I have a model in which A interacts with B to influence C which in turn influences D. A, B and C are continuous, and D is categorical. D has some missing data (4.5% out of around 300 people) and they do not seem to be random because most of those who are missing on D have low values on either A or B (-1 SD or more). The distribution of A is negatively skewed (skewness = -.626, S.D. = .142). A and B are moderately correlated (around .48).
Here are my questions. (1) I ran a path analysis without measurement model, and found that Bayesian analysis gives different results from the analysis using MLR. Why? (2) Which is a better way to handle missing data under what circumstances? Bayesian or MLR? (3) Which Bayesian method am I using when I specify ESTIMATOR=BAYES in the model described above?