How to improve the prediction based on citation impact percentiles for years shortly after the publication date?

被引:98
作者
Bornmann, Lutz [1 ]
Leydesdorff, Loet [2 ]
Wang, Jian [3 ,4 ,5 ]
机构
[1] Adm Headquarters Max Planck Soc, Div Sci & Innovat Studies, D-80539 Munich, Germany
[2] Univ Amsterdam, Amsterdam Sch Commun Res ASCoR, NL-1012 CX Amsterdam, Netherlands
[3] Inst Res Informat & Qual Assurance iFQ, D-10117 Berlin, Germany
[4] Katholieke Univ Leuven, Ctr R&D Monitoring ECOOM, B-3000 Louvain, Belgium
[5] Katholieke Univ Leuven, Dept Managerial Econ Strategy & Innovat, B-3000 Louvain, Belgium
关键词
Citation impact normalization; Percentile; Short citation window; ASSESSMENTS;
D O I
10.1016/j.joi.2013.11.005
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The findings of Bornmann, Leydesdorff, and Wang (2013b) revealed that the consideration of journal impact improves the prediction of long-term citation impact. This paper further explores the possibility of improving citation impact measurements on the base of a short citation window by the consideration of journal impact and other variables, such as the number of authors, the number of cited references, and the number of pages. The dataset contains 475,391 journal papers published in 1980 and indexed in Web of Science (WoS, Thomson Reuters), and all annual citation counts (from 1980 to 2010) for these papers. As an indicator of citation impact, we used percentiles of citations calculated using the approach of Hazen (1914). Our results show that citation impact measurement can really be improved: If factors generally influencing citation impact are considered in the statistical analysis, the explained variance in the long-term citation impact can be much increased. However, this increase is only visible when using the years shortly after publication but not when using later years. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:175 / 180
页数:6
相关论文
共 23 条
[1]  
[Anonymous], 2004, SIGNIFICANCE TESTING, DOI DOI 10.1037/10693-000
[2]  
[Anonymous], 2018, GEN LINEAR MODELS EX
[3]  
[Anonymous], **NON-TRADITIONAL**
[4]   What do citation counts measure? A review of studies on citing behavior [J].
Bornmann, Luti ;
Daniel, Hans-Dieter .
JOURNAL OF DOCUMENTATION, 2008, 64 (01) :45-80
[5]   Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P100) [J].
Bornmann, Lutz ;
Leydesdorff, Loet ;
Wang, Jian .
JOURNAL OF INFORMETRICS, 2013, 7 (04) :933-944
[6]   The problem of citation impact assessments for recent publication years in institutional evaluations [J].
Bornmann, Lutz .
JOURNAL OF INFORMETRICS, 2013, 7 (03) :722-729
[7]   Multilevel-statistical reformulation of citation-based university rankings: The Leiden ranking 2011/2012 [J].
Bornmann, Lutz ;
Mutz, Ruediger ;
Daniel, Hans-Dieter .
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 2013, 64 (08) :1649-1658
[8]   How to calculate the practical significance of citation impact differences? An empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects [J].
Bornmann, Lutz ;
Williams, Richard .
JOURNAL OF INFORMETRICS, 2013, 7 (02) :562-574
[9]   The validation of (advanced) bibliometric indicators through peer assessments: A comparative study using data from InCites and F1000 [J].
Bornmann, Lutz ;
Leydesdorff, Loet .
JOURNAL OF INFORMETRICS, 2013, 7 (02) :286-291
[10]   The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits [J].
Bornmann, Lutz ;
Leydesdorff, Loet ;
Mutz, Ruediger .
JOURNAL OF INFORMETRICS, 2013, 7 (01) :158-165