So is it going to be exploratory factor analysis or confirmatory
factor analysis? For the latter, you can use -gllamm- to integrate
over the missing data rather than trying to figure out the best way to
impute them.
On 7/12/07, Woolton Lee <[email protected]> wrote:
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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--
Stas Kolenikov
http://stas.kolenikov.name
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