Identification of interaction effects is of increasing importance to the social sciences; however, interaction (or moderator) effects have often been difficult to detect with continuous data. Structural equation modeling (SEM) methods have been touted as a solution to the problem of detecting moderators with continuous data because they are thought to account for the presence of measurement error. Also some of the optional fitting algorithms are thought to be less sensitive to non-normality, a common characteristic of the cross-product terms used in evaluation of interaction effects. Although much of the literature to date describes SEM methods to detect interactions among latent variables, the current study contrasts well known moderated multiple regression (MMR) as compared to various analogous SEM models for estimating moderation among manifest variables. While some SEM estimation methods were found inferior, no clear advantage of any SEM method over MMR was observed in the detection of interaction effects. Furthermore, SEM models, with stable Type I error rates, either failed to converge or reported errors about 10% of the time while MMR always yielded a solution
