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The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment

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  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

  • Bart Thijs

    (KU Leuven)

  • Koenraad Debackere

    (KU Leuven)

Abstract

The analysis of the high end of citation distributions represented by its tail provides important supplementary information on the citation profile of the unit under study. In a previous study by Glänzel (Scientometrics 97:13–23, 2013a), a parameter-free solution providing four performance classes has been proposed. Unlike in methods based on pre-set percentiles, this method is not sensitive to ties and ensures needless integration of measures of outstanding and even extreme performance into the standard tools of scientometric performance assessment. The applicability of the proposed method is demonstrated for both subject analysis and the combination of different subjects at the macro and meso level.

Suggested Citation

  • Wolfgang Glänzel & Bart Thijs & Koenraad Debackere, 2014. "The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 939-952, November.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1247-1
    DOI: 10.1007/s11192-014-1247-1
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    References listed on IDEAS

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    1. Jonathan Adams & Karen Gurney & Stuart Marshall, 2007. "Profiling citation impact: A new methodology," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(2), pages 325-344, August.
    2. Loet Leydesdorff & Lutz Bornmann & Rüdiger Mutz & Tobias Opthof, 2011. "Turning the tables on citation analysis one more time: Principles for comparing sets of documents," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    3. Beirlant, Jan & Glänzel, Wolfgang & Carbonez, An & Leemans, Herlinde, 2007. "Scoring research output using statistical quantile plotting," Journal of Informetrics, Elsevier, vol. 1(3), pages 185-192.
    4. Wolfgang Glänzel & Bart Thijs & András Schubert & Koenraad Debackere, 2009. "Subfield-specific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(1), pages 165-188, January.
    5. Matthys, Gunther & Delafosse, Emmanuel & Guillou, Armelle & Beirlant, Jan, 2004. "Estimating catastrophic quantile levels for heavy-tailed distributions," Insurance: Mathematics and Economics, Elsevier, vol. 34(3), pages 517-537, June.
    6. Wolfgang Glänzel & András Schubert, 2003. "A new classification scheme of science fields and subfields designed for scientometric evaluation purposes," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 357-367, March.
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    Citations

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    Cited by:

    1. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    2. Pei-Shan Chi & Wolfgang Glänzel, 2017. "An empirical investigation of the associations among usage, scientific collaboration and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 403-412, July.
    3. Vîiu, Gabriel-Alexandru, 2018. "The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 401-415.
    4. Wolfgang Glänzel & Bart Thijs, 2018. "The role of baseline granularity for benchmarking citation impact. The case of CSS profiles," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 521-536, July.
    5. Albarrán, Pedro & Herrero, Carmen & Ruiz-Castillo, Javier & Villar, Antonio, 2017. "The Herrero-Villar approach to citation impact," Journal of Informetrics, Elsevier, vol. 11(2), pages 625-640.
    6. Georgios Stoupas & Antonis Sidiropoulos & Antonia Gogoglou & Dimitrios Katsaros & Yannis Manolopoulos, 2018. "Rainbow ranking: an adaptable, multidimensional ranking method for publication sets," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 147-160, July.
    7. Tomaz Bartol & Gordana Budimir & Primoz Juznic & Karmen Stopar, 2016. "Mapping and classification of agriculture in Web of Science: other subject categories and research fields may benefit," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 979-996, November.
    8. Zhiqi Wang & Wolfgang Glänzel & Yue Chen, 2020. "The impact of preprints in Library and Information Science: an analysis of citations, usage and social attention indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1403-1423, November.
    9. Wolfgang Glänzel & Sarah Heeffer & Bart Thijs, 2016. "A triangular model for publication and citation statistics of individual authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 857-872, May.
    10. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    11. Wolfgang Glänzel & Sarah Heeffer & Bart Thijs, 2017. "Lexical analysis of scientific publications for nano-level scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1897-1906, June.
    12. Abramo, Giovanni & D’Angelo, Ciriaco Andrea, 2016. "A farewell to the MNCS and like size-independent indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 646-651.
    13. Pei-Shan Chi & Juan Gorraiz & Wolfgang Glänzel, 2019. "Comparing capture, usage and citation indicators: an altmetric analysis of journal papers in chemistry disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1461-1473, September.
    14. Andrea Bonaccorsi & Tindaro Cicero & Peter Haddawy & Saeed-UL Hassan, 2017. "Explaining the transatlantic gap in research excellence," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 217-241, January.
    15. Vîiu, Gabriel-Alexandru, 2017. "Disaggregated research evaluation through median-based characteristic scores and scales: a comparison with the mean-based approach," Journal of Informetrics, Elsevier, vol. 11(3), pages 748-765.
    16. Bart Thijs & Koenraad Debackere & Wolfgang Glänzel, 2017. "Improved author profiling through the use of citation classes," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 829-839, May.
    17. Pei-Shan Chi & Wolfgang Glänzel, 2018. "Comparison of citation and usage indicators in research assessment in scientific disciplines and journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 537-554, July.
    18. Wolfgang Glänzel & Bart Thijs & Pei-Shan Chi, 2016. "The challenges to expand bibliometric studies from periodical literature to monographic literature with a new data source: the book citation index," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2165-2179, December.
    19. Wolfgang Glänzel & Lin Zhang, 2018. "Scientometric research assessment in the developing world: A tribute to Michael J. Moravcsik from the perspective of the twenty-first century," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1517-1532, June.
    20. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.
    21. Lutz Bornmann & Alexander Tekles & Loet Leydesdorff, 2019. "How well does I3 perform for impact measurement compared to other bibliometric indicators? The convergent validity of several (field-normalized) indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1187-1205, May.
    22. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.

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