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**Cohen's d**

When you're comparing two groups, like in an independent samples t-test, the most common method for assessing the size of the effect is by using Cohen's *d*. In this instance, we are simply standardizing the difference between the groups.

**Computing Cohen's d**

Starting from the "old school" method, we can compute Cohen's *d* using a basic formula:

where:

- M1 and M2 represent the sample means for the two groups being compared and
- Sp represents the pooled estimated population standard deviation.

Most of the time the actual population standard deviation is not known, this is why we estimate it using a pooled standard deviation from our two groups. This means we have another formula:

Here N represents the mean for each group (as numbered) and S^2 represents the variance for each group. Thankfully, most students aren't asked to do these calculations manually. Instead, we can simply use technology to compute Cohen's *d*.

**Cohen's d using SPSS**

If you're using SPSS version 27 or higher, you can use SPSS to include an effect size estimate with your output for your independent samples t-test. Simply check the box next to "Estimate effect sizes" in the **Independent Samples T-Test** dialogue window, as shown below.

This will prompt SPSS to include the following table in the output:

Here we look in the top row, where it says "Cohen's d" and we look at the **Point Estimate** value. We would report this in the text as *d* = 1.084.

**Interpreting Cohen's d**

The general guidelines for interpreting the effect size are as follows:

- 0.2 = small effect
- 0.5 = moderate effect
- 0.8 = large effect

You should refer to your course resources to verify this is the same guideline followed by your readings, as some sources use slightly different interpretation values.

**For additional assistance with computing and interpreting the effect size for your analysis, attend the SPSS: T-tests group session**

- Last Updated: Mar 12, 2023 6:02 PM
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