Sample-size justification is an essential aspect of rigorous research in the behavioral and social sciences and helps to ensure studies are adequately powered, minimize resource waste, and reduce participant burden. However, researchers often face challenges in navigating the array of sample-size-planning methods available, particularly when balancing inferential goals and statistical frameworks. The SampleSizePlanner (SSP), originally developed to assist researchers in selecting appropriate sample-size determination methods for two-group designs, has been expanded to address 2 × 2 analysis-of-variance (ANOVA) designs. In this article, we introduce novel 2 × 2 design extensions to the SSP, including tools for Bayesian methods, such as the Bayes factor equivalence interval and the region of practical equivalence, and a frequentist approach. The SSP offers an accessible ShinyApp interface and R package, enabling researchers to streamline decision-making and apply various sample-size-planning methods with minimal computational overhead. Ready-to-use reporting templates foster transparency in sample-size justification. In the article, we address the practical application of these tools through comprehensive examples, demonstrating their relevance to scenarios such as interaction testing and equivalence estimation. By providing a standardized and accessible approach to sample-size planning, this work supports researchers in conducting reproducible and well-powered studies while addressing gaps in sample-size planning for 2 × 2 ANOVA designs.
Lortz et al. (Thu,) studied this question.