The CORR Procedure
- Overview
- Getting Started
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Syntax
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Details
Pearson Product-Moment Correlation Spearman Rank-Order Correlation Kendall’s Tau-b Correlation Coefficient Hoeffding Dependence Coefficient Partial Correlation Fisher’s z Transformation Polyserial Correlation Cronbach’s Coefficient Alpha Confidence and Prediction Ellipses Missing Values In-Database Computation Output Tables Output Data Sets ODS Table Names ODS Graphics
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Examples
Computing Four Measures of Association Computing Correlations between Two Sets of Variables Analysis Using Fisher’s z Transformation Applications of Fisher’s z Transformation Computing Polyserial Correlations Computing Cronbach’s Coefficient Alpha Saving Correlations in an Output Data Set Creating Scatter Plots Computing Partial Correlations
- References
| Output Data Sets |
If you specify the OUTP=, OUTS=, OUTK=, or OUTH= option, PROC CORR creates an output data set that contains statistics for Pearson correlation, Spearman correlation, Kendall’s tau-b, or Hoeffding’s 
, respectively. By default, the output data set is a special data set type (TYPE=CORR) that many SAS/STAT procedures recognize, including PROC REG and PROC FACTOR. When you specify the NOCORR option and the COV, CSSCP, or SSCP option, use the TYPE= data set option to change the data set type to COV, CSSCP, or SSCP.
The output data set includes the following variables:
BY variables, which identify the BY group when using a BY statement
_TYPE_ variable, which identifies the type of observation
_NAME_ variable, which identifies the variable that corresponds to a given row of the correlation matrix
INTERCEPT variable, which identifies variable sums when specifying the SSCP option
VAR variables, which identify the variables listed in the VAR statement
You can use a combination of the _TYPE_ and _NAME_ variables to identify the contents of an observation. The _NAME_ variable indicates which row of the correlation matrix the observation corresponds to. The values of the _TYPE_ variable are as follows:
SSCP, uncorrected sums of squares and crossproducts
CSSCP, corrected sums of squares and crossproducts
COV, covariances
MEAN, mean of each variable
STD, standard deviation of each variable
N, number of nonmissing observations for each variable
SUMWGT, sum of the weights for each variable when using a WEIGHT statement
CORR, correlation statistics for each variable
If you specify the SSCP option, the OUTP= data set includes an additional observation that contains intercept values. If you specify the ALPHA option, the OUTP= data set also includes observations with the following _TYPE_ values:
RAWALPHA, Cronbach’s coefficient alpha for raw variables
STDALPHA, Cronbach’s coefficient alpha for standardized variables
RAWALDEL, Cronbach’s coefficient alpha for raw variables after deleting one variable
STDALDEL, Cronbach’s coefficient alpha for standardized variables after deleting one variable
RAWCTDEL, the correlation between a raw variable and the total of the remaining raw variables
STDCTDEL, the correlation between a standardized variable and the total of the remaining standardized variables
If you use a PARTIAL statement, the statistics are calculated after the variables are partialled. If PROC CORR computes Pearson correlation statistics, MEAN equals zero and STD equals the partial standard deviation associated with the partial variance for the OUTP=, OUTK=, and OUTS= data sets. Otherwise, PROC CORR assigns missing values to MEAN and STD.