Simulation-Based Power Analysis for Factorial Analysis of Variance Designs

The paper

Lakens & Caldwell (2021). Simulation-Based Power Analysis for Factorial Analysis of Variance Designs. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920951503

Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analyses often need [Formula: see text] or Cohen’s f as input, but these effect sizes are not intuitive and do not generalize to different experimental designs. We have created the R package Superpower and online Shiny apps to enable researchers without extensive programming experience to perform simulation-based power analysis for ANOVA designs of up to three within- or between-participants factors. Predicted effects are entered by specifying means, standard deviations, and, for within-participants factors, the correlations. The simulation provides the statistical power for all ANOVA main effects, interactions, and individual comparisons. The software can plot power across a range of sample sizes, can control for multiple comparisons, and can compute power when the homogeneity or sphericity assumption is violated. This Tutorial demonstrates how to perform a priori power analysis to design informative studies for main effects, interactions, and individual comparisons and highlights important factors that determine the statistical power for factorial ANOVA designs.

Contribution

new method

Data basis

synthetic

Document

full article

Review stage

peer reviewed

Disciplines

statistics, psychology

Access

open

Existing implementations

Code the authors or the community already shipped. This is what decides how far up the ladder a work can climb — no implementation, no tools.

• cranSuperpowerMITby Aaron Caldwell, Daniel Lakensofficialruns here

What it can do

• Anova power exactanova_power_exact

  Power of every ANOVA effect and pairwise comparison, from one exact data set.

• Anova power simulationanova_power_simulation

  Power of every ANOVA effect and pairwise comparison, by Monte Carlo simulation.

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Provenance

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