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Citation count distributions for large monodisciplinary journals


  • Thelwall, Mike


Many different citation-based indicators are used by researchers and research evaluators to help evaluate the impact of scholarly outputs. Although the appropriateness of individual citation indicators depends in part on the statistical properties of citation counts, there is no universally agreed best-fitting statistical distribution against which to check them. The two current leading candidates are the discretised lognormal and the hooked or shifted power law. These have been mainly tested on sets of articles from a single field and year but these collections can include multiple specialisms that might dilute their properties. This article fits statistical distributions to 50 large subject-specific journals in the belief that individual journals can be purer than subject categories and may therefore give clearer findings. The results show that in most cases the discretised lognormal fits significantly better than the hooked power law, reversing previous findings for entire subcategories. This suggests that the discretised lognormal is the more appropriate distribution for modelling pure citation data. Thus, future analytical investigations of the properties of citation indicators can use the lognormal distribution to analyse their basic properties. This article also includes improved software for fitting the hooked power law.

Suggested Citation

  • Thelwall, Mike, 2016. "Citation count distributions for large monodisciplinary journals," Journal of Informetrics, Elsevier, vol. 10(3), pages 863-874.
  • Handle: RePEc:eee:infome:v:10:y:2016:i:3:p:863-874
    DOI: 10.1016/j.joi.2016.07.006

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    References listed on IDEAS

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    2. Thelwall, Mike, 2016. "The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression," Journal of Informetrics, Elsevier, vol. 10(2), pages 336-346.
    3. Thelwall, Mike & Wilson, Paul, 2014. "Regression for citation data: An evaluation of different methods," Journal of Informetrics, Elsevier, vol. 8(4), pages 963-971.
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    8. Didegah, Fereshteh & Thelwall, Mike, 2013. "Which factors help authors produce the highest impact research? Collaboration, journal and document properties," Journal of Informetrics, Elsevier, vol. 7(4), pages 861-873.
    9. Thelwall, Mike & Fairclough, Ruth, 2015. "Geometric journal impact factors correcting for individual highly cited articles," Journal of Informetrics, Elsevier, vol. 9(2), pages 263-272.
    10. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    11. López-Illescas, Carmen & de Moya-Anegón, Félix & Moed, Henk F., 2008. "Coverage and citation impact of oncological journals in the Web of Science and Scopus," Journal of Informetrics, Elsevier, vol. 2(4), pages 304-316.
    12. Thelwall, Mike, 2016. "Are the discretised lognormal and hooked power law distributions plausible for citation data?," Journal of Informetrics, Elsevier, vol. 10(2), pages 454-470.
    13. Thelwall, Mike & Wilson, Paul, 2014. "Distributions for cited articles from individual subjects and years," Journal of Informetrics, Elsevier, vol. 8(4), pages 824-839.
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    Cited by:

    1. Mike Thelwall & Kayvan Kousha, 2017. "ResearchGate versus Google Scholar: Which finds more early citations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 1125-1131, August.
    2. Thelwall, Mike, 2017. "Three practical field normalised alternative indicator formulae for research evaluation," Journal of Informetrics, Elsevier, vol. 11(1), pages 128-151.
    3. Vîiu, Gabriel-Alexandru, 2018. "The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 401-415.
    4. Mike Thelwall & Kayvan Kousha & Mahshid Abdoli, 2017. "Is medical research informing professional practice more highly cited? Evidence from AHFS DI Essentials in," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 509-527, July.
    5. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2018. "Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 869-892, May.
    6. Thelwall, Mike & Fairclough, Ruth, 2017. "The accuracy of confidence intervals for field normalised indicators," Journal of Informetrics, Elsevier, vol. 11(2), pages 530-540.
    7. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2019. "Evaluating research and researchers by the journal impact factor: Is it better than coin flipping?," Journal of Informetrics, Elsevier, vol. 13(1), pages 314-324.


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