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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? |
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Yes, that is possible. |
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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. |
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I mean the latter, the latent class analysis. |
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And I want to keep the items that indicate the factors in the model. |
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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 |
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You say f BY i1-i5; y on f; - and you don't have to say anything to have the y means vary as a function of c. The i10-i30 variables are the c indicators by default in the sense that when you say f BY i1-i5; the i1-i5 variables will not have their means/thesholds varying across classes c because of the BY. Only i10-i30 will have means/thresholds varying across classes c, which is what you want for LCA. |
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I'm sorry, I just can't see where c is in this model. To me it looks like only f (and i1-i5) are in the regression, but c and i10-i30 are not used at all as far as I can tell. |
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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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