FACTOR Procedure
The FACTOR procedure performs a variety of common factor and component analyses and rotations.
The following are highlights of the procedure's features:
 supports the following factor extraction methods:
 principal component analysis
 principal factor analysis
 iterated principal factor analysis
 unweighted least squares factor analysis
 maximum likelihood (canonical) factor analysis
 alpha factor analysis
 image component analysis
 Harris component analysis
 supports the following rotation methods:
 varimax
 quartimax
 biquartimax
 equamax
 parsimax
 factor parsimax
 quartimin
 biquartimin
 covarimin
 orthomax with userspecified gamma
 CrawfordFerguson family with userspecified weights on variable parsimony and factor parsimony
 generalized CrawfordFerguson family with userspecified weights
 direct oblimin with userspecified tau
 CrawfordFerguson family with userspecified weights on variable parsimony and factor parsimony
 generalized CrawfordFerguson family with userspecified weights
 promax with userspecified exponent
 HarrisKaiser case II with userspecified exponent
 Procrustes with a userspecified target pattern
 provides a variety of methods for prior communality estimation
 input can be multivariate data, a correlation matrix, a covariance matrix, a factor pattern,
or a matrix of scoring coefficients

 enables you to factor either the correlation or covariance matrix
 processes output from other procedures
 produces the following output:
 means
 standard deviations
 correlations
 Kaiser's measure of sampling adequacy
 eigenvalues
 a scree plot
 path diagrams
 eigenvectors
 prior and final communality estimates
 the unrotated factor pattern
 residual and partial correlations
 the rotated primary factor pattern
 the primary factor structure
 interfactor correlations
 the reference structure
 reference axis correlations
 the variance explained by each factor both ignoring and eliminating other factors
 plots of both rotated and unrotated factors
 squared multiple correlation of each factor with the variables
 standard error estimates
 confidence limits
 coverage displays
 scoring coefficients
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 enables you to use relative weights for each observation in the input data set
 creates a SAS data set that corresponds to any table
 automatically creates graphs by using ODS Graphics

For further details see the SAS/STAT User's Guide:
The FACTOR Procedure
( PDF  HTML )
Examples