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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
Daniel Seddig – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The latent growth model (LGM) is a popular tool in the social and behavioral sciences to study development processes of continuous and discrete outcome variables. A special case are frequency measurements of behaviors or events, such as doctor visits per month or crimes committed per year. Probability distributions for such outcomes include the…
Descriptors: Growth Models, Statistical Analysis, Structural Equation Models, Crime
Rohit Batra; Silvia A. Bunge; Emilio Ferrer – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Studying development processes, as they unfold over time, involves collecting repeated measures from individuals and modeling the changes over time. One methodological challenge in this type of longitudinal data is separating retest effects, due to the repeated assessments, from developmental processes such as maturation or age. In this article,…
Descriptors: Children, Adolescents, Longitudinal Studies, Test Reliability