Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures.

混合效应模型是同时对固定效应和随机效应建模的强大工具，但不能提供可行的分析解决方案来估计检验正确拒绝原假设的概率。然而，能够估计这种概率对于样本量规划至关重要，因为功效与实证结果的可靠性和可复制性密切相关。一种灵活且非常直观的分析电源解决方案替代方案是基于仿真的功率分析。尽管有各种工具可用于对混合效应模型进行基于仿真的功率分析，但缺乏关于如何适当使用它们的指导。在本教程中，我们将讨论如何估计不同用例中混合效应模型的功效：首先，如何使用适合可用（例如已发布）数据的模型来确定样本量；第二，如何确定足够功率所需的刺激数量；最后，如何在没有可用数据的情况下进行样本量规划。我们的示例涵盖线性和广义线性模型，我们提供代码和资源，用于对可公开访问的数据集执行基于仿真的功率分析。因此，目前的工作通过总结资源，帮助研究人员在使用混合效应模型时导航合理的研究设计，