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Sampling issues in bibliometric analysis

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  • Williams, Richard
  • Bornmann, Lutz

Abstract

Bibliometricians face several issues when drawing and analyzing samples of citation records for their research. Drawing samples that are too small may make it difficult or impossible for studies to achieve their goals, while drawing samples that are too large may drain resources that could be better used for other purposes. This paper considers three common situations and offers advice for dealing with each. First, an entire population of records is available for an institution. We argue that, even though all records have been collected, the use of inferential statistics, significance testing, and confidence intervals is both common and desirable. Second, because of limited resources or other factors, a sample of records needs to be drawn. We demonstrate how power analyses can be used to determine in advance how large the sample needs to be to achieve the study's goals. Third, the sample size may already be determined, either because the data have already been collected or because resources are limited. We show how power analyses can again be used to determine how large effects need to be in order to find effects that are statistically significant. Such information can then help bibliometricians to develop reasonable expectations as to what their analysis can accomplish. While we focus on issues of interest to bibliometricians, our recommendations and procedures can easily be adapted for other fields of study.

Suggested Citation

  • Williams, Richard & Bornmann, Lutz, 2016. "Sampling issues in bibliometric analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1225-1232.
  • Handle: RePEc:eee:infome:v:10:y:2016:i:4:p:1225-1232
    DOI: 10.1016/j.joi.2015.11.004
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    References listed on IDEAS

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    1. Schneider, Jesper W., 2013. "Caveats for using statistical significance tests in research assessments," Journal of Informetrics, Elsevier, vol. 7(1), pages 50-62.
    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. Lutz Bornmann, 2014. "How are excellent (highly cited) papers defined in bibliometrics? A quantitative analysis of the literature," Research Evaluation, Oxford University Press, vol. 23(2), pages 166-173.
    4. A. Colin Cameron & Pravin K. Trivedi, 2010. "Microeconometrics Using Stata, Revised Edition," Stata Press books, StataCorp LP, number musr, March.
    5. 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.
    6. Jesper W. Schneider, 2015. "Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 411-432, January.
    7. Opthof, Tobias & Leydesdorff, Loet, 2010. "Caveats for the journal and field normalizations in the CWTS (“Leiden”) evaluations of research performance," Journal of Informetrics, Elsevier, vol. 4(3), pages 423-430.
    8. Diana Hicks & Paul Wouters & Ludo Waltman & Sarah de Rijcke & Ismael Rafols, 2015. "Bibliometrics: The Leiden Manifesto for research metrics," Nature, Nature, vol. 520(7548), pages 429-431, April.
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    Cited by:

    1. Robin Haunschild & Lutz Bornmann, 2018. "Field- and time-normalization of data with many zeros: an empirical analysis using citation and Twitter data," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 997-1012, August.
    2. Lutz Bornmann & Klaus Wohlrabe, 2019. "Normalisation of citation impact in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 841-884, August.
    3. Thelwall, Mike, 2017. "Three practical field normalised alternative indicator formulae for research evaluation," Journal of Informetrics, Elsevier, vol. 11(1), pages 128-151.
    4. Lutz Bornmann, 2017. "Confidence intervals for Journal Impact Factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1869-1871, June.
    5. Bornmann, Lutz & Haunschild, Robin, 2018. "Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data," Journal of Informetrics, Elsevier, vol. 12(3), pages 998-1011.
    6. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.
    7. Claveau, François, 2016. "There should not be any mystery: A comment on sampling issues in bibliometrics," Journal of Informetrics, Elsevier, vol. 10(4), pages 1233-1240.
    8. Bornmann, Lutz & Williams, Richard, 2017. "Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data," Journal of Informetrics, Elsevier, vol. 11(3), pages 788-799.
    9. Thelwall, Mike & Fairclough, Ruth, 2017. "The accuracy of confidence intervals for field normalised indicators," Journal of Informetrics, Elsevier, vol. 11(2), pages 530-540.
    10. Ahmed Sabab Sharek & Kalim U. Shah, 2021. "Tracking the quality of scientific knowledge inputs in reports generated by the Intergovernmental Panel on Climate Change (IPCC)," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 11(4), pages 586-594, December.

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