The analyses of mediation effects are frequently applied to the studies of psychology, education, and other social science disciplines. More than one mediator may be involved when the relationship among more than three variables is concerned. For a model with multiple mediators, there are three kinds of mediation effects: total mediation effect, specific mediation effect through a specified path, and contrast mediation effects for the comparison of two or more specific effects. Compared to analyzing multiple mediators by building up several separate models with single mediator, an equivalent model with multiple mediators by structural equation modeling (SEM) has many advantages. For example, specific mediation effects can be tested in the condition controlling other mediators in the model; total mediation effect which is the sum of the specific mediation effects can be tested; contrast mediation effects can be calculated to determine the relative magnitudes of the different specific mediation effects. The purpose of the present study is to summary an effective procedure for analyzing multiple mediators based on structural equation modeling. There are at least three weaknesses frequently found in the present empirical studies involved multiple mediation effects. First, not all of the three kinds of mediation effects were considered, leading to the incomplete analyses of multiple mediation effects. Second, Sobel’s testing method was dominantly used, but the test method was based on the normality assumption that was typically violated by any kind of the mediation effects because they included the product of two parameters. Third, the computations of standard errors of multiple mediation effects often required manual calculations. At the present study, we propose a procedure to analyze the model with multiple mediators. The procedure is able to deal with both manifest and latent variables, and overcome all the three weaknesses described above. The first step is to establish a model including multiple mediators based on the theoretical frame in the field. In the second step, some auxiliary (phantom) variables are introduced into the model. These auxiliary variables will help researchers to obtain all the three kinds of mediation effects if the output of SEM software does not provide them directly. In the third step, bias-corrected percentile Bootstrap method, which can be implemented easily by MPLUS software, is recommended to analyze multiple mediation effects. It shows that the corresponding mediation effect is significant if a confidence interval does not include zero. Of course, the results of Bootstrap SEM analysis are acceptable only when the SEM model is fitted well. We used an example to illustrate how to conduct the proposed procedure by using MPLUS software. MPLUS program is attached to facilitate the implementation of bias-corrected percentile Bootstrap method to analyze multiple mediation effects. The programs can be managed easily by empirical researchers. In fact, in addition to Bootstrap method, Bayesian method also can be selected to analyze multiple mediation effects, the results of Bayesian SEM analysis are acceptable only when the SEM model is fitted well and the Markov chain is convergence. It is possible for Bayesian method to improve the power to detect mediation effects by incorporating prior information about the indirect effect.