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The validation of (advanced) bibliometric indicators through peer assessments: A comparative study using data from InCites and F1000

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  • Bornmann, Lutz
  • Leydesdorff, Loet

Abstract

The data of F1000 and InCites provide us with the unique opportunity to investigate the relationship between peers’ ratings and bibliometric metrics on a broad and comprehensive data set with high-quality ratings. F1000 is a post-publication peer review system of the biomedical literature. The comparison of metrics with peer evaluation has been widely acknowledged as a way of validating metrics. Based on the seven indicators offered by InCites, we analyzed the validity of raw citation counts (Times Cited, 2nd Generation Citations, and 2nd Generation Citations per Citing Document), normalized indicators (Journal Actual/Expected Citations, Category Actual/Expected Citations, and Percentile in Subject Area), and a journal based indicator (Journal Impact Factor). The data set consists of 125 papers published in 2008 and belonging to the subject category cell biology or immunology. As the results show, Percentile in Subject Area achieves the highest correlation with F1000 ratings; we can assert that for further three other indicators (Times Cited, 2nd Generation Citations, and Category Actual/Expected Citations) the “true” correlation with the ratings reaches at least a medium effect size.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:7:y:2013:i:2:p:286-291
    DOI: 10.1016/j.joi.2012.12.003
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    References listed on IDEAS

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    1. Lutz Bornmann & Rüdiger Mutz & Hans‐Dieter Daniel, 2008. "Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(5), pages 830-837, March.
    2. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Ryan Chute, 2009. "A Principal Component Analysis of 39 Scientific Impact Measures," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-11, June.
    3. Martin, Ben R. & Irvine, John, 1993. "Assessing basic research : Some partial indicators of scientific progress in radio astronomy," Research Policy, Elsevier, vol. 22(2), pages 106-106, April.
    4. Lutz Bornmann & Rüdiger Mutz & Werner Marx & Hermann Schier & Hans‐Dieter Daniel, 2011. "A multilevel modelling approach to investigating the predictive validity of editorial decisions: do the editors of a high profile journal select manuscripts that are highly cited after publication?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 857-879, October.
    5. Franceschet, Massimo & Costantini, Antonio, 2011. "The first Italian research assessment exercise: A bibliometric perspective," Journal of Informetrics, Elsevier, vol. 5(2), pages 275-291.
    6. Loet Leydesdorff, 2009. "How are new citation‐based journal indicators adding to the bibliometric toolbox?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(7), pages 1327-1336, July.
    7. Eduardo Figueredo, 2006. "The numerical equivalence between the impact factor of journals and the quality of the articles," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(11), pages 1561-1561, September.
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