Although PROC FACTOR can be used for common factor analysis, the default method is principal components. PROC FACTOR produces the same results as PROC PRINCOMP except that scoring coefficients from PROC FACTOR are normalized to give principal component scores with unit variance, whereas PROC PRINCOMP by default produces principal component scores with variance equal to the corresponding eigenvalue. PROC PRINCOMP can also compute scores standardized to unit variance. Both procedures produce graphical results through ODS Graphics.
PROC PRINCOMP has the following advantages over PROC FACTOR:
PROC PRINCOMP is slightly faster if a small number of components is requested.
PROC PRINCOMP can analyze somewhat larger problems in a fixed amount of memory.
PROC PRINCOMP can output scores from an analysis of a partial correlation or covariance matrix.
PROC PRINCOMP is simpler to use.
PROC FACTOR has the following advantages over PROC PRINCOMP for principal component analysis:
PROC FACTOR produces more output.
PROC FACTOR does rotations.
If you want to perform a common factor analysis, you must use PROC FACTOR instead of PROC PRINCOMP. Principal component analysis should never be used if a common factor solution is desired (Dziuban and Harris 1973; Lee and Comrey 1979).