Message/Author 

Jake Lant posted on Friday, March 09, 2012  1:29 pm



Hello fellow Mplususers and creators, I am using MLR to run mediation models but it is the first time I do it with missing data. It runs it perfectly but it skips the observations with missing data. Is there any way around this? I was under the impression MLR automatically estimated these but it doesn't seem to be doing that haha. I specified MISSING ARE variables (999.00), etc. Any help is appreciated. Best, Jake 


I suspect that the cases that are being deleted have missing on the observed exogenous x variables. Missing data theory does not apply to these variables as the model is estimated conditioned on them. 

Jake Lant posted on Friday, March 09, 2012  6:22 pm



Thank you, Linda. And yes you're right (of course), that's precisely the error message that I got. So this means I will HAVE to exclude these variables? 


You can bring them into the model by mentioning their variances in the MODEL command. They will then be treated as dependent variables and distributional assumptions will be made about them. 

Jake Lant posted on Friday, March 09, 2012  6:38 pm



Also, I think this is important. These variables were somewhat "expected" to be missing because the participant didn't meet the criteria so we got them to check a box saying that it didn't apply just so that we have explanations for these missing variables. Do you think there is a method that would probably fit this scenario better? Thanks again. 

Jake Lant posted on Friday, March 09, 2012  8:14 pm



I did it (mentioned variances) and it worked, but I am not really liking the results because it really makes a somewhat big difference from excluding versus FIML, and I want to make sure there isn't a better way. So I am thinking I should use the PATTERN IS command (missing by design) but I had set these as missing (i.e., 99, 100). But what kind of coding can I use to get it to accept it as a "pattern"? I had done, for example, MISSING ARE p1 (999.00) p2 (992.00) p3 (100.99) I tried: PATTERN IS p1 (999.00) p2 (992.00) p3 (100.99) looking for a shortcut, ;) but of course it failed. The example says, PATTERN IS design (1= y1 y3 y5 2= y2 y3 y4 3= y1 y4 y5). But I am not 100% as to what 1 = y1 y3 y5 would be here. Any help to clarify this would be appreciated. Thank you. 


Missing data as you describe should not be treated as true missing data. You should not bring the variances of these variables into the MODEL command. Data like these should be analyzed in subsets. 


1. I conducted a path analysis with three exogenous (two of them are binary data) and two endogenous variables. Except for one endogenous variables, all other variables have missing data. I have known the default of missingness method of mplus is FIML in VERSION 6.0. To avoid the loss of information, I do not want to use listwise or pairwise approaches. Thus, if I apply FIML, how the missing values are considered in FIML? 2. I have found that you explained that mentioning the variances of exogenous variables in the MODEL part in mplus is the way to make the exogenous variables be considered as a dependent variable. Is this way is appropriate for my model as well? If it is, can you let me know how to make the command in the model part? always thanks! 


See Chapter 1 of the user's guide where missing data handling in Mplus is described. See also Version History on the website under Version 6.1. Here you will find an explanation of mentioning the variances of exogenous variable in the MODEL command. 


Do you mean this below? https://www.statmodel.com/Version6.1xLanguage.pdf If it is, I could not find the info.. 


Go the the homepage of the website. Click on Version History in the left margin. Go to Version 6.1. 

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