Introduction to Mediation with SEM Model

Up to this point, we have focused on how one construct can directly influence another con- struct in a SEM model. Let’s now examine how the influence between two constructs may take an indirect path through a third variable called a mediator. In these situations, the third variable will intervene on the influence of the two constructs (Hair et al. 2009). In testing if “mediation” or the presence of a mediator is in a model, you need to understand some of the terminology that is used, such as direct effect, indirect effect, and total effects. A direct effect is simply a direct relationship between an independent variable and a dependent variable. An indirect effect is the relationship that flows from an independent variable to a mediator and then to a dependent variable. The term total effect is the combined influence of the direct effect between two constructs and the indirect effect flowing through the mediator.

Mediation can take numerous forms in a model. You can have what is called full media- tion (also called indirect only mediation) where the direct effect between two constructs is non-significant, but an indirect effect through a mediator does have a significant relationship. Partial mediation is another form that mediation can take. This is where the direct effect between two constructs is significant, and so is the indirect effect through a mediator. Lastly, you can have complementary and competitive mediation. Complementary mediation is where the direct effect and the indirect effect have a similar influence in regard to directionality. For instance, the direct effect may a have positive influence, and the indirect effect has a positive influence as well. A competitive mediation is where you have different directionality between the direct effect and indirect effect. The direct effect might have a negative influence, but the indirect effect might have a positive influence.With this type of mediation, the presence of the mediator can change the directionality of the influence.

Let’s look at a simple mediation model to give some context to our discussion.We have an independent variable (we will label it “X”) that has a proposed direct influence on a depend- ent variable (we will call it “Y”). We will also propose that the influence of X to Y might flow through a mediator variable (we will call it “M”).We have three variables in this simple model. We are going to examine the direct effect of X toY and also the indirect effect of X to M toY. The indirect effect is calculated by taking the product of the X to M relationship and the M to Y relationship. We will simply multiply the regression coefficients for each of those relation- ships to get the indirect effect.

From a statistical standpoint, you will often see the paths in a mediation model referred to as the “A path”, the “B path”, and the “C path”. The commonly referred-to A path is the relationship from the independent variable to the mediator (X to M). The B path is used to refer to the relationship from the mediator to the dependent variable (M to Y). The C path is used when referring to the direct path from the independent variable to the dependent vari- able (X to Y). This is common vernacular when discussing mediation, and you need to take note of these parameter labels.To help clarify our discussion, the different types of mediation are represented in graphical form. See Example 6.1.

The indirect effect can take different forms:

Source: Thakkar, J.J. (2020). “Procedural Steps in Structural Equation Modelling”. In: Structural Equation Modelling. Studies in Systems, Decision and Control, vol 285. Springer, Singapore.