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Direct linkXiao Liu; Zhiyong Zhang; Kristin Valentino; Lijuan Wang – Grantee Submission, 2024
Parallel process latent growth curve mediation models (PP-LGCMMs) are frequently used to longitudinally investigate the mediation effects of treatment on the level and change of outcome through the level and change of mediator. An important but often violated assumption in empirical PP-LGCMM analysis is the absence of omitted confounders of the…
Descriptors: Mediation Theory, Bayesian Statistics, Growth Models, Monte Carlo Methods
Xiao Liu; Zhiyong Zhang; Kristin Valentino; Lijuan Wang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Parallel process latent growth curve mediation models (PP-LGCMMs) are frequently used to longitudinally investigate the mediation effects of treatment on the level and change of outcome through the level and change of mediator. An important but often violated assumption in empirical PP-LGCMM analysis is the absence of omitted confounders of the…
Descriptors: Mediation Theory, Bayesian Statistics, Growth Models, Monte Carlo Methods
Lee, Daniel Y.; Harring, Jeffrey R. – Journal of Educational and Behavioral Statistics, 2023
A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation-maximization algorithm, (c) multiple imputation, (d) a two-stage multiple…
Descriptors: Monte Carlo Methods, Research Problems, Statistical Inference, Bayesian Statistics
