Traditional statistical models with random effects account for heterogeneity in the population with respect to the location of the response in a subject-specific way. This approach ignores that also uncertainty of the responses can vary across individuals and items: for example, subject-specific indecision may play a role in the rating process relative to questionnaire items. In this setting, a generalized mixture model is advanced that accounts for subjective heterogeneity in response behaviour for multivariate ordinal responses: to this aim, random effects are specified for the individual propensity to a structured or an uncertain response attitude. Simulations and a case study illustrate the effectiveness of the proposed model and its implications.

Subjective heterogeneity in response attitude for multivariate ordinal outcomes / Simone, Rosaria; Tutz, Gerhard; Iannario, Maria. - In: ECONOMETRICS AND STATISTICS. - ISSN 2452-3062. - 14:(2020), pp. 145-158. [10.1016/j.ecosta.2019.04.002]

Subjective heterogeneity in response attitude for multivariate ordinal outcomes

Simone Rosaria
;
TUTZ, GERHARD;Iannario Maria
2020

Abstract

Traditional statistical models with random effects account for heterogeneity in the population with respect to the location of the response in a subject-specific way. This approach ignores that also uncertainty of the responses can vary across individuals and items: for example, subject-specific indecision may play a role in the rating process relative to questionnaire items. In this setting, a generalized mixture model is advanced that accounts for subjective heterogeneity in response behaviour for multivariate ordinal responses: to this aim, random effects are specified for the individual propensity to a structured or an uncertain response attitude. Simulations and a case study illustrate the effectiveness of the proposed model and its implications.
2020
Subjective heterogeneity in response attitude for multivariate ordinal outcomes / Simone, Rosaria; Tutz, Gerhard; Iannario, Maria. - In: ECONOMETRICS AND STATISTICS. - ISSN 2452-3062. - 14:(2020), pp. 145-158. [10.1016/j.ecosta.2019.04.002]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/755410
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