Table of Contents
Statistical Power for Correlation Coefficient
Arguments (Parameters)
Among sample size, correlation, significance level, and power, one and only one can be left blank.
Sample Size
Provide the number of participants. Multiple sample sizes can be provided in two ways. First, multiple sample sizes can be supplied separated by white spaces, e.g., 100 150 200 will calculate power for the three sample sizes. A sequence of sample sizes can be generated using the method s:e:i with s denoting the starting sample size, e as ending sample size, and i as the interval. For example, 100:150:10 will generate a sequence 100 110 120 130 140 150.
By default, the sample size is 100.
Effect Size
The effect size (correlation) to be used. Multiple effect sizes or a sequence of effect sizes can be supplied using the same method for sample size. By default, the value is 0.1. Cohen defines small, medium, and large effect size as
| small | medium | large |
|---|---|---|
| 0.1 | 0.3 | 0.5 |
# of vars partialed out
The correlation can be one after partialing out some other variables. This argument tells how many variables are partialed out. By default, it is set to 0 so that no variable is partialed out in calculating the correlation.
Significance Level (alpha)
The significance level (Type I error rate) for power calculation with the default 0.05.
Power
The power of the test.
H1
Specifying the alternative hypothesis, can be “two.sided” (default), “greater” or “less”
Power curve
Whether to generate the power curve.
Note
A note (less than 200 characters) can be provided to provide basic information on the analysis.
Examples
Example 1
Example 2 - Power curve
References
- Brent, R. (1973) Algorithms for Minimization without Derivatives. Englewood Cliffs, NJ: Prentice-Hall.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.