I have carried out a LCGA with a continuous outcome for depressive symptoms, and found a 3-class solution. My main interest is in identifying groups of women who display a similar course of depressive symptoms over time.
However, the plots of the estimated means and observed individual values show quite a lot of variation regarding intercept, slope, and quadratic effect for the individual growth curves. Therefore, a colleague of mine proposed that I try a GMM solution, thus with free estimation of variances and covariances. However, I am now reading the book by Nagin on group-based modeling and start to doubt about what I should use - LCGA or GMM, as GMM really alters the conception of "group".
Thank you for the article, that was very helpful. I have a very new and fast computer so that cannot be the problem; my sample size is 1428.
How do I know whether I have non-normal outcomes? (I am sorry, I do not know exactly what this means)
Yesterday I kept the model running for 9 hours and then it looked like it was kind of "stuck"; nothing happened anymore. However, I did not get any error messages. Should I just let such a model continue to run? I don't know whether it's normal that it takes this long?
As a related question (I am sorry if it is a stupid question but I am relatively new to Mplus)
If I do happen to obtain results for the GMM models in the foreseeable future: do I have to start over the process of selecting the number of classes, based on BIC, BLRT, LMR-LRT etc, as I have done for the LCGA model? Or could I say something like: the LCGA pointed to a 3-class solution, and model fit increased (i.e. BIC decreased) when we performed a GMM, thus we chose the 3-class GMM model?
Thank you in advance.
- And another question (maybe this also is very stupid): When I perform a LCGA, and then I remove the line i-q@0, does this make it a GMM with equal variances for the trajectory growth factors? And then when I add %C#1% i s q; for each class, does that make it a GMM with class-specific variances