I am an Mplus novice. I ran a latent profile analysis and identified 3 profiles. I then put profile assignment into a path analysis as an endogenous variables. I ran the path analysis with profile assignment as one variable and I also broke it into dummy variables. The model with dummy variables fit better but I'm having trouble interpreting the model results.
My questions are: 1) Is it valid to turn a categorical variable into dummy variables?
2) How do I interpret the model results? For example, when I regressed my mediator (Eff) onto one of the dummy variables (Cls2D) I got a weight of -.064. What does this mean? Is it like an odds ratio in logistic regression?
Path analysis with a nominal DV is a tough task for any analyst, and certainly for a novice. Probably the easiest approach for you is to work with one 0/1 dummy variable at a time as the DV (one of the 3 categories vs the 2 other). But even so, you get into new, advanced methods because path analysis (by which I assume you mean mediation analysis) needs special attention with a binary DV using counterfactually-defined indirect and direct effects (see our Mediation page on our website for sources to study).
I assume that when you say "regress my mediator (eff) onto ...Cls2D" you really mean the opposite - that is, Cls2D is your DV which is regressed on the mediator (eff).
TchPrac = teacher practice Eff = Self-efficacy Cls2D = Class 2 dummy-coded Cls3D = Class 3 dummy-coded (I left out Cls1 to use as a reference)
The main intention is to see how class membership predicts teaching practice, using efficacy as a mediator. (I had a few other mediators as well but the model fit was not good so I took them out.) Is there a better way to do this?