2.50
Hdl Handle:
http://hdl.handle.net/2436/619221
Title:
Citation count distributions for large monodisciplinary journals
Authors:
Thelwall, Mike ( 0000-0001-6065-205X )
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.
Publisher:
Elsevier
Journal:
Journal of Informetrics
Issue Date:
Aug-2016
URI:
http://hdl.handle.net/2436/619221
Additional Links:
http://www.sciencedirect.com/science/article/pii/S1751157716300517
Type:
Article
Language:
en
ISSN:
1751-1577
Appears in Collections:
Statistical Cybermetrics Research Group

Full metadata record

DC FieldValue Language
dc.contributor.authorThelwall, Mikeen
dc.date.accessioned2016-09-01T11:54:54Z-
dc.date.available2016-09-01T11:54:54Z-
dc.date.issued2016-08-
dc.identifier.issn1751-1577en
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.en
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.typeArticleen
dc.identifier.journalJournal of Informetricsen
dc.date.accepted2016-07-
rioxxterms.funderInternalen
rioxxterms.identifier.projectUoW010916MTen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2017-07-31en
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