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Yuan Fang; Lijuan Wang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…
Descriptors: Structural Equation Models, Bayesian Statistics, Monte Carlo Methods, Longitudinal Studies
Saijun Zhao; Zhiyong Zhang; Hong Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
Liu, Hui; Powers, Daniel A. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
This article applies growth curve models to longitudinal count data characterized by an excess of zero counts. We discuss a zero-inflated Poisson regression model for longitudinal data in which the impact of covariates on the initial counts and the rate of change in counts over time is the focus of inference. Basic growth curve models using a…
Descriptors: Smoking, Structural Equation Models, Longitudinal Studies, Regression (Statistics)