and if you saw those previous videos on the chi-square goodness of fit, you'll
recall that we needed to weight the cases, because SPSS will just interpret this 25
as a value, like on a quiz or test or what have you, rather than indicating 25
people. But we want SPSS to interpret these as frequencies, that is 25 people, 75
people, and so on. So to do that we need to go to Data on the menu bar,
so go ahead and click on that, and then go down to our last option,
Weight Cases. Click on Weight Cases and then here we want to weight the Frequency
variable, so click Frequency, and then select the radio button here Weight Cases
by. So click on that and then go ahead and click on the arrow. The frequency
variable is now moved over to the Frequency Variable box, so go ahead and
click OK. And notice down here you see Weight On at the bottom of the window. That
indicates that the frequencies have in fact been weighted. So that looks good. So
now we can run the chi square test of Independence. To do that select Analyze,
and then Descriptive Statistics, and then we want to go ahead and select Crosstabs. And
here we're going to move gender into our first box here, the Row, and aggressive
driving into the Columns box. And it really isn't a big deal which variable
is in which box,
the only thing you want to make sure is that we keep the frequency variable over
in the left. We already took care of the frequency variable when we weighted the
cases a few moments ago.
Next, we'll click on Statistics and then here it is very important to note that
notice the Chi-square here, this box is unchecked by default. So if we don't
check this box, we will not get a chi-square statistic reported. So it's
very important to check this box. And then we'll also check this box, Phi and
Cramer's V, this is our measure of effect size for the chi-square test of
independence. Go ahead and click Continue. And then go to Cells. And under Counts,
let's select Expected.
And then under percentages we'll select Row. Click Continue. And that looks good,
everything's ready to go, so click OK. And here we get our output in the viewer
window. Our first table here we'll just briefly cover. This tells us that we had
200 people total, and that there were zero cases or people missing.
So, in other words, all of the values were processed, which is great, that's exactly
what we want. Our next table we'll pass over for a few moments and we'll come back
to it shortly.
Let's go ahead and move down to our next table, the Chi-Square Tests table. This is
the table we're going to look at to assess whether our results are
statistically significant or not. And what we want to do here is look at the
results for the Pearson chi-square. We're going to be using these values in
this first row. We won't be concerned with these other values, these other p values,
all we want is the first row of values. And notice it says Pearson chi-square. If
you recall at the beginning of this presentation, we had said that the
chi-square measures whether there is a relationship between two categorical
variables.
Well if you saw the correlation videos, you might recall that we were using the
Pearson correlation, and it measures whether there was a linear relationship
between two variables. So Pearson actually created the correlation, and he
also developed the chi-square, and both of these procedures assess whether or
not there is a relationship between two variables. Whereas chi-square deals with
categorical variables, Pearson's correlation deals with variables that
are quantitative in nature. That is, they have a number of different values like
height and weight they aren't categorical, where a person falls into one of two, or
a few categories.
Alright, so as always, to assess this test we're going to look at the p-value, which
is reported here under Asymptotic significance. Notice our p-value is .005.
With an alpha of .05, we'll use our decision rule as always. And we want to ask ourselves,
which of these two conditions does the result fall into.