Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm. CRESST Report 833

Autor/inn/en	Yang, Ji Seung; Cai, Li
Institution	National Center for Research on Evaluation, Standards, and Student Testing
Titel	Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm. CRESST Report 833
Quelle	(2013), (42 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Context Effect; Computation; Hierarchical Linear Modeling; Mathematics; Maximum Likelihood Statistics; Efficiency; Comparative Analysis; International Programs; Testing Programs; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Mathematik; Effectiveness; Effektivität; Wirkungsgrad
Abstract	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 algorithm can produce FIML estimates and their standard errors efficiently, and the efficiency of MH-RM was more prominent for a cross-level interaction model, which requires five dimensional integration. Simulations, with various sampling and measurement structure conditions, were conducted to obtain information about the performance of nonlinear multilevel latent variable modeling compared to traditional hierarchical linear modeling. Results suggest that nonlinear multilevel latent variable modeling can more properly estimate and detect a contextual effect and a cross-level interaction than the traditional approach. As empirical illustrations, two subsets of data extracted from The Programme for International Student Assessment (PISA, 2000; OECD, 2000) were analyzed. Two appendices are included: (1) Observed and complete data likelihoods; and (2) First and second order derivatives of the complete data models. [The work reported herein received additional support from the Society of Multivariate Experimental Psychology Dissertation Support Awards.] (As Provided).
Anmerkungen	National Center for Research on Evaluation, Standards, and Student Testing (CRESST). 300 Charles E Young Drive N, GSE&IS Building 3rd Floor, Mailbox 951522, Los Angeles, CA 90095-1522. Tel: 310-206-1532; Fax: 310-825-3883; Web site: http://www.cresst.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01