I conducted mixture modeling on 5 subscale scores 2 ways:
1. Using raw subscale scores.
2. re-scaling the scores by dividing them by the number of items on the subscale, because there was a different number of items on each subscale. This re-scaling made the means within class comparable.
Fit indices (e.g., log-likelihood, BIC)weren’t the same across the two ways. Why? When will fit indices be the same and when will they differ?