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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. 

DONGHO CHOI posted on Wednesday, December 11, 2019  6:29 pm



Hi. I am trying to run 2 level MGMM but then I got this error: *** ERROR The following MODEL statements are ignored: * Statements in Class 1 of the BETWEEN level: SB ON TREAT I hope to know how I should modify the syntax. I have one covariate (P1PPL). WITHIN = P1PPL; !(1 or 0) BETWEEN = TREAT; !(1 or 0) CLUSTER = TEACHER; CATEGORICAL = training3; CLASSES = lc(2); MODEL: %WITHIN% %OVERALL% iw sw  SSIS0@0 SSIS1@1 SSIS2@2 SSIS3@3; [iw@0 sw@0]; iw*; sw@0; SSIS0SSIS3*(1); iw on P1PPL; lc#1 on P1PPL; %BETWEEN% %OVERALL% ib sb  SSIS0@0 SSIS1@1 SSIS2@2 SSIS3@3; SSIS0SSIS3@0; [ib sb]; ib sb on TREAT; ib* sb@0; lc#1*1; lc#1 with ib; !compliers %lc#1% [training3$1@15]; [ib sb]; sb on TREAT*; !nevertakers %lc#2% [training3$1@15]; [ib sb]; sb on TREAT@0; 


We need to see your full output  send to Support along with your license number. 

DONGHO CHOI posted on Monday, January 06, 2020  10:53 pm



Dear Dr. Muthen, Hi. I've got informative advice from Mplus Support. Thank you very much! Now I am watching through Mplus video lectures, (https://www.youtube.com/watch?v=UBzgxWib4Zo) and I noticed that several handouts are revised new, and thus not covered in the video. Could you let me know of the syntax used for Multilevel Growth Mixture Modeling Example given at page 170 of this handout below? (https://www.statmodel.com/download/2011%20Topic%208v5%20[Compatibility%20Mode].pdf) This is about reanalyzing data from Baltimore randomized field trial. Thanks again! 


Slide 170 of Topic 8 refers to an analysis done in line with the input of slides 168169. 

DONGHO CHOI posted on Saturday, January 11, 2020  9:06 pm



Dear Dr. Muthen, Oh I see. I asked this question because it seems withinlevel estimates are constrained to be equal across classes in ex 10.10. I wanted to get different treatment effects for each class. Is this correct model specification to do so? ~ MODEL: %WITHIN% %OVERALL% iw sw  y1@0 y2@1 y3@2 y4@3; iw sw ON x; ! treatment effect c ON x; %CB#1.C#1% iw sw ON x*; %CB#1.C#2% iw sw ON x*; %CB#2.C#1% iw sw ON x*; %CB#2.C#2% iw sw ON x*; %BETWEEN% %OVERALL% ib sb  y1@0 y2@1 y3@2 y4@3; ib2  y1y4@1; y1y4@0; ib sb ON w; c#1 ON w; sb@0; c#1; ib2@0; cb ON w; ~ Thanks! 


Slide 170 of Topic 8 has the treatment on the Between level (classrooms), not on Within (students). 

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