TY - INPR A1 - Koch, Timo A1 - Pargent, Florian A1 - Kleine, Anne-Kathrin A1 - Lermer, Eva A1 - Gaube, Susanne T1 - A Tutorial on Tailored Simulation-Based Power Analysis for Experimental Designs with Generalized Linear Mixed Models T2 - PsyArXiv Preprints N2 - When planning experimental research, determining an appropriate sample size and using suitable statistical models are crucial for robust and informative results. However, the recent replication crisis underlines the need for more rigorous statistical methodology and well-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. Yet, available methods and software for a priori power analyses for GLMMs are often limited to specific designs, while data simulation approaches offer more flexibility. Based on a practical case study, the current tutorial equips researchers with a step-by-step guide and corresponding code for conducting tailored a priori power analyses to determine appropriate sample sizes with GLMMs. Finally, we give an outlook on the increasing importance of simulation-based power analysis in experimental research. KW - data simulation KW - generalized linear mixed model KW - power analysis KW - sample size Y1 - 2023 U6 - https://doi.org/10.31234/osf.io/rpjem ER - TY - JOUR A1 - Pargent, Florian A1 - Koch, Timo K. A1 - Kleine, Anne-Kathrin A1 - Lermer, Eva A1 - Gaube, Susanne T1 - A Tutorial on Tailored Simulation-Based Sample-Size Planning for Experimental Designs With Generalized Linear Mixed Models JF - Advances in Methods and Practices in Psychological Science KW - tutorial KW - sample-size planning KW - generalized linear mixed model KW - power analysis KW - data simulation KW - GLMM Y1 - 2024 U6 - https://doi.org/10.1177/25152459241287132 SN - 2515-2459 VL - 7 IS - 2024,4 ER -