Plotting combined conditional indirec...
Message/Author
 hr13@gre.ac.uk posted on Wednesday, January 07, 2015 - 4:12 am
Dear Drs Muthen and Muthen

I am estimating a model which has multiple x, m and y variables and one z.

I would like to plot the conditional indirect effects of x on y through multiple mediators. I have followed the Preacher syntax from the Mplus user guide and successfully plotted the conditional indirect effects through each mediator, each on a separate plot.

However, I wondered if there is a way to plot the combined conditional indirect effect of x->m1 m2 m3->y in one plot.

Bex.
 Bengt O. Muthen posted on Wednesday, January 07, 2015 - 5:16 pm
Are you using the LOOP plot?
 hr13@gre.ac.uk posted on Friday, January 09, 2015 - 3:51 am
Yes, I am using LOOP.

I also had a related question about how to see the estimates and CI for the indirect effect at different levels of the moderator (e.g. -1SD, M, +1SD). I can't seem to find which output would give me these data.

many thanks.
 Bengt O. Muthen posted on Friday, January 09, 2015 - 3:56 pm
In both cases you would have to express the effect (combined or at different moderator levels) in Model Constraint. The combined effect (the sum of the individual indirect effects) can then be plotted using LOOP. The effect at different moderator values would appear in the regular estimates section under New/additional parameters.
 hr13@gre.ac.uk posted on Tuesday, April 07, 2015 - 8:01 am
Thank you for your response (my apologies for the delay responding).

Could you help me with the syntax for that?

This is what I currently have in Model Constraint, to model each indirect effect individually (a, c and r, which I have specified in the Model section):

plot(ind_a ind_c ind_r);
LOOP(mod,-1,1,0.1);
ind_a = a*(gamma1a+gamma2a*mod);
ind_c = c*(gamma1c+gamma2c*mod);
ind_r = r*(gamma1a+gamma2r*mod);

How would I express the combined effect and the CI for the indirect effect at different levels of the moderator as you have suggested?
 hr13@gre.ac.uk posted on Tuesday, April 07, 2015 - 9:12 am
I have worked out how to plot the combined indirect effect (very simple now that I know!)

But I would still apreciate guidance on how I can request the CI for the indirect effect at different levels of the moderator.

many thanks
 Bengt O. Muthen posted on Tuesday, April 07, 2015 - 10:50 am
You see the 95% CI in the plot.
 Xu, Man posted on Wednesday, August 02, 2017 - 3:45 pm
I am setting up a simple mediation analysis with two mediators:
y ON x m1 m2 m1*m2;
m1 ON x;
m2 ON x;

When estimating/plotting the moderated mediation effect, I was wondering if there should be a preference whether using the LOOP or bootstrap?

Thanks a lot!

Kate
 Xu, Man posted on Thursday, August 03, 2017 - 7:34 am
Dear Drs Muthen,

I am a little new with the concept of counter-factual output from the MODEL INDIRECT with MOD option, and still trying to understand the meaning of the plots as well.

May I ask what would be the best reference to understand the output and plot on the mediation effect from Mplus, please?

Another question is, I am using complex design with WEIGHT. My sample is reasonably large ( a few thousands and population representative). But it seems I cannot use bootstrap in this condition. Is there a way around it? I presume what I get now is the sobel method.
 Bengt O. Muthen posted on Thursday, August 03, 2017 - 2:36 pm
Answer to the Aug 2 posting:

LOOP and bootstrap can be used together and serve different purposes.
 Bengt O. Muthen posted on Thursday, August 03, 2017 - 2:38 pm

You should read our book Regression and Mediation Analysis using Mplus.

Having weights makes mediation analysis a little tricky. Bootstrapping is not available with weights and neither is Bayes (which also gives non-symmetric CIs).

Instead of using weights, you can add as covariates the variables that the weights are based on.
 Xu, Man posted on Thursday, August 03, 2017 - 3:24 pm
Thank you Dr. Muthen for your suggestions. I was trying to order the book but the website seems to have issues - will come back later to check.

Another question: the book seems to focus on single level models. In the case of random intercept, fixed slope multilevel models (because this is mostly like the case of my research), will the effect/logic applied in the book hold, or much will be different in the context of a random intercept multilevel model?

This is probably too general of a question. Maybe I should first go through the relevant chapters in the book and come back to ask specific questions when I do analysis.
 Bengt O. Muthen posted on Thursday, August 03, 2017 - 5:22 pm
Yes, the book does not cover multilevel matters. There should be no problem with the website for it.