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Composite indicators (CIs) are useful tools for performance evaluation in policy analysis and public communication. 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 …