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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 biascorrected 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; 


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; 


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? 


For instance, Model: .... x (varx); y on x (b); y (resvary); Model Constraint: New(sd); sd = sqrt(b*b*varx+resvary); 

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