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Multivariate approach to classify research institutes according to their outputs: The case of the CSIC's institutes

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  • Ortega, José Luis
  • López-Romero, Elena
  • Fernández, Inés

Abstract

This paper attempts to build a classification model according to the research products created by those institutes and hence to design specific evaluation processes. Several scientific input/output indicators belonging to 109 research institutes from the Spanish National Research Council (CSIC) were selected. A multidimensional approach was proposed to resume these indicators in various components. A clustering analysis was used to classify the institutes according to their scores with those components (principal component analysis). Moreover, the validity of the a priori classification was tested and the most discriminant variables were detected (linear discriminant analysis). Results show that there are three types of institutes according to their research outputs: Humanistic, Scientific and Technological. It is argue that these differences oblige to design more precise assessment exercises which focus on the particular results of each type of institute. We conclude that this method permits to build more precise research assessment exercises which consider the varied nature of the scientific activity.

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  • Ortega, José Luis & López-Romero, Elena & Fernández, Inés, 2011. "Multivariate approach to classify research institutes according to their outputs: The case of the CSIC's institutes," Journal of Informetrics, Elsevier, vol. 5(3), pages 323-332.
    • Handle: RePEc:eee:infome:v:5:y:2011:i:3:p:323-332
    DOI: 10.1016/j.joi.2011.01.004
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    2. Cova, Tânia F.G.G. & Jarmelo, Susana & Formosinho, Sebastião J. & de Melo, J. Sérgio Seixas & Pais, Alberto A.C.C., 2015. "Unsupervised characterization of research institutions with task-force estimation," Journal of Informetrics, Elsevier, vol. 9(1), pages 59-68.
    3. Seiler, Christian & Wohlrabe, Klaus, 2012. "Ranking economists on the basis of many indicators: An alternative approach using RePEc data," Journal of Informetrics, Elsevier, vol. 6(3), pages 389-402.
    4. Gnewuch, Matthias & Wohlrabe, Klaus, 2018. "Super-efficiency of education institutions: an application to economics departments," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 26, pages 610-623.
    5. Cena, Anna & Gagolewski, Marek & Mesiar, Radko, 2015. "Problems and challenges of information resources producers’ clustering," Journal of Informetrics, Elsevier, vol. 9(2), pages 273-284.
    6. Lee, Seonghee & Lee, Hakyeon, 2015. "Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach," Journal of Informetrics, Elsevier, vol. 9(4), pages 942-953.
    7. Gabriel-Alexandru Vîiu & Mihai Păunescu & Adrian Miroiu, 2016. "Research-driven classification and ranking in higher education: an empirical appraisal of a Romanian policy experience," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 785-805, May.
    8. Judith Czellar & Jacques Lanarès, 2013. "Quality of research: which underlying values?," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 1003-1021, June.
    9. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia & Nicolas Robinson-García & Daniel Torres-Salinas, 2013. "Benchmarking research performance at the university level with information theoretic measures," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 435-452, April.
    10. Seiler, Christian & Wohlrabe, Klaus, 2013. "Archetypal scientists," Journal of Informetrics, Elsevier, vol. 7(2), pages 345-356.

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