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.
I have the case of having two highly correlated moderator variables. Actually, I run the moderating effects in two different models due to model complexity issues (showing different explained variances). But is there anything like VIF or tolerance values for Moderators to Show the importance of Accounting for both Moderators? How would you suggest to proceed?