Missing values, MI, and growth mixtur... PreviousNext
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 Shige Song posted on Saturday, March 10, 2007 - 12:08 am
Dear Linda and Bengt,

Here is something that has been bothering for a while: I want to do a growth mixture model to identify latent sub-groups. Some of the covariates I choose to predict latent class membership have significant amount of missing values. From what I have read, I can 1) give the covariates some distributional assumptions and make them as part of the model or, 2) I can do a multiple imputation.

In case of growth mixture model, is multiple imputation still a viable option? I mean, will the latent class group membership parameters be combined in the same way as other parameters using Rubin's rule? Does Mplus automatically use these combined class membership parameters to classify individuals into latent classes?

In short, what is the optimal method to handle missing covariate values in a growth mixture model?

Thanks!

Best,
Shige
 Linda K. Muthen posted on Saturday, March 10, 2007 - 7:44 am
We think that multiple imputation is a viable option for growth mixture modeling. One issue is that when you analyze the imputed data sets, give good starting values so that you do not run into label switching.
 Shige Song posted on Saturday, March 10, 2007 - 4:37 pm
Hi Linda,

Thanks!

Do you have examples showing how to do growth mixture modeling using imputed data sets?

Shige
 Linda K. Muthen posted on Saturday, March 10, 2007 - 5:05 pm
No. You just use the IMPUTATION option of the data command instead of a single data set. There is nothing else different from any other growth mixture model other than using starting values to avoid label switching.
 Myong Hwa Lee posted on Saturday, April 17, 2010 - 6:31 am
Dear Linda and Bengt,

I’m running growth mixture models with 4 binary covariates and one binary distal outcome. The growth outcome variables are ordinal (3 categories). All variables have some missing cases (1% - 30%). I created 5 imputed datasets by using ICE in STATA and then used “type=imputation” in Mplus. The outputs looked good.

But the output did not print both the results of probability scale of distal outcome in each class and the latent class odds ratio results. I want to know how the classes are related to distal outcome. How can I get these results? (If I don’t use “type=imputation” option, these outputs were always printed.)

I appreciate your help!
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