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    Stopped sum models and proposed variants for citation data

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    Name:
    Stopped sum models and proposed ...
    Embargo:
    2107-01-29
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    Authors
    Low, Wan Jing
    Wilson, Paul
    Thelwall, Mike
    Issue Date
    2016-01-30
    
    Metadata
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    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
    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
    Journal article
    Language
    en
    ISSN
    0138-9130
    1588-2861
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11192-016-1847-z
    Scopus Count
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    Faculty of Science and Engineering

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