You'll see several correlation coefficients generated from a real
study. In this investigation, people were measured in terms of
two variables (isometric strength and job performance), with two
different measures taken on each of these variable. The bivariate
correlation coefficients are presented in a correlation matrix,
with each r tested to see if it was statistically significant.
Sky Huck's Question to you:
What kind of relationship exists between the values of r and p
in the correlation matrix? Is it a direct relationship or an indirect
This interactive resource allows you to see if there is a statistically
significant difference between two correlation coefficients. By
exerting control over (1) the size of the difference between the
two rs and (2) the sample sizes, you'll be able to see that large
sample sizes can turn a small difference into something "significant."
What to Do:
Click on the colored title of this on-line resource: "Comparing
Make 2 private decisions: (a) your level of significance
and (b) whether you want to do a one-tailed test or a two-tailed
Enter .50 and .55 as the r values for Samples A and B.
Enter 100 for each sample size.
Click "Calculate" and then look to see if you have a statistically
significant finding; this will be the case if the computed
p is smaller that the alpha level you selected in Step #2.
If you don't have a significant finding, repeat Steps #4
and #5 again and again (each time increasing each n by 100)
until you finally can cross over into "The Wonderful Land
Sky Huck's Puzzle Question:
What do you think will happen to the computed value of p if you
(1) set each n equal to 100, (2) make the two values of r different
by .20, and (3) change the "location" of your two r values on
the "correlation continuum" (that extends from -1.00 to +1.00)?