Robust analysis of bibliometric data

Francesca De Battisti () and Silvia Salini ()

Statistical Methods & Applications, 2013, vol. 22, issue 2, 269-283

Abstract: This work stems from the idea of describing the scientific productivity of Italian statisticians. There are several problems that must be addressed in achieving this goal: What data should be used? Have the data been cleaned? What techniques can be used? We propose the use of multiple sources and multiple metrics to get a complete information base. We check the correctness of the data using multivariate outlier identification techniques. We appropriately transform the data. We apply robust clustering to verify the existence of homogeneous groups. We suggest the use of forward search to establish a ranking among scholars. The proposed methodology, which, in this case, allowed us to group scholars into four homogeneous groups and sort them according to multidimensional data, can be applied to other similar applications in bibliometrics. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Bibliometric indicators; Multivariate transformation; Cluster analysis; Forward search (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (8)

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Working Paper: Robust Analysis of Bibliometric Data (2011)
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DOI: 10.1007/s10260-012-0217-0

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