Resources

We would like to acknowledge some excellent online and peer-reviewed material about power simulation and mixed effects models, which we have borrowed from liberally in the tutorial. These resources represent an excellent next step in your exploration of simulation-based power analysis.

R

• DeBruine & Barr (2021) paper on using simulation to understand mixed effects models: https://journals.sagepub.com/doi/epdf/10.1177/2515245920965119

• Kumle et al. (2021) paper on estimating power in GLMMs: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613146/

Julian Quandt’s 4-part blog series on power analysis via simulation:

1. https://julianquandt.com/posts/2020-04-22-power-analysis-by-data-simulation-in-r-part-i.html
2. https://julianquandt.com/posts/2020-05-12-power-analysis-by-data-simulation-in-r-part-ii.html
3. https://julianquandt.com/posts/2020-06-16-power-analysis-by-data-simulation-in-r-part-iii.html
4. https://julianquandt.com/posts/2020-08-05-power-analysis-by-data-simulation-in-r-part-iv.html

Stata

Chuck Huber’s 4-part blog series on power analysis via simulation:

1. https://blog.stata.com/2019/01/10/calculating-power-using-monte-carlo-simulations-part-1-the-basics/
2. https://blog.stata.com/2019/01/29/calculating-power-using-monte-carlo-simulations-part-2-running-your-simulation-using-power/
3. https://blog.stata.com/2019/08/13/calculating-power-using-monte-carlo-simulations-part-3-linear-and-logistic-regression/
4. https://blog.stata.com/2019/08/20/calculating-power-using-monte-carlo-simulations-part-4-multilevel-longitudinal-models/