Citation count distributions for large monodisciplinary journals

TL;DR

This study compares statistical models for citation counts in large journals, finding the discretised lognormal distribution fits better than the hooked power law, which impacts how citation impact is analyzed.

Contribution

It provides evidence that the discretised lognormal distribution is more suitable for modeling citation data in individual journals, challenging previous findings at broader levels.

Findings

Discretised lognormal fits better than hooked power law in most cases.

Results challenge previous findings at the subcategory level.

Improved software for fitting the hooked power law is included.

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.