Ken Reed <[EMAIL PROTECTED]> wrote:
> What is the basis for deciding when to use principal components analysis and
> when to use factor analysis. Could anyone describe a problem that
> illustrates the difference?
PCA is simply a reparameterization of your data, sort of analogous to
taking the Fourier transform of a time series. It retains all the
properties of your data; it simply lets you look at them from a different
perspective.
FA, OTOH, involves assuming that your data can be described by a very
specific kind of linear model and then fitting such a model to your data.
Like all models, it will be wrong, but it might be useful. By doing FA,
you're choosing to discard some information from your data in the hopes
that what remains will be interpretable. You're blurring some of the
trees in order to get a better idea of the shape of the forest.
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