It is many years since I was current on the factor analysis
literature. I have retired and no longer have access to databases of
DIALOG, ORBIT, or PsychInfo. If you have a friend in a university or
a government agency they might be able to do a search for you.
Since most clustering grew up around grouping cases (rows in the
original data matrix), how is transposing the data matrix and using the
same algorithms problematic in clustering variables (columns)? Just
the opposite, one of the oldest methods of clustering cases was to
standardize then transpose the data matrix and factor it. (this
approach was big in the 1960's & 1970's).
I have a gut feeling (not a thought out opinion) that an oblique
solution means that you end up with measures that do not have
SPSS has had many varieties of factor analysis for many years. It has
used 2 kinds of data, 7 kinds of extraction, and 4 kinds of rotation.
(56 different "methods"!) Maybe some of those combinations would meet
your needs. [For those of us who use methods that other create, it
sure would be nice if someone were to use this framework and produce a
document advising on when to use the options. ]
to get details like algorithms and lit cites go to
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then <catpca> <catreg> <cluster> <discriminant>
<factor> <overals> <proximities> <quick
cluster> <twostep cluster>
The ANSWERTREE add-on and new TREE procedure in the base module may
also be relevant.
kinds of data: SPSS can work on a correlation matrix or a covariance
matrix. In Psych, the means of variables are usually arbitrary, so
correlations are more common. However, much of the development of
factoring was from psych and ed. Perhaps the math psych list would
have more current people .
Mathematical Psychology: MPSYCH Listserv
quote from SPSS about the extractions available
Available methods are principal components, unweighted least squares,
generalized least squares, maximum likelihood, principal axis
factoring, alpha factoring, and image factoring.
there are more details in the <help>.
quote from SPSS <help> about the rotations available. These
Varimax Method. An orthogonal rotation method that
minimizes the number of variables that have high loadings on each
factor. It simplifies the interpretation of the factors.
Method. A method for oblique
(nonorthogonal) rotation. When delta equals 0 (the default), solutions
are most oblique. As delta becomes more negative, the factors become
less oblique. To override the default delta of 0, enter a number less
than or equal to 0.8.
A rotation method that
minimizes the number of factors needed to explain each variable. It
simplifies the interpretation of the observed variables.
Equamax Method. A rotation method that is a combination of
the varimax method, which simplifies the factors, and the quartimax
method, which simplifies the variables. The number of variables that
load highly on a factor and the number of factors needed to explain a
variable are minimized.
Promax Rotation. An oblique rotation, which allows factors
to be correlated. It can be calculated more quickly than a direct
oblimin rotation, so it is useful for large datasets.
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Wolfgang M. Hartmann wrote:
Thank you for the nice response,
I kmow that in practice transposing
the matrix is a common, but do not think
of it as a very valid approach. (Higher order) Factor analysis with oblique
and restrictions penalizing nonzero loadings would sound good for
you know of any references for such
In SPSS all of the few dozen Proximity (similarity measures)
can be applied to variables. (After the data are transformed and
transposed) The Proximity matrix can then be read into the variety of
cluster procedures. Or the transposed data can be read directly into
the CLUSTER, or Quick cluster procedure. I see no reason (given that
you want to cluster variables) that the TWOSTEP cluster could not read
a transposed data matrix.
Of course there are all of the varieties of factor analysis which are
more commonly used to group variables. The CATPCA procedure factors
When the variables are used to classify or differentiate a categorical
variable, there are procedures like DISCRIMINANT or the various