A Two-level Moderated Latent Variable Model with Single Level Data

Abstract

With single-level data, Yuan, Cheng and Maxwell developed a two-level regression model for more accurate moderation analysis. This article extends the two-level regression model to a two-level moderated latent variable (2MLV) model, and uses a Bayesian approach to estimate and test the moderation effects. Monte Carlo results indicate that: 1) the new method yields more accurate estimate of the interaction effect than those via the product-indicator (PI) approach and latent variable interaction (LVI) with single-level model, both are also estimated via Bayesian method; 2) the coverage rates of the credibility interval following the 2MLV model are closer to the nominal 95% than those following the other methods; 3) the test for the existence of the moderation effect is more reliable in controlling Type I errors than both PI and LVI, especially under heteroscedasticity conditions. Moreover, a more interpretable measure of effect size is developed based on the 2MLV model, which directly answers the question as to what extent a moderator can account for the change of the coefficient between the predictor and the outcome variable. A real data example illustrates the application of the new method.