Fisher’s Exact Test is used to determine whether or not there is a significant association between two categorical variables.
It is typically used as an alternative to the Chi-Square Test of Independence when one or more of the cell counts in a 2×2 table is less than 5.
This tutorial explains how to perform Fisher’s Exact Test in SPSS.
Example: Fisher’s Exact Test in SPSS
Suppose we want to know whether or not gender is associated with political party preference at a particular college. To explore this, we randomly poll 25 students on campus. The number of students who are Democrats or Republicans, based on gender, is shown in the table below:
To determine if there is a statistically significant association between gender and political party preference, we can use the following steps to perform Fisher’s Exact Test in SPSS:
Step 1: Enter the data.
First, enter the data as shown below:
Each row shows an individual’s ID, their political party preference, and their gender.
Step 2: Perform Fisher’s Exact Test.
Click the Analyze tab, then Descriptive Statistics, then Crosstabs:
Drag the variable Gender into the box labelled Rows and the variable Party into the box labelled Columns. Then click the button labelled Statistics and make sure that the box next to Chi-square is checked. Then click Continue.
Next, click the button labelled Exact and make sure the box next to Exact is checked. Then click Continue.
Lastly, click OK to perform Fisher’s Exact Test.
Step 3: Interpret the results.
Once you click OK, the results of Fisher’s Exact Test will be displayed:
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 Fisher’s Exact Test. We can see the following two p-values for the test:
- Two-sided p-value: .115
- One-sided p-value: .081
The null hypothesis for Fisher’s Exact Test is that the two variables are independent. In this case, our null hypothesis is that gender and political party preference are independent, which is a two-sided test so we would use the two-sided p-value of 0.115.
Since this p-value is not less than 0.05, we do not reject the null hypothesis. Thus, we don’t have sufficient evidence to say that there is a significant association between gender and political party preference.