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

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

This tutorial explains how to perform a Chi-Square Test of Independence in SPSS.

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

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 followingÂ table shows the results of the survey:

 Â 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 SPSS to determine if gender is associated with political party preference.

Step 1: Enter the data.

First, enter the data in the following format:

Step 2: Use weighted cases.

In order for the test to work correctly, we need to tell SPSS that the variables Party and Gender should be weighted by the variable Count.

Click theÂ DataÂ tab, thenÂ Weight Cases:

In the new window that pops up, drag the variableÂ CountÂ into the box labelled Test Variable List. Then clickÂ OK.

Step 3: Perform the Chi-Square Goodness of Fit Test.

Click the AnalyzeÂ tab, thenÂ Descriptive Statistics, thenÂ Crosstabs:

In the new window that pops up, drag the variableÂ GenderÂ into the box labelledÂ Rows and the variableÂ PartyÂ into the box labelled Columns. Then clickÂ StatisticsÂ and make sure the box next toÂ Chi-squareÂ is checked. ClickÂ Continue. Then clickÂ OK.

Step 4: Interpret the results.

Once you clickÂ OK, the results of the Chi-Square Test of Independence will appear:

The first table displays the number of missing cases in the dataset. We can see that there are 0 missing cases in this example.

The second table displays a crosstab of the total number of individuals by gender and political party preference.

The third table shows the results of the Chi-Square Test of Independence. The test statistic isÂ .864Â and the corresponding two-sided p-value isÂ .649.

The null hypothesis for the Chi-Square Test of Independence is that the two variables are independent. In this case, our null hypothesis is that gender and political party preference are independent.

Since the p-value (.649) 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.