NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 7 results Save | Export
IRT Item Parameter Recovery with Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
PDF on ERIC Download full text
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
PDF on ERIC Download full text
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
Numerical Differentiation Methods for Computing Error Covariance Matrices in Item Response Theory Modeling: An Evaluation and a New Proposal
Peer reviewed Peer reviewed
Direct linkDirect link
Tian, Wei; Cai, Li; Thissen, David; Xin, Tao – Educational and Psychological Measurement, 2013
In item response theory (IRT) modeling, the item parameter error covariance matrix plays a critical role in statistical inference procedures. When item parameters are estimated using the EM algorithm, the parameter error covariance matrix is not an automatic by-product of item calibration. Cai proposed the use of Supplemented EM algorithm for…
Descriptors: Item Response Theory, Computation, Matrices, Statistical Inference
Generalized Full-Information Item Bifactor Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Cai, Li; Yang, Ji Seung; Hansen, Mark – Psychological Methods, 2011
Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single-group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of…
Descriptors: Item Analysis, Item Response Theory, Factor Analysis, Maximum Likelihood Statistics
Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm
Direct linkDirect link
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