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Intraorganisational evaluation: are there ‘least evaluable units'?

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  • Jochen Gläser
  • Thomas H Spurling
  • Linda Butler

Abstract

An increasing number of universities and research organisations are introducing internal evaluations which are often based on quantitative indicators. However, it is likely that a ‘least evaluable unit’ (LEU) exists in every research organisation, and that below this level many standard quantitative performance indicators no longer provide a valid measure of performance. In this paper, the LEU of a research organisation is identified by analysing retrospective performance evaluations at different levels of aggregation and enhancing their interpretation with the detailed knowledge of the organisation's senior manager. The main obstacles to further disaggregation below the LEU are that indicators lose their statistical validity because of low numbers of publications and that the performance of subunits cannot be independently measured. The latter phenomenon is heightened at the level of scientists because work roles emerged that further clouded the application of performance measures to individuals. Copyright , Beech Tree Publishing.

Suggested Citation

  • Jochen Gläser & Thomas H Spurling & Linda Butler, 2004. "Intraorganisational evaluation: are there ‘least evaluable units'?," Research Evaluation, Oxford University Press, vol. 13(1), pages 19-32, April.
  • Handle: RePEc:oup:rseval:v:13:y:2004:i:1:p:19-32
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    File URL: http://hdl.handle.net/10.3152/147154404781776554
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    Cited by:

    1. Gordon Rogers & Martin Szomszor & Jonathan Adams, 2020. "Sample size in bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 777-794, October.
    2. Leonardo Reyes-Gonzalez & Claudia N. Gonzalez-Brambila & Francisco Veloso, 2016. "Using co-authorship and citation analysis to identify research groups: a new way to assess performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1171-1191, September.
    3. Domenico De Stefano & Susanna Zaccarin, 2013. "Modelling Multiple Interactions in Science and Technology Networks," Industry and Innovation, Taylor & Francis Journals, vol. 20(3), pages 221-240, April.

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