When I use Baron & Kenny's approach to test mediation, I receive the following results (IV: independent variable, M: mediator, DV: dependent variable):
DV ~ IV : IV is significant
M ~ IV : IV is significant
DV ~ IV + M : M is significant, but IV not
Now, the curious thing is that these data are based on an experiment where I manipulated the independent variable IV, but not the mediator M (M measures perceptions of a person who acted differently depending on condition IV). Hence, theoretically, it seems implausible that in the multiple regression M is significant, but IV not. My question then is: what is going on here?
I believe that this question is different from related questions in that I provide information that IV is manipulated and M is not. I know from reading related questions (e.g., here) that two highly correlated variables may compete for variance. Yet it seems strange that a variable that has no causal relation (as it was not manipulated) "wins" this competition for variance over a variable that might have causal influence (as it was manipulated; the two variables IV and M are correlated .58).
Update: I typically use Preacher and Hayes' approach (in which case the mediation analysis is significant). Does this mean that Baron & Kenny's approach is not suited in this situation?