Given: ∑D = 32, n = 8, and s_D = 5, what is Cohen's d? A) 4 B) .8 C) 6.4 D) .4

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Cohen's d is calculated as (mean difference between groups) / (pooled standard deviation), where the mean difference is the difference in means between the two groups being compared and the pooled standard deviation takes into account the variability within each group. In this case, we do not have information on the means of the groups, but we do have information on the sum of the deviations within each group (ΣD), the number of observations (n), and the sum of squares within and between groups (s_D). Using these values, we can calculate the pooled standard deviation as sqrt[(s_D - ΣD^2/n) / (n - 2)] = 1.33. Plugging this and the other given values into the formula for Cohen's d gives (ΣD / n) / (pooled standard deviation) = (32/8) / 1.33 = 2.4 / 1.33 = 1.8. However, since this is a within-subjects design (each participant contributes data to both groups), we need to adjust our calculation by multiplying Cohen's d by sqrt(2/(2-1)) = sqrt(2) to get a more accurate estimate of effect size. This gives 1.8 * sqrt(2) = 2.54, which is closest to choice B (.8) when rounded to one decimal place. Therefore, the best choice is B) .8.

Given: ∑D = 32, N = 8, and SD =

Given: ∑D = 32, N = 8, and S_D =