Fitting mixture models for feeling and uncertainty for rating data analysis. (March 2022)

• Record Type: Journal Article
• Authors: Cerulli, Giovanni; Simone, Rosaria; Di Iorio, Francesca; Piccolo, Domenico; Baum, Christopher F.
• Is Part Of: Stata journal. Volume 22:Number 1(2022)
• Page Start: 195
• Page End: 223
• Publication Date: 2022-03
• Subjects: st0669 -- cub -- scattercub -- CUB -- mixture models -- rating data -- maximum likelihood estimation; Statistics -- Periodicals; Statistics -- Computer programs -- Periodicals; 001.422
• DOI: 10.1177/1536867X221083927
• Languages: English
• ISSNs: 1536-867X

Abstract

In this article, we present the commandcub, 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 ofcub using a case study on students' satisfaction for the orientation services provided by the University of Naples Federico II in Italy.