Sample size for repeated-measures ANOVA
Effect size is the difference in means between three observations of an outcome
A good rule of thumb is to overestimate the variance of the effect size. Researchers do this because it forces them to have to collect more observations of the outcome, which in turn leads to more precise and accurate measures of effect with repeated-measures ANOVA.
For example, let's say that researchers find quality evidence that people in the treatment group have an average pain score of 7.1 with a standard deviation of 1.6 at baseline, an average pain score of 4.3 with a standard deviation of 1.1, and a 6-month follow-up average score of 4.1 with a standard deviation of 1.4. Researchers could enter these values into G*Power and know exactly how many observations of the outcome they would need to collect to detect the hypothesized treatment effect.
The steps for calculating sample size for a repeated-measures ANOVA in G*Power
2. Under the Test family drop-down menu, select F tests.
3. Under the Statistical test drop-down menu, select ANOVA: Repeated measures, within factors.
4. Under the Type of power analysis drop-down menu, select A priori: Compute required sample size - given alpha, power, and effect size.
5. Click the Determine button.
6. Click on the Direct marker to highlight the menu.
7. In the Partial eta-squared box, enter one of the following values:
Enter ".01" if researchers believe there will be a small treatment effect.
Enter ".03" if researchers believe there will be a moderate treatment effect.
Enter ".05" if researchers believe there will be a large treatment effect.
8. Click Calculate.
9. Click Calculate and transfer to main window.
10. Enter .80 into the Power (1-beta err prob) box, unless researchers want to change the power according to the current empirical or clinical context.
11. In the Number of groups box, enter "1"
12. In the Number of measurements box, enter "3"
13. Click Calculate.
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