Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: st: mi impute chained with multilevel data
From
"JVerkuilen (Gmail)" <[email protected]>
To
[email protected]
Subject
Re: st: mi impute chained with multilevel data
Date
Tue, 7 May 2013 15:30:35 -0400
It's not 100% clear what to do. This is very much an open question
from what I understand. (Disclosure: Missing data is not my research
area but I've done it a lot in practice.)
One fairly workable solution is to put in a goodly number of level 2
variables and cross-level interactions with completely observed
variables, because they often soak up a lot of of the level 2 variance
and don't cost you in the imputation. I would be very careful with
outlying clusters, though. That can really mess things up. And I would
try a few different plausible imputation models and see if your
desired inferences are sensitive to those assumptions. Ideally they
won't be.
Jay
On Tue, May 7, 2013 at 1:35 PM, Newport-berra, Mchale
<[email protected]> wrote:
> I am using mi impute chained (Stata 12) to impute data for children nested in schools. I am then using the imputed data to do xtmixed. So far I have been able to do the multiple imputation without taking clustering into account, but I would really appreciate guidance about how to account for the multi-level structure of the data in the imputation. I have come across the following possibilities:
>
> One suggestion I have received is doing mi impute chained with a dataset of only the school-level variables, and then merging this with the child-level dataset and imputing the child-level variables. However, I'm not sure how mi estimate would make sense of the imputed variables from different rounds of imputation.
>
> Han (2008, Developmental Psychology) used Stata and assigned the same imputed values for school variables to students from the same school to preserve multilevel data structure in multiple imputation procedures, but I'm not sure how to do this.
>
> I also came across this Stata document: http://www.stata.com/support/faqs/statistics/clustering-and-mi-impute/ which describes the following 3 methods:
>
> 1. Include indicator variables for clusters in the imputation model
> 2. Impute data separately for each cluster.
> 3. Use a multivariate normal model to impute all clusters simultaneously.
>
> Since I have a lot of schools but not a lot of kids in each school, #1 and #2 won't work. I'm not sure if #3 work with mi impute chained.
>
> Has anyone used any of these strategies, or other strategies? Thank you!
>
>
>
>
>
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/faqs/resources/statalist-faq/
> * http://www.ats.ucla.edu/stat/stata/
--
JVVerkuilen, PhD
[email protected]
"They were careless people, Tom and Daisy - they smashed up things and
creatures and then retreated back into their money of their vast
carelessness, or whatever it was that kept them together, and let
other people clean up the mess they had made." -- F. Scott Fitzgerald
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/