Principles of model specification in ANOVA designs

Rouder, Jeffrey N., Schnuerch, Martin, Haaf, Julia M. and Morey, Richard D. ORCID: https://orcid.org/0000-0001-9220-3179 2023. Principles of model specification in ANOVA designs. Computational Brain & Behavior 6 (1) , pp. 50-63. 10.1007/s42113-022-00132-7

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Abstract

ANOVA—the workhorse of experimental psychology—seems well understood in that behavioral sciences have agreed-upon contrasts and reporting conventions. Yet, we argue this consensus hides considerable flaws in common ANOVA procedures, and these flaws become especially salient in the within-subject and mixed-model cases. The main thesis is that these flaws are in model specification. The specifications underlying common use are deficient from a substantive perspective, that is, they do not match reality in behavioral experiments. The problem, in particular, is that specifications rely on coincidental rather than robust statements about reality. We provide specifications that avoid making arguments based on coincidences, and note these Bayes factor model comparisons among these specifications are already convenient in the BayesFactor package. Finally, we argue that model specification necessarily and critically reflects substantive concerns, and, consequently, is ultimately the responsibility of substantive researchers. Source code for this project is at github/PerceptionAndCognitionLab/stat_aov2.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Springer
ISSN:	2522-0861
Date of First Compliant Deposit:	6 March 2023
Date of Acceptance:	20 January 2022
Last Modified:	03 May 2023 09:03
URI:	https://orca.cardiff.ac.uk/id/eprint/157516

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