# 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

 ```1 2``` ```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 [email protected]

## 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

 ```1 2 3 4 5``` ```## 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 July 8, 2018, 3:17 p.m.