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 radanielina-hita marie louise posted on Friday, May 31, 2013 - 4:12 pm
Dear Prof. Muthen,

I am running an SEM (with both direct and indirect effects). I also specified an interaction term for two latent variables to see if the joint effect of two of my predictors have an effect on another latent variable. I understand that I have to specify: 1) Type is random and 2) ALGORITHM=INTEGRATION; Each time I ran my model, I get an error message: "MODEL INDIRECT is not available for TYPE=RANDOM." I ran the model without the indirect effect and it worked. Does this mean that I cannot examine mediation and moderation at the same time? Thank you very much. Here's the code that I used.


ident on nmedia desire real norm f1 f2;
f1xf2 | f1 XWITH f2;
ident on f1xf2;
expectat on SIMILAR f1 f2;

Model indirect:

Expectat ind f2 nmedia;
Behav ind expect f2 nmedia;

CONVERGENCE = 0.00005;
 Bengt O. Muthen posted on Friday, May 31, 2013 - 6:45 pm
In this case you replace Model Indirect with Model Constraint where you define your indirect effect using labels given to your slope parameters in the Model command.
 radanielina-hita marie louise posted on Sunday, June 02, 2013 - 2:07 pm
Thank you very much for your answer. Unfortunately, I am still getting an error message after I replaced Model Indirect with Model Constraint.

" A parameter label or the constant 0 must appear on the left-hand side
of a MODEL CONSTRAINT statement. Problem with the following:
NMEDIA(0) =". I am not quite sure what I am doing wrong because this is the first time that I am testing both mediation and interaction effects.
1) Is there any such example in the Users' guide or on the website that I could use as a guide?
2) When I run the multiple group analysis to see the effect of gender on both the direct and indirect effects, do I just constrain the mediated paths as I would normally do with the direct effects?
3) I am using 6.12 but it won't give me a three-way interaction. Is this available in the 7.0?

Thank you in anticipation
 Linda K. Muthen posted on Monday, June 03, 2013 - 5:00 pm
Please send the output and your license number to
 Benjamin Miller posted on Monday, September 30, 2013 - 8:41 pm
I am modeling a bifactor model.Its factors are outcome variables. I test a 3-way interaction between continuous variables as predictors. I predict the 3 outcomes in separate models because I want the factors to remain orthogonal and do not want factors to correlate through predictor variables. Because I estimate missing data for X variables, I constrain the correlation between X's and the other outcome factors (not predicted) to 0.

I am having trouble interpreting the 3-way interaction, which is significant when all 3 predictors are centered at 0. But, when I center the moderator at high or low values, recalculate interaction terms, and re-estimate the model, the coefficient and p-value of the 3 way interaction term changes significantly. I know this 3-way interaction term should NOT change. I include all lower order terms (x,z,w,xz,zw,xw,and xzw)

I think this has to do with testing a 3 way interaction. Testing only a two-way interaction and centering at high or low values of the moderator doesn't change the 2-way interaction term. I think this also has to do with estimation of missing X variables (specify variances). When I don't specify variances of X, the coefficient/p-value of the 3-way interaction term does not change when I center the moderator at a high value and recalculate interaction terms.Is there something I am overlooking or is this an issue that has a solution? Any advice would be very much appreciated!
 Linda K. Muthen posted on Tuesday, October 01, 2013 - 3:47 pm
This question seems more appropriate for a general discussion forum like SEMNET.
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