A Model-Based Fuzzy Analysis of Questionnaires

TL;DR

This paper introduces a hybrid fuzzy-statistical model for analyzing questionnaire data, capturing inherent vagueness and uncertainty in customer satisfaction surveys using advanced fuzzy set theory and mixture models.

Contribution

It presents a novel model-based fuzzy analysis framework combining fuzzy evaluation and statistical modeling for ordinal data, with a focus on defuzzification and uncertainty measurement.

Findings

Effective in capturing shades of evaluation in surveys

Demonstrated on university orientation service data

Provides aggregated uncertainty measures

Abstract

In dealing with veracity of data analytics, fuzzy methods are more and more relying on probabilistic and statistical techniques to underpin their applicability. Conversely, standard statistical models usually disregard to take into account the inherent fuzziness of choices and this issue is particularly worthy of note in customers' satisfaction surveys, since there are different shades of evaluations that classical statistical tools fail to catch. Given these motivations, the paper introduces a model-based fuzzy analysis of questionnaire with sound statistical foundation, driven by the design of a hybrid method that sets in between fuzzy evaluation systems and statistical modelling. The proposal is advanced on the basis of \cub mixture models to account for uncertainty in ordinal data analysis and moves within the general framework of Intuitionistic Fuzzy Set theory to allow membership, non-membership, vagueness and accuracy assessments. Particular emphasis is given to defuzzification procedures that enable uncertainty measures also at an aggregated level. An application to a survey run at the University of Naples Federico II about the evaluation of Orientation Services supports the efficacy of the proposal.