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I was wondering if it was possible to have both a set of continuous latent factors indicated by a set of items and set of categorical latent factors measured by a separate set of indicators where both the continuous and categorical latent variables are predictors of another variable? 


Yes, that is possible. 


When you say categorical latent factor, do you mean a factor with categorical factor indicators or do you mean a categorical latent variable as used in latent class analysis or mixture modeling to capture unobserved classes. 


I mean the latter, the latent class analysis. 


And I want to keep the items that indicate the factors in the model. 


So for example, I have a continuous factor f that is measured by items i_1 through i_5 and a latent class variable c with say three classes measured by items i_10 through i_30 and now I want to know what the syntax would be to perform a regression of another variable Y on f and c 


You say f BY i1i5; y on f;  and you don't have to say anything to have the y means vary as a function of c. The i10i30 variables are the c indicators by default in the sense that when you say f BY i1i5; the i1i5 variables will not have their means/thesholds varying across classes c because of the BY. Only i10i30 will have means/thresholds varying across classes c, which is what you want for LCA. 


I'm sorry, I just can't see where c is in this model. To me it looks like only f (and i1i5) are in the regression, but c and i10i30 are not used at all as far as I can tell. 


It doesn't show in the Model specification because this is handled by default. You can see what Mplus does by looking either at the estimates or TECH1. TECH1 will show you thresholds/intercepts for the c indicators and for your y variable that vary across the latent classes. 

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