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Scores of a specific field-normalized indicator calculated with different approaches of field-categorization: Are the scores different or similar?

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  • Haunschild, Robin
  • Daniels, Angela D.
  • Bornmann, Lutz

Abstract

Usage of field-normalized citation scores is a bibliometric standard. Different methods for field-normalization are in use, but also the choice of field-classification system determines the resulting field-normalized citation scores. Using Web of Science data, we calculated field-normalized citation scores using the same formula but different field-classification systems to answer the question if the resulting scores are different or similar. Six field-classification systems were used: three based on citation relations, one on semantic similarity scores (i.e., a topical relatedness measure), one on journal sets, and one on intellectual classifications. Systems based on journal sets and intellectual classifications agree on at least the moderate level. Two out of the three sets based on citation relations also agree on at least the moderate level. Larger differences were observed for the third data set based on citation relations and semantic similarity scores. The main policy implication is that normalized citation impact scores or rankings based on them should not be compared without deeper knowledge of the classification systems that were used to derive these values or rankings.

Suggested Citation

  • Haunschild, Robin & Daniels, Angela D. & Bornmann, Lutz, 2022. "Scores of a specific field-normalized indicator calculated with different approaches of field-categorization: Are the scores different or similar?," Journal of Informetrics, Elsevier, vol. 16(1).
  • Handle: RePEc:eee:infome:v:16:y:2022:i:1:s1751157721001127
    DOI: 10.1016/j.joi.2021.101241
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    References listed on IDEAS

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    1. Strotmann, Andreas & Zhao, Dangzhi, 2010. "Combining commercial citation indexes and open-access bibliographic databases to delimit highly interdisciplinary research fields for citation analysis," Journal of Informetrics, Elsevier, vol. 4(2), pages 194-200.
    2. Wang, Qi & Waltman, Ludo, 2016. "Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus," Journal of Informetrics, Elsevier, vol. 10(2), pages 347-364.
    3. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    4. Bornmann, Lutz & Marx, Werner, 2015. "Methods for the generation of normalized citation impact scores in bibliometrics: Which method best reflects the judgements of experts?," Journal of Informetrics, Elsevier, vol. 9(2), pages 408-418.
    5. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    6. Richard Klavans & Kevin W. Boyack, 2017. "Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 984-998, April.
    7. Loet Leydesdorff & Tobias Opthof, 2013. "Citation analysis with medical subject Headings (MeSH) using the Web of Knowledge: A new routine," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1076-1080, May.
    8. Perianes-Rodríguez, Antonio, 2016. "A comparison of the Web of Science with publication-level classification systems of Science," UC3M Working papers. Economics we1602, Universidad Carlos III de Madrid. Departamento de Economía.
    9. John P A Ioannidis & Kevin Boyack & Paul F Wouters, 2016. "Citation Metrics: A Primer on How (Not) to Normalize," PLOS Biology, Public Library of Science, vol. 14(9), pages 1-7, September.
    10. Lundberg, Jonas, 2007. "Lifting the crown—citation z-score," Journal of Informetrics, Elsevier, vol. 1(2), pages 145-154.
    11. 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.
    12. Lutz Bornmann & Hermann Schier & Werner Marx & Hans-Dieter Daniel, 2011. "Is interactive open access publishing able to identify high-impact submissions? A study on the predictive validity of Atmospheric Chemistry and Physics by using percentile rank classes," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 61-71, January.
    13. Diana Hicks & Paul Wouters & Ludo Waltman & Sarah de Rijcke & Ismael Rafols, 2015. "Bibliometrics: The Leiden Manifesto for research metrics," Nature, Nature, vol. 520(7548), pages 429-431, April.
    14. Peter Sjögårde & Per Ahlgren & Ludo Waltman, 2021. "Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 853-869, July.
    15. Lutz Bornmann & Hans‐Dieter Daniel, 2008. "Selecting manuscripts for a high‐impact journal through peer review: A citation analysis of communications that were accepted by Angewandte Chemie International Edition, or rejected but published else," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(11), pages 1841-1852, September.
    16. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
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