How to interpret chi-square test result

In these results, the Pearson chi-square statistic is 11.

Chi-Square Test for Independence

Adjusted residuals. It cannot make comparisons between continuous variables or between categorical and continuous variables. In these results, the total for row 1 is 143, the total for row 2 is 155, and the total for row 3 is 110. It is used when categorical data from a sampling are being compared to expected or "true" results. With the Expected Count values shown, we can confirm that all cells have an expected value greater than 5.

For example, in an election survey, voters might be classified by gender male or female and voting preference Democrat, Republican, or Independent. The clustered bar chart option allows a relevant graph to be produced that highlights the group categories and the frequency of counts in these groups. About the Author. There is no relationship between the subjects in each group. Hypotheses The null hypothesis H 0 and alternative hypothesis H 1 of the Chi-Square Test of Independence can be expressed in two different but equivalent ways: Two or more categories groups for each variable.

SPSS Tutorials: Chi-Square Test of Independence

Depending on which text editor you're pasting into, you might have to add the italics to the site name. If you'd like to download the sample dataset to work through the examples, choose one of the files below:. SPSS Statistics Assumptions When you choose to analyse your data using a chi-square test for independence, you need to make sure that the data you want to analyse "passes" two assumptions. Assumption 2: Sciencing Video Vault. References Hobart and William Smith Colleges: You can either: The value of the test statistic is 3.

Chi-Square Test for Association using SPSS Statistics

Click the button to generate your output. Then click Continue. Cell Display window, which controls which output is displayed in each cell of the crosstab.