Bayesian Estimation of the Logistic Positive Exponent IRT Model
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
20/10/2012
20/10/2012
2010
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Resumo |
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. |
Identificador |
JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS, v.35, n.6, p.693-713, 2010 1076-9986 http://producao.usp.br/handle/BDPI/30458 10.3102/1076998610375834 |
Idioma(s) |
eng |
Publicador |
SAGE PUBLICATIONS INC |
Relação |
Journal of Educational and Behavioral Statistics |
Direitos |
restrictedAccess Copyright SAGE PUBLICATIONS INC |
Palavras-Chave | #achievement #assessment #item response theory (IRT) #mathematics education #ITEM RESPONSE MODELS #BINARY REGRESSION #PROBIT #CURVES #METHODOLOGY #LOGIT #Education & Educational Research #Social Sciences, Mathematical Methods #Psychology, Mathematical |
Tipo |
article original article publishedVersion |