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How to consider fractional counting and field normalization in the statistical modeling of bibliometric data: A multilevel Poisson regression approach


Mutz, Rüdiger; Daniel, Hans-Dieter (2019). How to consider fractional counting and field normalization in the statistical modeling of bibliometric data: A multilevel Poisson regression approach. Journal of Informetrics, 13(2):643-657.

Abstract

The numerical-algorithmic procedures of fractional counting and field normalization are often mentioned as indispensable requirements for bibliometric analyses. Against the background of the increasing importance of statistics in bibliometrics, a multilevel Poisson regression model (level 1: publication, level 2: author) shows possible ways to consider fractional counting and field normalization in a statistical model (fractional counting I). However, due to the assumption of duplicate publications in the data set, the approach is not quite optimal. Therefore, a more advanced approach, a multilevel multiple membership model, is proposed that no longer provides for duplicates (fractional counting II). It is assumed that the citation impact can essentially be attributed to time-stable dispositions of researchers as authors who contribute with different fractions to the success of a publication’s citation. The two approaches are applied to bibliometric data for 254 scientists working in social science methodology. A major advantage of fractional counting II is that the results no longer depend on the type of fractional counting (e.g., equal weighting). Differences between authors in rankings are reproduced more clearly than on the basis of percentiles. In addition, the strong importance of field normalization is demonstrated; 60% of the citation variance is explained by field normalization.

Abstract

The numerical-algorithmic procedures of fractional counting and field normalization are often mentioned as indispensable requirements for bibliometric analyses. Against the background of the increasing importance of statistics in bibliometrics, a multilevel Poisson regression model (level 1: publication, level 2: author) shows possible ways to consider fractional counting and field normalization in a statistical model (fractional counting I). However, due to the assumption of duplicate publications in the data set, the approach is not quite optimal. Therefore, a more advanced approach, a multilevel multiple membership model, is proposed that no longer provides for duplicates (fractional counting II). It is assumed that the citation impact can essentially be attributed to time-stable dispositions of researchers as authors who contribute with different fractions to the success of a publication’s citation. The two approaches are applied to bibliometric data for 254 scientists working in social science methodology. A major advantage of fractional counting II is that the results no longer depend on the type of fractional counting (e.g., equal weighting). Differences between authors in rankings are reproduced more clearly than on the basis of percentiles. In addition, the strong importance of field normalization is demonstrated; 60% of the citation variance is explained by field normalization.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Uncontrolled Keywords:Library and Information Sciences, Computer Science Applications
Language:English
Date:1 May 2019
Deposited On:11 Jul 2019 09:02
Last Modified:25 Sep 2019 00:38
Publisher:Elsevier
ISSN:1751-1577
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.joi.2019.03.007

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