Bayes Factors for Mixed Models: a Discussion

Open Access
Authors
  • G.E. Cox
  • C.P. Davis-Stober
  • A. Heathcote
  • D.W. Heck
  • M. Kalish
  • D. Kellen
  • D. Matzke
  • R.D. Morey
  • B. Nicenboim
  • D. van Ravenzwaaij
  • J.N. Rouder
  • D.J. Schad
  • R.M. Shiffrin
  • H. Singmann
  • S. Vasishth
  • J. Veríssimo
  • F. Bockting
  • S. Chandramouli
  • J.C. Dunn
  • Q.F. Gronau
  • M. Linde
  • S.D. McMullin
  • D. Navarro
  • M. Schnuerch
  • H. Yadav
  • F. Aust ORCID logo
Publication date 03-2023
Journal Computational Brain and Behavior
Volume | Issue number 6 | 1
Pages (from-to) 140–158
Number of pages 19
Organisations
  • Faculty of Social and Behavioural Sciences (FMG) - Psychology Research Institute (PsyRes)
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.

Document type Comment/Letter to the editor
Language English
Related publication Bayes Factors for Mixed Models Bayes Factors for Mixed Models: Perspective on Responses
Published at https://doi.org/10.1007/s42113-022-00160-3
Other links https://www.scopus.com/pages/publications/85148050348
Downloads
s42113-022-00160-3 (Final published version)
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