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Simulating power for mixed-effects models

3 References and Further Reading

3.1 Further reading

  • How to justify your sample size (Lakens, 2022).

  • My tutorial with Lisa DeBruine (DeBruine & Barr, 2021) which covers much of the same ground, but is mainly focused on understanding how LMEMs work

  • Other articles on power in LMEMs: (Brysbaert & Stevens, 2018), (Kumle et al., 2021), (Westfall et al., 2014)

  • Monte Carlo simulations comparing methods for getting \(p\)-values: (Luke, 2017)

Check out the online textbooks and resources that my group and University of Glasgow have been developing. There are great resources to learn more about data wrangling and visualization, among other things.

3.2 R packages

3.3 References

Brysbaert, M., & Stevens, M. (2018). Power analysis and effect size in mixed effects models: A tutorial. Journal of Cognition, 1(1).

DeBruine, L. M., & Barr, D. J. (2021). Understanding mixed-effects models through data simulation. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920965119.

Green, P., & MacLeod, C. J. (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498.

Kumle, L., Võ, M. L.-H., & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53(6), 2528–2543.

Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267. https://doi.org/https://doi.org/10.1525/collabra.33267

Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in R. Behavior Research Methods, 49, 1494–1502.

Westfall, J., Kenny, D. A., & Judd, C. M. (2014). Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli. Journal of Experimental Psychology: General, 143(5), 2020.