In this video we'll take a look at how to run the one-way anova in SPSS. ANOVA
stands for analysis of variance and it's similar to the independent samples
t-test, in that is used to compare independent or unrelated groups. But
where the independent samples t compares only two groups, the ANOVA can be used to
compare 2, 3, 4, 5, 6, or more groups - as many as you need to test. And
on your screen you see an example where we have two variables. We have volume and
exam scores. And we used a similar example when we conducted the
independent samples t-test. In this example what I've done is I've added a
third group to help underscore the relationship between these two tests. And
let me show you what I mean here. First of all we have our exam scores variable,
as I said, where we have the scores on the exam.
This is also known as our dependent variable. And then we have our other
variable, volume, where as we take a look at this variable you can see there's
three groups, we have 1s, 2s, and 3s.
Now if you recall the independent samples t-test had two groups as I had said
before and we had 1s and 2s in our variable, our grouping variable. This is
also known as our independent variable, the variable that separates or divides
people into different groups. This would be what would be required for the t-test,
1s and 2s. But as we have a third group here notice how we added 3s
for the third group. And if you had a fourth group you would add 4s, and so
on,
5s for 5th group, etc. Now in this study what we had was we had a no music
group and then as was the case with the independent t we also had a high volume
group, but we added another group, and that was a low volume group. So as a
reminder as to the background of this study, we had people who listen to
either no volume, they had low volume now, that's our new condition, or
they had high volume playing while they were studying.
And then the next day they took the exam. And these once again are their exam
scores. So we'll use the ANOVA to see if there's a significant difference between
these groups, our 1s, 2s, and 3s or our no volume, low volume, and high
volume groups. Now the one-way ANOVA, the 'one-way' stands for one independent
variable, where in this example, as I said before, the independent variable is
volume. And our independent variable consists of three groups: no music, low
volume, and high volume. These groups can also be called levels that's l-e-v-e-l-s.
So we have three levels to our independent variable. When we start
doing ANOVA's we do get this new terminology, like levels which I already
mentioned, and instead of an independent variable, which this variable
is, it's an independent variable, we also will use the name, factor. And you'll see
that when we run the analysis in a minute. There's more than one-way
ANOVAs there's also a two-way ANOVA, for example. And if one-way ANOVA means one
independent variable, then you can probably guess what a two-way ANOVA
means, that would mean two independent variables. Such as we can have volume and
then we could have gender, males and females, and then exam scores. But we're
just going to focus on the one-way ANOVA here. so the one-way ANOVA will answer
the question, Does the volume of background noise while studying for an
exam have an impact on exam scores? So we want to know, Does volume make a
difference?
Do people do better in one or more conditions as compared to another? And
we'll run this test using alpha .05 and it's worth noting as well that the ANOVA,
unlike the t-test, which we could do a two-tailed test or a one-tailed test, the
ANOVA really is one tailed by design for our purposes. So we'll just be using a
one-tailed test, but that's the way it's set up to be naturally. OK one last
thing too, in this design. Of these 30 people in our study,
assume that they were randomly assigned to the three groups. So
if I was in the study, for example, whether I received the high volume, low
volume, or no volume condition was totally up to chance. That is, we random
assignment of people to the respective groups. All right let's go and take a
look at how to run the one-way ANOVA.