Large amount of missing on predictor PreviousNext
Mplus Discussion > Missing Data Modeling >
 Sun Kim posted on Friday, November 09, 2012 - 3:21 pm
Dear Dr. Muthen,

I am running a mediational model with around 50% of missing data on one of the predictors (I have 5 predictors, 3 mediators, and 1 outcome). Is this a serious proble with FIML estimation? I could drop all those missing on this specific predictor from the whole analysis (which cuts my sample in half), but I am not sure this is the best approach.

Also, what is the "number of observations" in the output? Is it the total N of the model (which indicates that none of the data has been listwise deleted)?
Thank you.

 Linda K. Muthen posted on Friday, November 09, 2012 - 8:23 pm
The N shown in the output is the total N for the analysis. Listwise deletion is not the default.

If the predictor is not that important, I would not include it in the analysis. One would hope for less than 20% missing.
 Sun Kim posted on Saturday, November 10, 2012 - 12:43 am
Dear Dr. Muthen,

Thank you so much for your quick response.

The predictor is actually important (central to the research question), and I am not sure what to do in this case-- should I drop the cases missing on this predictor or still just run everything on Mplus since it does do FIML estimation.

Another question is about the indirect effects-- is "MODEL INDIRECT" giving me estimates equivalent to what I would obtain if I conducted the Sobel test of mediation (so I read in another article)?

Thank you so much for your help.

 Linda K. Muthen posted on Saturday, November 10, 2012 - 2:13 pm
You would need to decide whether you want to do listwise deletion or bring all of your covariates into the model and make distributional assumptions about them. Having 50% missing is not desirable.
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