Authors¶
Sophia Man Yang, Nianbo Dong, Rebecca Maynard
PyPowerUp¶
PyPowerUp is the Python implementation for the research article “PowerUp!: A Tool for Calculating Minimum Detectable
Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-experimental Design Studies (Dong & Maynard, 2013)”. It is a
power analysis tool for 21 experimental and quasi-experimental designs.
Given study design, PyPowerUp computes minimum detectable effect sizes effect_size, power power,
and minimum required samples sizes sample size.
To install PyPowerUp, run this command in your terminal:
$ pip install pypowerup
To use the functions:
from pypowerup import effect_size, power, sample_size
Individual Random Assignment Designs¶
Cluster Random Assignment Designs¶
Quasi-experimental Designs¶
Credit and disclaimer¶
This document is heavily built on https://www.causalevaluation.org/uploads/7/3/3/6/73366257/powerup.xlsm. All the design and variable explanations are from this sheet.
References¶
Dong, N. & Maynard, R. A. (2013). PowerUp!: A tool for calculating minimum detectable effect sizes and minimum required sample sizes for experimental and quasi- experimental design studies, Journal of Research on Educational Effectiveness, 6(1), 24-67. doi: 10.1080/19345747.2012.673143. https://www.causalevaluation.org/uploads/7/3/3/6/73366257/powerup.xlsm
Bulus, M., Dong, N., Kelcey, B., & Spybrook, J. (2019). PowerUpR: Power Analysis Tools for Multilevel Randomized Experiments. R package version 1.0.4. https://CRAN.R-project.org/package=PowerUpR