the one-way ANOVA. To run the one way
ANOVA, we want to go to Analyze and then
Compare Means and then select the last
option down there, One-Way ANOVA. And the
One-Way ANOVA dialog box opens and what we
want to do is put our dependent variable,
the exam scores, into the Dependent List,
and we want to put volume, our
independent variable, into the Factor box.
And recall that I said that the
independent variable in ANOVA can
also be called a factor, so that's where
this term comes from.
OK next we want to go to Options, and
then select Descriptive, and then click
Continue. And then one more thing we want
to do is click on Post Hoc. Under
Post Hoc, let's select Tukey. And I'll
talk about post hoc tests, such as what
they are, when we use them, and so forth,
when we discuss our results in just a
little while.
One other thing worth mentioning here,
the default level for the significance
level is .05, and since we're using
alpha .05, that's what we want to leave
this at. If we were going to use alpha of
.01, then we would want to change this
accordingly. But since we're using .05,
we'll leave it as it is. Next click
Continue. And then everything looks good
here, so let's click OK. And as we do that,
our output or viewer window opens. And we
have a few tables here. First of all, we
have the Descriptives table, and we'll
just take a quick glance at that. Notice
our three groups here and once again
remember the labels are output here,
because I created value labels. There are
10 people in each group,
this looks good. And notice here no music
and low-volume both have means and the
84s, no music 84.9, low-volume 84.2. Whereas
the high volume group had a mean at 77.5.
So this group, at least in the sample, is
definitely lower than these two groups,
with the no music group having the best
exam performance overall. Now this
is just describing the results in the
sample; we don't know whether these
results are significant yet or not. We'll
take a look at that next. Next, we go to
our ANOVA table, and this is where we get
the results of whether or not the groups
are significantly different from one
another. That is, does the level of volume
in the background played while studying
have an impact on exam performance? And
with the ANOVA we get an F value, instead
of a t, which we got with the t test, but
here we get an F, and most importantly as
always we have our significance value,
or our p-value, and this is what we want
to assess to see whether or not the test
is statistically significant. And as a
reminder our decision rule for assessing
this test is as follows using alpha .05,
if p is less than or equal to
.05, the test is significant, and this
indicates that the test scores differ
significantly somewhere between the
groups. If p is greater than
.05, on the other hand, the test is
not significant, indicating that the test
scores do not differ significantly
between the groups. So we want to look at
our p-value here, compare it to these two
conditions, and see which of the two it
falls into. So with a p of .019, it
falls into the first condition, since
it's less than equal to .05. Therefore, we
will conclude that the test is
significant, indicating that the test
scores differ significantly somewhere
between the groups. And we can write our
results as follows: the level of volume
of music played while studying had a
significant impact on exam performance.
And what this is saying is the
background volume of music played did
influence the exam performance. In other
words, exam scores differed for
different volume groups. And then we have
our APA format
and notice now we have an F instead of a
t since we're using an F-test, which
you can see right here. And next we put
our degrees of freedom and these degrees
of freedom come from this column right
here in our table, where we have between
groups 2, and within groups 27, and you
see here 2, 27. So if you're
using APA format, you want to use these
two values in your written results. Next
we have an F of 4.62 which, when
rounding to two decimal places, you can
see right here. And finally our p-value
is .019, which is reported
here in the p value column shown as Sig.
And, as always, we could state that p is
less than .05, instead of p is
equal to .019, although
reporting the exact p-value is more
informative, and therefore is recommended.
In our next video, we'll take a look at
how to interpret the post-hoc tests for
the ANOVA. Thanks for watching.