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Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis-Hastings Robbins-Monro Algorithm
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Monroe, Scott; Cai, Li – Educational and Psychological Measurement, 2014
In Ramsay curve item response theory (RC-IRT) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's EM algorithm, which yields maximum marginal likelihood estimates. This method, however, does not produce the…
Descriptors: Item Response Theory, Models, Computation, 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; 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
Estimation of the Correlation Coefficient Based on Selected Data
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Hagglund, Gosta; Larsson, Rolf – Journal of Educational and Behavioral Statistics, 2006
In psychometrics, it is often the case that one encounters data that may not be considered random but selected in a systematic way according to some explanatory variable. In this article, maximum likelihood estimation is considered when data are supposed to arise from a bivariate normal distribution that is truncated in an extreme way. Two methods…
Descriptors: Psychometrics, Correlation, Computation, Methods
On the Estimation of Hierarchical Latent Linear Models for Large Scale Assessments. Research Report. ETS RR-06-37
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Deping, Li; Oranje, Andreas – ETS Research Report Series, 2006
A hierarchical latent regression model is suggested to estimate nested and nonnested relationships in complex samples such as found in the National Assessment of Educational Progress (NAEP). The proposed model aims at improving both parameters and variance estimates via a two-level hierarchical linear model. This model falls naturally within the…
Descriptors: Hierarchical Linear Modeling, Computation, Measurement, Regression (Statistics)