Jo Brown posted on Wednesday, May 16, 2012 - 4:47 am
I am exploring a simple mediation model and would like to adjust it for confounders (z1 - z5) which I believe may influence the c path (X--> Y).
I am not sure where/how to specify them. Should they be included in all regressions statements or just the Y on X one (as below)?
Should they also be specified in the MODEL INDIRECT command as well?
Model: Y on m ; Y on X z1 z2 z3 z4 z5; m on X;
MODEL INDIRECT: Y IND X;
Emil Coman posted on Wednesday, May 16, 2012 - 11:11 am
I would try to take a stab at this, if I may: Judd & Kenny (2009) list this as the most problematic assumption, that m and Y have no common causes (p. 117), so you are doing what few researcher do (great!). In your case, it seems you want to add : m on z1 z2 z3 z4 z5; And the INDIRECT command should be fine, just make it Y IND m X; INDIRECT is going to estimate the effect flowing from X through m, which will not go through the confounders too. Judd, C., & Kenny, D. (2009). Data Analysis in Social Psychology: Recent and Recurring Issues. In S. T. Fiske, D. T. Gilbert & G. Lindzey (Eds.), Handbook of Social Psychology, Volume One (pp. 113-137). Hoboken, NJ: Wiley. Cheers, emil
I have a mediation model with measures collected at 3 time points: x1->m2->y3.
I recently read the paper by Cole & Maxwell (2003) who suggest that to adjust a mediation model it is necessary to adjust path a for the mediator at baseline (i.e. time 1) to capture the actual change. I am not sure whether it is sufficient to do so or whether I should also adjust the other paths with m1 so that
Student posted on Friday, April 10, 2015 - 8:36 am
I have questions about figuring out exactly which confounding variables to control for in a simple mediation model (X -> M -> Y):
First, I believe you need to determine which variables are associated with BOTH M and Y, and control for those variables. Is this correct?
Second, do you also recommend controlling for variables that are associated with X and M (or Y) as well?
Third question, what is the best statistical way to determine significant confounding variables? For example, would you regress all of the potential covariates on your M latent variable, then separately on the Y latent variable? Or would you have M and Y in the same model and regress all the covariates on both?
In general, is it advisable to test individual or limited groups of covariates (e.g., substance abuse variables only) in these models, instead of all at once?