I ran a series of GMM models (1-5 classes) with BMI data; fit indices indicated that a 3 class model was the best fitting. I then ran models (classes 1-5) separately by gender to evaluate if the BMI trajectories differed by gender. For both genders, a three class model was identified as the best fitting, and the models yielded trajectories that were very similar for boys and girls. I then ran models to test for measurement invariance (using KNOWNCLASS), and found that the unrestricted model was a significant improvement over the restricted model. However, a close look at the data indicated that for the first and second class, BMI trajectories completely overlapped across gender. But for the third class, the trajectories visually differed by gender. So then I tested for partial invariance, in which I restricted the parameters for the first two classes across gender to be equal, and then allowed the parameters for the third class to remain free. Based on the loglikehood diff test, this partially restricted model was a significant improvement over the unrestricted model, which is great. However, even though this third class is statistically different by gender, it isn't meaningful interpretation-wise, and we plan to combine the 3rd class across genders. So, where do I go from here? I would like to test for diffs in covariates by class membership, but I am not sure if I should do so in the full sample 3-class model or in the partially restricted model.