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Citation count distributions for large monodisciplinary journals

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  • Thelwall, Mike

Abstract

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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    Citations

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    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. Katchanov, Yurij L. & Markova, Yulia V. & Shmatko, Natalia A., 2023. "Uncited papers in the structure of scientific communication," Journal of Informetrics, Elsevier, vol. 17(2).
    5. Yang, Jinqing & Liu, Zhifeng, 2022. "The effect of citation behaviour on knowledge diffusion and intellectual structure," Journal of Informetrics, Elsevier, vol. 16(1).
    6. Brito, Ricardo & Navarro, Alonso Rodríguez, 2021. "The inconsistency of h-index: A mathematical analysis," Journal of Informetrics, Elsevier, vol. 15(1).
    7. Mike Thelwall & Kayvan Kousha & Mahshid Abdoli, 2017. "Is medical research informing professional practice more highly cited? Evidence from AHFS DI Essentials in drugs.com," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 509-527, July.
    8. 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.
    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. 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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