In the previous video I was talking about t-tests and how the experimental design we
use determines what kind of t-test we need to run. Specifically, I showed you how to
do a paired samples t-test which you do when you gather multiple measurements from the
same group of people. The other way to do it, which I'm going to go over now, is how
to compare measurements obtained from different groups of people. This is called an independent
sample t-test. So I'll reuse the same data from the previous video but I'll rename the
variables. Instead of pre-college IQ I'll say these scores are the IQ scores of people
who had no college and these people represent the IQ scores -- or these represent the IQ
scores to people who did go to college. So the process of doing the independent sample
t-test is pretty similar. You go to Analyze, Compare Means, and do Independent Samples
T-Test. Now at this point it's different from a paired samples t test. You can try moving
the variables over but when you try and run the analysis and click OK it's not letting
you do it so something is wrong here. The things that's wrong is you need to set up
the data differently for an independent samples t-test. This way is great for paired samples
won't work for independent samples t-test. You see with a paired samples t-test this
pairing is very important. This is a person's IQ score before they went to college and this
is that person's IQ score -- same person's IQ score after they went to college but that
pairing has no basis for independent samples t-test. This is one person this is a different
person. So when you run an independent samples t-test you need to have all your IQ scores
or all your dependent variable measurement in one column. So I'll do that, I'll call
that IQ Scores and I'll take these and put them all in the same column. The way I differentiate
which one which one of these IQ scores came from college graduates and which ones came
from non college graduates is I create another column where I code for group membership and
the way I do that can be simple. I'll say the people who did not go to college they're
ones and people who did go to college they're twos and if I'm so inclined I can even go
to Variable View and create Data Labels -- or Variable Labels where one means no college
and two means college. So there we go, I have it nicely labeled. And we did a really similar
to this in the frequency counts video. So now that we have all our scores in one column
I don't need these things anymore -- they're useless. We have all of our scores in one
variable, we have our coding scheme as another variable, now we can do the independent samples
t-test. Let's go to Analyze, Compare Means, Independent Samples. When the window pops
up move over your dependent variable which is your scores under Test Variable and move
whatever variable you have that contains the coding scheme for group membership into the
thing that says Group Variable. Now you'll notice I can't run the analysis yet. The OK
button is still faded it's because under Grouping Variable I have to have little question marks.
SPSS doesn't know what our coding scheme is, we have to tell it. Click Define Groups and
tell it Group 1 we labeled with a one Group Two we labeled with a two. Hit Continue. Now
we can run our analysis. Hit OK and our output window pops up. Just like with before we get
one table with a descriptive stats. It tells the sample size, the mean, the standard deviation,
the standard error for each one of our groups of people no college education versus college
graduates. And below that we see the table containing the inferential statistics and
you can mostly focus on this middle part of the table. We have the t-score, degrees of
freedom for our two groups, and the p value that corresponds to that t score at those
degrees of freedom which we use to determine if there's a significant difference or not.
If p is less than .05 we can say yes there is a significant difference. What is that
difference? Just look at the means it looks like the college graduates on average have
higher IQ scores than the non college graduates. Over to the right we see the confidence intervals
which tell us the range of differences that likely contain the real mean difference in
IQ scores for our populations. Everybody in the world who is a college graduate versus
everybody in the world who is not a college graduate instead of just the samples of twenty
people we used. So, that's about it. The last thing I want to mention is that there are
two different rows with different results for equal variances assumed and equal variances
not assumed. If you're new to stats and have no idea what equal variances means just don't
worry about it. For the most part assuming equal variances it okay so just stick with
this first row. So that's about it. One last thing I wanted to mention is in the spreadsheet
these scores don't need to be in any particular order. You don't even need to group the college
graduates separately from the non college graduates and I can prove that. If you go
up to Data and do Sort Cases and sort by IQ score ascending order it will kind of jumble
up those scores. Now I have some no college graduates and a few college graduates interspersed
with college graduates. If I go back and rerun the analysis I get exactly the same thing.
So the order I pop these things in really doesn't matter. So that's about it for t test
in SPSS. You just follow the menu options which is pretty easy. The only trick is knowing
what kind of experimental design you used and setting up your data properly. Particularly
for independent samples t test make sure all your dependent variable measurements are in
one column and you have another column that contains a coding scheme for group membership.