> Adrian Teo wrote:
>
> Judging from this response below and the other ad hominems directed at
> Vince, I think George is not interested in discussion. I think this may be
> a joke, but with all due respect to George, if he really takes this
> seriously, then I should not insult him by calling it a joke. So
> respectfully, George, I don't think you have a case here and I am not
> persuaded by your thesis.
[Hammond]
Well, I'm sorry, but the rules of academic debate include
a statement of one's academic and professional credentials
before we can judge is "you're not persuaded" actually means
anything in the real world.
After all, any hillbilly can come on this open group
and commandingly assert "Hammond's theory is wrong" even if
he has a 3rd grade education. It naturally doesn't mean
anything for a theory which requires a competency in
Psychometry, Factor Analysis, Biology, Neuroscience, and
Physics including Relativity. Sorry to disappoint you
but Hammond does not accept grand and sweeping theoretical
pronouncements from unqualified people as meaningful.
Neither does any other competent scientific body. What you
are ciriticising has been published in the peer reviewed
academic literature. I'm afraid we have to trust their
judgement over yours until you tell us what your CV is.
>
> Adrian.
>
> Note: The peer-reviewed published paper offers no new analyses, only
> theoretical speculations - hardly proof of anything, let alone how the
> models can actually be integrated.
[Hammond]
WRONG. LOL.
> -----Original Message-----
> From: George Hammond [mailto:ghammond@mediaone.net]
> Sent: Sunday, May 20, 2001 3:38 PM
> To: asa@calvin.edu
> Subject: Re: Just a short comment on Factor Analysis, and on God...
>
> > Adrian Teo wrote:
> >
> > Vince is essentially correct in his claim that factor analysis (FA) does
>
> > not interpret. To be more precise, FA is not even a single method, but
> it
> > refers to a collection of related algebraic manipulations which is part
> of
> > a large family of analyses of covariance matrices. FA can be
> exploratory,
> > where you allow the data to "speak for itself" or confirmatory, where
> you
> > test particular hypotheses about the underlying structure of the data
> set.
> > The data reduction approach that Vince and I think Hammond is talking
> > about sounds like Principle Components Analysis, an exploratory
> approach.
> > For a quick, easy and reliable reference, Sage University has a series
> of
> > booklets on statistical procedures and there is one by Kim and Mueller
> > that introduces this class of analysis. This discussion has sometimes
> > given the impression that FA is some complex, exotic statistical
> approach
> > that few understand, when in fact, it is commonly used and discussed in
> > personality assessments, aptitude and achievement test constructions,
> and
> > diagnostic measures. Many graduate students in various branches of
> > psychology take such a course in their second or third year.
> >
>
> [Hammond]
> This is all true and of course might have been copied off the
> first page of any (modern) textbook on Factor Analysis.
> However, you don't have to know anything about Factor Analysis
> mathematics to figure out what the scientific proof of God is...
> all the F.A. work has already been done and it took thousands of
> scientists a hundred years to do it.
> The bottom line is that all evidence converges to E,N,P,g
> at the 2nd order, and that these 3 dimensions (eigenvecors, Factors)
> are caused by the gross macroscopic structural geometry of the
> brain (Hammond 1994). Most of you have heard of "Sperrian
> Lateralization",
> well, that's just the 1st-axis, turns out there are 2-more just like
> Sperry's axis (Bell-Magendie, and the Neuraxis itself). This
> causes E,N,P. When you add IQ to that, which is a "time dimension"
> (mental speed = IQ), then you have 3-space axes and 1-time axis,
> and SURE ENOUGH, you can show how they are physically, mechanically,
> causally caused by the 4-axis of space-time (X,Y,Z,t so called)
> of real space. Now let me repeat that, X,Y,Z,t PHYSICALLY
> MECHANICALLY CAUSES E,N,P,g in Psychology.... they are not just
> "similar", there is a direct chain of physical causation (brain
> geometry is caused by space geometry).
> OK, from there (all of which has now been overwhelmingly proven),
> it is only a trivial step to the scientific proof of God.... in fact,
> all you do is factor the 4x4 correlation matrix of E,N,P,g (which
> can only have a single factor), and that factor is GOD.
> QED, God exists.
>
> > And BTW, the ENP by Hans Eysenck is only one of several models that
> > reduces personality measurements to common factors. A much more widely
> > accepted model is the Big Five (as the name suggests, there are not 3,
> but
> > 5 factors). Eysenck's ENP has not been consistently supported in the
> > literature.
>
> [Hammond]
> A little bit of knowledge is dangerous (fortunately
> not dangerous enough). Turns out 3 of the Big-5 dimension
> ARE IN FACT identical to Eysenck's E,N,P.. and the other
> two are simply two diagonals in the E-N plane. I have published
> the proof of this in the peer reviewed literature (Hammond 1994):
>
> HAMMOND G.E. (1994) The Cartesian Theory: Unification of
> Eysenck and Gray, in: New Ideas In Psychology,
> Vol 12(2) pp 153-167, Pergamon Press
>
> And it reconciles ALL of the known and published F.A. models in
> the literature including Eysencks Giant 3, AVA 4, Big-5, Brand's
> Big-6, K&J's 7F, Saucier's 9F, and finally Cattell's 13F 2nd
> order model. as is proven by Hammond (1994), ALL OF THESE MODELS
> are just the various symmetric redactions of the 13-Symmetry axes
> of the common cube. This is proven to two decimal point accuracy
> by simply taking the arcosine the published correlation coefficients
> and showing that form said geometrical structure. Cattell, the old
> master, is of course the only one to have actually resolved all
> 13 actors of the cube. The Big-5 was discovered by Norman, Goldberg,
> Costa & McCrae etc. who are basically academic types equipped
> with a desktop computer, commercial Factor Programs, and readily
> available captive test subjects (students, patients) etc.
> Of course the stronger the redaction (lower the number of redacted
> axes) that you take in the cube, the MORE ROBUST the result, since
> you're forcing all of the variance into fewer factors. In fact,
> Eysenck's-3 (ENP) is the STRONGEST simply because of this, while
> Cattell's "all 13 cubic axes" is the hardest to clearly resolve
> because the variance is spread among all 13 axis.
> BTW, you can look at a cube and count the axes; 3-Normals, 4-Body
> Diagonals, and 6 "face diagonals" (see any geometry book). So,
> 3+4+6=13.
>
> For your reading convenience and enjoyment I have placed a
> fully illustrated facsimile copy on my website at:
>
> http://people.ne.mediaone.net/ghammond/cart.html
-- BE SURE TO VISIT MY WEBSITE, BELOW: ----------------------------------------------------------- George Hammond, M.S. Physics Email: ghammond@mediaone.net Website: http://people.ne.mediaone.net/ghammond/index.html -----------------------------------------------------------
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