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Rank Order Data In Dea

In: Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

Author

Listed:
  • Wade D. Cook

    (York University)

  • Joe Zhu

    (Worcester Polytechnic Institute)

Abstract

In data envelopment analysis (DEA), performance evaluation is generally assumed to be based upon a set of quantitative data. In many real world settings, however, it is essential to take into account the presence of qualitative factors when evaluating the performance of decision making units (DMUs). Very often rankings are provided from best to worst relative to particular attributes. Such rank positions might better be presented in an ordinal, rather than numerical sense. The chapter develops a general framework for modeling and treating qualitative data in DEA, and provides a unified structure for embedding rank order data into the DEA framework. We show that the approach developed earlier in Cook et al (1993, 1996) is equivalent to the IDEA methodology given in Chapter 3. It is shown that, like IDEA, the approach given her for dealing with qualitative data lends itself to treatment by conventional DEA methodology.

Suggested Citation

  • Wade D. Cook & Joe Zhu, 2007. "Rank Order Data In Dea," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 13-34, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-71607-7_2
    DOI: 10.1007/978-0-387-71607-7_2
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    Cited by:

    1. Färe, Rolf & Grosskopf, Shawna, 2013. "DEA, directional distance functions and positive, affine data transformation," Omega, Elsevier, vol. 41(1), pages 28-30.
    2. Beatriz García-Cornejo & José A. Pérez-Méndez & David Roibás & Alan Wall, 2020. "Efficiency and Sustainability in Farm Diversification Initiatives in Northern Spain," Sustainability, MDPI, vol. 12(10), pages 1-18, May.

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