In this contribution we describe a novel procedure to represent uncertainty in rating scales in terms of fuzzy numbers. Following the rationale of fuzzy conversion scale, we adopted a two-step procedure based on a psychometric model (i.e., Item Response Theory-based tree) to represent the process of answering survey questions. This provides a coherent context where fuzzy numbers, and the related fuzziness, can be interpreted in terms of decision uncertainty that usually affects the rater’s response process. We reported results from a simulation study and an empirical application to highlight the characteristics and properties of the proposed approach.

fIRTree: An Item Response Theory Modeling of Fuzzy Rating Data

Calcagnì, Antonio
2021

Abstract

In this contribution we describe a novel procedure to represent uncertainty in rating scales in terms of fuzzy numbers. Following the rationale of fuzzy conversion scale, we adopted a two-step procedure based on a psychometric model (i.e., Item Response Theory-based tree) to represent the process of answering survey questions. This provides a coherent context where fuzzy numbers, and the related fuzziness, can be interpreted in terms of decision uncertainty that usually affects the rater’s response process. We reported results from a simulation study and an empirical application to highlight the characteristics and properties of the proposed approach.
2021
Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
978-94-6239-423-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3400192
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