User Guide

449
Factor Analysi
s

Principal Axis F actoring. A method of extracting factors from the original
correlatio
n matrix with squared multiple correlation coefficients placed in the
diagonal as initial estimates of the communalities. These factor loadings are used
to estimate new communalities that replace the old communality estimates in the
diagonal. I
terations continue until the changes in the communalities from one
iteration to the next satisfy the convergence criterion for extraction.
 Alpha. A factor extraction method that considers the variables in the analysis to
be a sample from the universe of potential variables. It maximizes the alpha
reliabili
ty of the factors.
 Image Fac
toring.
A factor extraction method developed by Guttman and based
on image theory. The common part of the variable, called the partial image, is
defined as its linear regression on remaining variables, rather than a function of
hypothet
ical factors.
Analyze. Allows you to specify either a correlation matrix or a covariance matrix.
 Correlation matrix. Useful if variables in your analysis are measured on different
scales.
 Covaria
nce matrix.
Useful when you want to apply your factor analysis to multiple
groups with different variances for each variable.
Extract.
You can either retain all factors whose eigenvalues exceed a specified value
or retain a specific number of factors.
Display. Allows you to request the unrotated factor solution and a scree plot of the
eigenval
ues.
 Unrotat
ed Factor Solution.
Displays unrotated factor loadings (factor pattern
matrix), communalities, and eigenvalues for the factor solution.
 Scree plot. A plot of the variance associated with each factor. It is used to
determine how many factors should be kept. Typically the plot shows a distinct
break be
tween the steep slope of the large factors and the gradual trailing of the
rest (the scree).
Maximum
Iterations for Convergence.
Allows you to specify the maximum number of
steps the algorithm can take to estimate the solution.