Bayesian Estimation of the Logistic Positive Exponent IRT Model

Heleno Bolfarine and Jorge Luis Bazan
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Heleno Bolfarine: Universidade de Sao Paulo
Jorge Luis Bazan: Pontificia Universidad Católica, Perú

Journal of Educational and Behavioral Statistics, 2010, vol. 35, issue 6, 693-713

Abstract: 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.

Keywords: achievement; assessment; item response theory (IRT); mathematics education (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:35:y:2010:i:6:p:693-713

DOI: 10.3102/1076998610375834

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