TY - JOUR
T1 - Sample size determination for bayesian hierarchical models commonly used in psycholinguistics
AU - Vasishth, Shravan
AU - Yadav, Himanshu
AU - Schad, Daniel J.
AU - Nicenboim, Bruno
N1 - Funding Information:
Open Access funding enabled and organized by Projekt DEAL. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 317633480, SFB 1287 (2021-2025), Project Q, PIs: Shravan Vasishth and Ralf Engbert.
Publisher Copyright:
© 2022, The Author(s).
PY - 2023/3
Y1 - 2023/3
N2 - We discuss an important issue that is not directly related to the main theses of the van Doorn et al. (Computational Brain and Behavior, 2021) paper, but which frequently comes up when using Bayesian linear mixed models: how to determine sample size in advance of running a study when planning a Bayes factor analysis. We adapt a simulation-based method proposed by Wang and Gelfand (Statistical Science 193–208, 2002) for a Bayes factor-based design analysis, and demonstrate how relatively complex hierarchical models can be used to determine approximate sample sizes for planning experiments
AB - We discuss an important issue that is not directly related to the main theses of the van Doorn et al. (Computational Brain and Behavior, 2021) paper, but which frequently comes up when using Bayesian linear mixed models: how to determine sample size in advance of running a study when planning a Bayes factor analysis. We adapt a simulation-based method proposed by Wang and Gelfand (Statistical Science 193–208, 2002) for a Bayes factor-based design analysis, and demonstrate how relatively complex hierarchical models can be used to determine approximate sample sizes for planning experiments
KW - Sample size determination
KW - Bayesian data analysis
KW - Hierarchical models
UR - http://www.scopus.com/inward/record.url?scp=85125649295&partnerID=8YFLogxK
U2 - 10.1007/s42113-021-00125-y
DO - 10.1007/s42113-021-00125-y
M3 - Article
SN - 2522-0861
VL - 6
SP - 102
EP - 126
JO - Computational Brain & Behavior
JF - Computational Brain & Behavior
ER -