Minimum: Anybody can reproduce the computations (without ambiguities and guessing), and get to the same result.
Optimal: Key choices are justified. Most importantly: the assumed effect size. But also: Why α=5% Why power = 80%?
What information do you need to compute a power analysis?
Type of power analysis:
A priori: compute N, given alpha, power, ES
Post-hoc: compute power, given alpha, N, ES
Criterion: compute alpha, given power, ES, N
Sensitivity: compute ES, given alpha, power, N
Design: Correlations? t-test? Two-group or paired? Linear model?
α = .05? .005?
Power = 80%?
One-sided or two-sided test?
Expected / Minimally interesting effect size -The metric of the effect size (d? r? \(f^2\)?)
Write-Up
Let’s check an issue from a journal …
How are power analyses reported in practice?
Example 1
# 10.1002/ejsp.2710: f2 = 0.07# u: Number of predictors in the model (without intercept). Here presumably 3.# n = v + u + 1; v = n- u - 1pwr.f2.test(u =3, v =152-3-1, sig.level =0.05, power =0.8)# -> f2 = 0.07366273
Example 2
Example 3
Example 4
# 10.1002/ejsp.2713# guessing the ES and alphapwr.f2.test(u=3, f2=0.0435, sig.level =0.05, power =0.8)# n -> 255
Example 5
# 10.1002/ejsp.2715pwr.f2.test(u=1, f2=0.05, sig.level =0.05, power =0.8)# n = 158