Cross-tab (Chi-square Test) Tutorial

1. When do we use Cross-tab (Chi-square Test)?

Chi-square Test is a method that two variables are independent of each other and are used to determine the correlation between two variables. The large Chi-square value implies that it is useful when classifying data and acts as an important variable. For example, test whether the proportion of patients with and without vomiting is the same between the treated and the untreated groups, and test for correlation between the treatment drug and the symptoms of vomiting.

2. Find the “Statistics” section under the black banner at the top and click “Cross-tab (Chi-square Test)”.

3. Choose either your own file or sample to run the Chi-square Test. Let’s use the San Francisco Airport Satisfaction Data in 2018 (SFO 2018). Here, we are going to test whether there is a significant association between X (Dependent Variable) and Y (Independent Variable).

4. Click the “Select” button after you finish deciding.

5. Cross-tab is a frequency table of two or three variables. You have to decide which variable will be located in the row. Click the Row Variable. Be aware that it should be categorical variable.

6. Then, choose the Column Variable. Here, you have to choose the variable that will be located in the column. Be aware that this variable is categorical variable.

7. Click the “Run” button.

8. Check the result. Since the p-value is less than 0.01 (), we can reject the null hypothesis (H0). So there is significant association between age and language. If p-value is greater than , we can not reject the null hypothesis (there is no difference) and if p-value is less than , we can reject the null hypothesis (there is a significant difference).