Including confounders in a mediation ... PreviousNext
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 Jo Brown posted on Wednesday, May 16, 2012 - 4:47 am
Hi There,

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;

Thanks
 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
 Linda K. Muthen posted on Wednesday, May 16, 2012 - 11:12 am
I would also include the confounders in the m regression:

m on X z1 z2 z3 z4 z5;

There is no need to change the IND statement.
 Jo Brown posted on Thursday, May 17, 2012 - 5:10 am
Thanks for your replies.

Just to double-check my model should be:

Model:
Y on m ;
Y on X z1 z2 z3 z4 z5;
m on X z1 z2 z3 z4 z5;

MODEL INDIRECT:
Y IND X;

Jo
 Linda K. Muthen posted on Thursday, May 17, 2012 - 10:12 am
This is correct.
 Jo Brown posted on Friday, May 18, 2012 - 7:32 am
Thanks again!
 Jo Brown posted on Wednesday, May 23, 2012 - 4:06 am
Hi Linda,

I set the model as you suggested:


Model:
Y on m ;
Y on X z1 z2 z3 z4 z5;
m on X z1 z2 z3 z4 z5;

MODEL INDIRECT:
Y IND X;

and asked standardised estimates using

output: sampstat;

However, the output for the indirect and direct effect produces estimates of .00 and does not show p value unless I specify the bootstrap option; does this seem reasonable?

Jo
 Linda K. Muthen posted on Wednesday, May 23, 2012 - 5:44 am
Please send the output and your license number to support@statmodel.com.
 arcis_pau@yahoo.co.uk posted on Thursday, July 12, 2012 - 5:44 am
Hi Linda,

could you direct me towards a paper/website which explain the difference between using the two mediation commands below and what is the rational for using one over the other:

Model:
Y on m ;
Y on X z1 z2 z3 z4 z5;
m on X;

and

Model:
Y on m ;
Y on X z1 z2 z3 z4 z5;
m on X z1 z2 z3 z4 z5;
 Linda K. Muthen posted on Thursday, July 12, 2012 - 9:53 am
I would use the second case to avoid leaving out an important predictor of m.
 Joseph posted on Monday, June 10, 2013 - 12:57 pm
Dear Linda,

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

y on x m1 m2;
m2 on x m2;

Do you have any views on this?

thanks!
 Bengt O. Muthen posted on Monday, June 10, 2013 - 3:00 pm
This is a good, general question for SEMNET.
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