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 Eunice Campiran posted on Thursday, April 16, 2015 - 8:46 pm
I'm trying to conduct multiple imputation in a multilevel dataset and I have some questions:
1) I have missing values in the dependent variable and in the covariates. Mplus can make imputations in such case?

2) I am using negative binomial regression in my multilevel model, Mplus can make imputations?

3) Can I include interactions in my multilevel model and Mplus still can do imputations?

Many thanks!
 Linda K. Muthen posted on Friday, April 17, 2015 - 6:28 am
1. Yes.
2. All variables are treated as continuous unless they are on the CATEGORICAL list. No other types of variables can be imputed.
3. I would include the two variables and create the interaction after the imputation.
 Hsien-Yuan Hsu posted on Sunday, April 26, 2015 - 8:37 pm
Hi, Dr. Muthen,
I have two questions.
1) Following Ex. 9.1, I specify a two-level random intercept model with a categorical DV, which has five categories (1-5) with missing value (.). The output showed that I have 7 categories:
Category 1 0.098 1573.000
Category 2 0.327 5235.000
Category 3 0.265 4240.000
Category 4 0.163 2619.000
Category 5 0.101 1621.000
Category 6 0.046 730.000
Category 7 0.000 1.000

I have no idea why Mplus presents 7 categories. Any thought?

2) What is the syntax to save the predicted Y values for both levels?

Thank you so much!
 Bengt O. Muthen posted on Monday, April 27, 2015 - 11:12 am
Please send input, output, and data to support along with your license number.
 Eunice Campiran posted on Wednesday, April 29, 2015 - 5:27 pm
Hi, Dr. Muthen,
I have missing values in the two level variable OCC.
In the imputed data from Mplus, I got variations for OCC within clusters, which should not be.
For instance, for cluster progidx=5,
progidx OCC
5 45.7
5 65.8
5 43.3
5 12.6
5 46.4

It should be constant within this cluster.

my input file:

file = Missing.csv;

names=q98days progidx q25_lic occ female homeless
usevariables=q98days q25_lic occ female homeless;
count is q98days (nb);
categorical is q25_lic female homeless;
cluster = progidx;
within = female homeless;
between = q25_lic occ;

missing = all (-99);

bseed = 2828;
bconvergence = 0.01;
q98days on female homeless;
q98days on occ q25_lic;

ndatasets = 20;
save =Missing*.dat;
thin = 500;
 Bengt O. Muthen posted on Wednesday, April 29, 2015 - 6:02 pm
We need the data and your input and output to help. Send to support along with your license number.
 Eunice Campiran posted on Friday, May 08, 2015 - 1:10 pm
I am using negative binomial regression in my multilevel model, this variable does not have missing values.
MPlus can make imputations in other continuous variables?

I got the following error:
The DATA IMPUTATION command is not available for analysis with count, continuous-time
survival, censored or nominal variables.
 Linda K. Muthen posted on Friday, May 08, 2015 - 1:17 pm
You must be saying you want to impute for the negative binomial. Example 11.5 goes over the various options that can be used with DATA IMPUTATION.
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