Bayesian estimation of the logistic positive exponent irt model

Heleno Bolfarine, Jorge Luis Bazan

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

28 Citas (Scopus)


A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered. © 2010 AERA.
Idioma originalEspañol
Páginas (desde-hasta)693-713
Número de páginas21
PublicaciónJournal of Educational and Behavioral Statistics
EstadoPublicada - 1 ene. 2010
Publicado de forma externa

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