Factor Analysis
as a Classification Method  Rotating the Factor Structure
We could plot the factor loadings shown above in a scatterplot. In that
plot, each variable is represented as a point. In this plot we could rotate
the axes in any direction without changing the relative locations of the
points to each other; however, the actual coordinates of the points, that
is, the factor loadings would of course change. In this example, if you
produce the plot it will be evident that if we were to rotate the axes
by about 45 degrees we might attain a clear pattern of loadings identifying
the work satisfaction items and the home satisfaction items.
Rotational
strategies. There are various rotational strategies that
have been proposed. The goal of all of these strategies is to obtain a
clear pattern of loadings, that is, factors that are somehow clearly marked
by high loadings for some variables and low loadings for others. This
general pattern is also sometimes referred to as simple
structure (a more formalized definition can be found in most standard
textbooks). Typical rotational strategies are varimax, quartimax, and
equamax; these are described in greater detail in the context of the Rotation dialog.
We have described the idea of the varimax rotation before (see Extracting Principal Components),
and it can be applied to this problem as well. As before, we want to find
a rotation that maximizes the variance on the new axes; put another way,
we want to obtain a pattern of loadings on each factor that is as diverse
as possible, lending itself to easier interpretation. Below is the table
of rotated factor loadings.
STATISTICA
FACTOR
ANALYSIS 
Factor
Loadings (Varimax normalized)
Extraction:
Principal components

Variable 
Factor 1 
Factor 2 
WORK_1 
.862443 
.051643 
WORK_2 
.890267 
.110351 
WORK_3 
.886055 
.152603 
HOME_1 
.062145 
.845786 
HOME_2 
.107230 
.902913 
HOME_3 
.140876 
.869995 
Expl.Var 
2.356684 
2.325629 
Prp.Totl 
.392781 
.387605 