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dc.contributor.authorThelwall, Mike
dc.date.accessioned2016-09-01T11:54:54Z
dc.date.available2016-09-01T11:54:54Z
dc.date.issued2016-07-25
dc.identifier.issn1751-1577en
dc.identifier.doi10.1016/j.joi.2016.07.006
dc.identifier.urihttp://hdl.handle.net/2436/619221
dc.description.abstractMany 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.
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S1751157716300517en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectScientometricsen
dc.subjectBibliometricsen
dc.subjectcitation countsen
dc.subjecthooked power lawen
dc.subjectLomax distributionen
dc.subjectlognormal distributionen
dc.titleCitation count distributions for large monodisciplinary journalsen
dc.typeJournal article
dc.identifier.journalJournal of Informetricsen
dc.date.accepted2016-07-14
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUoW010916MTen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2017-07-25en
dc.source.volume10
dc.source.issue3
dc.source.beginpage863
dc.source.endpage874
refterms.dateFCD2019-03-20T10:29:28Z
refterms.versionFCDAM
refterms.dateFOA2017-07-31T00:00:00Z
html.description.abstractMany 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.


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