Stopped sum models and proposed variants for citation data

2.50
Hdl Handle:
http://hdl.handle.net/2436/609864
Title:
Stopped sum models and proposed variants for citation data
Authors:
Low, Wan Jing; Wilson, Paul; Thelwall, Mike
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.
Citation:
Stopped sum models and proposed variants for citation data 2016, 107 (2):369 Scientometrics
Publisher:
Springer
Journal:
Scientometrics, May 2016, Volume 107, Issue 2, pp 369-384
Issue Date:
30-Jan-2016
URI:
http://hdl.handle.net/2436/609864
DOI:
10.1007/s11192-016-1847-z
Additional Links:
http://link.springer.com/10.1007/s11192-016-1847-z
Type:
Article
Language:
en
ISSN:
0138-9130; 1588-2861
Appears in Collections:
FSE

Full metadata record

DC FieldValue Language
dc.contributor.authorLow, Wan Jingen
dc.contributor.authorWilson, Paulen
dc.contributor.authorThelwall, Mikeen
dc.date.accessioned2016-05-19T11:39:54Zen
dc.date.available2016-05-19T11:39:54Zen
dc.date.issued2016-01-30en
dc.identifier.citationStopped sum models and proposed variants for citation data 2016, 107 (2):369 Scientometricsen
dc.identifier.issn0138-9130en
dc.identifier.issn1588-2861en
dc.identifier.doi10.1007/s11192-016-1847-zen
dc.identifier.urihttp://hdl.handle.net/2436/609864en
dc.description.abstractIt 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.en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://link.springer.com/10.1007/s11192-016-1847-zen
dc.rightsArchived with thanks to Scientometricsen
dc.subjectStopped sum modelsen
dc.subjectCitation countsen
dc.subjectDiscretised lognormalen
dc.subjectNegative binomialen
dc.subjectAICen
dc.subjectStandard erroren
dc.titleStopped sum models and proposed variants for citation dataen
dc.typeArticleen
dc.identifier.journalScientometrics, May 2016, Volume 107, Issue 2, pp 369-384en
All Items in WIRE are protected by copyright, with all rights reserved, unless otherwise indicated.