Residual Structural Equation Modeling with Nonnormal Distribution.

Abstract

This study primarily investigates the impact of ignoring nonnormal distributions in RSEM models on the estimation of parameters in the second residual structure. The results of the simulation studies demonstrate that when the RSEM model follows a nonnormal distribution, it is crucial to test and estimate the nonnormal distribution while constructing mixture RI-AR or mixture RI-CLPM models. This approach guarantees the unbiased estimation of autoregressive parameters and cross-lagged parameters in the second residual structure. If, during the construction of an empirical model, the nonnormal distribution of mixture RI-AR models or mixture RI-CLPM models is not taken into account, or if a normal distribution is assumed directly for analysis, the resulting parameter estimates for autoregressive parameters and cross-lagged parameters will be biased, leading to erroneous inferences.