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Modeling human ratings data subject to raters' decision uncertainty is anattractive problem in applied statistics. In view of the complex interplaybetween emotion and decision making in rating processes, final raters' choicesseldom reflect the true underlying raters' responses. Rather, they areimprecisely observed in the sense that they are subject to a non-randomcomponent of uncertainty, namely the decision uncertainty. The purpose of thisarticle is to illustrate a statistical approach to analyse ratings data whichintegrates both random and non-random components of the rating process. Inparticular, beta fuzzy numbers are used to model raters' non-random decisionuncertainty and a variable dispersion beta linear model is instead adopted tomodel the random counterpart of rating responses. The main idea is to quantifycharacteristics of latent and non-fuzzy rating responses by means of randomobservations subject to fuzziness. To do so, a fuzzy version of theExpectation-Maximization algorithm is adopted to both estimate model'sparameters and compute their standard errors. Finally, the characteristics ofthe proposed fuzzy beta model are investigated by means of a simulation studyas well as two case studies from behavioral and social contexts.

Modeling random and non-random decision uncertainty in ratings data: A fuzzy beta model