Assignment: Testing for Bivariate Categorical Analysis
You have had plenty of practice with data analysis in the Discussions and hopefully you have received helpful and encouraging feedback from your colleagues. Now, for the last time in the course, it is time once again to put all of that good practice to use and answer a social research question using categorical statistical tools. As you begin the Assignment, be sure and pay close attention to the assumptions of the test. Specifically, make sure the variables are categorical level variables.
For this Assignment, you will consider three different scenarios. Each of these scenarios include a research question. You will examine each scenario, choose a categorical data analysis and run a sample test.
To prepare for this Assignment:
Review Chapters 10 and 11 of the Frankfort-Nachmias & Leon-Guerrero course text and the media program found in this weeks Learning Resources related to bivariate categorical tests.
Using the SPSS software, open the Afrobarometer dataset found in this weeks Learning Resources.
Next, review the Chi Square Scenarios found in this weeks Learning Resources and consider each research scenario for this Assignment.
Based on the dataset you chose and for each research scenario provided, using the SPSS software, choose a categorical data analysis and run a sample test.
Once you perform your categorical data analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.
Bivariate Categorical Tests
Bivariate Categorical Tests
Program Transcript
[MUSIC PLAYING]
MATT JONES: Up to this point, we’ve been focusing on statistical tests that
require metric or variables-- that is, variables measured at the interval or ratio
level. But there are a lot of categorical variables that are of use to the social
scientist. We’re going to cover the chi-square test for independence and
associated measures of effect of Cramer’s V in SPSS.
Let’s go to SPSS. We can test for the relationship between two variables by
using the chi-square test for independence. Let’s go ahead and test the
relationship between gender and views on marijuana legalization.
To do this, we go to Analyze, Descriptive Statistics, and Crosstabs. Here, you will
see a place to put a variable in a row and a column. I’m going to scroll down and
find my SHOULD MARIJUANA BE MADE LEGAL variable and enter that into my
row, and I will scroll down to find the respondent’s gender and place that into my
column.
I’m going to go ahead and hit OK to show you the output that we receive. And
here, you will see some output that are basic Descriptive Statistics. These are
counts of the number of males and the number of females who felt that marijuana
should be either LEGAL or NOT LEGAL.
However, this does not statistically test for a relationship between these
variables. We can request the chi-square statistic by, again, going back into our
Crosstabs box. So I perform the same procedure of going to Descriptive
Statistics, Crosstabs, and all of my information is still there, so I can select
Statistics. You’ll see that the Chi-square statistic comes first, but I have to go
ahead and activate that.
I’m also going to go into the section Nominal to ask for Phi and Cramer’s V. This
will tell me something about the strength of the relationship between the two
variables. As you know from your reading, the chi-square tells us whether there
is a relationship, but it doesn’t tell us anything about the strength of that
relationship. Find Cramer’s V help us with that follow-up should we have a
significant relationship with a chi-square. Continue.
OK. So I’m going to hit Cells. Just for ease of interpretation, I’m going to request
Percentages for Columns.
I’ll hit Continue and OK. Here, you see, I receive some Case Processing
Summary. This tells me that there are 920 valid cases in this analysis. 580 cases
are missing. So out of the 1,500 cases or respondents of the survey, we have
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Bivariate Categorical Tests
quite a few of them that either didn’t answer, refused to answer, or just left that
blank.
The next piece of output is the Crosstabulation table. You can see this looks
similar to the Crosstabs I asked for in Descriptive Statistics with just the raw
counts, but now, I also requested for the percent within respondent’s sex. This
tells me 55% of the males believe that marijuana should be made LEGAL and
44.6% of the males believe that marijuana should be NOT LEGAL for a
cumulative percentage of 100%. I can interpret the female column as the same
way. 41% of females believe that marijuana should be LEGAL while 59% believe
that it should be NOT LEGAL. If there was no relationship between these two
variables, we would see approximately equal percentages.
To statistically test for this, we can look to our chi-square statistic. Here, we see a
critical value of 18.993 with an associated p-value of 0.001. This test is significant
at the 0.01 level and certainly well below the common 0.05 threshold. Therefore,
we can reject the null hypothesis that there is no relationship between the two
variables assuming that there is some sort of relationship between gender and
position on marijuana legalization.
But once again, we don’t know the strength of that relationship. We can scroll
down to our Cramer’s V correlation, which tells us about the strength of this
relationship. A value of 0 indicates no relationship whatsoever, and a value of 1.0
indicates a very strong, perfect relationship.
We can see here, we have a value of 0.144. So while there is a relationship, it’s
important to do the follow-up test to determine the strength of that relationship. In
this case, the relationship between these two variables, which is statistically
significant at the 0.01 level, is rather weak.
[MUSIC FADING]
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