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Cluster methods for assessing research performance: exploring Spanish computer science

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  • Alfonso Ibáñez

    (Universidad Politécnica de Madrid)

  • Pedro Larrañaga

    (Universidad Politécnica de Madrid)

  • Concha Bielza

    (Universidad Politécnica de Madrid)

Abstract

The objective of this paper is to propose a cluster analysis methodology for measuring the performance of research activities in terms of productivity, visibility, quality, prestige and international collaboration. The proposed methodology is based on bibliometric techniques and permits a robust multi-dimensional cluster analysis at different levels. The main goal is to form different clusters, maximizing within-cluster homogeneity and between-cluster heterogeneity. The cluster analysis methodology has been applied to the Spanish public universities and their academic staff in the computer science area. Results show that Spanish public universities fall into four different clusters, whereas academic staff belong into six different clusters. Each cluster is interpreted as providing a characterization of research activity by universities and academic staff, identifying both their strengths and weaknesses. The resulting clusters could have potential implications on research policy, proposing collaborations and alliances among universities, supporting institutions in the processes of strategic planning, and verifying the effectiveness of research policies, among others.

Suggested Citation

  • Alfonso Ibáñez & Pedro Larrañaga & Concha Bielza, 2013. "Cluster methods for assessing research performance: exploring Spanish computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 571-600, December.
    • Handle: RePEc:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-0985-9
    DOI: 10.1007/s11192-013-0985-9
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    1. Teodoro Luque-Martínez & Ignacio Luque-Raya, 2024. "Spanish scientific research by field and subject. Strategic analysis with ARWU indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5265-5285, September.
    2. Telcs, András & Kosztyán, Zsolt Tibor & Banász, Zsuzsanna & Csányi, Vivien Valéria, 2019. "Felsőoktatási ligák, parciális rangsorok képzése biklaszterezési eljárásokkal [How to rate higher education systems partial rankings using bi-clustering methods]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 905-931.
    3. Guannan Xu & Weijie Hu & Yuanyuan Qiao & Yuan Zhou, 2020. "Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2057-2081, September.
    4. Harlley Lima & Thiago H. P. Silva & Mirella M. Moro & Rodrygo L. T. Santos & Wagner Meira & Alberto H. F. Laender, 2015. "Assessing the profile of top Brazilian computer science researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 879-896, June.
    5. Federico Scarpa & Vincenzo Bianco & Luca A. Tagliafico, 2018. "The impact of the national assessment exercises on self-citation rate and publication venue: an empirical investigation on the engineering academic sector in Italy," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 997-1022, November.

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