Please use this identifier to cite or link to this item: https://doi.org/10.1080/10705511.2013.742404
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dc.titleImplementing Restricted Maximum Likelihood Estimation in Structural Equation Models
dc.contributor.authorCheung, M.W.-L.
dc.date.accessioned2014-04-02T10:09:52Z
dc.date.available2014-04-02T10:09:52Z
dc.date.issued2013-01
dc.identifier.citationCheung, 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
dc.identifier.issn10705511
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/49849
dc.description.abstractStructural 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1080/10705511.2013.742404
dc.sourceScopus
dc.subjectmeta-analysis
dc.subjectmultilevel modeling
dc.subjectrestricted maximum likelihood estimation
dc.subjectstructural equation modeling
dc.typeArticle
dc.contributor.departmentPSYCHOLOGY
dc.description.doi10.1080/10705511.2013.742404
dc.description.sourcetitleStructural Equation Modeling
dc.description.volume20
dc.description.issue1
dc.description.page157-167
dc.identifier.isiut000314213800010
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