Structurally relationship of variables is important in deeply analysis of path models, but the process of effect distribution must be concerned. In this situation, one or more variable would be a mediator variable which assessing effect of an independent to a dependent variable. We studied the simple mediation model that is one of path analytical models which contain of one independent variable, dependent variable and mediator variable. A necessary component of mediation is effectiveness that is a statistically significant indirect effect, formal significance tests of indirect effects are early conducted by Sobel (1982). According to sequential regression analysis on a simple mediation model, a mediator variable come after an independent variable exist in the model, the contribution of upcoming variable to the model could be obtained. We argue the importance of investigating empirical relationship between the significance of indirect effects and sequential contribution of mediator variable with a normal theory approach using Microsoft Excel simulation tools developed by Myerson (2000). We find that the higher contribution of mediator variable to the model, the more effectiveness is. this result comes up with three level correlation of independent and dependent variable which each 1000 times iteration that gives relatively immediate information about the recent empirical relationship between the significance of indirect ffects and sequential contribution of mediator in the simple mediation models.