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Subjective heterogeneity in response attitude for multivariate ordinal outcomes

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  • Simone, Rosaria
  • Tutz, Gerhard
  • Iannario, Maria

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.

Suggested Citation

  • Simone, Rosaria & Tutz, Gerhard & Iannario, Maria, 2020. "Subjective heterogeneity in response attitude for multivariate ordinal outcomes," Econometrics and Statistics, Elsevier, vol. 14(C), pages 145-158.
    • Handle: RePEc:eee:ecosta:v:14:y:2020:i:c:p:145-158
    DOI: 10.1016/j.ecosta.2019.04.002
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    References listed on IDEAS

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    1. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
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    7. Rosaria Simone & Gerhard Tutz, 2018. "Modelling uncertainty and response styles in ordinal data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 224-245, August.
    8. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
    9. Gerhard Tutz & Micha Schneider & Maria Iannario & Domenico Piccolo, 2017. "Mixture models for ordinal responses to account for uncertainty of choice," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(2), pages 281-305, June.
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    Cited by:

    1. Manisera, Marica & Zuccolotto, Paola, 2022. "A mixture model for ordinal variables measured on semantic differential scales," Econometrics and Statistics, Elsevier, vol. 22(C), pages 98-123.
    2. Rosaria Simone, 2021. "An accelerated EM algorithm for mixture models with uncertainty for rating data," Computational Statistics, Springer, vol. 36(1), pages 691-714, March.
    3. Cong, Lin & Yao, Weixin, 2021. "A Likelihood Ratio Test of a Homoscedastic Multivariate Normal Mixture Against a Heteroscedastic Multivariate Normal Mixture," Econometrics and Statistics, Elsevier, vol. 18(C), pages 79-88.

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