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Robust analysis of bibliometric data

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  • Francesca De Battisti
  • Silvia Salini

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

Suggested Citation

  • Francesca De Battisti & Silvia Salini, 2013. "Robust analysis of bibliometric data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 269-283, June.
    • Handle: RePEc:spr:stmapp:v:22:y:2013:i:2:p:269-283
    DOI: 10.1007/s10260-012-0217-0
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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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. 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.
    8. 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.
    9. 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.

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