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st: Re: Factor analysis after multiple imputation in STATA


From   "Rodrigo A. Alfaro" <raalfaroa@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: Factor analysis after multiple imputation in STATA
Date   Thu, 12 Jul 2007 20:40:45 -0400

///
It is not clear to me what you can get averaging the factor loading. For a reasonable number of factors, you could print out your output for each imputed-dataset and try to find if there is any pattern. For example, variable x1 has some load between 0.7 and 0.74... if you get x2 has loads between -0.2 and 0.8, then I would think more carefully about the imputation method.

Alternative, you could try EM-algorithm to get the variance-covariance matrix under normality assumption. With that it would be clear how to obtain the associated factors (there will be just one).

Good luck, R




----- Original Message ----- From: "Woolton Lee" <finished07@gmail.com>
To: <statalist@hsphsun2.harvard.edu>
Sent: Thursday, July 12, 2007 4:44 PM
Subject: st: Factor analysis after multiple imputation in STATA



I am working with a dataset with many missing values across the
variables and have used multiple imputation via chained equations
(created by Patrick Royston) to generate 5 multiply imputed datasets
with the objective of running factor analysis to analyze the
relationships among the variables in the dataset. However, it seems
that MICOMBINE is only tailored for regression type procedures and is
not appropriate for application when implementing factor analysis
after multiple imputation. Is there a STATA command such as MICOMBINE
that can be used to obtain factor loadings from the multiply imputed
data or will I have to apply Rubin (1987) 's formula manually (via
MATA or programming) to obtain the factor loadings after running
factor analysis separately on each of the imputed datasets?

As a side note, I think that Rubin (1987)'s formula applied to factor
analysis would simply be the mean of the of the factor loadings across
the imputed datasets (I have 5 imputed datasets) , but is this
correct, or should I be using a different formula for the factor
loadings across imputed datasets?

I would greatly appreciate any assistance,

Woolton
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