Message/Author |
|
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 u1-u10; 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 u1-u10; 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. |
|
Back to top |