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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Extensions of the skew-normal ogive item response model

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Author(s):
Bazan, Jorge Luis [1] ; Branco, Marcia D. [2] ; Bolfarine, Heleno [2]
Total Authors: 3
Affiliation:
[1] Pontificia Univ Catolica Peru, Dept Ciencias, Lima 32 - Peru
[2] Univ Sao Paulo, Dept Estatist, BR-05508090 Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS; v. 28, n. 1, p. 1-23, FEB 2014.
Web of Science Citations: 0
Abstract

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. (AU)