Statsmodels is a powerful library in Python for performing statistical analysis and modeling. One key aspect of statistical analysis is understanding the effect size of a particular variable or the impact of a treatment on the outcome. In this blog post, we will explore how to calculate the effect size using Statsmodels in Python.

What is Effect Size?

Effect size measures the magnitude of the difference or relationship between variables in a statistical model. It allows us to determine the practical significance of our findings, independent of sample size. Effect size helps to understand the real-world implications and is particularly useful when comparing different treatments or interventions.

Cohen’s d Effect Size

Cohen’s d is a commonly used effect size measure, especially in comparing the means between two groups. It is calculated as the difference between the means divided by the pooled standard deviation. A larger Cohen’s d indicates a larger effect size.

To calculate Cohen’s d effect size using Statsmodels in Python, we can follow these steps:

Step 1: Import Required Libraries

import pandas as pd
import numpy as np
import statsmodels.api as sm

Step 2: Prepare the Data

Let’s assume we have a dataframe called df with two groups, where we want to calculate the effect size between them. Make sure to clean and preprocess the data as per your requirements.

Step 3: Perform t-tests

We need to perform a t-test between the two groups to calculate the effect size using Cohen’s d. Statsmodels provides the ttest_ind function for independent samples t-test.

# Perform t-test
result = sm.stats.ttest_ind(group1, group2)

Step 4: Calculate Cohen’s d

After performing the t-test, we can extract the necessary values to calculate Cohen’s d effect size.

# Extract values
mean_diff = np.mean(group1) - np.mean(group2)
pooled_std = np.sqrt((np.std(group1, ddof=1) ** 2 + np.std(group2, ddof=1) ** 2) / 2)

# Calculate Cohen's d
cohens_d = mean_diff / pooled_std

Conclusion

Calculating the effect size is crucial in statistical analysis as it provides a standardized measure of the impact of variables or treatments. Cohen’s d is a popular effect size measure, particularly when comparing means between groups. By using Statsmodels in Python, we can easily calculate the Cohen’s d effect size and gain deeper insights into our data.