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

["Rodrigo A. Alfaro" <raalfaroa@gmail.com>]

///
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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