Covary interaction terms PreviousNext
Mplus Discussion > Structural Equation Modeling >
 Elissa Hamlat posted on Friday, September 22, 2017 - 2:56 pm
Hello again,

If I create an interaction term with my exogenous variables,
for example, define x1xgen = x1*gender.

and then regress my (latent) endogenous variables on the exogenous variables as such:

Factor1 ON x1 gender x1xgen;

do I allow the interaction terms to covary or do I set the relationship to zero, i.e.,

x1 WITH x1xgen@0;

Thank you for this service.
 Bengt O. Muthen posted on Friday, September 22, 2017 - 4:46 pm
The interaction terms covary without you saying anything because they are "x variables".
 Elissa Hamlat posted on Saturday, September 23, 2017 - 9:03 am
Yes, that makes sense. But this is ok in the context of my model, to have the x variables covary?

I prefer not to constrain them but I am not used to seeing this in regression w/ interactions (as I am relatively new to Mplus/SEM).

Thank you.
 Bengt O. Muthen posted on Saturday, September 23, 2017 - 3:56 pm
I don't think it is ok - why would you want x1 to have zero correlation with x1*gender?
 Elissa Hamlat posted on Saturday, September 23, 2017 - 4:40 pm
I agree. Thank you.

A follow-up question is that the SR model contains method factors as there are multiple informants (child, parent) as well as random intercept factors.

I am tempted to just let my x variables covary with these. Is there any reason why I should constrain these relationship?

 Bengt O. Muthen posted on Monday, September 25, 2017 - 5:13 pm
No, just covary or regress the factors on the covariates.
 Elissa Hamlat posted on Tuesday, September 26, 2017 - 4:46 pm
Thank you. So I should constrain the correlation between x variables and these factors to O?
 Bengt O. Muthen posted on Tuesday, September 26, 2017 - 5:32 pm
No - the opposite.
 Elissa Hamlat posted on Thursday, September 28, 2017 - 6:40 am
ok, thank you.
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