If I am running a mediation analysis (X: independent; Y: dependent; M: mediator; also applies to moderation), but I am also interested in the simple correlation between X and Y (i.e., for my first hypothesis), should I interpret the full model including the mediator, or interpret a separate model that only includes X?
In other words, when interested in the correlation between X and Y, should I interpret a model that only includes X, to 'isolate' the association with Y, or should I interpret the model with X and M?
Often, in my experience, in line with the old Baron and Kenny method, several hypotheses are drafted prior to estimating mediation:
H1: "X has a significantly positive association with Y"; X -> Y
H2: X -> M
H3: M -> Y
H4: Mediation hypothesis
I keep hearing different opinions about this. I've heard it is a good idea to isolate the effect, as you would not have controlled for M if you would not have had the mediator idea or dataset. I've also heard it is a good idea to include M specifically because it acts as a control variable.