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Re: st: Factor Analysis: which explained variance?
From
Alan Acock <[email protected]>
To
[email protected]
Subject
Re: st: Factor Analysis: which explained variance?
Date
Fri, 19 Mar 2010 09:25:36 -0700
Stata offers pca and factor, pcf. It's been noted that many people use principal component analysis with rotations in scale development. I think what they are often using is SPSS factor analysis that defaults to what Stata does with factor, pcf and not pca. If I'm wrong on this, I would like a clarification since pca and factor, pcf are producing very different results.
I strongly agree with the point that it doesn't make much sense to do factor, pcf, then a varimax rotation only to combine items from what have been estimated to be orthogonal dimensions. It seems to me if you have groups of items that are unrelated to each other than to say they are measuring a single dimension is a mistake. I still see people doing this, however.
Alan Acock
[email protected]
On Dec 21, 2009, at 6:26 AM, Francesco Burchi wrote:
> polychoric Var1 Var2 Var3 Var4
> matrix R = r(R)
> factormat R, n(6926) ipf factor(1)
> rotate, horst blanks(.3)
> predict FACTORROTATE
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