng & Nian Cai Liu, 2015. "Improving the normalization effect of mean-based method from the perspective of optimization: optimization-based linear methods and their performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 587-607, January.
    9. Marcello D’Agostino & Valentino Dardanoni & Roberto Ghiselli Ricci, 2017. "How to standardize (if you must)," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 825-843, November.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    2. Zhihui Zhang & Ying Cheng & Nian Cai Liu, 2015. "Improving the normalization effect of mean-based method from the perspective of optimization: optimization-based linear methods and their performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 587-607, January.
    3. Bouyssou, Denis & Marchant, Thierry, 2016. "Ranking authors using fractional counting of citations: An axiomatic approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 183-199.
    4. Ruiz-Castillo, Javier & Waltman, Ludo, 2015. "Field-normalized citation impact indicators using algorithmically constructed classification systems of science," Journal of Informetrics, Elsevier, vol. 9(1), pages 102-117.
    5. Ruiz-Castillo, Javier & Costas, Rodrigo, 2018. "Individual and field citation distributions in 29 broad scientific fields," Journal of Informetrics, Elsevier, vol. 12(3), pages 868-892.
    6. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2012. "How important is choice of the scaling factor in standardizing citations?," Journal of Informetrics, Elsevier, vol. 6(4), pages 645-654.
    7. Ruiz-Castillo, Javier & Costas, Rodrigo, 2014. "The skewness of scientific productivity," Journal of Informetrics, Elsevier, vol. 8(4), pages 917-934.
    8. Herranz, Neus & Ruiz-Castillo, Javier, 2012. "Sub-field normalization in the multiplicative case: Average-based citation indicators," Journal of Informetrics, Elsevier, vol. 6(4), pages 543-556.
    9. Marcello D’Agostino & Valentino Dardanoni & Roberto Ghiselli Ricci, 2017. "How to standardize (if you must)," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 825-843, November.
    10. T. S. Evans & N. Hopkins & B. S. Kaube, 2012. "Universality of performance indicators based on citation and reference counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 473-495, November.
    11. Loet Leydesdorff, 2013. "An evaluation of impacts in “Nanoscience & nanotechnology”: steps towards standards for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 35-55, January.
    12. Javier Ruiz-Castillo, 2013. "The role of statistics in establishing the similarity of citation distributions in a static and a dynamic context," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 173-181, July.
    13. Giancarlo Ruocco & Cinzia Daraio, 2013. "An empirical approach to compare the performance of heterogeneous academic fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 601-625, December.
    14. Ludo Waltman & Nees Jan Eck & Thed N. Leeuwen & Martijn S. Visser & Anthony F. J. Raan, 2011. "Towards a new crown indicator: an empirical analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 467-481, June.
    15. Vîiu, Gabriel-Alexandru, 2018. "The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 401-415.
    16. Loet Leydesdorff, 2012. "Alternatives to the journal impact factor: I3 and the top-10% (or top-25%?) of the most-highly cited papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 355-365, August.
    17. Abramo, Giovanni & D’Angelo, Ciriaco Andrea, 2016. "A farewell to the MNCS and like size-independent indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 646-651.
    18. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2013. "Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P1," Journal of Informetrics, Elsevier, vol. 7(4), pages 933-944.
    19. Waltman, Ludo & van Eck, Nees Jan & van Leeuwen, Thed N. & Visser, Martijn S. & van Raan, Anthony F.J., 2011. "Towards a new crown indicator: Some theoretical considerations," Journal of Informetrics, Elsevier, vol. 5(1), pages 37-47.
    20. Wang, Xing & Zhang, Zhihui, 2020. "Improving the reliability of short-term citation impact indicators by taking into account the correlation between short- and long-term citation impact," Journal of Informetrics, Elsevier, vol. 14(2).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:101:y:2014:i:3:d:10.1007_s11192-014-1294-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.