# How to Perform a Chi-Square Test of Independence in SAS

A Chi-Square Test of Independence is used to determine whether or not there is a significant association between two categorical variables.

The following example shows how to perform a Chi-Square Test of Independence in SAS.

### Example: Chi-Square Test of Independence in SAS

Suppose we want to know whether or not gender is associated with political party preference. We take a simple random sample of 500 voters and survey them on their political party preference.

The following table shows the results of the survey:

 Republican Democrat Independent Total Male 120 90 40 250 Female 110 95 45 250 Total 230 185 85 500

Use the following steps to perform a Chi-Square Test of Independence in SAS to determine if gender is associated with political party preference.

Step 1: Create the data.

First, we will create a dataset in SAS to hold the survey responses:

/*create dataset*/
data my_data;
input Gender \$ Party \$ Count;
datalines;
Male Rep 120
Male Dem 90
Male Ind 40
Female Rep 110
Female Dem 95
Female Ind 45
;
run;

/*print dataset*/
proc print data=my_data;

Step 2: Perform the Chi-Square Test of Independence.

Next, we can use the following code to perform the Chi-Square Test of Independence:

/*perform Chi-Square Test of Independence*/
proc freq data=my_data;
tables Gender*Party / chisq;
weight Count;
run;

There are two values of interest in the output:

• Chi-Square Test Statistic: 0.8640
• Corresponding p-value: 0.6492

Recall that the Chi-Square Test of Independence uses the following null and alternative hypotheses:

• H0: The two variables are independent.
• HA: The two variables are not independent.

Since the p-value (0.6492) of the test is not less than 0.05, we fail to reject the null hypothesis.

This means we do not have sufficient evidence to say that there is an association between gender and political party preference.

In other words, gender and political party preference are independent.