The FACTOR Procedure

ODS Table Names

PROC FACTOR assigns a name to each table that it creates. You can use these names to refer to the table when using the Output Delivery System (ODS) to select tables and create output data sets. These names are listed in the Table 6. For more information about ODS, see Chapter 23, Using the Output Delivery System.

Table 6: ODS Tables Produced by PROC FACTOR

ODS Table Name Description Option
AlphaCoef Coefficient alpha for each factor METHOD=ALPHA
AveParCorrControlPC Average partial correlations after controlling for principal components MAP or NFACTORS=MAP, MAP2, or MAP4
CanCorr Squared canonical correlations METHOD=ML
CondStdDev Conditional standard deviations SIMPLE with PARTIAL
ConvergenceStatus Convergence status METHOD=PRINIT, ALPHA, ML, or ULS
Corr Correlations CORR
Eigenvalues Eigenvalues Default
Eigenvectors Eigenvectors EIGENVECTORS
FactorPattern Factor pattern Default except METHOD=PATTERN or SCORE
FactorStructure Factor structure ROTATE= any oblique rotation
FactorWeightRotate Factor weights for rotation HKPOWER=
FinalCommun Final communalities Default except METHOD=ALPHA, ML, or HARRIS
FinalCommunWgt Final communalities with weights METHOD=ALPHA, ML, or HARRIS; METHOD=IMAGE, PRINCIPAL, PRINIT, or ULS with WEIGHT
FitMeasures Measures of fit METHOD=ML
ImageCoef Image coefficients METHOD=IMAGE
ImageCov Image covariance matrix METHOD=IMAGE
ImageFactors Image factor matrix METHOD=IMAGE
InputFactorPattern Input factor pattern METHOD=PATTERN with PRINT or ALL
InputScoreCoef Standardized input scoring coefficients METHOD=SCORE with PRINT or ALL
InterFactorCorr Interfactor correlations ROTATE= any oblique rotation
InvCorr Inverse correlation matrix ALL
IterHistory Iteration history METHOD=PRINIT, ALPHA, ML, or ULS
MultipleCorr Squared multiple correlations METHOD=IMAGE or HARRIS
NObs Number of records and observations, input data type Default
NormObliqueTrans Normalized oblique transformation matrix ROTATE=PROCRUSTES or PROMAX
ObliqueRotFactPat Rotated factor pattern ROTATE= any oblique rotation
ObliqueTrans Oblique transformation matrix HKPOWER= or ROTATE= any oblique rotation except PROCRUSTES or PROMAX
OrthRotFactPat Rotated factor pattern ROTATE= any orthogonal rotation
OrthTrans Orthogonal transformation matrix ROTATE= any orthogonal rotation
ParallelAnalysis Parallel analysis results PARALLEL or NFACTORS=PARALLEL
ParCorrControlFactor Partial correlations after controlling for factors RESIDUAL
ParCorrControlVar Partial correlations after controlling for other variables MSA
PartialCorr Partial correlations MSA, CORR with PARTIAL
PriorCommunalEst Prior communality estimates PRIORS=, METHOD=ML or ALPHA
ProcrustesTarget Target matrix for Procrustean transformation ROTATE=PROCRUSTES or PROMAX
ProcrustesTrans Procrustean transformation matrix ROTATE=PROCRUSTES or PROMAX
RMSOffDiagPartials Root mean square off-diagonal partials RESIDUAL
RMSOffDiagResids Root mean square off-diagonal residuals RESIDUAL
ReferenceAxisCorr Reference axis correlations ROTATE= any oblique rotation
ReferenceStructure Reference structure ROTATE= any oblique rotation
ResCorrUniqueDiag Residual correlations with uniqueness on the diagonal RESIDUAL
SamplingAdequacy Kaiser’s measure of sampling adequacy MSA
SignifTests Significance tests METHOD=ML
SimpleStatistics Simple statistics SIMPLE
StdScoreCoef Standardized scoring coefficients SCORE
VarExplain Variance explained Default except METHOD=ALPHA, ML, or HARRIS
VarExplainWgt Variance explained with weights METHOD=ALPHA, ML, or HARRIS; METHOD=IMAGE, PRINCIPAL, PRINIT, or ULS with WEIGHT
VarFactorCorr Squared multiple correlations of the variables with each factor SCORE
VarWeightRotate Variable weights for rotation NORM=WEIGHT, ROTATE=


Last updated: December 09, 2022