Sample Size Determination for Bayesian Hierarchical Models Commonly Used in Psycholinguistics
Shravan Vasishth, Himanshu Yadav, Daniel J. Schad, Bruno Nicenboim
Abstract
Abstract 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.
Topics & Concepts
Bayesian probabilitySample size determinationBayes' theoremBayes factorComputer scienceSample (material)Artificial intelligencePsycholinguisticsBayesian hierarchical modelingHierarchical database modelLinear modelMultilevel modelMachine learningStatisticsMathematicsData miningPsychologyCognitionPhysicsNeuroscienceThermodynamicsSensory Analysis and Statistical MethodsAdvanced Text Analysis TechniquesMulti-Criteria Decision Making