Hello friendly and helpful TAs (and/or Mari),
I have a question related to GLM assumptions for a mediated model. When we utilize Baron and Kenny's three model approach for testing mediation, there are three separate regressions performed to test the mediation; during these steps the 'predictor' and 'outcome' variables change according to what you want to analyze. (The relationship of the true predictor to true outcome, relationship of true predictor to mediator, and relationship of both true predictor and mediator to true outcome).
So, my questions:
1) Do we have to test each seperate relationship for GLM assumptions? I assume that at least the first two models do need to be tested, as the relationship of the predictor to outcome and the relationship of the predictor to mediator would have to conform to the assumptions to be valid, but I am unsure of the last model.
2) If assumptions are not met and a transormation needs to be tested on the outcome variable, should I transform for each step of B & K's mediation approach? If I do, this makes interpretation sort of hairy in the second step of the model, as the "outcome" variable to be transformed is no longer the outcome of true interest. Or should I just transform my true outcome on the first regression? In the case that I need to transform for all steps of B & K's approach, how do I interpret the relationship at each step?
Gracias,
Chris
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1 comment:
1) In mediated models, you may test the assumptions for regressions 1 & 2 and skip 3 if you like.
2) If you need to transform a variable at one step of the model testing, keep it transformed for all steps. The unstandardized betas are still interpretable; you will just need to be careful in doing so.
That is, rather that 1 original unit change in X being associated with B change in Y (in untransformed variables), you will have either (a) one square root unit change in X associated with B change in Y, (b) one natural log unit change in X being associated with B% change in Y, or (c) one inverse unit change being associated with -B change in Y.
Looking at the standardized betas may well help you in understanding the relationship. Remember, however, that the unstandardized betas are still used in the comparison.
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