  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.
