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Sean Joo; Montserrat Valdivia; Dubravka Svetina Valdivia; Leslie Rutkowski – Journal of Educational and Behavioral Statistics, 2024
Evaluating scale comparability in international large-scale assessments depends on measurement invariance (MI). The root mean square deviation (RMSD) is a standard method for establishing MI in several programs, such as the Programme for International Student Assessment and the Programme for the International Assessment of Adult Competencies.…
Descriptors: International Assessment, Monte Carlo Methods, Statistical Studies, Error of Measurement
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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2021
Large-scale assessments (LSAs) use Mislevy's "plausible value" (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Statistical Inference
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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
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Lee, Taehun; Cai, Li – Journal of Educational and Behavioral Statistics, 2012
Model-based multiple imputation has become an indispensable method in the educational and behavioral sciences. Mean and covariance structure models are often fitted to multiply imputed data sets. However, the presence of multiple random imputations complicates model fit testing, which is an important aspect of mean and covariance structure…
Descriptors: Statistical Inference, Structural Equation Models, Goodness of Fit, Statistical Analysis