Description Usage Arguments Value Author(s) References Examples
Compute an alpha value adjusted for sample size. The adjusted value is based on Perez and Pericchi's (2014) formula (equation 11, see below) using a reference sample, which can be defined a priori or estimated using the sample size calculation from power.
α * √(n0 times (log(n0) + χ^2_α(1))) / √(n* times (log(n*) + χ^2_α(1)))
1 2 3 4 5 6 7 8 9 10 |
test |
Type of statistical test being used. Can be any of the tests listed |
ref.n |
n0 in the above equation. Reference sample size. If sample size was determined a priori, then the reference number of participants can be set. This removes the calculation of sample size based on power |
n |
n* in the above equation. Number of participants in the experiment sample (or per group) |
alpha |
α in the above equation.
Alpha value to adjust.
Defaults to |
power |
Power (1 - β) value.
Used to estimate the reference sample size (n0).
Defaults to |
efxize |
Effect size to be used to estimate the reference sample size.
Effect sizes are based on Cohen (1992).
Numeric values can be used.
Defaults to |
groups |
Number of groups (only for |
df |
Number of degrees of freedom (only for |
A list containing the following objects:
adapt.a |
The adapted alpha value |
crit.value |
The critical value associated with the adapted alpha value |
orig.a |
The original alpha value |
ref.n |
The reference sample size based on alpha, power, effect size, and test |
exp.n |
The sample size of the experimental sample |
power |
The power used to determine the reference sample size |
test |
The type of statistical test used |
Alexander Christensen <alexpaulchristensen@gmail.com>
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.
Perez, M. E., & Pericchi, L. R. (2014). Changing statistical significance with the amount of information: The adaptive a significance level. Statistics & Probability Letters, 85, 20-24.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | #ANOVA
adapt.anova <- adapt.a(test = "anova", n = 200, alpha = .05, power = .80, groups = 3)
#Chi-square
adapt.chisq <- adapt.a(test = "chisq", n = 200, alpha = .05, power = .80, df = 3)
#Correlation
adapt.cor <- adapt.a(test = "cor", n = 200, alpha = .05, power = .80)
#One-sample t-test
adapt.one <- adapt.a(test = "one.sample", n = 200, alpha = .05, power = .80)
#Two-sample t-test
adapt.two <- adapt.a(test = "two.sample", n = 200, alpha = .05, power = .80)
#Paired sample t-test
adapt.paired <- adapt.a(test = "paired", n = 200, alpha = .05, power = .80, efxize = "medium")
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