Estimating Latent Variable Interactions With Non-Normal Observed Data: A Comparison of Four Approaches.

ABSTRACT: A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approach

SUBMITTER: Cham H 

PROVIDER: S-EPMC3583564 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature