power.indep.cor: Power analysis for two independent correlations

In m-Py/cower: Power Analysis for Correlation Coefficients

Description

This function can be used to conduct power analyses for the comparison of independent correlation coefficients, as tested via Fisher's z-test. It can (1) determine the power for detecting a significant difference between 2 correlations given N ('post hoc'), and (2) determine the required N to achieve a desired power ('a-priori').

Usage

power.indep.cor(r1, r2, n1 = NULL, n2 = NULL, power = NULL,
  sig.level = 0.05, alternative = "two.sided")

Arguments

r1	Value of first Pearson correlation coefficient, must be passed.
r2	Value of second correlation coefficient, must be passed.
n1	Sample size of group 1; pass only for 'post hoc' analysis.
n2	Sample size of group 2; pass only for 'post hoc' analysis.
power	The statistical power; pass only for 'a-priori' analysis.
sig.level	The employed alpha level, defaults to 0.05.
alternative	Must be 'greater', 'less', or 'two.sided'. 'greater' (r1 > r2) or 'less' (r1 < r2) will result in one sided tests (to be used if a directional hypothesis exists). Default test is two.sided.

Value

A list that contains all passed and computed values.

r1	passed correlation coefficient r1
r2	passed correlation coefficient r2
q	The effect size. Represents the difference between the z-transformed values of r1 and r2
n1	sample size in group 1, either passed ('post hoc'), or computed ('a-priori')
n2	sample size in group 1, either passed ('post hoc'), or computed ('a-priori')
power	Power to detect a significant difference between the two correlation coefficients r1 and r2, either passed ('a-priori') or computed ('post hoc')
sig.level	The significance level that was employed for the power analysis
hypothesis	Passed argument 'alternative'

Author(s)

Martin Papenberg [e-mail]

References

Fisher RA. Statistical Methods for Research Workers. Edinburgh, Scotland: Oliver and Boyd; 1925. Available: http://psychclassics.yorku.ca.

Cohen, J. (1969). Statistical power analysis for the behavioural sciences. New York: Academic Press.

Examples

## A priori analysis
power.indep.cor(r1 = 0.4, r2 = 0.3, power = 0.8)

## Post-hoc analysis (one-sided)
power.indep.cor(r1 = 0.4, r2 = 0.3, n1 = 450, n2 = 450, alternative = "greater")

m-Py/cower documentation built on May 21, 2019, 9:16 a.m.