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A MIXTURE RASCH MODEL WITH A COVARIATE:A SIMULATION STUDY VIA BAYESIAN MARKOV CHAIN MONTE CARLO ESTIMATION

dc.contributor.advisorMislevy, Robert Jen_US
dc.contributor.authorDai, Yunyunen_US
dc.date.accessioned2010-02-19T06:54:10Z
dc.date.available2010-02-19T06:54:10Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1903/9926
dc.description.abstractMixtures of item response theory models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying structures. In particular, the impact of auxiliary variables, or covariates, for examinees in estimation has not been systematically explored. The goal of this dissertation is to carry out a systematically designed simulation study to investigate the performance of mixture Rasch model (MRM) under Bayesian estimation using Markov Chain Monte Carlo (MCMC) method. The dependent variables in this study are (1) the proportion of cases in which the generating mixture structure is recovered, and (2) among those cases in which the structure is recovered, the bias and root mean squared error of parameter estimates. The foci of the study are to use a flexible logistic regression model to parameterize the relation between latent class membership and the examinee covariate, to study MCMC estimation behavior in light of effect size, and to provide insights and suggestions on model application and model estimation.en_US
dc.titleA MIXTURE RASCH MODEL WITH A COVARIATE:A SIMULATION STUDY VIA BAYESIAN MARKOV CHAIN MONTE CARLO ESTIMATIONen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMeasurement, Statistics and Evaluationen_US
dc.subject.pqcontrolledEducation, Tests and Measurementsen_US
dc.subject.pquncontrolledBayesian estimationen_US
dc.subject.pquncontrolledDifferential item functioningen_US
dc.subject.pquncontrolledEffect sizeen_US
dc.subject.pquncontrolledMarkov Chain Monte Carloen_US
dc.subject.pquncontrolledMixed Rasch model with covariateen_US


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