> I hope that someone can help clarify some interesting output I came across
> from a Factor Analysis that I just conducted. I have a 24-item instrument
> that I used Principal Axis Factoring for. By default, SPSS suggested that
> there were 5 factors based on the eigenvalue greater than 1 rule. I then
> tried to run the same procedure but altered the number of factors to be
> extracted to 3. The eigenvalues for the 3-factor solution were not the
> same values as the first 3 eigenvalues from the 5-factor solution (all
> values are based on prerotation). The values are close but shouldn't they
> be identical given that factors are extracted ortogonally? Thanks!
I am not an expert on factor analysis, but what I have read suggests that
the "eigenvalues > 1" rule (aka Kaiser's criterion) is not a particularly
good one much of the time. See the following article for, for example.
"When all else fails, RTFM."
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