Consequences of Misspecifying Across-Cluster Time-Specific Residuals in Multilevel Latent Growth Curve Models

摘要

This Monte Carlo study evaluates, in the context of multilevel latent growth curve models, the consequences of under-and overspecifying across-cluster time-specific residuals (i.e., Tb) on the estimation of the fixed effects, their corresponding standard errors, the variances and covariances of the random effects, Type I error rates, and the statistical power of detecting fixed effects. The results show that underspecifyingTbwith all elements ofTbfixed at zero results in a large underestimation of the between-and within-level random effect and standard errors of fixed effect estimates, which, in turn, leads to serious bias in significant testing. Underspecifying Tb with diagonal elements of Tb constrained to equality, or overspecifying Tbwith diagonal elements of Tb constrained to equality or freely estimated and residual covariances fixed at zero also leads to bias in the estimation of the between-and within-level random effects. Implications of the compensatory relationship occurring at the covariance level are discussed.