Citation count distributions for large monodisciplinary journals. Issue 3 (August 2016)

• Record Type: Journal Article
• Authors: Thelwall, Mike
• Is Part Of: Journal of informetrics. Volume 10:Issue 3(2016:Jul.)
• Page Start: 863
• Page End: 874
• Publication Date: 2016-08
• Subjects: Citation distributions -- Discretised lognormal distribution -- Lognormal distribution -- Hooked power law -- Citation analysis -- Shifted power law; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2016.07.006
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: The discretised lognormal fits better than the hooked power law for relatively pure citation distributions. The discretised lognormal fits better than the hooked power law for large monodisciplinary journals. Both the discretised lognormal and the hooked power law have imperfect shapes for citation distributions. 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.