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

Modeling human ratings data subject to raters' decision uncertainty is an attractive problem in applied statistics. In view of the complex interplay between emotion and making rating processes, final choices seldom reflect true underlying responses. Rather, they are imprecisely observed sense that a non-random component uncertainty, namely uncertainty. The purpose this article illustrate statistical approach analyse which integrates both random components process. particular, beta fuzzy numbers used model variable dispersion linear instead adopted counterpart main idea quantify characteristics latent non-fuzzy responses by means observations fuzziness. To do so, version Expectation-Maximization algorithm estimate model's parameters compute their standard errors. Finally, proposed investigated simulation study as well two case studies from behavioral social contexts.