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How to improve the prediction based on citation impact percentiles for years shortly after the publication date?

Author

Listed:
  • Bornmann, Lutz
  • Leydesdorff, Loet
  • Wang, Jian

Abstract

The findings of Bornmann, Leydesdorff, and Wang (2013b) revealed that the consideration of journal impact improves the prediction of long-term citation impact. This paper further explores the possibility of improving citation impact measurements on the base of a short citation window by the consideration of journal impact and other variables, such as the number of authors, the number of cited references, and the number of pages. The dataset contains 475,391 journal papers published in 1980 and indexed in Web of Science (WoS, Thomson Reuters), and all annual citation counts (from 1980 to 2010) for these papers. As an indicator of citation impact, we used percentiles of citations calculated using the approach of Hazen (1914). Our results show that citation impact measurement can really be improved: If factors generally influencing citation impact are considered in the statistical analysis, the explained variance in the long-term citation impact can be much increased. However, this increase is only visible when using the years shortly after publication but not when using later years.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:infome:v:8:y:2014:i:1:p:175-180
    DOI: 10.1016/j.joi.2013.11.005
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. James W. Hardin & Joseph W. Hilbe, 2012. "Generalized Linear Models and Extensions, 3rd Edition," Stata Press books, StataCorp LP, edition 3, number glmext, April.
    4. Guerrero-Bote, Vicente P. & Moya-Anegón, Félix, 2012. "A further step forward in measuring journals’ scientific prestige: The SJR2 indicator," Journal of Informetrics, Elsevier, vol. 6(4), pages 674-688.
    5. 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.
    6. Vieira, E.S. & Gomes, J.A.N.F., 2010. "Citations to scientific articles: Its distribution and dependence on the article features," Journal of Informetrics, Elsevier, vol. 4(1), pages 1-13.
    7. 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.
    8. 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.
    9. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, April.
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    Cited by:

    1. Babak Sohrabi & Hamideh Iraj, 2017. "The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 243-251, January.
    2. repec:spr:scient:v:104:y:2015:i:3:d:10.1007_s11192-015-1573-y is not listed on IDEAS
    3. repec:eee:respol:v:46:y:2017:i:8:p:1416-1436 is not listed on IDEAS
    4. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    5. Schreiber, Michael, 2015. "Restricting the h-index to a publication and citation time window: A case study of a timed Hirsch index," Journal of Informetrics, Elsevier, vol. 9(1), pages 150-155.
    6. Iman Tahamtan & Askar Safipour Afshar & Khadijeh Ahamdzadeh, 2016. "Factors affecting number of citations: a comprehensive review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1195-1225, June.
    7. Bornmann, Lutz & Leydesdorff, Loet, 2015. "Does quality and content matter for citedness? A comparison with para-textual factors and over time," Journal of Informetrics, Elsevier, vol. 9(3), pages 419-429.
    8. Nader Ale Ebrahim & H. Ebrahimian & Maryam Mousavi & Farzad Tahriri, 2015. "Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 1(3), pages 6-16, February.
    9. repec:eee:infome:v:12:y:2018:i:1:p:191-202 is not listed on IDEAS
    10. repec:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2390-2 is not listed on IDEAS
    11. Cao, Xuanyu & Chen, Yan & Ray Liu, K.J., 2016. "A data analytic approach to quantifying scientific impact," Journal of Informetrics, Elsevier, vol. 10(2), pages 471-484.
    12. Bornmann, Lutz & Stefaner, Moritz & de Moya Anegón, Felix & Mutz, Rüdiger, 2014. "What is the effect of country-specific characteristics on the research performance of scientific institutions? Using multi-level statistical models to rank and map universities and research-focused in," Journal of Informetrics, Elsevier, vol. 8(3), pages 581-593.
    13. repec:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2389-8 is not listed on IDEAS

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