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Hi Linda and Bengt, I am exploring mediation and need to estimate the indirect effect. I used multiple imputation to deal with missing data and I am now running the analyses on the imputed data. Hence, I specify type=imputation. Following earlier posts, to obtain the indirect effect I am using the MODEL CONSTRAINT subcommand to obtain the indirect effect in this way: Model: y on m (p1); y on y; m on x (m); MODEL CONSTRAINT: new(ind1); ind1 = p1*m; In the output line I request standardised estimates by specifying std (output: std). However, in the output 'ind1' is estimated for the unstandardised but not for the standardised results. I could manually multiply the corresponding standardised estimates for 'm' and 'p1' but I would like to obtain the standard error and p values as well. Is there any way I could specify my model so that it calculates the standardised indirect effects? Many thanks 


You would need to standardize it in MODEL CONSTRAINT to obtain the standard error. 


Thanks Linda, I am afraid I am not very familiar with this  I would I achieve it? 


See Example 5.20. 


Hi Linda, I had a go at following example 5.20 to obtain the standard errors for the standardised estimates. However, I must have set up the model incorrectly as I received an error message which suggested I should specify PARAMETERIZATION=THETA and when I specified it (to see what would happen) the model could not even be identified. Could you recommend a reference related to example 5.20? 


Please send your output and license number to support@statmodel.com. 


Dear Linda, To calculate the direct/indirect effects on imputed data, I used the commands Model: y on m (p1); y on x; m on x (m); MODEL CONSTRAINT: new(ind1); ind1 = p1*m; I then went on to adjust the model to account for potential confounders (cov1  cov5). Model: y on m (p1); y on x cov1  cov5; m on x cov1  cov5 (m); MODEL CONSTRAINT: new(ind1); ind1 = p1*m; The results I obtain are much more different than the ones on the complete cases where I am able to use the model indirect command: Model: y on m ; y on x cov1  cov5; m on x cov1  cov5; Model indirect: Y on X; However, if I aply the model constrain command to the complete cases the results are in line with those of the imputed data. It would be great if you could help me to identify where I have gone wrong... Many thanks 


The variable y have five more predictors in the second model. The regression coefficients are partial regression coefficients controlling for all of the predictors. This is why the indirect effect changes. 


Thanks Linda! 


Hi Linda, I just realised that my email was not 100% clear. I used the following line on complete cases to adjust for 5 confounders: Model: y on m ; y on x cov1  cov5; m on x cov1  cov5; Model indirect: Y IND X; And I now want to do the same for imputed data so I need to reflect the process in the lines above when writing up the model constrained. So I use: Model: y ON cov1cov5; y on m (p1); y on x cov1  cov5; m on x cov1  cov5 (m); MODEL CONSTRAINT: new(ind1); ind1 = p1*m; This produces results equivalent to the ones in with the model indirect command but I wanted to check with you that this is sound. 


The following line holds all of the regression coefficient for m equal: m on x cov1  cov5 (m); I think you want: m on x (m) cov1  cov5; 


The line above does not reproduce the results I had obtained using model indirect in the complete cases. However, if I rewrite it as below I do replicate the results: m on cov1cov5; m on x (m); Is it OK to regress the covariates on the mediator to reproduce the adjustment I originally made on the IV> m path ( m on x cov1  cov5;)? 


The statement m on x (m) cov1  cov5; is the same as m on cov1cov5; m on x (m); If you don't get the same results, send those 2 outputs to Support. It is certainly ok to add covariates in the m regression on x. 


Thank you Bengt, You are right the two lines produce the same results  not sure what happened earlier. Many thanks 


Dear Dr. Muthen, I would like to calculate the standardized indirect coefficient using the WLSMV estimator. I have used "Model Constraint" command because one of the dependent variables was binary, and sampling weights and multiple imputation were used. I would like to calculate the standardized indirect coefficient for the path from hp(binary)> na >dp. DATA: FILE IS "data.dat" ; Type=Imputation; VARIABLE: Names are hp dp sa1sa5 cont1 cont5 ; Categorical= hp; Missing are all (9999); Weight= RWTR; ANALYSIS: Estimator= WLSMV; MODEL: na by sa1  sa5 ; dp on hp na (a1) cont1 cont5 ; na on hp (b1) cont1  cont5 ; hp on cont1  cont5; dp (v2); hp (v1); Model Constraint: New(a1b1 stdab ); a1b1=a1* b1; stdab = a1b1*SQRT(v1)/SQRT(a1b1**2*v1+v2); When I ran this syntax, I received an error message saying that " PARAMETERIZATION=THETA" was needed. However, when I added theta parameterization to the analysis command, all of the standard errors and pvalues for coefficients were zero. I was wondering if my specification for the standardization of indirect path is incorrect and how I could fix this problem. Thank you very much. 


Please send the output and your license number to support@statmodel.com. 

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