Assessing risk perception by means of ordinal models

Abstract

This paper presents a discrete mixture model as a suitable approach for the analysis of data concerning risk perception, when they are expressed by means of ordered scores (ratings). The model, which is the result of a personal feeling (risk perception) towards the object and an inherent uncertainty in the choice of the ordinal value of responses, reduces the collective information, synthesising different risk dimensions related to a preselected domain. After a brief introduction to risk management, the presentation of the CUB model and related inferential issues, we illustrate a case study concerning risk perception for the workers of a printing press factory.