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 Trang Q. Nguyen posted on Saturday, November 10, 2012 - 2:26 pm

I am trying to impute missing data in a complex survey data set, and appreciate your help in getting it right. For design I have variables strata for strata, com for clusters and wt for individual weights. I did:

NAMES = ...;
CLUSTER = com;
WEIGHT = wt;
AUXILIARY = id strata ...;

IMPUTE = ...;
SAVE = impute*.dat;


I read the forum and thought TYPE = BASIC TWOLEVEL would help incorporate the cluster and weight information at least, although it does not use strata information.

However the output says:

"Weight variable (cluster-size scaling)

which suggests what I did is incorrect. My wt variable is individual weights, not cluster size.

Sorry I am new to Mplus in general and brand new to imputation in Mplus. Please let me know how I should do it.

Thanks much!

 Tihomir Asparouhov posted on Monday, November 12, 2012 - 2:54 pm
The best solution is to add the weight variable in the imputation


and remove the command

WEIGHT = wt;

Usually the weight variable is computed from other variables such as race gender SES. If that is the case, the best solution is to have these variables in the imputation instead of the weight variable.

You can also add dummy variables for each stratum if you want to use that information.

Bayesian estimation (which is used for the imputation) currently can not use the weight variable directly.
 Trang Q. Nguyen posted on Tuesday, November 13, 2012 - 4:04 pm
Thank you, Tihomir.

I'll add dummy variables for the strata.

The weight variable is actually sampling probability based, and it depends on which stratum/cluster one is in and not on individual characteristics. I think because I am going to include the stratum variable, the weight variable will not carry any additional information.

And so should I still do CLUSTER = com and
TYPE = BASIC TWOLEVEL? Or should I do just TYPE = BASIC?

 Tihomir Asparouhov posted on Wednesday, November 14, 2012 - 10:41 am
You should use TYPE = BASIC TWOLEVEL if you can unless the cluster effects are very small. Look at the ICC of the variables and also take a look at
in particular Section 3.3 and the other sections on multilevel imputations.
 Trang Q. Nguyen posted on Monday, November 19, 2012 - 9:16 am
Thanks much, Tihomir!
 Peter Liberman posted on Wednesday, February 06, 2013 - 9:30 pm
Is it possible to use the DEFINE command to create variables in multiply imputed datasets? I would like to obtain exogenous indicator variables from a multiply imputed ordinal variable. (This seems preferable to doing this in the imputation stage, as it sacrifices information about the ordinal relationship of the indicators).

But after adding the define command, the input file that had been working normally now produces an output file with only input file instructions--without results or error messages. Also, the "Mplus" activity box does not show that multiple datasets are being analyzed.

(using Mplus 7 for Mac)
 Linda K. Muthen posted on Thursday, February 07, 2013 - 9:55 am
Please send your files and license number to so we can look into this.
 mdehne posted on Tuesday, February 11, 2020 - 6:24 am
I was wondering whether my output is right after 25 multiple imputations. In videos concerning multiple imputations in Mplus (maybe of older Mplus versions), the output was printed without information regarding mean and standard deviation of my fit indices based on multiple imputations. I am using Mplus 8.4 and always get the means that to my understanding are my actual model fit indices. Am I right with this assumption?

Kind regards and many thanks in advance,
 Tihomir Asparouhov posted on Wednesday, February 12, 2020 - 8:19 am
You should get the average fit indices if the model you are running has those available. You can check that by running one of the imputed data sets alone. If you are using the ML estimation with all continuous variables then the actual combined fit index is computed. If this is not what is happening for you send you example to
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