In statistics, a mediation model is one that seeks to identify and explicate the mechanism that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable. Rather than hypothesizing a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the independent variable causes the mediator variable, which in turn causes the dependent variable. The mediator variable, then, serves to clarify the nature of the relationship between the independent and dependent variables (MacKinnon, 2008). While the concept of mediation as defined within psychology is theoretically appealing the methods used to study mediation empirically have been challenged by statisticians and epidemiologists and formally derived by Pearl (2001).

Direct versus indirect effects

In the diagram shown above, assuming linear relationships, the indirect effect is the product of paths coefficients A and B. In general, including nonlinear models, the total effect is equal to the difference between the direct effect and the indirect effect of a unit decrease in the independent variable. In contrast, the indirect effect (sometimes referred to as mediated effect) refers to the extent to which the dependent variable changes when the independent variable is held fixed and the mediator variable changes to the level it would have attained had the independent variable increased by one unit.