Can't make sense of calculated results and hope I'll find help here.
I've collected answers from about 600 persons concerning three
variables. I hypothesise those three variables to be components (or
indicators) of one latent factor. In order to reduce data (vars), I
had the following idea: Calculate the factor underlying these three
vars. Use the loadings and the original var values to construct an new
(artificial) var: (B1 * X1) + (B2 * X2) + (B3 * X3) = ArtVar (brackets
for readability). Use ArtVar for further analysis of the data, that
is, as predictor etc.
In my (I realise, elementary) psychological statistics readings I was
taught to use pca for these problems. Referring to Venables & Ripley
(2002, chapter 11), I applied "princomp" to my vars. But the outcome
shows 4 components -- which is obviously not what I want. Reading
further I found "factanal", which produces loadings on the one
specified factor very fine. But since this is a contradiction to
theoretical introductions in so many texts I'm completely confused
whether I'm right with these calculations.
(1) Is there an easy example, which explains the differences between
pca and pfa? (2) Which R procedure should I use to get what I want?
Thank you for your help
Sören
Refs.:
Venables, W. N., and Ripley, B. D. (2002). Modern applied statistics
with S (4th edition). New York: Springer.
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