No matter whether you are doing t-tests, ANOVAs, or some other kind of test you need to know whether your samples are
independent or dependent.
And you need to know that before you enter your data in SPSS
because the type of test that you will run will determine how the data are entered.
When conducting an independent samples t-test, the data are structured or entered
differently in SPSS than when you are doing a repeated measures t-test.
It is important to set up your SPSS file correctly for the type of test that you want to do.
If you set up or structure your data wrong,
you will not be able to run the test that you want to run.
You should decide what tests you will run before you enter data in SPSS.
Ideally, you should know what type of tests you will run before you even do data collection.
For independent samples t-tests you need one
quantitative dependent variable.
Quantitative is also called continuous or scale level. You also need one categorical
independent variable.
Categorical would be nominal or ordinal.
All of the continuous data will go in one column, and the categorical data,
1 and 2, will go in the second column.
For paired sample t-tests you need two quantitative variables.
Quantitative is also called continuous or scale level.
The first scale variable represents the first measurement for each subject, and the second scale variable
represents the second measurement for the same subject or the
paired subject if you were doing a matched design.
This pattern for data entry is true for any
independent vs repeated measures design.
And I should also point out
that the PuppyTraining.sav data set that we used
has been specifically set up to run both kinds of t-tests.
I created these first two columns to run an independent samples t-test,
and then I created the third and fourth columns to run a paired samples t-test.
This setup for these data
is very uncommon, in fact, this is a stupid way to set up your data.
I have never seen a data set like this in the wild. No
self-respecting researcher would ever set up data this way.
I only did this because I wanted you to see that the data were exactly the same for both
tests, and so that you would only have one file for learning the two kinds of tests. This set up is only
for teaching purposes. Do not follow this as an example.
Instead you should know whether you are doing an independent or dependent
measures test, and then set up a single data set appropriate to the type of test that you are conducting.
Finally, so why did I do this? If you look at these data for these two tests you will notice that the data are
exactly the same. The group means are the same in both tests, and yet, the
independent samples t-test was not significant, but the paired-samples t-test,
with exactly the same numbers, was significant.
I set up the data set in this goofy way to better illustrate the difference in power between
tests based upon your research design,
and it gives me one more chance to remind you that you should consider the power of your test
when you do your research design.
This consideration is called a power analysis. I will cover how to do a power analysis in another video.