I am running a multilevel model (I compare the Intercept-only-Modell with a Model 1 (Control Variables), a Model 2 (Direct Effects of Predictor Variables) and a Model 3 (Adding Interaction Terms). Some results of Model 3 seem to be weird (a standardized estimate for an interaction term in Model 3 over 1.0 (1.3) and the Chi-Square-Difference Test (comparing Model 3 to Model 2) seems not to be possible because the -2*LogLikelihood Value of Model 3 is HIGHER than the -2*LogLikelihood Value of Model 2.
Question 1: Is it possible that the -2*LogLikelihood Value of Model 3 is higher than the one of Model 2, even if the models are nested (Model 3 includes additionally the interaction terms)?
Question2: When I look at the correlation between my predictor variables I subsume that there ist multicollinearity between the interaction term (which has this weird estimate)and a predictor. A there - like in SPSS - any opportunities in Mplus to test for multicollinearity through a Test like VIF or Tolerance?
...As I run the model in Mplus, I am not sure how to transfer the results to SPSS to check for multicollinearity...
Q1: No - send to Support along with your license number.
Q2: You can have standardized values greater than 1 if you have more than one predictor. But as you say, this may point to high correlations among the predictors. Perhaps you didn't center the 2 variables going into the interaction. No VIF in Mplus.