Using GAMMs to model trial-by-trial fluctuations in experimental data: more risks but hardly any benefit

Thul, R., Conklin, K. and Barr, D. J. (2021) Using GAMMs to model trial-by-trial fluctuations in experimental data: more risks but hardly any benefit. Journal of Memory and Language, 120, 104247. (doi: 10.1016/j.jml.2021.104247)

Abstract

Data from each subject in a repeated-measures experiment form a time series, which may include trial-by-trial fluctuations arising from human factors such as practice or fatigue. Concerns about the statistical implications of such effects have increased the popularity of Generalized Additive Mixed Models (GAMMs), a powerful technique for modeling wiggly patterns. We question these statistical concerns and investigate the costs and benefits of using GAMMs relative to linear mixed-effects models (LMEMs). In two sets of Monte Carlo simulations, LMEMs that ignored time-varying effects were no more prone to false positives than GAMMs. Although GAMMs generally boosted power for within-subject effects, they reduced power for between-subject effects, sometimes to a severe degree. Our results signal the importance of proper subject-level randomization as the main defense against statistical artifacts due to by-trial fluctuations.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Barr, Dr Dale
• Authors: Thul, R., Conklin, K., and Barr, D. J.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Journal of Memory and Language
• Publisher: Elsevier
• ISSN: 0749-596X
• ISSN (Online): 1096-0821
• Published Online: 20 April 2021
• Copyright Holders: Copyright © 2021 Elsevier Inc.
• First Published: First published in Journal of Memory and Language 120: 104247
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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