A **three-way ANOVA** is used to determine whether or not there is a statistically significant difference between the means of three or more independent groups that have been split on three factors.

The following example shows how to perform a three-way ANOVA in Python.

**Example: Three-Way ANOVA in Python**

Suppose a researcher wants to determine if two training programs lead to different mean improvements in jumping height among college basketball players.

The researcher suspects that gender and division (Division I or II) may also affect jumping height so he collects data for these factors as well.

His goal is to perform a three-way ANOVA to determine how training program, gender, and division affect jumping height.

Use the following steps to perform this three-way ANOVA in Python:

**Step 1: Create the Data**

First, let’s create a pandas DataFrame to hold the data:

**import numpy as np
import pandas as pd
#create DataFrame
df = pd.DataFrame({'program': np.repeat([1, 2], 20),
'gender': np.tile(np.repeat(['M', 'F'], 10), 2),
'division': np.tile(np.repeat([1, 2], 5), 4),
'height': [7, 7, 8, 8, 7, 6, 6, 5, 6, 5,
5, 5, 4, 5, 4, 3, 3, 4, 3, 3,
6, 6, 5, 4, 5, 4, 5, 4, 4, 3,
2, 2, 1, 4, 4, 2, 1, 1, 2, 1]})
#view first ten rows of DataFrame
df[:10]
program gender division height
0 1 M 1 7
1 1 M 1 7
2 1 M 1 8
3 1 M 1 8
4 1 M 1 7
5 1 M 2 6
6 1 M 2 6
7 1 M 2 5
8 1 M 2 6
9 1 M 2 5
**

**Step 2: Perform the Three-Way ANOVA**

Next, we can use the **anova_lm()** function from the **statsmodels** library to perform the three-way ANOVA:

**import statsmodels.api as sm
from statsmodels.formula.api import ols
#perform three-way ANOVA
model = ols("""height ~ C(program) + C(gender) + C(division) +
C(program):C(gender) + C(program):C(division) + C(gender):C(division) +
C(program):C(gender):C(division)""", data=df).fit()
sm.stats.anova_lm(model, typ=2)
sum_sq df F PR(>F)
C(program) 3.610000e+01 1.0 6.563636e+01 2.983934e-09
C(gender) 6.760000e+01 1.0 1.229091e+02 1.714432e-12
C(division) 1.960000e+01 1.0 3.563636e+01 1.185218e-06
C(program):C(gender) 2.621672e-30 1.0 4.766677e-30 1.000000e+00
C(program):C(division) 4.000000e-01 1.0 7.272727e-01 4.001069e-01
C(gender):C(division) 1.000000e-01 1.0 1.818182e-01 6.726702e-01
C(program):C(gender):C(division) 1.000000e-01 1.0 1.818182e-01 6.726702e-01
Residual 1.760000e+01 32.0 NaN NaN**

**Step 3: Interpret the Results**

The **Pr(>F)** column shows the p-value for each individual factor and the interactions between the factors.

From the output we can see that none of the interactions between the three factors were statistically significant.

We can also see that each of the three factors (program, gender, and division) were statistically significant with the following p-values:

- P-value of
**program**: 0.00000000298 - P-value of
**gender**: 0.00000000000171 - P-value of
**division**: 0.00000185

In conclusion, we would state that training program, gender, and division are all significant predictors of the jumping height increase among players.

We would also state that there are no significant interaction effects between these three factors.

**Additional Resources**

The following tutorials explain how to fit other ANOVA models in Python:

How to Perform a One-Way ANOVA in Python

How to Perform a Two-Way ANOVA in Python

How to Perform a Repeated Measures ANOVA in Python