

Handling missing data: Bayesian vs. MLR 

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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. 


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 noninformative priors. 

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I have a path model where most endogenous variables are normally distributed, but there is one mediator that is not normally distributed (example is below). I would like to use a Bayesian model to obtain asymmetric confidence intervals for the mediator (the model is multilevel and thus cannot use bootstrap estimates). I wanted to ask about how missing data would be handled for the variable that is nonnormal. Example model: Y1 ON X1 X2 Y2 ON X1 X2 Y1 Y3 ON X1 X2 Y1 Y2 MODEL INDIRECT: Y3 IND X1 X2 Y1; Y2 is the variable that has a nonnormal distribution. My question is: 1. If Y2 has a different distribution than the other variables (e.g., Dirichlet), is it assumed MAR based on X1 X2 Y1 2. Or, Is Y2 assumed MAR based on X1 X2 Y1 and also Y3 (i.e., including all variables listed in the path model, including ones that are not predictors of Y2) 3. Or are neither of these correct and there is another answer 


2. is correct. And, normality is assumed for all variables conditional on the covariates X1, X2. Typically ML under MAR has some robustness to missing on nonnormal variables. 

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