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Robust analysis of bibliometric data

Author

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  • Francesca DE BATTISTI

    ()

  • Silvia SALINI

    ()

Abstract

The aim of the work is to reproduce the image of the research profile of the Italian statisticians derived from querying of bibliometric databases. We highlighted the need for multiple sources in order to convey a truer picture and how the data could be combined in order to have a classification or an index of overall productivity, which took into account all sources and metrics. The data matrix contains a set of metrics from a variety of databases for each author and it is a sparse matrix (there are many zeros). Furthermore, the variables are leptokurtic and characterized by positive asymmetry. In order to apply the classical techniques of multivariate analysis, the data must be transformed first or alternatively robust analysis techniques have to be used. In the paper we will focus on this type of bibliometric data, describing their main characteristics and problems. In addition, a robust approach to the analysis of these data will be presented.

Suggested Citation

  • Francesca DE BATTISTI & Silvia SALINI, 2011. "Robust analysis of bibliometric data," Departmental Working Papers 2011-36, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2011-36
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    File URL: http://wp.demm.unimi.it/files/wp/2011/DEMM-2011_036wp.pdf
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    1. repec:spr:scient:v:87:y:2011:i:3:d:10.1007_s11192-011-0350-9 is not listed on IDEAS
    2. Baccini, Alberto & Barabesi, Lucio, 2011. "Seats at the table: The network of the editorial boards in information and library science," Journal of Informetrics, Elsevier, vol. 5(3), pages 382-391.
    3. Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008. "Outlier identification in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1694-1711, January.
    4. repec:spr:scient:v:67:y:2006:i:3:d:10.1556_scient.67.2006.3.5 is not listed on IDEAS
    5. Atkinson, A.C. & Riani, M., 2007. "Exploratory tools for clustering multivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 272-285, September.
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    Cited by:

    1. repec:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1361-0 is not listed on IDEAS
    2. repec:spr:scient:v:104:y:2015:i:3:d:10.1007_s11192-015-1608-4 is not listed on IDEAS
    3. Andrea Cerioli & Domenico Perrotta, 2014. "Robust clustering around regression lines with high density regions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 5-26, March.

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