Latent Variable Regression: A Technique for Estimating Interaction and Quadratic Coefficients

Abstract

The article proposes a technique to estimate regression coefficients for interaction and quadratic latent variables that combines regression analysis with the measurement model portion of structural equation analysis (e.g., analysis involving CALLS, EQS or LISREL). The measurement model provides parameter estimates that can be combined to correct the variance-covariance matrix used in regression, as Heise (1986) and others recommend. The proposed technique will provide coefficient estimates for regression models involving existing measures, or new measures for which a priori error estimates are not available.