SPSS Tutorial
- Performing a statistical analysis -
This part of the beginner's guide to SPSS will walk you through performing a
simple correlational test on the data file created in the first part. The
Pearson's Product Moment Correlation Coefficient tells us how well two sets of
continuous data correlate to each other. The value can fall between 0.00 (no
correlation) and 1.00 (perfect correlation). A p value tells us if the
Pearson's is significant or not. Generally p values under 0.05 are
considered significant.
Have open the data file you created in Part I.
Figure 1:
1) Select the option under Statistics:
Correlate: Bivariate. This will bring up a menu like the one in Figure
2.
Figure 2:
2) Highlight "Weight before" by
clicking on it once.
3) Transfer the variable to the box on the
right by clicking once on the arrow button. Repeat this procedure for the
"Height (inches)" variable. Now click on OK.
SPSS is going to generate some correlational data now. It should appear as in
Figure 3.
Figure 3:
Now let's interpret these dreams, Daniel . . . .
4) In general you'll notice that both
height and weight are set up in a matrix so that their columns
intersect. Where height and height intersect obviously there
is going to be a perfect correlation (1.00). Of course this is unimportant
(as well as painfully obvious), so there is no p value given.
5) Now for the data that actually makes
sense, note the values that occur in the intersections of the height and
weight columns/rows. The Pearson's value is the .876, and the
significance is the .000 (which doesn't mean that it is zero, only that
it is lower than .001). The asterisks (**) indicate significant values.
From this we can say that there is a significant correlation between height
and weight. Makes sense, right?
From following this walk-though guide you hopefullly have a feel for SPSS and
will be able to use it for some of the more difficult analyses.