Handling missing data: Bayesian vs. MLR PreviousNext
Mplus Discussion > Missing Data Modeling >
 Jieying Chen posted on Saturday, April 13, 2013 - 5:43 am
Dear Dr. Muthen,

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?

Thank you in advance for your consideration.
 Linda K. Muthen posted on Saturday, April 13, 2013 - 1:57 pm
1. This should not be the case. Please send the two outputs and your license number to support@statmodel.com.

2. They are the same.

3. A model with non-informative priors.
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