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Limited-Information Goodness-of-Fit Testing of Diagnostic Classification Item Response Theory Models. CRESST Report 840
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Hansen, Mark; Cai, Li; Monroe, Scott; Li, Zhen – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2014
It is a well-known problem in testing the fit of models to multinomial data that the full underlying contingency table will inevitably be sparse for tests of reasonable length and for realistic sample sizes. Under such conditions, full-information test statistics such as Pearson's X[superscript 2] and the likelihood ratio statistic G[superscript…
Descriptors: Goodness of Fit, Item Response Theory, Classification, Maximum Likelihood Statistics
Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm
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Yang, Ji Seung; Cai, Li – Journal of Educational and Behavioral Statistics, 2014
The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM). Results indicate that the MH-RM algorithm can produce estimates and standard…
Descriptors: Computation, Hierarchical Linear Modeling, Mathematics, Context Effect
Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm
Peer reviewed Peer reviewed
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Yang, Ji Seung; Cai, Li – Grantee Submission, 2014
The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM; Cai, 2008, 2010a, 2010b). Results indicate that the MH-RM algorithm can…
Descriptors: Computation, Hierarchical Linear Modeling, Mathematics, Context Effect
Limited-Information Goodness-of-Fit Testing of Diagnostic Classification Item Response Models
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Hansen, Mark; Cai, Li; Monroe, Scott; Li, Zhen – Grantee Submission, 2016
Despite the growing popularity of diagnostic classification models (e.g., Rupp, Templin, & Henson, 2010) in educational and psychological measurement, methods for testing their absolute goodness-of-fit to real data remain relatively underdeveloped. For tests of reasonable length and for realistic sample size, full-information test statistics…
Descriptors: Goodness of Fit, Item Response Theory, Classification, Maximum Likelihood Statistics
Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm. CRESST Report 833
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Yang, Ji Seung; Cai, Li – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2013
The main purpose of this study is to improve estimation efficiency in obtaining full-information maximum likelihood (FIML) estimates of contextual effects in the framework of a nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM; Cai, 2008, 2010a, 2010b). Results indicate that the MH-RM…
Descriptors: Context Effect, Computation, Hierarchical Linear Modeling, Mathematics