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Sample size in bibliometric analysis

Author

Listed:
  • Gordon Rogers

    (Institute for Scientific Information, Clarivate Analytics)

  • Martin Szomszor

    (Institute for Scientific Information, Clarivate Analytics)

  • Jonathan Adams

    (Institute for Scientific Information, Clarivate Analytics
    King’s College London)

Abstract

While bibliometric analysis is normally able to rely on complete publication sets this is not universally the case. For example, Australia (in ERA) and the UK (in the RAE/REF) use institutional research assessment that may rely on small or fractional parts of researcher output. Using the Category Normalised Citation Impact (CNCI) for the publications of ten universities with similar output (21,000–28,000 articles and reviews) indexed in the Web of Science for 2014–2018, we explore the extent to which a ‘sample’ of institutional data can accurately represent the averages and/or the correct relative status of the population CNCIs. Starting with full institutional data, we find a high variance in average CNCI across 10,000 institutional samples of fewer than 200 papers, which we suggest may be an analytical minimum although smaller samples may be acceptable for qualitative review. When considering the ‘top’ CNCI paper in researcher sets represented by DAIS-ID clusters, we find that samples of 1000 papers provide a good guide to relative (but not absolute) institutional citation performance, which is driven by the abundance of high performing individuals. However, such samples may be perturbed by scarce ‘highly cited’ papers in smaller or less research-intensive units. We draw attention to the significance of this for assessment processes and the further evidence that university rankings are innately unstable and generally unreliable.

Suggested Citation

  • Gordon Rogers & Martin Szomszor & Jonathan Adams, 2020. "Sample size in bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 777-794, October.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:1:d:10.1007_s11192-020-03647-7
    DOI: 10.1007/s11192-020-03647-7
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    References listed on IDEAS

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    1. Wolfgang Glänzel & Henk F. Moed, 2013. "Opinion paper: thoughts and facts on bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 381-394, July.
    2. Potter, Ross W.K. & Szomszor, Martin & Adams, Jonathan, 2020. "Interpreting CNCIs on a country-scale: The effect of domestic and international collaboration type," Journal of Informetrics, Elsevier, vol. 14(4).
    3. Per O. Seglen, 1994. "Causal relationship between article citedness and journal impact," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 45(1), pages 1-11, January.
    4. Fairclough, Ruth & Thelwall, Mike, 2015. "More precise methods for national research citation impact comparisons," Journal of Informetrics, Elsevier, vol. 9(4), pages 895-906.
    5. Zhesi Shen & Liying Yang & Zengru Di & Jinshan Wu, 2019. "Large enough sample size to rank two groups of data reliably according to their means," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 653-671, February.
    6. Thelwall, Mike, 2016. "The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 110-123.
    7. Per O. Seglen, 1992. "The skewness of science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 43(9), pages 628-638, October.
    8. Michael Levin & Stefan Krawczyk & Steven Bethard & Dan Jurafsky, 2012. "Citation‐based bootstrapping for large‐scale author disambiguation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(5), pages 1030-1047, May.
    9. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    10. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    11. Jochen Gläser & Thomas H Spurling & Linda Butler, 2004. "Intraorganisational evaluation: are there ‘least evaluable units'?," Research Evaluation, Oxford University Press, vol. 13(1), pages 19-32, April.
    12. María del Carmen Calatrava Moreno & Thomas Auzinger & Hannes Werthner, 2016. "On the uncertainty of interdisciplinarity measurements due to incomplete bibliographic data," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 213-232, April.
    13. Michael Levin & Stefan Krawczyk & Steven Bethard & Dan Jurafsky, 2012. "Citation-based bootstrapping for large-scale author disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(5), pages 1030-1047, May.
    14. Howard D. White & Belver C. Griffith, 1981. "Author cocitation: A literature measure of intellectual structure," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 32(3), pages 163-171, May.
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