I am conducting GMM with 2 parallel growth processes, and I have 4 time-invariant covariates; 2 of them are temperament variables and the rest of 2 are parenting variables.
I first started with unconditional models without any covariates and reached at stable solutions.
Then, I sequentially added covariates, first 2 temperaments and then added 2 more parenting variables.
1) However, the slopes of some classes have changed when I added 2 parenting covariates along with 2 temperament covariates. For example, with just 2 temperament covariates, one of the class had a significant negative slope but with 2 more parenting covariates, the slope of the same class turned out to be significantly positive.
How could this happen and how should I explain this kind of results?
2) Is it possible to examine hierachical effects of covarites in GMM?
In polynomial logistic regression part of GMM, I want to examine additive effects of parenting beyond temperaments.
How the slopes change depends on the relationships among the covariates and the relationship of the covariates to the dependent variable. The coefficients when there is more than one covariate are partial regression coefficients.