Factor Analysis
as a Classification Method  Hierarchical Factor Analysis
Instead of computing loadings for often difficult to interpret oblique
factors, the Factor
Analysis module in STATISTICA
uses a strategy first proposed by Thompson (1951) and Schmid and Leiman
(1957), which has been elaborated and popularized in the detailed discussions
by Wherry (1959, 1975, 1984). In this strategy, STATISTICA
first identifies clusters of items and rotates axes through those clusters;
next the correlations between those (oblique)
factors is computed, and that correlation matrix of oblique factors is
further factoranalyzed to yield a set of orthogonal factors that divide
the variability in the items into that due to shared or common variance
(secondary factors), and unique variance due to the clusters of similar
variables (items) in the analysis (primary factors). To return to the
example above, such a hierarchical
analysis might yield the following factor loadings:
STATISTICA
FACTOR
ANALYSIS 
Secondary
& Primary Factor Loadings

Factor 
Second. 1 
Primary 1 
Primary 2 
WORK_1 
.483178 
.649499 
.187074 
WORK_2 
.570953 
.687056 
.140627 
WORK_3 
.565624 
.656790 
.115461 
HOME_1 
.535812 
.117278 
.630076 
HOME_2 
.615403 
.079910 
.668880 
HOME_3 
.586405 
.065512 
.626730 
MISCEL_1 
.780488 
.466823 
.280141 
MISCEL_2 
.734854 
.464779 
.238512 
MISCEL_3 
.776013 
.439010 
.303672 
MISCEL_4 
.714183 
.455157 
.228351 
Careful examination of these loadings lead to the following conclusions:
There is a general (secondary)
satisfaction factor that likely affects all types of satisfaction measured
by the 10 items;
There appear to be two primary
unique areas of satisfaction that can best be described as satisfaction
with work and satisfaction with home life.
Wherry (1984) discusses in great detail examples of such hierarchical
analyses, and how meaningful and interpretable secondary factors can be
derived.
Confirmatory
Factor Analysis. Over the past 15 years, socalled confirmatory
methods have become increasingly popular (e.g., see Jöreskog and Sörbom,
1979). In general, one can specify a
priori, a pattern of factor loadings for a particular number of
orthogonal or oblique factors, and then test whether the observed correlation
matrix can be reproduced given these specifications. Confirmatory factor
analyses can be performed via STATISTICA's
general Structural
Equation Modeling (SEPATH) module. Note that the Examples section of SEPATH
includes several examples of such analyses.