Fitting mixture models for feeling and uncertainty for rating data analysis

Giovanni Cerulli, Rosaria Simone (), Francesca Di Iorio, Domenico Piccolo () and Christopher Baum
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Rosaria Simone: University of Naples Federico II

Stata Journal, 2022, vol. 22, issue 1, 195-223

Abstract: In this article, we present the command cub, which fits ordinal rating data using combination of uniform and binomial (CUB) models, a class of finite mixture distributions accounting for both feeling and uncertainty of the response process. CUB identifies the components that define the mixture in the baseline model specification. We apply maximum likelihood methods to estimate feeling and uncertainty parameters, which are possibly explained in terms of covariates. An extension to inflated CUB models is discussed. We also present a subcommand, scattercub, for visualization of results. We then illustrate the use of cub using a case study on students’ satisfaction for the orientation services provided by the University of Naples Federico II in Italy.

Keywords: cub; scattercub; CUB; mixture models; rating data; maximum likelihood estimation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1177/1536867X221083927

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