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Re: st: Re: Factor analysis after multiple imputation in STATA


From   "Rodrigo A. Alfaro" <raalfaroa@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Re: Factor analysis after multiple imputation in STATA
Date   Thu, 12 Jul 2007 21:35:21 -0400

///
Woolton,

You could check Schafer, J.L. (1997), Analysis of Incomplete Multivariate Data, New York: Chapman and Hall, as primary reference for Missing Data and how to run EM. Joe has an executable for Windows that computes EM algorithm (http://www.stat.psu.edu/~jls/norm203.exe), it is very fast and friendly. You should take the variance-covariance matrix, and construct the correlation matrix by hand (this is easy, it should be a shortcut... but I am out of ideas), then use -factormat- to solve your problem.

I checked the web (long life to gooogle) and there are other sources for your problem. Little and Rubin (2002) http://www.amazon.com/Statistical-Analysis-Missing-Data-Second/dp/0471183865 and Jamshidian, M. (1997). An EM algorithm for ML factor analysis with missing data. (Ed.). Berkane, M (Ed.). Latent Variable Modeling and Applications to Causality (p. 247-258). New York: Springer Verlag. The latter seems to what you want.

HTH, Rodrigo.



----- Original Message ----- From: "Woolton Lee" <finished07@gmail.com>
To: <statalist@hsphsun2.harvard.edu>
Sent: Thursday, July 12, 2007 8:58 PM
Subject: Re: st: Re: Factor analysis after multiple imputation in STATA



Thanks for your reply.  So it sounds as though you are saying that I
can use the EM algorthm to run factor analysis in the presence of
missing data.  Is that correct?  How might I go about implementing
this approach?  By the way, do you have any references I might use to
get some background on this approach?

Thank you for your help,

Woolton


On 7/12/07, Rodrigo A. Alfaro <raalfaroa@gmail.com> wrote:
///
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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