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 Mplus User posted on Wednesday, May 31, 2017 - 10:47 am
I have a mediation model, and I'm interested in testing whether two mediation effects are significantly different from each other in magnitude. I use the Model Constraint method to create a new variable "diff" (see below). I also requested for standardized effects (stdyx). The Mplus output provides standardized and unstandardized coefficients for all my variables except diff, in which only an unstandardized coefficient was provided. I also tried using bias-corrected bootstrapping, but again, only an unstandardized coefficient was provided. Is there a way to obtain the standardized coefficient for the new variable, diff?

MODEL CONSTRAINT:
NEW (ind_1 ind_2 );
ind_1 = a1*b1;
ind_2 = a2*b2;

NEW (diff);
diff = ind_1 - ind_2;
 Bengt O. Muthen posted on Wednesday, May 31, 2017 - 5:46 pm
You have to express the standardization yourself in Model Constraint. That is, express the standardized version of ind_1 and ind_2 and then express the difference.
 Mplus User posted on Thursday, June 01, 2017 - 10:01 am
Thank you so much, Bengt. This is probably a dumb question -- how do I create a standardized version of ind_1 and ind_2?

MODEL CONSTRAINT:
NEW (ind_1 ind_2 );
ind_1 = a1*b1;
ind_2 = a2*b2;

NEW (diff);
diff = ind_1 - ind_2;
 Mplus User posted on Thursday, June 01, 2017 - 10:42 am
I tried to answer my own question. Does this syntax create a standardized version of the two indirect effects? This is based on equation 9 in Preacher and Kelly (2011). I multiplied path a and path b by the standard deviation of X (.744) divided by the standard deviation of y (2.911). Is the syntax below correct?

MODEL CONSTRAINT:
NEW (ind_1 ind_2);
ind_1 = a1*b1*.744/2.911;
ind_2 = a2*b2*.744/2.911;

NEW (diff);
diff = ind_1 - ind_2;
 Bengt O. Muthen posted on Thursday, June 01, 2017 - 6:56 pm
The formula is right but when you insert numbers like this (e.g..744) you are not taking into account sampling error. You need to express the SDs in model parameter terms.
 Mplus User posted on Thursday, June 01, 2017 - 9:17 pm
How do I express the SD in model parameter terms?
 Bengt O. Muthen posted on Friday, June 02, 2017 - 1:42 pm
For instance,

Model:

....
x (varx);
y on x (b);
y (resvary);

Model Constraint:

New(sd);

sd = sqrt(b*b*varx+resvary);
 Irina Patwardhan posted on Tuesday, December 26, 2017 - 8:05 am
Hi everyone,

I am using a MODEL CONSTRAINT statement to estimate group differences in all paths in the multiple group model. The model works fine, but I don't understand how to interpret the output, what kind of statistics is calculated with the model constraint?

model:
DIFtas on Risk8;

MODEL MALE:

DIFtas on Risk8(p1) ;

MODEL FEMALE:

DIFtas on Risk8(p1f) ;

model constraint:
new diff1 (and so on for each of the 27 paths);

diff1=p1-p1f;

and I then get the estimates, SE, and p-values for the new/additional parameters in the bottom of my ouptput.

DIFF1 (Estimate= -0.142) (SE=0.129)(ESt/SE=-1.105)(p-value=0.269)


What kind of statistics is reported with the estimate? Looks like a Beta to me, is it correct?
 Bengt O. Muthen posted on Tuesday, December 26, 2017 - 4:08 pm
Like all parameters in the Model command, your p1 and p1f parameters are unstandardized regression coefficients. Diff1 is their difference.
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