Click Calculate and transfer to main window.ġ0. In the Partial eta-squared box, enter one of the following values:Įnter ".01" if researchers believe there will be a small treatment effect.Įnter ".03" if researchers believe there will be a moderate treatment effect.Įnter ".05" if researchers believe there will be a large treatment effect.ĩ. Click on the Direct marker to highlight the menu.ħ. Under the Type of power analysis drop-down menu, select A priori: Compute required sample size - given alpha, power, and effect size.Ħ. Under the Statistical test drop-down menu, select ANOVA: Repeated measures, within factors.Ĥ. Under the Test family drop-down menu, select F tests.ģ. 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.Ģ. 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.įor 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. Find articles in the literature that are conceptually or theoretically similar to the study of interest or use similar outcomes and use those values in the sample size calculation for repeated-measures ANOVA.Ī good rule of thumb is to overestimate the variance of the effect size. The absolute differences between these three mean values and their respective variances constitutes an evidence-based measure of effect size. In order to run an a priori sample size calculation for repeated-measures ANOVA, researcheres will need to seek out evidence that provides the means and standard deviations of the outcome at the three different observations.
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