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Mediation Analyses of Intensive Longitudinal Data with Dynamic Structural Equation Modeling

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DataCite Commons2024-07-10 更新2024-08-19 收录
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https://tandf.figshare.com/articles/dataset/Mediation_Analyses_of_Intensive_Longitudinal_Data_with_Dynamic_Structural_Equation_Modeling/25114512/1
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Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e., 1-1-1, 2-1-1, and 2-2-1) of intensive longitudinal mediation (ILM) analyses based on DSEM and RDSEM models. Specifically, each ILM model was demonstrated with a simulated example and illustrated with the corresponding annotated <i>Mplus</i> codes. We compared two types of detrending methods in mediation analyses and showed that RDSEM was superior to DSEM because the latter included the time<i><sub>tj</sub></i> variable as a Level 1 predictor. Lastly, we extended ILM analyses based on DSEM and RDSEM to multilevel autoregressive mediation models, cross-classified DSEM, and intensive longitudinal moderated mediation models.
提供机构:
Taylor & Francis
创建时间:
2024-01-30
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