The FACTOR Procedure

Output Data Sets

The OUT= Data Set

The OUT= data set contains all the data in the DATA= data set plus new variables called Factor1, Factor2, and so on, containing estimated factor scores. Each estimated factor score is computed as a linear combination of the standardized values of the variables that are factored. The coefficients are always displayed if the OUT= option is specified, and they are labeled "Standardized Scoring Coefficients."

If partial variables are specified in the PARTIAL statement, the factor analysis is on the residuals of the variables, which are regressed on the partial variables. In this case, the OUT= data set also contains the (unstandardized) residuals, which are prefixed by R_ by default. For example, the residual of variable X is named R_X in the OUT= data set. You might also assign the prefix by the PARPREFIX= option. Because the residuals are factor-analyzed, the estimated factor scores are computed as linear combinations of the standardized values of the residuals, but not the original variables.

The OUTSTAT= Data Set

The OUTSTAT= data set is similar to the TYPE=CORR or TYPE=UCORR data set produced by the CORR procedure, but it is a TYPE=FACTOR data set and it contains many results in addition to those produced by PROC CORR. The OUTSTAT= data set contains observations with _TYPE_=’UCORR’ and _TYPE_=’USTD’ if you specify the NOINT option.

The output data set contains the following variables:

the BY variables, if any
two new character variables, _TYPE_ and _NAME_
the variables analyzed—those in the VAR statement, or, if there is no VAR statement, all numeric variables not listed in any other statement. If partial variables are specified in the PARTIAL statement, the residuals are included instead. By default, the residual variable names are prefixed by R_, unless you specify something different in the PARPREFIX= option.

Each observation in the output data set contains some type of statistic as indicated by the _TYPE_ variable. The _NAME_ variable is blank except where otherwise indicated. The values of the _TYPE_ variable are as follows:

MEAN: means
STD: standard deviations
USTD: uncorrected standard deviations
N: sample size
CORR: correlations. The _NAME_ variable contains the name of the variable corresponding to each row of the correlation matrix.
UCORR: uncorrected correlations. The _NAME_ variable contains the name of the variable corresponding to each row of the uncorrected correlation matrix.
IMAGE: image coefficients. The _NAME_ variable contains the name of the variable corresponding to each row of the image coefficient matrix.
IMAGECOV: image covariance matrix. The _NAME_ variable contains the name of the variable corresponding to each row of the image covariance matrix.
COMMUNAL: final communality estimates
PRIORS: prior communality estimates, or estimates from the last iteration for iterative methods
WEIGHT: variable weights
SUMWGT: sum of the variable weights
EIGENVAL: eigenvalues
UNROTATE: unrotated factor pattern. The _NAME_ variable contains the name of the factor.
SE_UNROT: standard error estimates for the unrotated loadings. The _NAME_ variable contains the name of the factor.
RESIDUAL: residual correlations. The _NAME_ variable contains the name of the variable corresponding to each row of the residual correlation matrix.
PRETRANS: transformation matrix from prerotation. The _NAME_ variable contains the name of the factor.
PREFCORR: prerotated interfactor correlations. The _NAME_ variable contains the name of the factor.
SE_PREFC: standard error estimates for prerotated interfactor correlations. The _NAME_ variable contains the name of the factor.
PREROTAT: prerotated factor pattern. The _NAME_ variable contains the name of the factor.
SE_PREPA: standard error estimates for the prerotated loadings. The _NAME_ variable contains the name of the factor.
PRERCORR: prerotated reference axis correlations. The _NAME_ variable contains the name of the factor.
PREREFER: prerotated reference structure. The _NAME_ variable contains the name of the factor.
PRESTRUC: prerotated factor structure. The _NAME_ variable contains the name of the factor.
SE_PREST: standard error estimates for prerotated structure loadings. The _NAME_ variable contains the name of the factor.
PRESCORE: prerotated scoring coefficients. The _NAME_ variable contains the name of the factor.
TRANSFOR: transformation matrix from rotation. The _NAME_ variable contains the name of the factor.
FCORR: interfactor correlations. The _NAME_ variable contains the name of the factor.
SE_FCORR: standard error estimates for interfactor correlations. The _NAME_ variable contains the name of the factor.
PATTERN: factor pattern. The _NAME_ variable contains the name of the factor.
SE_PAT: standard error estimates for the rotated loadings. The _NAME_ variable contains the name of the factor.
RCORR: reference axis correlations. The _NAME_ variable contains the name of the factor.
REFERENC: reference structure. The _NAME_ variable contains the name of the factor.
STRUCTUR: factor structure. The _NAME_ variable contains the name of the factor.
SE_STRUC: standard error estimates for structure loadings. The _NAME_ variable contains the name of the factor.
SCORE: scoring coefficients to be applied to standardized variables if the SCORE option is specified on the PROC FACTOR statement. The _NAME_ variable contains the name of the factor.
USCORE: scoring coefficients to be applied without subtracting the mean from the raw variables if the SCORE option is specified on the PROC FACTOR statement. The _NAME_ variable contains the name of the factor.