

Correlation between latent variables 

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Dear Linda, Bengst: We are trying a MIMIC model with dichotomic data SRQ1SRQ20 (20 observed variables)with 25 observed covariates. The following warning message appears in the output: THE MODEL ESTIMATION TERMINATED NORMALLY WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE F3. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING PARAMETER 113. THE CONDITION NUMBER IS 0.587D17. Checking TECH4 option for output, two latent variables were found to be correlated at 1.011. What do you suggest to overcome this problem? Thanks for your advice. Kind regards, Y.P. Wang Institute of Psychiatry Sao Paulo University, Brazil 


You would need to change your model. I would suggest starting with an EFA. 

Derek Kosty posted on Tuesday, August 18, 2009  10:34 am



I would like to regress a variable (Z) on two correlated latent factors (X & Y). Is there a way to separate out the effects of a) the shared variance between X and Y, b) the unique variance of X, and c) the unique variance of Y on the dependent variable (Z)? This is how the model is currently setup and I believe the estimates for 'Z on X Y' reflect the effect of a) the total variance of X and b) the total variance of Y on Z. Is that correct? MODEL: X by var1 var2; Y by var1 var2 var3 var4 var5 var6 var7; Z on X Y; Again, thanks for your help. 


With correlated predictors (X & Y) I don't think one can separate out the effects. 

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