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Dallas posted on Wednesday, June 29, 2011  7:05 am



I have a model with 10 categorical (binary) items. I'd like to run the model allowing local dependence between two of the items. I'm using ML estimation. So, I get the following message "Variances for categorical outcomes can only be specified using PARAMETERIZATION=THETA with estimators WLS, WLSM, or WLSMV." How can I go about specifying correlated errors in Mplus using ML estimation? Thanks! 


The Theta parameterization is for weighted least squares analysis. With maximum likelihood each residual covariance requires one dimension of integration. To specify a residual covariance in this case, say f BY u1@1 u2; f@1; [f@0]; The residual covariance is found in the factor loading for u2. 

Dallas posted on Wednesday, June 29, 2011  8:12 am



Thanks! I see. In this case, then, wouldn't one want to constrain the covariance between the general factor and the factor representing the local dependence to zero? Say: f1 by u1u10; f2 by u1@1 u2; f2@1; [f2@0]; f1 with f2@0; To make the model equivalent to a WLSMV specified as follows: f1 by u1u10; u1 with u2; 


If the factor covariance is not zero as the default, then it should be fixed at zero. 

Dallas posted on Wednesday, June 29, 2011  8:21 am



Thanks. When I estimate the model, it does not appear fixed to zero as default. So I asked to make sure my understanding that it should be set to zero made sense. Thanks again. 

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