Extensions of the skew-normal ogive item response model

Jorge Luis Bazan, Márcia D. Branco, Heleno Bolfarine

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

5 Citas (Scopus)

Resumen

We introduce new applications of the skew-probit IRT model by considering a flexible skew-normal distribution for the latent variables and by extending this model to include an additional random effect for modeling dependence between items within the same testlet. A Bayesian hierarchical structure is presented using a double data augmentation approach. This can be easily implemented in WinBUGS or SAS by considering MCMC algorithms. Several Bayesian model selection criteria, such as DIC, EAIC and EBIC, have been considered; in addition, we use posterior sum of squares of the latent residuals as a global discrepancy measure to model comparison. Two applications illustrate the methodology, one data set related to a mathematical test and another related to reading comprehension, both applied to Peruvian students. Results indicate better performance of the more flexible models proposed in this paper. © Brazilian Statistical Association, 2014.
Idioma originalEspañol
Páginas (desde-hasta)1-23
Número de páginas23
PublicaciónBrazilian Journal of Probability and Statistics
Volumen28
EstadoPublicada - 1 ene. 2014
Publicado de forma externa

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