I am interested in testing a simple mediation model with one IV, one DV, and one mediator. The indirect effect is significant as tested by the Preacher and Hayes SPSS macro, which suggests the mediator does serve to statistically mediate the relationship.
When reading about mediation I have read things such as "Note that a mediational model is a causal model." - David Kenny. I can certainly appreciate the use of mediation models as causal models, and indeed, if a model is theoretically sound, I can see this as very useful.
In my model, however, the mediator (a trait considered to be a diathesis for anxiety disorders) is not caused by the independent variable (symptoms of an anxiety disorder). Rather, the mediator and independent variables are related, and I believe the association between the independent variable and the dependent variable can be explained largely by variance between the IV-mediator-DV. In essence I am trying to demonstrate that previous reports of the IV-DV relationship can be explained by a related mediator that is not caused by the IV.
Mediation is useful in this case because it explains how the IV-DV relationship can be statistically explained by the IV-Mediator-DV relationship. My problem is the question of causation. Could a review come back and tell us that the mediation is not appropriate because the IV does not in fact cause the mediator (which I would have never argued in the first place)?
Does this make sense? Any feedback on this matter would be greatly appreciated!
Edit: What I mean to say is that X is correlated with Y not because it causes Y, but because Z causes Y (partially) and because X and Z are highly correlated. A bit confusing, but that is it. The causal relationships in this instance are not really in question and this manuscript is not so much about causation. I simply seek to demonstrate that variance between X and Y can be explained by variance between Z and Y. So basically, that X is correlated indirectly to Y through Z (the "mediator" in this case).