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Bolfarine, Heleno; Bazan, Jorge Luis – Journal of Educational and Behavioral Statistics, 2010
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…
Descriptors: Markov Processes, Item Response Theory, Bayesian Statistics, Monte Carlo Methods
Hafdahl, Adam R. – Journal of Educational and Behavioral Statistics, 2007
The originally proposed multivariate meta-analysis approach for correlation matrices--analyze Pearson correlations, with each study's observed correlations replacing their population counterparts in its conditional-covariance matrix--performs poorly. Two refinements are considered: Analyze Fisher Z-transformed correlations, and substitute better…
Descriptors: Monte Carlo Methods, Correlation, Meta Analysis, Matrices
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2004
There is an increasing use of Markov chain Monte Carlo (MCMC) algorithms for fitting statistical models in psychometrics, especially in situations where the traditional estimation techniques are very difficult to apply. One of the disadvantages of using an MCMC algorithm is that it is not straightforward to determine the convergence of the…
Descriptors: Psychometrics, Mathematics, Inferences, Markov Processes