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The problem of citation impact assessments for recent publication years in institutional evaluations

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  • Bornmann, Lutz

Abstract

Bibliometrics has become an indispensable tool in the evaluation of institutions (in the natural and life sciences). An evaluation report without bibliometric data has become a rarity. However, evaluations are often required to measure the citation impact of publications in very recent years in particular. As a citation analysis is only meaningful for publications for which a citation window of at least three years is guaranteed, very recent years cannot (should not) be included in the analysis. This study presents various options for dealing with this problem in statistical analysis. The publications from two universities from 2000 to 2011 are used as a sample dataset (n=2652, univ 1=1484 and univ 2=1168). One option is to show the citation impact data (percentiles) in a graphic and to use a line for percentiles regressed on ‘distant’ publication years (with confidence interval) showing the trend for the ‘very recent’ publication years. Another way of dealing with the problem is to work with the concept of samples and populations. The third option (very related to the second) is the application of the counterfactual concept of causality.

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  • Bornmann, Lutz, 2013. "The problem of citation impact assessments for recent publication years in institutional evaluations," Journal of Informetrics, Elsevier, vol. 7(3), pages 722-729.
    • Handle: RePEc:eee:infome:v:7:y:2013:i:3:p:722-729
    DOI: 10.1016/j.joi.2013.05.002
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    References listed on IDEAS

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    2. 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.
    3. Lutz Bornmann & Loet Leydesdorff, 2013. "Statistical tests and research assessments: A comment on Schneider (2012)," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(6), pages 1306-1308, June.
    4. 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.
    5. Leo Egghe & Ronald Rousseau, 2006. "An informetric model for the Hirsch-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 121-129, October.
    6. Lutz Bornmann, 2013. "How to analyze percentile citation impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes, and top-cited papers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(3), pages 587-595, March.
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    9. Schneider, Jesper W., 2013. "Caveats for using statistical significance tests in research assessments," Journal of Informetrics, Elsevier, vol. 7(1), pages 50-62.
    10. 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.
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    12. Ronald Rousseau, 2012. "Basic properties of both percentile rank scores and the I3 indicator," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(2), pages 416-420, February.
    13. Bornmann, Lutz & Schier, Hermann & Marx, Werner & Daniel, Hans-Dieter, 2012. "What factors determine citation counts of publications in chemistry besides their quality?," Journal of Informetrics, Elsevier, vol. 6(1), pages 11-18.
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    Cited by:

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    3. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2014. "How to improve the prediction based on citation impact percentiles for years shortly after the publication date?," Journal of Informetrics, Elsevier, vol. 8(1), pages 175-180.
    4. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
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    7. Liu, Meijun & Hu, Xiao, 2021. "Will collaborators make scientists move? A Generalized Propensity Score analysis," Journal of Informetrics, Elsevier, vol. 15(1).
    8. Lutz Bornmann & Werner Marx, 2014. "How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 487-509, January.
    9. Mingyang Wang & Shi Li & Guangsheng Chen, 2017. "Detecting latent referential articles based on their vitality performance in the latest 2 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1557-1571, September.
    10. Kousha, Kayvan & Thelwall, Mike & Abdoli, Mahshid, 2018. "Can Microsoft Academic assess the early citation impact of in-press articles? A multi-discipline exploratory analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 287-298.
    11. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.
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    13. 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.

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