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Anonymous posted on Monday, August 26, 2002  9:18 am



In the MPLUS specification of a LCGA, does one need to explicitly specify that the variances of growth parameters and the covariances between growth parameter within class are equal to zero? I searched through your examples, but could not find a setup for the LCGA. Thank you! 


Yes, you do need to explicitly specify that the growth factor variances and covariances are zero. For example, i@0; s@0; i WITH s @0; 


Just to make sure: is that still the case ? 2002, in Mplus time, is already old... With continuous outcomes, the difference between LCGA and GMM is that in LCGA you specify : i@0; s@0; i WITH s @0; Whereas the difference between LCGA and GMM with noncontinuous outcomes is that you have to rely on numerical integration to obtain GMM. Is that it (from the Mplus input perspective)? 


The LCGA and GMM models are the same for continuous and categorical outcomes. LCGA fixes variances and covariances for the growth factors at zero while GMM has free growth factor variances and covariances. For categorical outcomes, numerical integration is required for model estimation for GMM but not for LCGA. 


Greetings and thanks for the answer, This confirm what I tought. But I think my question was not clear. In the Mplus input for noncontinuous outcomes LCGA, if we dont specify "numerical integration" then do we still have to specify i@0; s@0; i WITH s @0. In the manual examples, no such specification are given for the noncontinuous outcomes LCGA (ex 8.9, 8.10, 8.11). 


No such specification is given because it is not needed. 


Can a latent class growth analysis be performed when the indicators are dichotomous? Is it OK to construct a latent growth model with the latent intercept and slope factors as we typically think of them, when the indicators proceed in 01 patterns such as 0001 or 0111 or 0011? Or would this be inappropriate for a latent growth model? If so, is there another model that you can suggest? I am very interested in the mixture models in Chapter 8 of the Mplus manual, but I am not sure if they are appropriate for my analysis. Thank you for any guidance you can provide. 


Yes, this can be done as shown in Example 8.4 for GMM and Example 8.9 for LCGA. 


Thank you, could aspects of the models in Chapter 8 be combined? I like the model shown in EXAMPLE 8.7: A SEQUENTIAL PROCESS GMM, as we have two sequential processesbut could I adapt it: a) to have known classes as in Example 8.8, and b) to have dichotomous outcomes rather than continuous? Thank you. 


Yes, that can all be done. 

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