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Re: st: Factor Analysis and Multiple Imputation


From   gregor.hochschild@gmx.de
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Factor Analysis and Multiple Imputation
Date   Fri, 23 Jul 2010 12:30:00 +0200

-------- Original-Nachricht --------
> Datum: Thu, 22 Jul 2010 23:38:21 -0700 (PDT)
> Von: Maarten buis <maartenbuis@yahoo.co.uk>
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: Factor Analysis and Multiple Imputation

> --- On Thu, 22/7/10, gregor.hochschild@gmx.de wrote:
> > I would like to run a couple of regressions using the
> > factor score from an explorative factor analysis  as
> > the dependent variable but I am not sure how I should handle
> > missing data. In particular, I want to 
> > a) construct the dependent variable from 8 items using
> > explorative factor analysis
> > b) run some regressions using the factor score as the dep.
> > variable
> > 
> > There are missing values for pretty much all the variables
> > including the 8 items as well as the independent variables
> > in the regression. What is the best approach to handle the
> > missing data problem? What is the right imputation procedure
> > in this case? 
> > Should I first use all available information in the data to
> > recover the missing data across all the variables, and then
> > run the factor analysis? But how do I do this in Stata given
> > that mi does not support factor analysis?
> 
> The aim of an imputation model is to reproduce the observed patterns in
> the data on to the missing values. You need to make sure that you 
> reproduce the relevant patterns for your model of interest, but that 
> does not mean that you need to use the same model as you intend to use
> in your final analysis. The factor score is just a linear combination 
> of your observed items, so it is enough for the regression part of your
> model, to reproduce the association between the observed items and your 
> explanatory variables. Factor analysis just uses the correlation between 
> the observed items, so as long as your imputation model reproduces the 
> correlations between the items you are ok for the factor analysis part. 
> 
> So taking the two together: As long as your imputation model reproduces 
> the patterns between all the directly observed variables (items and 
> explanatory variables) you are ok, and your imputation model does not
> need to include the factor scores. You can use either the official Stata 
> -mi- commands for that or -ice- (see:  -findit ice- for several articles 
> on that, and download the software from SSC, type in Stata 
> -ssc install ice-)
> 
> Hope this helps,
> Maarten
> 
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> 
> http://www.maartenbuis.nl
> --------------------------

Thanks for your quick reply Maarten!!
I guess the step I am not sure about is the pooling for the factor analysis. Is it correct that I do not do the pooling for the factor analysis? First, I do the imputation step. Second, I run the factor analysis separately for each imputation and create the factor scores (as a result each imputed datasets m has the factor score variable and the values for certain observations differ across the datasets). Third, I run my data analysis on each imputation and the results are pooled to obtain a single multiple-imputation results. 

Is that correct?

Thanks again!

greg


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