Estimation of Contextual Effects Through Nonlinear Multilevel Latent Variable Modeling With a Metropolis–Hastings Robbins–Monro Algorithm

Ji Seung Yang and Li Cai
Additional contact information
Ji Seung Yang: University of Maryland
Li Cai: University of California

Journal of Educational and Behavioral Statistics, 2014, vol. 39, issue 6, 550-582

Abstract: 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.

Keywords: contextual effect; multilevel modeling; latent variable modeling; multilevel latent variable modeling (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998614559972 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:39:y:2014:i:6:p:550-582

DOI: 10.3102/1076998614559972

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().