Mediation Analyses of Intensive Longitudinal Data with Dynamic Structural Equation Modeling
被引:3
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作者:
Fang, Jie
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机构:
Guangdong Univ Finance & Econ, Guangzhou, Peoples R ChinaGuangdong Univ Finance & Econ, Guangzhou, Peoples R China
Fang, Jie
[1
]
Wen, Zhonglin
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机构:
South China Normal Univ, Guangzhou, Peoples R China
South China Normal Univ, Sch Psychol, Guangzhou 510631, Peoples R ChinaGuangdong Univ Finance & Econ, Guangzhou, Peoples R China
Wen, Zhonglin
[2
,4
]
Hau, Kit-Tai
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机构:
Chinese Univ Hong Kong, Hong Kong, Peoples R ChinaGuangdong Univ Finance & Econ, Guangzhou, Peoples R China
Hau, Kit-Tai
[3
]
机构:
[1] Guangdong Univ Finance & Econ, Guangzhou, Peoples R China
[2] South China Normal Univ, Guangzhou, Peoples R China
[3] Chinese Univ Hong Kong, Hong Kong, Peoples R China
[4] South China Normal Univ, Sch Psychol, Guangzhou 510631, Peoples R China
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 Mplus codes. We compared two types of detrending methods in mediation analyses and showed that RDSEM was superior to DSEM because the latter included the timetj 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.