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ERIC Number: EJ1048233
Record Type: Journal
Publication Date: 2014-Dec
Pages: 33
Abstractor: As Provided
Reference Count: 41
ISSN: ISSN-1076-9986
Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm
Yang, Ji Seung; Cai, Li
Journal of Educational and Behavioral Statistics, v39 n6 p550-582 Dec 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 errors efficiently. 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 contextual effects than the traditional approach. As an empirical illustration, data from the Programme for International Student Assessment were analyzed.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED); National Institute on Drug Abuse (DHHS/PHS)
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Program for International Student Assessment
IES Funded: Yes
Grant or Contract Numbers: R305D100039; R305D140046; R01DA026943; R01DA030466