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Hello everyone, I hope I am posting in the right place. Simple question, possibly: Can I combine the Variable declaration statements for COUNT and CENSORED? I have a dependent variable that is of type Count, I would like to specify it as (nb), and I would like to indicate that it is censored above and below. When I make both statements, the Output is only: Observed dependent variables Count SNDSCOR3 If I remove the count declaration, I get this: Observed dependent variables Censored SNDSCOR3 Here are the commands I have for the VARIABLE section: CENSORED = sndsCor3(a) sndsCor3(b); COUNT = sndsCor3 (nb); Any help is appreciate! Thanks, Andre. 


You cannot combine these options. 


Ah ok then, thanks for the response. 


Perhaps a mixture can fit this. 


Thanks for the suggestion! I will try it. I have another question related to the analysis; Instead of creating a new thread, perhaps you might suggest something here? I am using a complex twolevel model (stratification with 8 strata and a cluster variable). I have two count dependent measures. I would like to combine them into a latent variable and measure the impact of a level 2 independent on the latent variable (ORF) and then reuse the same ORF variable as the outcome for the within level. However, I get the error: "A latent variable declared on the within level cannot be used on the between level". Any further thoughts on how I might accomplish this? (Am I wrong in thinking that just because an observed dependent can be used on both, that a latent dependent could be used as well? If so, what am I missing?) Thanks again! 


You need to have an "f BY..." statement also on Between. In this case, the factor indicators are random intercepts for the count variables. You may or may not want to add equality of loadings across levels. 


Thanks for the response again. I did add the "by" statement for both, but I used the same variable name. I'm guessing that was the problem and I'll use another name. However, I'm not sure I understand that last part. Could you possibly provide a little more of an explanation? I would be really greatful. Thanks! 


Yes, use different names like fw and fb. If you have equality of loadings across levels you can view the between factor as a random intercept of the factor: y_ij = lambda*fw_ij + lambda*fb_j + errors_ij is the same as the multilevel expression y_ij = lambda*f_ij + errors_ij f_ij = fb_j + fw_ij where fb_j is the random intercept. 


Yes, use different names like fw and fb. If you have equality of loadings across levels you can view the between factor as a random intercept of the factor: y_ij = lambda*fw_ij + lambda*fb_j + errors_ij is the same as the multilevel expression y_ij = lambda*f_ij + errors_ij f_ij = fb_j + fw_ij where fb_j is the random intercept. 

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