A classification model for continuous responses: Identifying risk perception groups on health-related activities.

de Oliveira, Eduardo S B; Wang, Xiaojing; Bazán, Jorge L

de Oliveira, Eduardo S B; Wang, Xiaojing; Bazán, Jorge L.

Afiliação

de Oliveira ESB; Interinstitutional Postgraduate Program in Statistics UFSCAR-ICMC USP, São Carlos, São Paulo, Brazil.
Wang X; Department of Statistics, University of Connecticut, Storrs, Connecticut, USA.
Bazán JL; Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of São Paulo (SME/ICMC/USP), São Carlos, São Paulo, Brazil.

Biom J ; 65(4): e2100222, 2023 04.

Article em En | MEDLINE | ID: mdl-36782079

RESUMO

In the current literature on latent variable models, much effort has been put on the development of dichotomous and polytomous cognitive diagnostic models (CDMs) for assessments. Recently, the possibility of using continuous responses in CDMs has been brought to discussion. But no Bayesian approach has been developed yet for the analysis of CDMs when responses are continuous. Our work is the first Bayesian framework for the continuous deterministic inputs, noisy, and gate (DINA) model. We also propose new interpretations for item parameters in this DINA model, which makes the analysis more interpretable than before. In addition, we have conducted several simulations to evaluate the performance of the continuous DINA model through our Bayesian approach. Then, we have applied the proposed DINA model to a real data example of risk perceptions for individuals over a range of health-related activities. The application results exemplify the high potential of the use of the proposed continuous DINA model to classify individuals in the study.

Assuntos

Modelos Estatísticos; Modelos Teóricos; Humanos; Psicometria; Teorema de Bayes; Percepção

Palavras-chave

assessment; classification; cognitive diagnosis models; continuous responses; deterministic inputs, noisy, and gate (DINA) models

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos / Modelos Teóricos Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Biom J Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Alemanha

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