Message/Author |
|
mw posted on Tuesday, September 11, 2012 - 1:26 am
|
|
|
I'm all new to this and not particularly good! But I need to test a path model. I've got to grips with testing mediation, but now need to put a moderator in the model. I've trawled through some guides but still confused, I really just need some code to do it in Mplus. I'm better with visuals rather than trying to explain it, so have provided my model here... http://tinyurl.com/bnr3fzo |
|
|
One good approach is to let your moderator variable E be a grouping variable in a multiple-group mediation model. The User's Guide has several examples of multiple-group models. |
|
mw posted on Tuesday, September 11, 2012 - 10:47 am
|
|
|
Thank you for your reply. I really do want to keep the moderator as a continuous variable though and not split it. Is there a way of running the model without multiple groups? |
|
|
Then you have to think about how your moderator of the M ->Y relationship might interact with M (and X, Y). You have an interaction between your moderator and your mediator. Interactions involving DVs (the mediator in your case) may pose special analysis issues - for instance, the chi-square test of model fit is off because the variance of Y conditional on X and your moderator is not constant. |
|
mw posted on Tuesday, September 11, 2012 - 12:10 pm
|
|
|
I've had a look at the preacher and hayes mplus code for a similar kind of analysis (I think it is anyway!)assuming w is the moderator and m is the mediator y on m x w mw; m on x; w with m; mw with m; Would that be suitable, if I just added a few more 'x' variables to recreate my model? I suppose you are saying I need to work out if the moderator affects the A B and C variables in my model (pictured above) as well. To that I'm not really sure! It should correlate, but can I just specify that it will or will I end up in a huge over/non-identified mess? |
|
|
This code should work. It is a model in which the 'b' path is moderated by z, and it contains 3 x variables. TITLE: moderated mediation with 3 x's; DATA: FILE IS mplus.help3.dat; VARIABLE: NAMES ARE x1-x3 m z y; USEVARIABLES ARE all mz; DEFINE: mz=m*z; MODEL: y ON m x1-x3 z mz; m ON x1-x3; m WITH z mz; x1-x3; You would need to include a MODEL CONSTRAINT section that computes the conditional indirect effects that are of interest, but this is the basic model syntax. |
|
mw posted on Wednesday, September 12, 2012 - 6:26 am
|
|
|
Thank you! I'll run it and see! |
|
mw posted on Sunday, September 16, 2012 - 12:54 pm
|
|
|
I've decided to change my mediator to a latent variable. Will the above code be the same? or do I have to create/define the interaction differently with a latent variable. I've read something about XWITH (?) but not sure how I'd include that in Prof. Preacher's code above. |
|
|
Use XWITH to create the latent variable interaction and include it on the right-hand side of ON as shown above. |
|
marlies posted on Wednesday, April 17, 2013 - 10:10 am
|
|
|
Dear drs. Muthen, I have a question about a model with one moderation and a mediation, which is also moderated. This is my full model, with dependent variable y, predictor x, mediator m and second level moderator z. MODEL:%WITHIN% s1 | y ON x ; s2 | m ON x; y ON m; %BETWEEN% y m s1 s2 ON z; y m s1 s2 WITH y m s1 s2 ; In this model, the cross-level interaction between x and z on y is not signficant and neither is the cross-level interaction effect of x and z on m. However, if I take out the interaction of x and z on m, so I run this model: s1 | y ON x ; y ON m; m ON x; %BETWEEN% y s1 ON z; y WITH s1; then suddenly the interaction of x and z on y is significant (the p value changes from a non-significant 0.28 to a significant 0.047). However, it cannot have turned significant due to the mediation of m, since there was no interaction effect of x and z on m. Could it be that testing my full path model (so with both interaction effects)is statistically not okay? Thank you in advance for your answer! Kind regards, marlies |
|
|
I think your full model is correctly done. When you change m ON x from random to fixed, that may well influence the random y ON x regression. You may also want to try this out using Bayesian estimation to see how close to normal the posterior distributions are for the s ON z parameters; the Bayes results may be more trustworthy if they are not close to normal, as assumed with ML. |
|
marlies posted on Wednesday, April 17, 2013 - 11:47 pm
|
|
|
Thank you for your quick answer! Kind regards, Marlies |
|
|
Firstly, thank you again for the quick response! I am not a statistician so I am probably not expressing my thoughts well. Here is the model: http://i.imgur.com/meWWw2f.png My aim and questions are whether it is possible to add a lower level moderation in a 2-1-1 model on the 1-1 path in Mplus and whether there are some examples of such models or models with lower level moderation only (I could not find this)? Below, I followed the syntax from Preacher et al. (2010, 2011) for the 2-1-1 model. I tried to add the lower level moderator and construct the indirect effects as in Model 3 (Preacher et al., 2007). The model below gave no errors in Mplus. I'm sharing it for learning purposes. I find this forum incredibly helpful in that respect! Thank you! USEVARIABLES = Id sqy gmx gmm gmw mw; Missing are all (-99); BETWEEN IS gmx; CLUSTER IS Id; DEFINE: mw = gmm*gmw; ANALYSIS: TYPE IS TWOLEVEL RANDOM; MODEL: %WITHIN% sb | sqy ON gmm; mb | sqy ON mw; sqy ON gmw; %BETWEEN% gmm ON gmx(a); sqy ON gmm(b); sqy ON mw (b2); sqy ON gmx (c); sqy ON gmw; [sb](bw); [mb] (mbw); MODEL CONSTRAINT: NEW (bb indb bb2 indb2); bb=b+bw; indb=a*b; bb2=b2+mbw; indb2=a* (b+bb2); |
|
|
I apologize for the above message! It was posted in the wrong forum. My sincere apologies. Laura |
|
|
Ok; answered elsewhere. |
|
sharon posted on Monday, October 09, 2017 - 8:31 am
|
|
|
Hi everyone, i could really use some help for a moderation that i would like to try on Mplus, but i dont know how to code it. Does anyone have a syntax ? its a simple moderation analyse with x, m, and y. Thanks again for your help! |
|
|
See the Mplus runs starting at Table 2.25 for our Regression and Mediation book which are posted at http://www.statmodel.com/Mplus_Book_Tables.shtml |
|
S REN posted on Wednesday, March 21, 2018 - 7:57 am
|
|
|
I have a question related to the syntax that Prof. Preacher posted as below: The interaction term mz is defined as m*z Does this mean in moderation analysis, we do not need to centering the m and z to avoid multicollinearity issues? Thank you! This code should work. It is a model in which the 'b' path is moderated by z, and it contains 3 x variables. TITLE: moderated mediation with 3 x's; DATA: FILE IS mplus.help3.dat; VARIABLE: NAMES ARE x1-x3 m z y; USEVARIABLES ARE all mz; DEFINE: mz=m*z; MODEL: y ON m x1-x3 z mz; m ON x1-x3; m WITH z mz; x1-x3; |
|
|
Right. |
|
Back to top |