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Comparison of Methods for Estimating and Testing Latent Variable Interactions

Comparison of Methods for Estimating and Testing Latent Variable Interactions Structural equation modeling methods for estimating and testing hypotheses about an interaction between continuous variables were investigated. The methods were (a) Bollen's (1996) 2-stage least squares (TSLS) method, Ping's (1996) 2-step maximum likelihood (ML) method, and Jaccard and Wan's (1995) ML method for the Kenny-Judd model (Kenny & Judd, 1984); (b) a 2-step ML procedure and ML estimation of the Jöreskog-Yang model (Jöreskog & Yang 1996); and (c) ML estimation of a revised Jöreskog-Yang model. The TSLS procedure exhibited more bias and lower power than the other methods. Under ML estimation of the Jöreskog-Yang model, Type I error rates were not well controlled when robust standard errors were used. Among the remaining procedures, the Jaccard-Wan procedure and ML estimation of the revised Jöreskog-Yang procedure were most effective, with the latter having some small advantages over the former. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Structural Equation Modeling: A Multidisciplinary Journal Taylor & Francis

Comparison of Methods for Estimating and Testing Latent Variable Interactions

Comparison of Methods for Estimating and Testing Latent Variable Interactions


Abstract

Structural equation modeling methods for estimating and testing hypotheses about an interaction between continuous variables were investigated. The methods were (a) Bollen's (1996) 2-stage least squares (TSLS) method, Ping's (1996) 2-step maximum likelihood (ML) method, and Jaccard and Wan's (1995) ML method for the Kenny-Judd model (Kenny & Judd, 1984); (b) a 2-step ML procedure and ML estimation of the Jöreskog-Yang model (Jöreskog & Yang 1996); and (c) ML estimation of a revised Jöreskog-Yang model. The TSLS procedure exhibited more bias and lower power than the other methods. Under ML estimation of the Jöreskog-Yang model, Type I error rates were not well controlled when robust standard errors were used. Among the remaining procedures, the Jaccard-Wan procedure and ML estimation of the revised Jöreskog-Yang procedure were most effective, with the latter having some small advantages over the former.

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References (25)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1532-8007
eISSN
1070-5511
DOI
10.1207/S15328007SEM0901_1
Publisher site
See Article on Publisher Site

Abstract

Structural equation modeling methods for estimating and testing hypotheses about an interaction between continuous variables were investigated. The methods were (a) Bollen's (1996) 2-stage least squares (TSLS) method, Ping's (1996) 2-step maximum likelihood (ML) method, and Jaccard and Wan's (1995) ML method for the Kenny-Judd model (Kenny & Judd, 1984); (b) a 2-step ML procedure and ML estimation of the Jöreskog-Yang model (Jöreskog & Yang 1996); and (c) ML estimation of a revised Jöreskog-Yang model. The TSLS procedure exhibited more bias and lower power than the other methods. Under ML estimation of the Jöreskog-Yang model, Type I error rates were not well controlled when robust standard errors were used. Among the remaining procedures, the Jaccard-Wan procedure and ML estimation of the revised Jöreskog-Yang procedure were most effective, with the latter having some small advantages over the former.

Journal

Structural Equation Modeling: A Multidisciplinary JournalTaylor & Francis

Published: Jan 1, 2002

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