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).
Best Answer
A. "Mediation" conceptually means causation (as Kenny quote indicates). Path models that treat a variable as a mediator thus mean to convey that some treatment is influencing an outcome variable through its effect on the mediator, variance in which in turn causes the outcome to vary. But modeling something as a "mediator" doesn't mean it really is a mediator--this is the causation issue. Your post & comment in response to Macro suggest that you have in mind a path analysis in which a variable is modeled as a mediator but isn't viewed as "causal"; I'm not quite seeing why, though. Are you positing that the relationship is spurious--that there is some 3rd variable that is causing both the "independent variable" and the "mediator"? And maybe that both the "independent variable" & the "mediator" in your analysis are in fact mediators of the 3rd variable's influence on the outcome variable? If so, then a reviewer (or any thoughtful person) will want to know what the 3rd variable is & what evidence you have that it is responsible for spurious relationships between what are in fact mediators. This will get you into issues posed by Macro's answer.
B. To extend Macro's post, this is a notorious thicket, overgrown with dogma and scholasticism. But here are some highlights:
Some people think that you can only "prove" mediation if you experimentally manipulate the mediator as well as the influence that is hypothesized to exert the causal effect. Accordingly, if you did an experiment that manipulated only the causal influence & observed that its impact on the outcome variable was mirrored by changes in the mediator, they'd so "nope! Not good enough!" Basically, though, they just don't think observational methods ever support causal inferences & unmanipulated mediators in experiments are just a special case for them.
Other people, who don't exclude causal inferences from observational studies out of hand, nevertheless believe that if you use really really really complicated statistical methods (including but not limited to structural equation models that compare the covariance matrix for the posited mediating relationship with those for various alternatives), you can effectively silence the critics I just mentioned. Basically this is Baron & Kenny, but on steroids. Empirically speaking, they haven't silenced them; logically, I don't see how they could.
Still others, most notably, Judea Pearl, say that the soundness of causal inferences in either experimental or observational studies can never be proven w/ statistics; the strength of the inference inheres in the validity of the design. Statistics only confirm the effect causal inference contemplates or depends on.
Some readings (all of which are good, not dogmatic or scholastic):
Green, D.P., Ha, S.E. & Bullock, J.G. Enough Already about “Black Box” Experiments: Studying Mediation Is More Difficult than Most Scholars Suppose. The ANNALS of the American Academy of Political and Social Science 628, 200-208 (2010).
Sobel, M.E. Identification of Causal Parameters in Randomized Studies With Mediating Variables. Journal of Educational and Behavioral Statistics 33, 230-251 (2008).
Pearl, J. An Introduction to Causal Inference. The International Journal of Biostatistics 6, Article 7 (2010).
Last but by no means least, part of a cool exchange between Gelman & Pearl on causal inference in which mediation was central focus: http://andrewgelman.com/2007/07/identification/