Bayes factors for mixed models: A discussion

van Doorn, Johnny, Haaf, Julia M., Stefan, Angelika M., Wagenmakers, Eric-Jan, Edward Cox, Gregory, Davis-Stober, Clinton P., Heathcote, Andrew, Heck, Daniel W., Kalish, Michael, Kellen, David, Matzke, Dora, Morey, Richard D. ORCID: https://orcid.org/0000-0001-9220-3179, Nicenboim, Bruno, Ravenwaaij, Don Van, Rouder, Jeffrey N., Schad, Daniel J., Shi'frin, Richard M., Singmann, Henrik, Vasishth, Shravan, Verissimo, Joao, Bockting, Florence, Chandramouli, Suyog, Dunn, John C., Gronau, Quentin F., Linde, Maximilian, McMullin, Sara D., Navarro, Danielle, Schnuerch, Martin, Yadav, Himanshu and Aust, Frederik 2023. Bayes factors for mixed models: A discussion. Computational Brain & Behavior 6 , pp. 140-158. 10.1007/s42113-022-00160-3

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Publisher:	Springer
ISSN:	2522-0861
Date of First Compliant Deposit:	24 January 2023
Date of Acceptance:	25 October 2022
Last Modified:	03 May 2023 07:27
URI:	https://orca.cardiff.ac.uk/id/eprint/156159

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