Originally Posted by

**robertz**
Another very helpful explanation, much appreciated! Ok so let's see if I understand the process correctly:

1) I will draw each latent variable, including the observed variables, and place them next to each other in no particular order. So not in the hypothesized model structure, right?

2) Next I connect the latent variables with double headed arrows i.e. the correlations.

3) Calculate the estimates for both groups.

What are the values I need to look at in the output, and how do I compare them across the two groups (the invariance test) ?

After I correlated all my 13 variables (12 are latent) I looked at the correlations matrix for all data as one group, and didn't find anything that looked very unusual. A couple of correlations were not in line with the theory, so is there a way to adjust them? And do I need to do that first before I move on to testing for invariance? The output noted a covariance matrix with all 13 factors as not being positive definite.

UPDATE => I managed to fix the correlations, so the correlation matrix looks fine. However the non-positive definite coviarance matrix was still reported. Is this something to worry about (and to fix) or should I just focus on the correlation matrix for now?