Section author: Danielle J. Navarro and David R. Foxcroft

Effect size

The most commonly used measure of effect size for a t-test is Cohen’s d (Cohen, 1988). It’s a very simple measure in principle, with quite a few wrinkles when you start digging into the details. Cohen himself defined it primarily in the context of an independent samples t-test, specifically the Student test. In that context, a natural way of defining the effect size is to divide the difference between the means by an estimate of the standard deviation. In other words, we’re looking to calculate something along the lines of this:

d = (mean 1 - mean 2) / std. dev.

and he suggested a rough guide for interpreting d in Table 12. You’d think that this would be pretty unambiguous, but it’s not. This is largely because Cohen wasn’t too specific on what he thought should be used as the measure of the standard deviation (in his defence he was trying to make a broader point in his book, not nitpick about tiny details). As discussed by McGrath and Meyer (2006), there are several different versions in common usage, and each author tends to adopt slightly different notation. For the sake of simplicity (as opposed to accuracy), I’ll use d to refer to any statistic that you calculate from the sample, and use δ to refer to a theoretical population effect. Obviously, that does mean that there are several different things all called d.

My suspicion is that the only time that you would want Cohen’s d is when you’re running a t-test, and jamovi has an option to calculate the effect size for all the different flavours of t-test it provides.

Table 12 A (very) rough guide to interpreting Cohen’s d. My personal recommendation is to not use these blindly. The d-statistic has a natural interpretation in and of itself. It re-describes the difference in means as the number of standard deviations that separates those means. So it’s generally a good idea to think about what that means in practical terms. In some contexts a “small” effect could be of big practical importance. In other situations a “large” effect may not be all that interesting.
d-value rough interpretation
about 0.2 “small” effect
about 0.5 “moderate” effect
about 0.8 “large” effect

Cohen’s d from one sample

The simplest situation to consider is the one corresponding to a one-sample t-test. In this case, this is the one sample mean and one (hypothesised) population mean µo to compare it to. Not only that, there’s really only one sensible way to estimate the population standard deviation. We just use our usual estimate \(\hat{\sigma}\). Therefore, we end up with the following as the only way to calculate d:

\[d = \frac{\bar{X} - \mu_0}{\hat{\sigma}}\]

When we look back at the results in Fig. 87, the effect size value is Cohen’s d = 0.50. Overall, then, the psychology students in Dr Zeppo’s class are achieving grades (mean = 72.3%) that are about 0.5 standard deviations higher than the level that you’d expect (67.5%) if they were performing at the same level as other students. Judged against Cohen’s rough guide, this is a moderate effect size.

Cohen’s d from a Student’s t-test

The majority of discussions of Cohen’s d focus on a situation that is analogous to Student’s independent samples t test, and it’s in this context that the story becomes messier, since there are several different versions of d that you might want to use in this situation. To understand why there are multiple versions of d, it helps to take the time to write down a formula that corresponds to the true population effect size δ. It’s pretty straightforward, δ = (µ_sub:1 - µ_sub:2) /

where, as usual, µ_sub:1 and µ_sub:2 are the population means corresponding to group 1 and group 2 respectively, and σ is the standard deviation (the same for both populations). The obvious way to estimate δ is to do exactly the same thing that we did in the t-test itself, i.e., use the sample means as the top line and a pooled standard deviation estimate for the bottom line

\[d = \frac{\bar{X}_1 - \bar{X}_2}{\hat{\sigma}_p}\]

where \(\hat\sigma_p\) is the exact same pooled standard deviation measure that appears in the t-test. This is the most commonly used version of Cohen’s d when applied to the outcome of a Student t-test, and is the one provided in jamovi. It is sometimes referred to as Hedges’ g statistic (Hedges, 1981).

However, there are other possibilities which I’ll briefly describe. Firstly, you may have reason to want to use only one of the two groups as the basis for calculating the standard deviation. This approach (often called Glass’ Δ, pronounced delta) only makes most sense when you have good reason to treat one of the two groups as a purer reflection of “natural variation” than the other. This can happen if, for instance, one of the two groups is a control group. Secondly, recall that in the usual calculation of the pooled standard deviation we divide by N - 2 to correct for the bias in the sample variance. In one version of Cohen’s d this correction is omitted, and instead we divide by N. This version makes sense primarily when you’re trying to calculate the effect size in the sample rather than estimating an effect size in the population. Finally, there is a version based on Hedges and Olkin (1985), who point out there is a small bias in the usual (pooled) estimation for Cohen’s d. Thus they introduce a small correction by multiplying the usual value of d by (N - 3) / (N - 2.25).

In any case, ignoring all those variations that you could make use of if you wanted, let’s have a look at the default version in jamovi. In Fig. 91 Cohen’s d = 0.74, indicating that the grade scores for students in Anastasia’s class are, on average, 0.74 standard deviations higher than the grade scores for students in Bernadette’s class. For a Welch-test, the estimated effect size is the same (Fig. 93).

Cohen’s d from a paired-samples test

Finally, what should we do for a paired samples t-test? In this case, the answer depends on what it is you’re trying to do. jamovi assumes that you want to measure your effect sizes relative to the distribution of difference scores, and the measure of d that you calculate is:

\[d = \frac{\bar{D}}{\hat{\sigma}_D}\]

where \(\hat{\sigma}_D\) is the estimate of the standard deviation of the differences. In Fig. 97 Cohen’s d = 1.45, indicating that the time 2 grade scores are, on average, 1.45 standard deviations higher than the time 1 grade scores.

This is the version of Cohen’s d that gets reported by the jamovi Paired Samples T-Test analysis. The only wrinkle is figuring out whether this is the measure you want or not. To the extent that you care about the practical consequences of your research, you often want to measure the effect size relative to the original variables, not the difference scores (e.g., the 1% improvement in Dr Chico’s class over time is pretty small when measured against the amount of between-student variation in grades), in which case you use the same versions of Cohen’s d that you would use for a Student or Welch test. It’s not so straightforward to do this in jamovi; essentially you have to change the structure of the data in the spreadsheet view so I won’t go into that here,[1] but the Cohen’s d for this perspective is quite different: it is 0.22 which is quite small when assessed on the scale of the original variables.

[1]If you are interested, you can look at how this was done in the chico2 dataset