Power Analysis Calculator: Logistic Regression
Power analysis in logistic regression is crucial for determining the sample size required to detect an effect of a certain size with a specified level of confidence. It helps in planning studies and ensuring they are adequately powered.
- Enter the desired sample size, probability of success, desired power, and significance level.
- Click the “Calculate” button.
- View the results below the calculator.
The power analysis is based on the following formula: Power = 1 – β, where β is the probability of a type II error. The sample size (n) is calculated using the Cochran’s formula.
| Sample Size (n) | Probability of Success (p) | Desired Power | Significance Level (α) | Power |
|---|
- Always round up the calculated sample size to ensure adequate power.
- Consider using a power analysis tool to simplify calculations.
What is power in logistic regression?
Power in logistic regression refers to the probability of detecting an effect when there is one. It’s a measure of the study’s sensitivity.
How do I interpret the results of the power analysis?
The results tell you the power of your study to detect an effect of a certain size at a specified significance level. A power of 0.8 or 0.9 is generally considered adequate.
For more information, see the CDC’s guide on power analysis and the UCLA’s guide on power analysis for logistic regression.