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IRT Item Parameter Recovery with Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models
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Casabianca, Jodi M.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2015
Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation…
Descriptors: Item Response Theory, Maximum Likelihood Statistics, Computation, Comparative Analysis
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
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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
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
Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm
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Yang, Ji Seung – ProQuest LLC, 2012
Nonlinear multilevel latent variable modeling has been suggested as an alternative to traditional hierarchical linear modeling to more properly handle measurement error and sampling error issues in contextual effects modeling. However, a nonlinear multilevel latent variable model requires significant computational effort because the estimation…
Descriptors: Hierarchical Linear Modeling, Computation, Maximum Likelihood Statistics, Mathematics