Sample size determination for bayesian hierarchical models commonly used in psycholinguistics

Shravan Vasishth, Himanshu Yadav, Daniel J. Schad, Bruno Nicenboim

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

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
Original languageEnglish
Pages (from-to)102-126
Number of pages25
JournalComputational Brain & Behavior
Volume6
DOIs
Publication statusPublished - Mar 2023

Keywords

  • Sample size determination
  • Bayesian data analysis
  • Hierarchical models

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