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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