Please use this identifier to cite or link to this item: https://doi.org/10.1080/10705511.2013.742404
Title: Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models
Authors: Cheung, M.W.-L. 
Keywords: meta-analysis
multilevel modeling
restricted maximum likelihood estimation
structural equation modeling
Issue Date: Jan-2013
Source: Cheung, M.W.-L. (2013-01). Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models. Structural Equation Modeling 20 (1) : 157-167. ScholarBank@NUS Repository. https://doi.org/10.1080/10705511.2013.742404
Abstract: Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects modeling (LMM) such as cross-sectional multilevel modeling and latent growth modeling. It is well known that LMM can be formulated as structural equation models. However, one main difference between the implementations in SEM and LMM is that maximum likelihood (ML) estimation is usually used in SEM, whereas restricted (or residual) maximum likelihood (REML) estimation is the default method in most LMM packages. This article shows how REML estimation can be implemented in SEM. Two empirical examples on latent growth model and meta-analysis are used to illustrate the procedures implemented in OpenMx. Issues related to implementing REML in SEM are discussed. © 2013 Copyright Taylor and Francis Group, LLC.
Source Title: Structural Equation Modeling
URI: http://scholarbank.nus.edu.sg/handle/10635/49849
ISSN: 10705511
DOI: 10.1080/10705511.2013.742404
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

13
checked on Dec 13, 2017

WEB OF SCIENCETM
Citations

16
checked on Dec 13, 2017

Page view(s)

59
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.