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Preparing Your Data for DEA

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

Author

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  • Joe Sarkis

    (Clark University, 950 Main Street)

Abstract

DEA and its appropriate applications are heavily dependent on the data set that is used as an input to the productivity model. As we now know there are numerous models based on DEA. However, there are certain characteristics of data that may not be acceptable for the execution of DEA models. In this chapter we shall look at some data requirements and characteristics that may ease the execution of the models and the interpretation of results. The lessons and ideas presented here are based on a number of experiences and considerations for DEA. We shall not get into the appropriate selection and development of models, such as what is used for input or output data, but focus more on the type of data and the numerical characteristics of this data.

Suggested Citation

  • Joe Sarkis, 2007. "Preparing Your Data for DEA," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 305-320, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-71607-7_17
    DOI: 10.1007/978-0-387-71607-7_17
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