Power Calculation for Longitudinal Analysis
Power calculation for longitudinal analysis is crucial for ensuring your study has enough participants to detect a significant effect. It’s about planning your research to avoid false negatives.
How to Use This Calculator
- Enter the sample size you plan to use.
- Enter the expected effect size. This is the difference between the means of the two groups you’re comparing.
- Choose the significance level (α). This is typically 0.05 or 0.01.
- Click ‘Calculate’. The results will appear below the calculator.
Formula & Methodology
The formula used here is based on Cohen’s power analysis for two independent samples. It’s a bit complex, involving the non-central t-distribution and some statistical magic.
Real-World Examples
Data & Statistics
| Effect Size (d) | Power (1 – β) |
|---|---|
| 0.2 | 0.31 |
| 0.5 | 0.89 |
| 0.8 | 0.99 |
| Power (1 – β) | Effect Size (d) | Sample Size per Group |
|---|---|---|
| 0.8 | 0.2 | 124 |
| 0.8 | 0.5 | 32 |
| 0.8 | 0.8 | 12 |
Expert Tips
- Power analysis should be done before you start your study, not after.
- Be realistic about your effect size. Small effects are hard to detect.
- Consider using a power analysis software for more complex designs.
Interactive FAQ
What is power in statistics?
Power is the probability that a test will reject the null hypothesis when the alternative hypothesis is true. In other words, it’s the chance of detecting an effect if there really is one.
What is effect size?
Effect size is a measure of the magnitude of a phenomenon. In this context, it’s the expected difference between the means of the two groups you’re comparing.
For more information, see the Cohen’s d effect size calculator from the National Institutes of Health, or the power analysis tutorial from UCLA.