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Methods for the generation of normalized citation impact scores in bibliometrics: Which method best reflects the judgements of experts?

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  • Bornmann, Lutz
  • Marx, Werner

Abstract

Evaluative bibliometrics compare the citation impact of researchers, research groups and institutions with each other across time scales and disciplines. Both factors, discipline and period – have an influence on the citation count which is independent of the quality of the publication. Normalizing the citation impact of papers for these two factors started in the mid-1980s. Since then, a range of different methods have been presented for producing normalized citation impact scores. The current study uses a data set of over 50,000 records to test which of the methods so far presented correlate better with the assessment of papers by peers. The peer assessments come from F1000Prime – a post-publication peer review system of the biomedical literature. Of the normalized indicators, the current study involves not only cited-side indicators, such as the mean normalized citation score, but also citing-side indicators. As the results show, the correlations of the indicators with the peer assessments all turn out to be very similar. Since F1000 focuses on biomedicine, it is important that the results of this study are validated by other studies based on datasets from other disciplines or (ideally) based on multi-disciplinary datasets.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:2:p:408-418
    DOI: 10.1016/j.joi.2015.01.006
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    References listed on IDEAS

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    1. Loet Leydesdorff & Filippo Radicchi & Lutz Bornmann & Claudio Castellano & Wouter Nooy, 2013. "Field-normalized impact factors (IFs): A comparison of rescaling and fractionally counted IFs," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(11), pages 2299-2309, November.
    2. 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.
    3. Lutz Bornmann & Rüdiger Mutz, 2014. "From P100 to P100': A new citation-rank approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(9), pages 1939-1943, September.
    4. Bornmann, Lutz & Leydesdorff, Loet, 2013. "The validation of (advanced) bibliometric indicators through peer assessments: A comparative study using data from InCites and F1000," Journal of Informetrics, Elsevier, vol. 7(2), pages 286-291.
    5. Bornmann, Lutz & Leydesdorff, Loet & Mutz, Rüdiger, 2013. "The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits," Journal of Informetrics, Elsevier, vol. 7(1), pages 158-165.
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    7. Loet Leydesdorff & Lutz Bornmann, 2011. "How fractional counting of citations affects the impact factor: Normalization in terms of differences in citation potentials among fields of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 217-229, February.
    8. Schreiber, Michael, 2014. "Is the new citation-rank approach P100′ in bibliometrics really new?," Journal of Informetrics, Elsevier, vol. 8(4), pages 997-1004.
    9. Schreiber, Michael, 2014. "Examples for counterintuitive behavior of the new citation-rank indicator P100 for bibliometric evaluations," Journal of Informetrics, Elsevier, vol. 8(3), pages 738-748.
    10. Lutz Bornmann & Werner Marx, 2014. "The wisdom of citing scientists," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(6), pages 1288-1292, June.
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    12. Bornmann, Lutz & Williams, Richard, 2013. "How to calculate the practical significance of citation impact differences? An empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects," Journal of Informetrics, Elsevier, vol. 7(2), pages 562-574.
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    23. Richard Williams, 2012. "Using the margins command to estimate and interpret adjusted predictions and marginal effects," Stata Journal, StataCorp LP, vol. 12(2), pages 308-331, June.
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