Wim Beyers posted on Tuesday, February 01, 2005 - 6:43 am
Is it possible to estimate a Growth Mixture Model based on multivariate data? I mean, a classical cluster analysis often is based on multiple (relatively) independent variables (e.g., in parenting research: warmth and control). So, is this also possible in a LGMM (e.g., finding latent classes of trajectories in warmth ànd control)? I do not mean parallel LGMM, but rather 'combined' LGMM.
Wim Beyers posted on Thursday, February 03, 2005 - 8:55 am
OK, but do examples exist of such an analysis? To make myself more clear: I want to estimate latent classes that combine measures of at least two relatively independent variables (X1 and X2), measured repeatedly. So that, for instance, Class 1 consists of persons with 'stable high levels of X1' (high intercepts and low slopes) COMBINED with 'increasing X2 (low intercepts and high slopes).
bmuthen posted on Thursday, February 03, 2005 - 4:15 pm
I don't know that we have exactly such examples, but the idea and the modeling is clear. You would use 2 sets of growth factors (I assume you don't think they are the same for the 2 outcomes) but one latent class variable, where the latent classes are determined by the joint growth trajectories. You can use ex 6.13, deleting the last 2 lines of the model input, adding type = mixture and letting the defaults give you different growth factor means.