Robust Analysis of Bibliometric Data

Francesca De Battisti and Silvia Salini ()
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Francesca De Battisti: Department of Economics, Business and Statistics - University of Milan

No unimi-1113, UNIMI - Research Papers in Economics, Business, and Statistics from Universitá degli Studi di Milano

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.

Keywords: Bibliometric Indicators; Multivariate Transformation; Cluster Analysis; Forward Search (search for similar items in EconPapers)
Date: 2011-10-04
Note: oai:cdlib1:unimi-1113
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Robust analysis of bibliometric data (2013)
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