Over the past 30 years, most efforts to test for mediation have been based on cross-sectional data, which may not to 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 the development of longitudinal mediation analysis. First, time-varying effect of mediation effect was 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 was investigated. Random-effects Cross-lagged panel model (RE-CLPM) and Multilevel autoregressive mediation model (MAMM) should be adopted to analyze longitudinal mediation. Third, integration between different longitudinal mediation models, the outstanding performance is that is the integration of CPLM and MLM into MAMM. Fourth, the method testing mediation analysis was compared. Bayesian method should be adopted in mediation analysis of MAMM and MTVCM. Bootstrap method should be adopted in mediation analysis of LGM. Monte Carlo method should be adopted in mediation analysis of RE-CLPM. At 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 making a causal inference, go to the second step. Otherwise, go to the third step. In the second step, we decide whether it is necessary to test time-varying effect of mediation effect. If the aim of research is testing time-varying effect of mediation effect, CTM should be adopted to analyze longitudinal mediation. Otherwise, go to the fourth step. The third step is to decide whether it is need to test time-varying effect of mediation effect. If the aim of research is testing time-varying effect of mediation effect, MTVCM should be adopted to analyze longitudinal mediation. Otherwise, LGM or MLM should be adopted to analyze longitudinal mediation. The fourth step is to decide the model would fit by running a RE-CLPM model and CLPM. If AIC and BIC indictors of RE-CLPM are smaller than indictors of CLPM, RE-CLPM should be adopted to analyze longitudinal mediation. Otherwise, go to the fifth step. The fifth step is to decide whether it is necessary to investigate individuals-varying effect of mediation effect. If the aim of research is investigating individuals-varying effect of mediation effect, MAMM should be adopted to analyze longitudinal mediation. Otherwise, CLPM should be adopted to analyze longitudinal mediation.