http://spssx-discussion.165.s1.nabble.com/Have-to-use-4-factors-but-EFA-suggests-only-2-factors-tp5716773p5716788.html
If these highly correlated factors were scores on rating scales
about people, I would figure that there were only two "real" latent
scales, and don't try too hard to find four. But you say that these
are "institutional dimensions ... of a country."
Well, I don't know what that includes. But I do know that a lot of
people screw up badly on "country" data, in a few ordinary ways
that also may mess up the apparent dimensionality.
For instance, if a bunch of the numbers differ because of
population size or national area or total national wealth when those
aspects are supposed to be irrelevant... then the numerical covariances
of measuring this bosh might swamp the intended latent variables.
For instance, "per-capita income" is usually more interesting when
comparing countries than "gross domestic product".
On the other hand, if your variables *are* well chosen and well-measured,
then you probably don't have 4 factors. - When two "things" are correlated
0.95, I tend to want to look at something that reflects the difference between
them. Sometimes that is their ratio (or log of the ratio) and sometimes that
is some version of an arithmetic difference, like (mean1/SD1 - mean2/SD2) .
--
Rich Ulrich
> Date: Fri, 7 Dec 2012 11:03:42 -0800
> From:
[hidden email]> Subject: Have to use 4 factors, but EFA suggests only 2 factors
> To:
[hidden email]>
> Hey,
>
> I am trying to run a regression which takes four institutional dimensions
> (fixed) of a country into account.
> These four dimensions represent four independent latent variables. I managed
> to find pretty good variables which reflect each of the dimensions (in
> theory), so that I have 4 variables per dimension, so 16 in total. However,
> an exploratory factor analysis points to only 2 factors, and a confirmatory
> factor analysis with the above-mentioned 4 factors gives me a pretty poor
> fit. Indeed, factors 1 and 4 are highly correlated (0.95) and factor 2 and 3
> are also highly correlated (0.89).
>
> If I proceed regardless of this poor fit, do you think it is possible to
> draw proper conclusions from my regression? In theory, the variables measure
> things that fit to the respective dimensions very well.
>
> Thank you so much.
> ...