In this video we'll take a look at a second example involving correlation in
SPSS. And on your screen you can see two variables: we have height which is the
height of individuals in inches and then we have weight which is reported in
pounds. And what we have in this example is notice there are 20 people and for
each person their height and weight was recorded.
So for example the first person has a height of 60 inches or 5 feet and a
weight of 135 pounds. The second person is 64 inches and 145 8 00:00:38,630 --> 00:00:40,489 pounds, and so on.
OK so we'll run the correlation coefficient in SPSS in just a moment but
before we do that let's go ahead and use an alpha .01 in this
example instead of the typical value .05 that we use. And we'll go ahead and do
a two-tailed test which will allow for either positive or negative correlation
to exist. So once again we'll use alpha .01 in a two-tail test.
Now if you think about it before we begin the analysis
what type of relationship should we see if we see any at all between height and
weight?
Do you think that the taller people are they tend to also weigh more, or would it
be the taller they are they tend to weigh less?
Well it should make sense that taller people tend to weigh more.
There's certainly many exceptions, but that's the general pattern and people
who aren't as tall tend to weigh less.
So what type of correlation should that be, positive or negative?
There should be a positive correlation, right, because higher scores on X are
associated with higher scores on Y. Or, in this example, taller people tend to also
weigh more. People who aren't as tall tend to weight less. So let's go ahead and run
this analysis. We'll go to Analyze and then select Correlate and then Bivariate
and once again bivariate just means two variables. And then when you do that you
see the Bivariate Correlations
dialog box opens. Let's go ahead and move our variables over to the Variables box.
And then notice Pearson is selected by default. That's what we want to run once
again. We have a two-tailed test. We'll allow significant correlations to be flagged
and that means the asterisks that you saw in the previous video.
Everything looks good here so let's go ahead and click OK. And we see our output
here in the viewer window and if you recall from the last video we want to
see where our two variables intersect or meet. So we have height and weight here.
Notice the correlation between height and weight is .68, rounded to two
decimal places. And we have a p-value .001. And we'll go ahead
and use the same decision rule as always.
In this example we'll be using alpha .01.
So we're going to compare our p value to the following two conditions to see
which of the two it falls into. So with the p value of .001 you should see here
that it falls into this first condition,
p is less than or equal to .01. Notice that this is one-tenth
smaller, it's .001 where this (alpha) is .01.
OK so this is the case here here p is less than equal to .01, so that indicates that
the test is significant meaning that there is a significant relationship
between height and weight.
So the correlation is significant and you want to note if it's positive or
negative.
It is a positive correlation as we see a value .68 reported there's no negative
sign here and that makes sense
because taller people tend to weigh more. So we have a positive correlation of .68.
So next we'll write our results using APA format. And the written results are as
follows:
there is a significant positive relationship between height and weight.
Recall that we're using the Pearson r correlation here. So we have r and then
18 degrees of freedom.
Which once again while they're not reported in this table they're equal to N
minus 2. So if N is 20, the degrees of freedom are equal to 20-2 or 18.
And then we have a correlation of .68 and it's positive.
And finally our p value is equal to .001. And, as always, if
preferred you can report p is less than equal to alpha, which in this case is
.01, instead of reporting the exact p-value, if desired.
However, as always, it's recommended to report the exact p-value, as it really is
more informative than just less than .01.
Next let's take a few moments to produce a scatter plot. So to do that we'll go to
Graphs and then Legacy Dialogs and then select Scatter/Dot. Simple Scatter is
selected by default and that's what we want, so go ahead and click Define. And
then the Simple Scatterplot dialog box opens.
We can go ahead and move height to the Y-axis and then weight to the X-axis and
then click OK.
SPSS produces our scatterplot and you can see here, if these points are well
represented by a straight line on this plot the way it's shown our line would
look something like, would go about right here, more or less, to here. But a straight
line does do a good job at representing these data points.
This concludes the presentation on correlation in SPSS. Thanks for watching.