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.
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Sarkis, J. (2007). Preparing Your Data for DEA. In: Zhu, J., Cook, W.D. (eds) Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-71607-7_17
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DOI: https://doi.org/10.1007/978-0-387-71607-7_17
Publisher Name: Springer, Boston, MA
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