Bayes Factors for Mixed Models: Perspective on Responses

Doorn, Johnny van; Aust, Frederik; Haaf, Julia M.; Stefan, Angelika; Wagenmakers, Eric‐Jan

doi:10.1007/s42113-022-00158-x

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…MRE ANOVA is most appropriate when the population average is of primary interest, and it is more robust to outlying individuals. We also refer interested readers to a recent special issue on Bayes factors for linear mixed-effect models that further discusses the choice between SFR-and MRE-model specifications (Rouder et al, 2023;Singmann et al, 2023;van Doorn et al, 2021van Doorn et al, , 2022.…”

Section: Different Model Specificationsmentioning

confidence: 99%

Bayesian Repeated-Measures Analysis of Variance: An Updated Methodology Implemented in JASP

Bergh

Wagenmakers

Aust

2023

Advances in Methods and Practices in Psychological Science

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Analysis of variance (ANOVA) is widely used to assess the influence of one or more experimental (or quasi-experimental) manipulations on a continuous outcome. Traditionally, ANOVA is carried out in a frequentist manner using p values, but a Bayesian alternative has been proposed. Assuming that the proposed Bayesian ANOVA is closely modeled after its frequentist counterpart, one may be surprised to find that the two can yield very different conclusions when the design involves multiple repeated-measures factors. We illustrate such a discrepancy with a real data set from a two-factorial within-subject experiment. For this data set, the results of a frequentist and Bayesian ANOVA are in a disagreement about which main effect accounts for the variance in the data. The reason for this disagreement is that frequentist and the proposed Bayesian ANOVA use different model specifications. As currently implemented, the proposed Bayesian ANOVA assumes that there are no individual differences in the magnitude of effects. We suspect that this assumption is neither obvious to nor desired by most analysts because it is untenable in most applications. We argue here that the Bayesian ANOVA should be revised to allow for individual differences. As a default, we suggest the standard frequentist model specification but discuss a recently proposed alternative and provide guidance on how to choose the appropriate model specification. We end by discussing the implications of the revised model specification for previously published results of Bayesian ANOVAs.

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Section: Different Model Specificationsmentioning

confidence: 99%

Bayesian Repeated-Measures Analysis of Variance: An Updated Methodology Implemented in JASP

Bergh

Wagenmakers

Aust

2023

Advances in Methods and Practices in Psychological Science

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The role of frontal cortex in novel-word learning and consolidation: Evidence from focal transcranial direct current stimulation

Riemann,

van Lück,

Rodríguez-Fornells

et al. 2024

Cortex

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Effects of training length on adaptation to noise-vocoded speech

Drouin,

Flores

2024

The Journal of the Acoustical Society of America

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Listeners show rapid perceptual learning of acoustically degraded speech, though the amount of exposure required to maximize speech adaptation is unspecified. The current work used a single-session design to examine the length of auditory training on perceptual learning for normal hearing listeners exposed to eight-channel noise-vocoded speech. Participants completed short, medium, or long training using a two-alternative forced choice sentence identification task with feedback. To assess learning and generalization, a 40-trial pre-test and post-test transcription task was administered using trained and novel sentences. Training results showed all groups performed near ceiling with no reliable differences. For test data, we evaluated changes in transcription accuracy using separate linear mixed models for trained or novel sentences. In both models, we observed a significant improvement in transcription at post-test relative to pre-test. Critically, the three training groups did not differ in the magnitude of improvement following training. Subsequent Bayes factors analysis evaluating the test by group interaction provided strong evidence in support of the null hypothesis. For these stimuli and procedure, results suggest increased training does not necessarily maximize learning outcomes; both passive and trained experience likely supported adaptation. Findings may contribute to rehabilitation recommendations for listeners adapting to degraded speech signals.

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Bayes Factors for Mixed Models: Perspective on Responses

Cited by 5 publications

References 51 publications

Bayesian Repeated-Measures Analysis of Variance: An Updated Methodology Implemented in JASP

Bayesian Repeated-Measures Analysis of Variance: An Updated Methodology Implemented in JASP

The role of frontal cortex in novel-word learning and consolidation: Evidence from focal transcranial direct current stimulation

Effects of training length on adaptation to noise-vocoded speech

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