To run the correlation we go to Analyze, and then Correlate, and then Bivariate.
And bivariate if you think about it, 'bi' just means two and variate it is just
another name for a variable so this is the correlation we use when we have two
variables, like we do here. So select Bivariate and then here notice hours
media and college GPA are on the left-hand side here of our Bivariate
correlations box. You should see on your screen hours media is already selected,
so go ahead and click the right arrow button to move it to the Variables box.
And then select college GPA and click the right arrow button once again to
move it over as well.
Now notice here under Correlation Coefficients that Pearson is chosen by
default.
That's Pearson's r, that's what we want to run here in this analysis. If you want
to run something like say Spearman's you could check that box, but what we want to run
is the Pearson correlation, which is the most commonly used correlation
coefficient by far. So that looks good Pearson is selected.
Everything else looks great let's go ahead and click OK. And when we
click OK, the correlation analysis is presented in our output here. And the way
you read this here is you want to see where hours media and college GPA meet
or intersect.
So these two variables if you see they meet right here in this box here and
it's also shown here once again as it's redundant. So either box is fine to look
at. Notice that our correlation where it's shown here is Pearson correlation is negative,
so it is negative, .66 if I round to two decimal places and then the p-value is
.001. So using an alpha of .05 and a two-tailed test where a two-tailed test
allows for a positive correlation or a negative correlation, we can use the
following decision rule as we have for all of our tests: if p is less than or equal
to .05, the test is significant which here would indicate that there is a
significant relationship
between hours of media watched and college GPA. Whereas if p is greater than
.05, the test is not significant, indicating that there is not
a significant relationship between hours of media watched and college GPA. Our
p-value in our output is once again .001. So applying the p value to our
decision rule, we can see that .001 is in fact less than .05, which
indicates the test is significant. In other words in our example there is a
significant relationship between hours of media watched and college GPA.
Next we'll write our results using APA format. We could write our results as
follows:
There is a significant negative relationship between hours of media
watched and college GPA. And then we have r, which is for Pearsons r, we have 18
degrees of freedom. Now the degrees of freedom
interestingly are not reported in this table here instead we just get an N.
But the degrees of freedom for pearson's r are equal to the total number
of people in your study, or your sample size, minus two. So N - 2. But we have
20 people, N - 2 or 20-2 is 18, so that's where I got this here. Next we have
our correlation coefficient of negative .66 which you can see in the output here
when we round to two decimal places. And then we have a p-value of .001,
which is shown in our table here. Notice as well in the output here that SPSS uses
asterisks to indicate if the test is significant and they might be a little
hard to see but there are two asterisks here. And notice the note down here where
the two asterisks are showing and it says correlation is significant at the
.01 level, two tailed. Now if you saw one asterisk showing instead of two, this
would read correlation is significant at the .05
level, two tailed. And it's just the way SPSS outputs it by default to make
it a little more user-friendly. We could turn that off if we wanted to
in the Bivariate correlations box but we just left it as is.