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The CORR Procedure |
PROC CORR <option(s)>; |
To do this | Use this option | |
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Specify the input data set | DATA= | |
Create output data sets | ||
Specify an output data set to contain Hoeffding's D statistics | OUTH= | |
Specify an output data set to contain Kendall correlations | OUTK= | |
Specify an output data set to contain Pearson correlations | OUTP= | |
Specify an output data set to contain Spearman correlations | OUTS= | |
Control statistical analysis | ||
Exclude observations with nonpositive weight values from the analysis | EXCLNPWGT | |
Request Hoeffding's measure of dependence, D | HOEFFDING | |
Request Kendall's tau-b | KENDALL | |
Request Pearson product-moment correlation | PEARSON | |
Request Spearman rank-order correlation | SPEARMAN | |
Control Pearson correlation statistics | ||
Compute Cronbach's coefficient alpha | ALPHA | |
Compute covariances | COV | |
Compute corrected sums of squares and crossproducts | CSSCP | |
Exclude missing values | NOMISS | |
Specify singularity criterion | SINGULAR= | |
Compute sums of squares and crossproducts | SSCP | |
Specify the divisor for variance calculations | VARDEF= | |
Control printed output | ||
Specify the number and order of correlation coefficients | BEST= | |
Suppress Pearson correlations | NOCORR | |
Suppress all printed output | NOPRINT | |
Suppress significance probabilities | NOPROB | |
Suppress descriptive statistics | NOSIMPLE | |
Change the order of correlation coefficients | RANK |
Options |
Main discussion: | Cronbach's Coefficient Alpha |
Restriction: | If you use a WITH statement, ALPHA is invalid. |
Interaction: | ALPHA invokes PEARSON. |
Interaction: | If you specify OUTP=, the output data set also contains six observations with Cronbach's coefficient alpha. |
Interaction: | When you use the PARTIAL statement, PROC CORR calculates Cronbach's coefficient alpha for partialled variables. |
See also: | OUTP= option |
Featured in: | Computing Cronbach's Coefficient Alpha |
Interaction: | When you specify HOEFFDING, PROC CORR prints the D statistics in order from highest to lowest. |
Range: | 1 to the maximum number of variables |
Interaction: | COV invokes PEARSON. |
Interaction: | If you specify OUTP=, the output data set contains the covariance matrix and the _TYPE_ variable value is COV. |
Interaction: | When you use the PARTIAL statement, PROC CORR computes a partial covariance matrix. |
See also: | OUTP= option |
Featured in: | Computing Rectangular Correlation Statistics with Missing Data and Storing Partial Correlations in an Output Data Set |
Interaction: | CSSCP invokes PEARSON. |
Interaction: | If you specify OUTP=, the output data set contains a CSSCP matrix and the _TYPE_ variable value is CSSCP. If you use a PARTIAL statement, the output data set contains a partial CSSCP matrix. |
Interaction: | When you use a PARTIAL statement, PROC CORR prints both an unpartial and a partial CSSCP matrix. |
See also: | OUTP= option |
Main discussion: | Input Data Sets |
Requirement: | You must use a WEIGHT statement. |
See also: | WEIGHT Statement |
Main discussion: | Hoeffding's Measure of Dependence, D |
Restriction: | When you use a WEIGHT or PARTIAL statement, HOEFFDING is invalid. |
Featured in: | Computing Pearson Correlations and Other Measures of Association |
Main discussion: | Kendall's tau-b |
Restriction: | When you use a WEIGHT statement, KENDALL is invalid. |
Interactions: | When you use a PARTIAL statement, probability values for Kendall's partial tau-b are not available. |
Featured in: | Storing Partial Correlations in an Output Data Set |
Interaction: | If you specify OUTP=, the data set type remains CORR. To change the data set type to COV, CSSCP, or SSCP, use the TYPE= data set option. |
See also: | Output Data Sets |
Featured in: | Computing Cronbach's Coefficient Alpha |
Main discussion: | Missing Values |
Tip: | Using NOMISS is computationally more efficient. |
Featured in: | Computing Cronbach's Coefficient Alpha |
Tip: | Use NOPRINT when you want to create an output data set only. |
Featured in: | Computing Rectangular Correlation Statistics with Missing Data |
Main discussion: | Output Data Sets |
Interaction: | OUTH= invokes HOEFFDING. |
Main discussion: | Output Data Sets |
Interaction: | OUTK= option invokes KENDALL. |
Main discussion: | Output Data Sets |
Interaction: | OUTP= invokes PEARSON. |
Interaction: | If you specify ALPHA, the output data set also contains six observations with Cronbach's coefficient alpha. |
Featured in: | Storing Partial Correlations in an Output Data Set |
Main discussion: | Output Data Sets |
Interaction: | OUTS= invokes SPEARMAN. |
Main discussion: | Pearson Product-Moment Correlation |
Featured in: | Computing Pearson Correlations and Other Measures of Association |
Interaction: | If you use HOEFFDING, PROC CORR prints the D statistics in order from highest to lowest. |
Main discussion: | Partial Correlation |
Default: | 1E-8 |
Range: | between 0 and 1 |
Main discussion: | Spearman Rank-Order Correlation |
Restriction: | When you specify a WEIGHT statement, SPEARMAN is invalid. |
Featured in: |
Interaction: | SSCP invokes PEARSON. |
Interaction: | When you specify OUTP=, the output data set contains a SSCP matrix and the _TYPE_ variable value is SSCP. If you use a PARTIAL statement, the output data set does not contain an SSCP matrix. |
Interaction: | When you use a PARTIAL statement, PROC CORR prints the unpartial SSCP matrix. |
Featured in: | Computing Rectangular Correlation Statistics with Missing Data |
Possible Values for VARDEF= shows the possible values for divisor and associated divisors where k is the number of PARTIAL statement variables.
Value | Divisor | Formula |
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DF | degrees of freedom | n - k - 1 |
N | number of observations | n |
WDF | sum of weights minus one | (_{i}w_{i}) - k - 1 |
WEIGHT|WGT | sum of weights | _{i}w_{i} |
Default: | DF |
Tip: | When you use the WEIGHT statement and VARDEF=DF, the variance is an estimate of , where the variance of the ith observation is and is the weight for the ith observation. This yields an estimate of the variance of an observation with unit weight. |
Tip: | When you use the WEIGHT statement and VARDEF=WGT, the computed variance is asymptotically (for large n) an estimate of , where is the average weight. This yields an asymptotic estimate of the variance of an observation with average weight. |
Main discussion: | Weighted statistics Example . |
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Copyright 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.