[R] (no subject)
pdalgd at gmail.com
Mon Apr 15 16:07:07 CEST 2013
On Apr 15, 2013, at 14:30 , ilovestats wrote:
> Hi, I'm trying to decide between doing a FA or PCA and would appreciate some
> pointers. I've got a questionnaire with latent items which the participants
> answered on a Likert scale, and all I want to do at this point is to explore
> the data and extract a number of factors/components. Would FA or PCA be most
> appropriate in this case?
Not really an R question, is it?
Stats.StackExchange.com is -----> that way!
In terms of theory, PCA is essentially FA with the same residual variance in all responses. With all-Likert scales, it is unlikely that there will be much of a difference.
In practical terms:
- factanal can diverge (Heywood cases) which is a bit of a bother
on the other hand
- factor rotation is based on factanal() output; may require a little extra diddling to work with prcomp().
I think I'd try factanal() first, and if it acts up, switch to prcomp().
Peter Dalgaard, Professor
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com
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