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The Chi-Square test is a statistical method used to determine the association between two categorical variables. In SPSS, this test is used to analyze data and calculate the probability of obtaining a particular set of results, based on the assumption of independence between the variables. The results of the Chi-Square test in SPSS include a Chi-Square value, degrees of freedom, and a p-value. These values are used to determine whether the observed data supports or rejects the null hypothesis, which states that there is no relationship between the variables. A significant p-value indicates that there is a significant association between the variables, while a non-significant p-value suggests that there is no significant relationship. The Chi-Square test in SPSS is a valuable tool for researchers and analysts in various fields, as it provides a statistical approach to understanding the relationship between categorical variables.
Interpret Chi-Square Test Results in SPSS
A is used to determine whether or not there is a significant association between two categorical variables.
The following example shows how to interpret the results of a Chi-Square test of Independence in SPSS.
Example: How to Interpret Chi-Square Test of Independence Results in SPSS
Suppose we want to know whether or not gender is associated with political party preference.
We take a simple random sample of 50 voters and survey them on their political party preference.
The following screenshot shows how to enter this data into SPSS:
To perform a Chi-Square test of Independence, click the Analyze tab, then click Descriptive Statistics, then click Crosstabs:
In the new window that appears, drag Gender into the Rows panel, then drag Party into the Columns panel:
Next, click the Cells button. Then check the boxes next to Observed and Expected:
Then click Continue.
Next, click the Statistics button. Then check the box next to Chi-square:
Then click Continue.
Then click OK.
Case Processing Summary
This table displays the number of valid observations and missing observations in the dataset.
We can see that there are 50 valid observations and 0 missing observations.
Crosstabulation
This table displays a crosstab of the total number of individuals by gender and political party preference, including the observed count for each group and the expected count.
Refer to for an explanation of how to calculate expected counts in a Chi-Square test.
Chi-Square Tests
This table shows the results of the Chi-Square Test of Independence.
The Chi-Square test statistic is 1.118 and the corresponding two-sided p-value is .572.
Recall the hypotheses used for a Chi-Square Test of Independence:
- H0: The two variables are independent.
- HA: The two variables are not independent, i.e. they are associated.
In this particular example, our null hypothesis is that gender and political party preference are independent.
Since the p-value (.572) of the test is not less than 0.05, we fail to reject the null hypothesis.
Thus, we do not have sufficient evidence to say that there is an association between gender and political party preference.
Additional Resources
The following tutorials explain how to perform other common tasks in SPSS: