Bayes Factors for Mixed Models: a Discussion

Johnny van Doorn, Julia M. Haaf, Angelika M. Stefan, Eric Jan Wagenmakers, Gregory Edward Cox, Clintin P. Davis-Stober, Andrew Heathcote, Daniel W. Heck, Michael Kalish, David Kellen, Dora Matzke, Richard D. Morey, Bruno Nicenboim, Don van Ravenzwaaij, Jeffrey N. Rouder, Daniel J. Schad, Richard M. Shiffrin, Henrik Singmann, Shravan Vasishth, João VeríssimoFlorence Bockting, Suyog Chandramouli, John C. Dunn, Quentin F. Gronau, Maximilian Linde, Sara D. McMullin, Danielle Navarro, Martin Schnuerch, Himanshu Yadav, Frederik Aust

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison.

Original languageEnglish (US)
Pages (from-to)140-158
Number of pages19
JournalComputational Brain and Behavior
Volume6
Issue number1
DOIs
StatePublished - Mar 2023

Keywords

  • Bayes factors
  • Mixed effects
  • Mixed models
  • Random effects

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Developmental and Educational Psychology

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