Bayesian Estimation of the Logistic Positive Exponent IRT Model


Autoria(s): BOLFARINE, Heleno; BAZAN, Jorge Luis
Contribuinte(s)

UNIVERSIDADE DE SÃO PAULO

Data(s)

20/10/2012

20/10/2012

2010

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

http://dx.doi.org/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