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.