**Chronbach’s Alpha** is a way to measure the internal consistency of a questionnaire or survey.

Cronbach’s Alpha ranges between 0 and 1, with higher values indicating that the survey or questionnaire is more reliable.

The following example shows how to calculate Cronbach’s Alpha in Python.

**Example: Calculating Cronbach’s Alpha in Python**

Suppose a restaurant manager wants to measure overall satisfaction among customers, so she sends out a survey to 10 customers who can rate the restaurant on a scale of 1 to 3 for various categories.

The following pandas DataFrame shows the results of the survey:

**import pandas as pd
#enter survey responses as a DataFrame
df = pd.DataFrame({'Q1': [1, 2, 2, 3, 2, 2, 3, 3, 2, 3],
'Q2': [1, 1, 1, 2, 3, 3, 2, 3, 3, 3],
'Q3': [1, 1, 2, 1, 2, 3, 3, 3, 2, 3]})
#view DataFrame
df
Q1 Q2 Q3
0 1 1 1
1 2 1 1
2 2 1 2
3 3 2 1
4 2 3 2
5 2 3 3
6 3 2 3
7 3 3 3
8 2 3 2
9 3 3 3**

To calculate Cronbach’s Alpha for the survey responses, we can use the **cronbach_alpha()** function from the **pingouin** library.

First, we’ll install the pingouin library:

**pip install pingouin
**

Next, we’ll use the **cronbach_alpha()** function to calculate Cronbach’s Alpha:

**import pingouin as pg
pg.cronbach_alpha(data=df)
(0.7734375, array([0.336, 0.939]))**

Cronbach’s Alpha turns out to be **0.773**.

The 95% confidence interval for Cronbach’s Alpha is also given: **[.336, .939]**.

**Note:** This confidence interval is extremely wide because our sample size is so small. In practice, it’s recommended to use a sample size of at least 20. We used a sample size of 10 here for simplicity sake.

The default confidence interval is 95%, but we can specify a different confidence level using the **ci** argument:

**import pingouin as pg
#calculate Cronbach's Alpha and corresponding 99% confidence interval
pg.cronbach_alpha(data=df, ci=.99)
(0.7734375, array([0.062, 0.962]))**

The value for Cronbach’s Alpha remains the same, but the confidence interval is much wider since we used a higher confidence level.

The following table describes how different values of Cronbach’s Alpha are usually interpreted:

Cronbach’s Alpha |
Internal consistency |
---|---|

0.9 ≤ α | Excellent |

0.8 ≤ α < 0.9 | Good |

0.7 ≤ α < 0.8 | Acceptable |

0.6 ≤ α < 0.7 | Questionable |

0.5 ≤ α < 0.6 | Poor |

α < 0.5 | Unacceptable |

Since we calculated Cronbach’s Alpha to be **0.773**, we would say that the internal consistency of this survey is “Acceptable.”

**Bonus:** Feel free to use this Cronbach’s Alpha Calculator to find Cronbach’s Alpha for a given dataset.