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:
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:
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**