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2 messages · ilovestats, Peter Dalgaard
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? Cheers, Hannah
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 Phone: (+45)38153501 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com