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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
Efficient Handling of Predictors and Outcomes Having Missing Values
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Shin, Yongyun – Grantee Submission, 2013
Hierarchical organization of schooling in all nations insures that international large-scale assessment data are multilevel where students are nested within schools and schools are nested within nations. Longitudinal follow-up of these students adds an additional level. Hierarchical or multilevel models are appropriate to analyze such data. A…
Descriptors: Hierarchical Linear Modeling, Data Analysis, International Assessment, Predictor Variables