Unreliability of measures produces bias in regression coefficients. Such measurement error is particularly problematic with the use of product terms in multiple regression because the reliability of the product terms is generally quite low relative to its component parts. The use of confirmatory factor analysis as a means of dealing with the problem of unreliability was explored in a simulation study. The design compared traditional regression analysis (which ignores measurement error) with approaches based on latent variable structural equation models that used maximum-likelihood and weighted least squares estimation criteria. The results showed that the latent variable approach coupled with maximum-likelihood estimation methods did a satisfactory job of interaction analysis in the presence of measurement error in terms of Type I and Type II errors.
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