Thanks to all for the multiple and prompt answers. The latter posts
are losing me, but they may be of interest to more statistically
curious researchers (by the way, I did also try a factor, ml command,
and the results also seemed different than the pca results). The basic
explanation of what was going on with the factor command was helpful
to understand the negative eigen values and unblocked the discussion
with my statistician colleagues. I basically wanted to check the
dimensionality of an index for a latent concept. I am going to put
more stock in the pca results, and after looking at the factor
loadings, I feel in a better position to interpret the results.
Thanks (PS. if "Thanks emails" are inappropriate for the listserv,
please let me know off list and I'll refrain in the future...as I feel
I will be asking more questions soon)
Cheers,
Jean-Gael "JG" Collomb
PhD candidate
School of Natural Resources and Environment / School of Forest
Resources and Conservation
University of Florida
[email protected]
[email protected]
+1 (352) 870 6696
On Apr 28, 2009, at 3:43 PM, Verkuilen, Jay wrote:
Michael I. Lichter wrote:
So what are you advocating instead? ML, which isn't very robust to
weirdness (technical term) in the data? Or something other than >FA/
PCA?
No, I'm following the research of people like Rod McDonald (retired to
his beloved Sydney), Albert Maydeu-Olivares (Barcelona) and Bud
McCallum
(UNC-Chapel Hill), who support the use of the Unweighted Least Squares
criterion function, or multi-stage estimation methods that uses ULS
followed by a few iterations of ML or GLS. ULS is mathematically
equivalent to MINRES. Stata doesn't have ULS/MINRES (alas).
One of the downsides to ULS is the fact that it doesn't generate
standard errors and GoF statistics, but Albert's been rectifying that
situation in a sequence of articles in various journals
(Psychometrika,
JASA, etc.): http://www.ub.edu/gdne/psicometriaen.html.
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