The CORR Procedure

Testing Whether a Population Correlation Is Equal to a Given Value

You can use the following statements to test the null hypothesis against a two-sided alternative . The test is requested with the option FISHER(RHO0=0.5).

title 'Analysis for Batch 1';
proc corr data=Sim (where=(Batch=1)) fisher(rho0=.5);
   var X Y;
run;

Output 2.4.1 displays the results based on Fisher’s transformation. The null hypothesis is rejected since the -value is less than .

Output 2.4.1 Fisher’s Test for

Analysis for Batch 1

The CORR Procedure

Pearson Correlation Statistics (Fisher's z Transformation)
Variable	With Variable	N	Sample Correlation	Fisher's z	Bias Adjustment	Correlation Estimate			H0:Rho=Rho0
							95% Confidence Limits		Rho0	p Value
X	Y	150	0.22081	0.22451	0.0007410	0.22011	0.062034	0.367409	0.50000	<.0001

Testing for Equality of Two Population Correlations

You can use the following statements to test for equality of two population correlations, and . Here, the null hypothesis is tested against the alternative .

ods output FisherPearsonCorr=SimCorr;
title 'Testing Equality of Population Correlations';
proc corr data=Sim (where=(Batch=1 or Batch=2)) fisher;
   var X Y;
   by Batch;
run;

The ODS OUTPUT statement saves the "FisherPearsonCorr" table into an output data set in the CORR procedure. The output data set SimCorr contains Fisher’s statistics for both batches.

The following statements display (in Figure 2.4.2) the output data set SimCorr:

proc print data=SimCorr;
run;

The -value for testing is derived by treating the difference as a normal random variable with mean zero and variance , where and are Fisher’s transformation of the sample correlations and , respectively, and where and are the corresponding sample sizes.

The following statements compute the -value in Output 2.4.3:

data SimTest (drop=Batch);
   merge SimCorr (where=(Batch=1) keep=Nobs ZVal Batch
                  rename=(Nobs=n1 ZVal=z1))
         SimCorr (where=(Batch=2) keep=Nobs ZVal Batch
                  rename=(Nobs=n2 ZVal=z2));
   variance = 1/(n1-3) + 1/(n2-3);
   z = (z1 - z2) / sqrt( variance );
   pval = probnorm(z);
   if (pval > 0.5) then pval = 1 - pval;
   pval = 2*pval;
run;

proc print data=SimTest noobs;
run;

In Output 2.4.3, the -value of 0.2795 does not provide evidence to reject the null hypothesis that . The sample sizes and are not large enough to detect the difference at a significance level of .

Combining Correlation Estimates from Different Samples

Assume that sample correlations and are computed from two independent samples of and observations, respectively. A combined correlation estimate is given by , where is the weighted average of the transformations of and :

The following statements compute a combined estimate of by using Batch 1 and Batch 3:

ods output FisherPearsonCorr=SimCorr2;
proc corr data=Sim (where=(Batch=1 or Batch=3)) fisher;
   var X Y;
   by Batch;
run;

data SimComb (drop=Batch);
   merge SimCorr2 (where=(Batch=1) keep=Nobs ZVal Batch
                   rename=(Nobs=n1 ZVal=z1))
         SimCorr2 (where=(Batch=3) keep=Nobs ZVal Batch
                   rename=(Nobs=n2 ZVal=z2));
   z = ((n1-3)*z1 + (n2-3)*z2) / (n1+n2-6);
   corr = tanh(z);
   var = 1/(n1+n2-6);
   zlcl = z - probit(0.975)*sqrt(var);
   zucl = z + probit(0.975)*sqrt(var);
   lcl= tanh(zlcl);
   ucl= tanh(zucl);
   pval= probnorm( z/sqrt(var));
   if (pval > .5)  then pval= 1 - pval;
   pval= 2*pval;
run;

proc print data=SimComb noobs;
   var n1 z1 n2 z2 corr lcl ucl pval;
run;

Output 2.4.4 displays the combined estimate of . The table shows that a correlation estimate from the combined samples is . The confidence interval is , using the variance of the combined estimate. Note that this interval contains the population correlation .