In this video we'll examine how to run the chi-square test of independence in
SPSS. The chi-square test of Independence measures whether there is a relationship
between two categorical variables. And in our example we're going to look at
whether there is a relationship between gender and aggressive driving behavior.
And what happened in this hypothetical study is we have 200 people, where we
have 100 males and 100 females, and then we asked each of these persons through a
series of questions on a survey, whether or not they engaged in
aggressive driving behavior.
So, for example, do they run stoplights, did they tailgate anyone, this sort of
thing.
And based on the survey responses, they were coded either yes, they engaged in
aggressive driving behavior, or no, they did not.
So notice here we have our two categorical variables, gender, that's
categorical, as it is either male or female, and then aggressive driving
behavior is also categorical in this example, as it's either characterized as
a yes or no.
And once again the chi-square test of Independence measures whether there is a
relationship between two categorical variables. So the chi-square test of
Independence would be appropriate here, since we have our two categorical
variables, gender and aggressive driving behavior.
So let's go ahead and take a look at our data here in the Data View window. Notice
there's three variables, we have gender, where we have 1s and 2s, and then we
have aggressive driving, where we also have 1s and 2s, and then we have our
frequency variable, and you saw this in the other chi-square we talked about, the
chi-square goodness of fit,
where the frequency variable indicates the number of people in a given category.
So this 25 means there's 25 people who have a 1 on gender & a 1 on aggressive
driving, there are 75 people who have a 1 on gender and a 2 on aggressive driving, and
so forth. And we went ahead and we already coded these values into SPSS and
I'll show you their values here by clicking on the Variable View tab. And if
we go to gender, we can see that 1 is male and 2 is female, and then for
aggressive driving,
we have 1 is coded as yes, they engaged in aggressive driving, and 2 is no, they
did not.
So if we go back to our Data View window, and I can click on the Value Labels
button up here and you'll see these codes. So we had 25 people who were male and
did engage in aggressive driving behavior, whereas we had 75 people who
are male who did not engage in aggressive driving behavior.
For females, 10 engaged in aggressive driving behavior, and 90 females did not
engage in aggressive driving behavior.
It's a good idea to take a few moments to make sure that it's clear how to set
up the data file for the chi-square test of independence. Notice that we have our
1s and 2s for gender and then we have our 1s and 2s alternated for
aggressive driving behavior, and then we have our frequencies. Notice that there's
no repeat here of a combination of 1s and 2s.
Here we have 1,1 and then we have 1,2 and then 2,1 and then 2,2. And then we have the
frequencies for each of these different combinations of 1s and 2s.
So the 1s and 2s never repeat the same sequence again.
So 1,1 1,2 2,1 2,2, and looking at their actual labels that gives us male
aggressive, male not aggressive, female aggressive, female not aggressive.
So we want to have all different combinations expressed here in our Data
View window. And then the only other thing you need to make sure is that you get
the correct frequencies for the category. So we had 25 males who were
aggressive, and 75 males who are not aggressive, for example.
So in setting up the chi-square test of independence,
we have all of our different categories expressed here, and then you just want to
make sure you get the correct frequencies in each category.
Next we'll go ahead and run our chi-square test of independence and
let's use an alpha of .05 here, and the chi square test is naturally a
one-tailed test.
So to run the chi-square test, because we have our frequency variable here,
first we need to weight the cases