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Robust Analysis of Bibliometric Data

Author

Listed:
  • Francesca De Battisti

    (Department of Economics, Business and Statistics - University of Milan)

  • Silvia Salini

    (Department of Economics, Business and Statistics - University of Milan)

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," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1113, Universitá degli Studi di Milano.
    • Handle: RePEc:bep:unimip:unimi-1113
    Note: oai:cdlib1:unimi-1113
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    Cited by:

    1. Claudio Giachetti & Giancarlo Manzi & Cinzia Colapinto, 2019. "Entry Mode Degree of Control, Firm Performance and Host Country Institutional Development: A Meta-Analysis," Management International Review, Springer, vol. 59(1), pages 3-39, February.
    2. Yajie Zhang & Qiang Yu, 2020. "What is the best article publishing strategy for early career scientists?," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 397-408, January.
    3. Waleed M. Sweileh & Sa’ed H. Zyoud & Samah W. Al-Jabi & Ansam F. Sawalha, 2014. "Bibliometric analysis of diabetes mellitus research output from Middle Eastern Arab countries during the period (1996–2012)," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 819-832, October.
    4. Silvia Salini & Andrea Cerioli & Fabrizio Laurini & Marco Riani, 2016. "Reliable Robust Regression Diagnostics," International Statistical Review, International Statistical Institute, vol. 84(1), pages 99-127, April.
    5. Arabinda Bhandari, 2023. "Design Thinking: from Bibliometric Analysis to Content Analysis, Current Research Trends, and Future Research Directions," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(3), pages 3097-3152, September.
    6. Chioma Okoro & Oluwatobi Mary Owojori & Nnedinma Umeokafor, 2022. "The Developmental Trajectory of a Decade of Research on Mental Health and Well-Being amongst Graduate Students: A Bibliometric Analysis," IJERPH, MDPI, vol. 19(9), pages 1-20, April.
    7. Atree, Manish Kumar & Tripathy, Naliniprava, 2025. "Cryptocurrency research: Bibliometric review and content analysis," International Review of Economics & Finance, Elsevier, vol. 98(C).
    8. Lorna Wildgaard, 2015. "A comparison of 17 author-level bibliometric indicators for researchers in Astronomy, Environmental Science, Philosophy and Public Health in Web of Science and Google Scholar," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 873-906, September.
    9. 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.
    10. Cerioli, Andrea & Farcomeni, Alessio & Riani, Marco, 2014. "Strong consistency and robustness of the Forward Search estimator of multivariate location and scatter," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 167-183.

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