Inferential confidence intervals for fuzzy analysis of teaching satisfaction

Donata Marasini, Piero Quatto and Enrico Ripamonti ()
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Donata Marasini: Università degli Studi di Milano-Bicocca
Piero Quatto: Università degli Studi di Milano-Bicocca
Enrico Ripamonti: Università degli Studi di Milano-Bicocca

Quality & Quantity: International Journal of Methodology, 2017, vol. 51, issue 4, No 5, 1513-1529

Abstract: Abstract Fuzzy sets are an extension of classical sets, used to mathematically model indefinite concepts, such as that of customer satisfaction. This is obtained by introducing a membership function expressing the degree of membership of the elements to a set. Intuitionistic fuzzy sets represent an extension of the theory of fuzzy sets, in which also a suitable non-membership function is defined. In this paper we aim at quantifying a latent construct, namely satisfaction, using fuzzy sets and intuitionistic fuzzy sets. We put forth a general evaluation method: first, we introduce a fuzzy satisfaction index to obtain membership values. Second, inferential confidence intervals (ICI), calculated through Bootstrap-t and percentile procedures, are used to assess the uncertainty underpinning membership and non-membership estimates. Third, we address the problem of optimal and multiple ICI, as well as their generalization through p values and q-values. In particular, we consider the problem of analyzing the responses to evaluation questionnaires. We apply this new method to a national program of evaluation of University courses and we discuss our framework in comparison with other evaluation techniques.

Keywords: Fuzzy sets; Intuitionistic fuzzy sets; Teaching evaluation; Bootstrap methods; Inferential confidence intervals; Satisfaction indices; Positive false discovery rate (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11135-016-0349-7

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