Publication: Sample size planning for complex study designs: A tutorial for the mlpwr package
Sample size planning for complex study designs: A tutorial for the mlpwr package
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Zimmer, F., Henninger, M., & Debelak, R. (2023). Sample size planning for complex study designs: A tutorial for the mlpwr package. Behavior Research Methods, 56(4), 4217–4217. https://doi.org/10.3758/s13428-023-02269-0
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A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool in guiding the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). mlpwr can be used to search for the optimal
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- Zimmer, Felix
- Henninger, Mirka
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Zimmer, F., Henninger, M., & Debelak, R. (2023). Sample size planning for complex study designs: A tutorial for the mlpwr package. Behavior Research Methods, 56(4), 4217–4217. https://doi.org/10.3758/s13428-023-02269-0
