When should I use Glass's delta instead of Cohen's d?

Glass's Δ = (M₁ – M₂) / SD_control uses only the control group standard deviation. It is preferred when the treatment may affect the within-group variance (common in education intervention studies where treatment groups become more homogeneous) or when control SD better estimates the natural population variability.

How do I interpret Cohen's d in practical terms?

For benchmarking: d = 0.2 ≈ the difference between ages 15 and 16 on a cognitive test; d = 0.5 ≈ the typical effect of a new teaching intervention; d = 0.8 ≈ the difference between gifted and average students. Sawilowsky (2009) extended Cohen's scale: d = 1.0 large, 1.2 very large, 2.0 huge.

Cohen's d Effect Size Calculator for Research

Formula Definitions

The Cohen's d Effect Size Calculator for Research works by applying a well-defined formula to the values you enter. Understanding the formula behind the calculation helps you interpret the result and check that your inputs are correct. Below we break down the key components that drive the Cohen's d.

The core formula used by this calculator is:

Result = f(inputs, settings)

Each variable in the formula has a specific meaning:

inputs — Values entered into the Cohen's d.
settings — Options, units, or parameters that adjust how the calculation is performed.
Result — Computed output for the chosen inputs and settings.

How to Use This Calculator

Enter the required values. Enter the required values into the Cohen's d Effect Size Calculator for Research input fields.
Adjust settings. Adjust any settings, select applicable options, or choose units as needed.
Calculate the result. Press Calculate to compute the result using the underlying formula.
Interpret the output. Review and interpret your result using the provided context, reference ranges, or explanatory notes.

Glossary and Definitions

Input: A value entered into the calculator to be processed.
Output: The computed result returned by the calculator for the given inputs.
Setting: An option that adjusts how the calculation is performed, such as unit selection or mode.
Cohen's d: A key concept referenced by the Cohen's d: Calculate Cohen's d = (M₁ – M₂) / S_pooled for two-group comparisons. Interpret using Cohen's benchmarks: small 0.2, medium 0.5, large 0.8. Also supports Hedges' g correction for small samples and Glass's Δ using control group SD only.
Effect size: A key concept referenced by the Cohen's d: Calculate Cohen's d = (M₁ – M₂) / S_pooled for two-group comparisons. Interpret using Cohen's benchmarks: small 0.2, medium 0.5, large 0.8. Also supports Hedges' g correction for small samples and Glass's Δ using control group SD only.
Two group comparison: A key concept referenced by the Cohen's d: Calculate Cohen's d = (M₁ – M₂) / S_pooled for two-group comparisons. Interpret using Cohen's benchmarks: small 0.2, medium 0.5, large 0.8. Also supports Hedges' g correction for small samples and Glass's Δ using control group SD only.
Standardised difference: A key concept referenced by the Cohen's d: Calculate Cohen's d = (M₁ – M₂) / S_pooled for two-group comparisons. Interpret using Cohen's benchmarks: small 0.2, medium 0.5, large 0.8. Also supports Hedges' g correction for small samples and Glass's Δ using control group SD only.
Practical significance: A key concept referenced by the Cohen's d: Calculate Cohen's d = (M₁ – M₂) / S_pooled for two-group comparisons. Interpret using Cohen's benchmarks: small 0.2, medium 0.5, large 0.8. Also supports Hedges' g correction for small samples and Glass's Δ using control group SD only.
Hedges g: A key concept referenced by the Cohen's d: Calculate Cohen's d = (M₁ – M₂) / S_pooled for two-group comparisons. Interpret using Cohen's benchmarks: small 0.2, medium 0.5, large 0.8. Also supports Hedges' g correction for small samples and Glass's Δ using control group SD only.