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Quantitative and qualitative methods and measures in the evaluation of research

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  • David Roessner

Abstract

This paper proposes that the choice of quantitative versus qualitative measures in research evaluation is a false one, especially for evaluators isolated from the real world. This choice, sometimes substantially client-driven, should be tempered by professional judgment. It is sometimes easier to develop quantitative ‘indicators’ of performance than to work out what the program has to accomplish. As a result legislators and others increasingly ask public agencies for quantitative measures of research performance, and in so doing generate all kinds of mischief. Unfortunately, the fallacy of misplaced concreteness in research evaluation is still alive if not necessarily well. Copyright , Beech Tree Publishing.

Suggested Citation

  • David Roessner, 2000. "Quantitative and qualitative methods and measures in the evaluation of research," Research Evaluation, Oxford University Press, vol. 9(2), pages 125-132, August.
    • Handle: RePEc:oup:rseval:v:9:y:2000:i:2:p:125-132
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    File URL: http://hdl.handle.net/10.3152/147154400781777296
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    Cited by:

    1. Sarah Seus & Susanne Bührer, 2022. "The evaluation of the Austrian START programme: an impact analysis of a research funding programme using a multi-method approach," The Journal of Technology Transfer, Springer, vol. 47(3), pages 673-698, June.
    2. Martin Ricker, 2015. "A numerical algorithm with preference statements to evaluate the performance of scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 191-212, April.
    3. Peter A. Schulz & Edmilson J. T. Manganote, 2012. "Revisiting country research profiles: learning about the scientific cultures," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 517-531, November.
    4. Ismael Rafols & Alan Porter & Loet Leydesdorff, 2009. "Overlay Maps of Science: a New Tool for Research Policy," SPRU Working Paper Series 179, SPRU - Science Policy Research Unit, University of Sussex Business School.
    5. Denis O. Gray & Harm-Jan Steenhuis, 2003. "Quantifying the benefits of participating in an industry university research center: An examination of research cost avoidance," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(2), pages 281-300, October.
    6. Rafols, Ismael & Stirling, Andy, 2020. "Designing indicators for opening up evaluation. Insights from research assessment," SocArXiv h2fxp, Center for Open Science.
    7. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    8. Sebastian Grauwin & Pablo Jensen, 2011. "Mapping scientific institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 943-954, December.
    9. Matteo Pedrini & Valentina Langella & Mario Alberto Battaglia & Paola Zaratin, 2018. "Assessing the health research’s social impact: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1227-1250, March.
    10. Mathew Manimala & K. Thomas, 2013. "Learning Needs of Technology Transfer: Coping with Discontinuities and Disruptions," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 4(4), pages 511-539, December.
    11. Rafols, Ismael & Leydesdorff, Loet & O’Hare, Alice & Nightingale, Paul & Stirling, Andy, 2012. "How journal rankings can suppress interdisciplinary research: A comparison between Innovation Studies and Business & Management," Research Policy, Elsevier, vol. 41(7), pages 1262-1282.
    12. Fu, Junying & Frietsch, Rainer & Tagscherer, Ulrike, 2013. "Publication activity in the Science Citation Index Expanded (SCIE) database in the context of Chinese science and technology policy from 1977 to 2012," Discussion Papers "Innovation Systems and Policy Analysis" 35, Fraunhofer Institute for Systems and Innovation Research (ISI).

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