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:
Democrat | Republican | |
---|---|---|
Female | 8 | 4 |
Male | 4 | 9 |
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.
Hi Zach
From my survey, I was told to do Fishers test on the variable to the responses in a 5 point Likert scale. I don’t have the neat 2×2 for this. For example, years experience has 6 categories and there response has 5 (strongly disagree to strongly agree).
How do you do this where there is a 5×6 table?
Thanks
Hi. I performed Fisher’s exact test in SPSS, on a sample of 141 diabetic patients who have diabetic foot and I wanted to explore if there is an association between smoking levels on the rows side (nonsmoker, smoker, exsmoker) and diabetic foot prognosis on the columns side (No amputation, Amputation). I had the data in a 3 by 2 table, and I got SPSS output table showing the following numbers:
first a value of 1.791 on the left side adjacent to “Fisher’s exact test” sentence and I wonder what does it represent? and how is it calculated.
second: a P value in the same row (0.454).
third: a value named “The standardized statistic is -1.042.” in the notes below the table, and I wonder what does it mean? and how is it calculated?
Interestingly, on the same sample of 141 diabetic patients, when I wanted to explore the association between diabetes mellitus type on the rows side (type 1, type 2) and diabetic foot prognosis on the columns side (No amputation, Amputation), I got SPSS output table showing empty cell adjacent to “Fisher’s exact test” sentence on the left side. and a P value in the same row (0.720) and the term “The standardized statistic is -0.339.” in the notes below the table mean. I wish if I could upload an image showing these results better than words.