Chi-square test for independence

This test is used to explore the relationship between two categorical variables. Each of these variables can have two or more categories. It is based on a crosstabulation table, with cases classified according to the categories in each variable.

1. Click on Analyze\Descriptive Statistics\Crosstabs
2. Move one of your categorical variables into the box marked Row(s).
3. Move the other categorical variable into the box marked Column(s).
4. Click on the Statistics button and tick Chi-square and Phi and Cramer’s V. Click on Continue.
5. Click on the Cells button: in the Counts box, make sure there is a tick for Observed; in the Percentage section, click on the Row, Column and Total boxes.
6. Click on Continue and OK.

Cross tabulation table

This table shows you the percentage of each category that falls under each of the categories for the second variable. It also displays the total percentages for each category.

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Significance of the test

Check the significance of Pearson Chi-Square within the Chi-Square tests table to assess whether the relationship between the two variables is statistically significant. In this example, the relationship is not significant.

Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation. SPSS Inc. was acquired by IBM in October, 2009.

Effect size

The effect size, or magnitude of the association between the two variables, is given in the Symmetric Measures table.

Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation. SPSS Inc. was acquired by IBM in October, 2009.

The most commonly used statistic is the Phi coefficient, which ranges from 0 to 1. Higher values indicate a stronger correlation between the two variables.