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"With great power comes great randomized controlled trials." - J-PAL & Stan Lee
Lesson Prerequisites
Lesson Prerequisites
This lesson assumes that you have completed the Statistical Inference lesson or a course in hypothesis testing. This lesson also assumes that you have beginner Stata skills and can complete the exercises in the lessons on Intro to Stata and Stata Best Practices. The last module requires Stata version 15 or later to compute power for a clustered design using the power command. If you have an earlier version of Stata, then you will need to use a different command to do clustered power calculations; we recommend the user-written clustersampsi command.
0. Intro to the lesson
0. Intro to the lesson
Power calculations are a key step when designing an impact evaluation. Power calculations are most often used to compute the required sample size, and are useful for modeling other design considerations as well.
1. What is power? (part 1)
1. What is power? (part 1)
Power is the likelihood that your evaluation design enables you to detect a treatment effect of a certain size.
2. What is power? (part 2)
2. What is power? (part 2)
Power is the likelihood that your evaluation design enables you to detect a treatment effect of a certain size.
3. Components of power: alpha, delta, and sigma
3. Components of power: alpha, delta, and sigma
Different parameters have different effects on the power of the evaluation.
4. Components of power: n and rho
4. Components of power: n and rho
Intracluster correlation can have a huge effect on power in clustered evaluations.
5. Individual-level power calculations in Stata
5. Individual-level power calculations in Stata
The power command in Stata is versatile and efficient.
6. Clustered designs in Stata
6. Clustered designs in Stata
Use the power command for clustered designs if you have Stata 15 or later. Use the clustersampsi command for clustered designs if you have Stata 14 or earlier.
Additional Resources
Additional Resources
Djimeu and Houndolo (2016) "Power calculation for causal inference in social science" 3ie Working Paper 26.
Duflo, Glennerster, and Kremer (2007) "Using Randomization in Development Economics Research: A Toolkit" CEPR Discussion Paper No. 6059.
Kondylis and Loeser (2020) "Back-of-the-envelope power calcs" World Bank Development Impact Blog.
Banner photo: Florence Nightingale's polar area diagram showing the causes of death in the Crimean War, 1858. Accessed from https://en.wikipedia.org/wiki/Florence_Nightingale#/media/File:Nightingale-mortality.jpg.
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