Sample size for point biserial
Effect size is the hypothesized association between a categorical and a continuous variable
A good rule of thumb is to use the effect size criteria identified by Cohen (1988)*.
Small effect size = .10
Moderate effect size = .30
Large effect size = .50
The steps for calculating sample size for a point biserial in G*Power
2. Under the Test family drop-down menu, select t tests.
3. Under the Statistical test drop-down menu, select Correlation: Point biserial model.
4. Under the Type of power analysis drop-down menu, select A priori: Compute required sample size - given alpha, power, and effect size.
5. In the Tail(s) drop-down menu, select One if researchers have a definitive and literature-based reason for believing that the association/correlation that the correlation travels in a certain direction (either positive or negative). Select Two if researchers are unsure whether the association/correlation will be positive or negative.
6. Click on the Determine => button.
Enter ".01" into the Coefficient of determination p2 box if researchers believe there will be a small treatment effect.
Enter ".09" into the Coefficient of determination p2 box if researchers believe there will be a moderate treatment effect.
Enter ".25" into the Coefficient of determination p2 box if researchers believe there will be a large treatment effect.
7. Leave the alpha value at 0.05, unless researchers want to change the alpha value according to the current empirical or clinical context.
8. 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.
9. Click Calculate.
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