Introduction:

For linear models (e.g., multiple regression), the following functions are used:

pwr.f2.test(u =, v = , f2 = , sig.level = , power = )

where u and v are the numerator and denominator degrees of freedom. We use f2 as the effect size measure.

R² = population squared multiple correlation

R²_A = variance accounted for in the population by variable set A

R²_AB = variance accounted for in the population by variable set A and B together

The first formula is appropriate when we are evaluating the impact of a set of predictors on an outcome.

The second formula is appropriate when we are evaluating the impact of one set of predictors above and beyond a second set of predictors (or covariates).

Cohen suggests f2 values of 0.02, 0.15, and 0.35 represent small, medium, and large effect sizes.