Two-level DEA approaches in research evaluation

Wei Meng, Daqun Zhang, Qi Li and Wenbin Liu

Omega, 2008, vol. 36, issue 6, 950-957

Abstract: It is well known that the discrimination power of data envelopment analysis (DEA) models will be much weakened if too many input or output indicators are used. It is a dilemma if decision makers wish to select comprehensive indicators, which often have some hierarchical structures, to present a relatively holistic evaluation using DEA. In this paper we show that it is possible to develop DEA models that utilize hierarchical structures of input-output data so that they are able to handle quite large numbers of inputs and outputs. We present two approaches in a pilot evaluation of 15 institutes for basic research in the Chinese Academy of Sciences using the DEA models.

Keywords: Hierarchical; structures; Discrimination; power; DEA; Research; evaluation (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (30)

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