In Effect Size we introduce the notion of effect size, and briefly mention Cohen’s d. We will now explain this concept further.
Definition 1: Cohen’s d, a statistic which is independent of the sample size and is defined as
where m_{1} and m_{2} represent two means and σ_{pooled} is some combined value for the standard deviation.
The effect size given by d is conventionally viewed as small, medium or large as follows:
- d = 0.20 – small effect
- d = 0.50 – medium effect
- d = 0.80 – large effect
For single sample hypothesis testing of the mean, we use the following value for d
Example 1: National norms for a school mathematics proficiency exam are distributed N(80,20). A random sample of 60 students from New York City is taken showing a mean proficiency score of 75 (as in Example 1 of Single Sample Hypothesis Testing). Find the effect size for the sample mean.
Per Definition 1,
which indicates a small effect. Note that the effect size is independent of the sample size. We should interpret d to mean that the sample mean is a quarter of a population standard deviation below the population mean.
Is it allowed to calculate Cohen’s d for nonparametric tests like the Mann-Whitney U?
Marleen,
I don’t know of a Cohen’s d for Mann-Whitney.
I suggest that you use the effect size described on the following webpage
http://www.real-statistics.com/non-parametric-tests/wilcoxon-rank-sum-test/
It is not Cohen’s d, but it does use the same criterion as Cohen’s d.
Charles
Hi Charles,
Is there any tool in the Statistics Resource Pack that you can use to calculate Effect Size using Cohen’s D w/ excel?
Thank you
Hi Jonathan,
The effect size (Cohen’s d) is included in a number of data analysis tools. E.g. see T Tests and Non-parametric Equivalents data analysis tool.
Charles
Do you only need to calculate effect size on those who are significantly different to each other?
Also on calculating some effect size a few of my answers were negative i.e. -3.065 and -0.385 is that ok? and if so then how do you interpret it?
Thank you
Helen,
In my view, you should calculate an effect size in any case, but it is probably most useful when you have a significant result.
Depending on the effect size measure that you use, you could get a negative value. This just indicates the direction of the effect. E.g. in calculating the effect size for the difference between the means of sample A and sample B where A has a higher mean, you will get a positive value if you subtract A from B and a negative value if you do the subtraction in the opposite order. Often it is the absolute value that is used and so the negative sign goes away.
Charles
Thank you so much Mr. All of your explanation so clear and good. That’s very helpful for my thesis. And I want to say, again. Many thanks. I don’t have many word to say because I’m very happy I get what I want from this web.