Design qualitative data in DEA With admixture indicators

Composite indicators (CIs) are useful tools for performance evaluation in policy analysis and public communication. It must be pointed out that among various performance evaluation methodologies; data envelopment analysis (DEA) has recently received considerable attention in the construction of CIs. In basic DEA-based CI models, obtainment of measurable and quantitative indicators is commonly the prerequisite of the evaluation. However, it becomes more and more difficult to be guaranteed in today s complex performance evaluation activities, because the natural uncertainty of reality often leads up to the imprecision and vagueness inherent in the information that can only be represented by means of qualitative data. In this study, we investigate two approaches within the DEA framework for modeling both quantitative and qualitative data in the context of composite indicators construction. They are imprecise DEA (IDEA) and fuzzy DEA (FDEA), respectively. It would be worth mentioning that, we propose two new models of IDEA-based CIs and FDEA-based CIs in road safety management evaluation by creating a composite road safety policy performance index for 25 countries. The results verify the robustness of the index scores computed from both models, and further imply the effectiveness and reliability of the proposed two approaches for modeling qualitative data. It is taken for granted that, as a data-oriented technique, the applicability of DEA in the construction of CIs relies mostly on the quality of information about the indicators. In other words, obtainment of measurable and quantitative indicators is commonly the prerequisite of the evaluation. Under many conditions, however, quantitative data are inadequate or inappropriate to model real world situations due to the complexity and uncertainty of the reality. Therefore, it is essential to take into account the presence of qualitative indicators when making a decision on the performance of a DMU.

Keywords:

Composite indicators , Qualitative data , systematic data , Interval DEA , Fuzzy DEA