NDLI: Stopped sum models and proposed variants for citation data

Content Provider	Springer Nature Link
Author	Low, Wan Jing Wilson, Paul Thelwall, Mike
Copyright Year	2016
Abstract	It is important to identify the most appropriate statistical model for citation data in order to maximise the potential of future analyses as well as to shed light on the processes that may drive citations. This article assesses stopped sum models and some variants and compares them with two previously used models, the discretised lognormal and negative binomial, using the Akaike Information Criterion (AIC). Based upon data from 20 Scopus categories, some of the stopped sum variant models had lower AIC values than the discretised lognormal models, which were otherwise the best (with respect to AIC). However, very large standard errors were returned for some of these variant models, indicating the imprecision of the estimates and the impracticality of the approach. Hence, although the stopped sum variant models show some promise for citation analysis, they are only recommended when they fit better than the alternatives and have manageable standard errors. Nevertheless, their good fit to citation data gives evidence that two different, but related, processes may drive citations.
Starting Page	369
Ending Page	384
Page Count	16
File Format	PDF
ISSN	01389130
Journal	Scientometrics
Volume Number	107
Issue Number	2
e-ISSN	15882861
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-01-30
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Stopped sum models Citation counts Discretised lognormal Negative binomial AIC Standard error Information Storage and Retrieval Library Science
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Computer Science Applications

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