When fitting a univariate model with continuous moderator, I keep getting positive log-likelihood (and naturally negative -2LL). The main variable is log transformed BMI. As far as I understand this is caused due to its small SD (SD=0.13 with mean=3.13). I saw in one thread (http://openmx.psyc.virginia.edu/thread/329 ) that it is recommended to avoid using variables with small variance. Would it be advised to use original BMI variable instead of log-transformed despite its skewness (1.22 vs 0.62 of the log-transformed)?
I fit models with BMI as well and it seems more natural to compare positive -2LL. Besides, models with BMI were more stable (with log_BMI Mx code RED was obtained quite often). The results though were slightly different, but this might be due to the model choice. When comparing nested models using log_bmi, in many cases reduced model was significantly worse than the model above, but appropriate in comparison to the saturated model. With BMI, this happened only a few times.
Would be very grateful for an advice!