A Tutorial on Tailored Simulation-Based Sample-Size Planning for Experimental Designs With Generalized Linear Mixed Models

When planning experimental research, determining an appropriate sample size and using suitable statistical models are crucial for robust and informative results. The recent replication crisis underlines the need for more rigorous statistical methodology and adequately powered designs. Generalized linear mixed models (GLMMs) offer a flexible statistical framework to analyze experimental data with complex (e.g., dependent and hierarchical) data structures. However, available methods and software for a priori sample-size planning for GLMMs are often limited to specific designs. Tailored data-simulation approaches offer a more flexible alternative. Based on a practical case study in which we focus on a binomial GLMM with two random intercepts and discrete predictor variables, the current tutorial equips researchers with a step-by-step guide and corresponding code for conducting tailored a priori sample-size planning with GLMMs. We not only focus on power analysis but also explain how to use the precision of parameter estimates to determine appropriate sample sizes. We conclude with an outlook on the increasing importance of simulation-based sample-size planning.