纵向数据的中介效应分析

Over the past 30 years, most efforts on testing for mediation have been based on cross-sectional data, which may not get causal inference. A possible solution for this could be to collect longitudinal data and perform a longitudinal mediation analysis. There are three causal arrows in a simple mediation model for analyzing a system of causality. If there is at least one causal arrow where the effect arises sometime after the cause, a longitudinal mediation design will be necessary for effectively observing the causation. There are three types of longitudinal mediation analysis approaches: (1) cross-lagged panel model (CLPM); (2) multilevel mediation model (MLM); (3) latent growth mediation model (LGM). There are four types of development of longitudinal mediation analysis. First, time-varying effect of mediation effect is tested. Continuous time models (CTM) would illustrate how mediating effects vary as a function of lag. Multilevel time-varying coefficient model (MTVCM) can capture direct and indirect effects over time. Second, individuals-varying effect of mediation effect is investigated. Random-effects cross-lagged panel model (RE-CLPM) and Multilevel autoregressive mediation model (MAMM) should be adopted to analyze longitudinal mediation. Third, during integration between different longitudinal mediation models, the outstanding performance is the integration of CLPM and MLM into MAMM. Fourth, the method testing mediation analysis is compared. Bayesian method should be adopted in mediation analysis of MAMM and MTVCM. Bootstrap method should be adopted in mediation analysis of LGM. In the present study, we propose a procedure to analyze longitudinal mediation analysis. The first step is to decide whether it is necessary to make a causal inference. If the aim of research is to make a causal inference, then proceed with the second step. Otherwise, LGM or MLM should be adopted to analyze longitudinal mediation. In the second step, we decide whether it is necessary to test time-varying effect of mediation effect. If the aim of research is to test the time-varying effect of mediation effect, CTM should be adopted to analyze longitudinal mediation. Otherwise, proceed with the third step. The third step is to decide whether it is necessary to investigate the individuals-varying effect of mediation effect. If the aim of research is to investigate the individuals-varying effect of mediation effect, RE-CLPM model or MAMM should be adopted to analyze longitudinal mediation. Otherwise, CLPM should be adopted to analyze longitudinal mediation.