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Dear Dr. Muthen, I am selflearning Mplus. I would like to use path analysis with binary mediator and continuous outcome. The missing pattern for outcome variable is not missing at random. I'd like to use multiple imputation first and then do the path analysis. However, I do not know how to combine path analysis with multiple imputation in syntax. Could you please give me some example? thank you. Thank you! This is a part of the command for path analysis without imputation. ANALYSIS: BOOTSTRAP = 1000; MODEL: acrfreg2 ON d_wk2 d_wk3 d_wk4 age female married income_h bd001 ja001_2 chnsg cd_w1 srh_1 srh_2 acrfreg; cd_w2 ON d_wk2 d_wk3 d_wk4 age female married income_h bd001 ja001_2 chnsg cd_w1 srh_1 srh_2 h1kcntf h1fcany acrfreg2 acrfreg; MODEL INDIRECT: cd_w2 IND d_wk2; cd_w2 IND d_wk3; cd_w2 IND d_wk4; OUTPUT: standardized CINTERVAL (BCBOOTSTRAP); 


There is no need for multiple imputation  no advantage. Just use ML and its default MAR method for handling missing data. Also, you need a counterfactual approach to estimate indirect effects with a binary mediator  see http://www.statmodel.com/Mediation.shtml 


Thank you so much! I still have another question. I am now using WLSMV. I am not sure either WLSMV or ML is better for my situation. I think both of them could handle missing values of DV. And my continuous DV is a little bit right skewness. So do you think which estimator is better? 


I would use MLR. 


Thank you again! 


Dear Dr. Muthen, I found the MLR or ML with bootstrap doesn't give some model fit statistics as WLSMV. So, 1. how to convince the model is fit well? 2. Could I use MLR or ML with bootstrap to get coefficients and SEs, because of I have missing data for my outcome variable. Then I also use WLSMV to show there are good CFI and RMSEA. 3. I am not sure what is the difference between Type=Complex and Type=general, if I'd like do a path analysis with national survey data. Thank you! 


1. You can use TECH10 or you can use "neighboring models", that is, a less restrictive model where you check if its extra parameters are significant. 2. No, you have to stick to one estimator. 3. Read the intro to UG chapter 9 and the references given there. 


Thank you for so much help! 1. TECH10 cannot be printed because of having covariates in my model. 2. My mediator is binary, but my outcome variable is continuous. So could I calculate other fit indices? 3. I know ML is better to handle missing values than WLSMV. But if the results didn't change much. Could I use WLSMV instead in order to get fit indices. I have 16% of missingness for the continuous outcome variable. Best, 


2. Try "neighboring models" that are less restrictive as I suggested earlier. 3. You can mention the WLSMV fit but you can't fully rely on it. You can also try the Bayes estimator which gives fit info (but you need to study our Bayes videos first). 

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