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Re: st: Factor Analysis and Multiple Imputation

From   Stas Kolenikov <>
Subject   Re: st: Factor Analysis and Multiple Imputation
Date   Fri, 23 Jul 2010 07:43:46 -0500

On Thu, Jul 22, 2010 at 4:35 PM,  <> 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?

Whatever goes after -mi:- or -ice:- prefix must be a single command.
You would need to write a small wrapper to combine both factor
analysis and regression commands.

I personally would have little trust in your analysis, missing data or
not, as I have little trust in EFA to begin with. For one thing, the
two-step procedure you describe will likely underestimate the standard
errors, since the regression step is not aware of the fact that your
dependent variable is generated with an extra measurement error.
Multiple imputation will not overcome this, as it still uses the wrong
standard errors from the regression step. For another thing, there are
just too many options of getting the scores, and justifications for
them are ad hoc at best. If I were in your shoes, I would wrap things
up into a MIMIC model and estimate it with -gllamm-.

Stas Kolenikov, also found at
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