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I am analyzing multilevel longitudinal data with observations nested within students and students nested within teachers. I have structured the model so that class assignment is occurring at the student level. I am freeing most variances to vary by class, but I have not been successful in getting the teacher level intercept variance to vary by class. When I set up the syntax, I get the following error message: "Variances of between level variables are not allowed to vary across classes." Is there a work around for this? I am not using any covariates in the model. Thank you. 


You can capture the variance by a loading for a latent variable that you introduce. So with y referring to the latent intercept on level2 say: f by y* (lam); f@1; y@0; That lam parameter can then vary across classes, where the variance is lam*lam. 


Thank you for your quick reply. I am missing something because when I tried to run the syntax you provided, I got the error message, "unknown variable: lam". Here is my between cluster syntax for a 2 class model. %between% %overall% Ib1 Sb1 y1@0 y2@1 y3@2 y4@3 y5@4; sb1@0; f by ib1*(lam); f@1; ib1@0; %c#1% lam; %c#2% lam; 


Sorry, lam is a parameter label that I chose to communicate more easily to you. You should replace that in your classspecific statements with f by ib1*; 


Thank you again for your quick reply. I am getting closer. To make sure I understand, I will provide the syntax. It does not contain class specific parameters. %between% %overall% Ib1 Sb1 y1@0 y2@1 y3@2 y4@3 y5@4; sb1@0; f by ib1*; f@1; ib1@0; 1. Is this correct? 2. Is the square of the factor loading the variance of ib1? 3. How would this change for a parallel process model with ib1, ib2, and ib3? Here are two possibilities: f by ib1* ib2* ib3*; or f1 by ib1*; f2 by ib2*; f3 by ib3*; 4. Will this change the latent class structure? Thank you. 


Yes on 1 and 2. 3. The latter is correct. 4. Yes. 


Thank you for your response. Could you please provide an interpretation of the between cluster variance when the above mentioned technique is not used? Is it the between cluster between class or is it the between cluster within class? I ran a set of models, and the between cluster variance is not statistically significant even though the ICC is close to 30% in the multilevel model without latent class or growth mixture. I found this surprising. Could you recommend any literature on the interpretation of the variance for multilevel growth mixture models? Thank you again. 


Unless you can point to an example in the UG or in HenryMuthen, we need to see the output to know exactly which model you refer to  please send to Support along with your license number. 

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