Examples of mixed-effects modeling with crossed random effects and with binomial data

Quené, Hugo; van den Bergh, Huub

doi:10.1016/j.jml.2008.02.002

Cited by 534Statistical Methods and Bayesian InferenceNeurobiology of Language and BilingualismCategorization, perception, and language

Examples of mixed-effects modeling with crossed random effects and with binomial data

Quené, Hugo, van den Bergh, Huub·2008·Journal of Memory and Language

DOI 10.1016/j.jml.2008.02.002

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Generalized linear mixed-effects probit/logit models are more appropriate than ANOVA or t-test on participant averages for analyzing binary accuracy data.
“As a result, there is a growing body of literature that argues it is more appropriate to use generalized linear mixed-effects probit/logit models to analyze these types of accuracy data (Dixon, 2008; Jaeger, 2008; Quené and van den Bergh, 2008; Song et al., 2017).”
From Cognitive Mapping Without Vision: Comparing Wayfinding Performance After Learning From Digital Touchscreen-Based Multimodal Maps vs. Embossed Tactile Overlays (2020) · cites this paper as Quené and van den Bergh, 2008

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Authors: Quené, H., & van den Bergh, H.
Affiliations: Utrecht University (NL)
Corresponding author: Quené, Hugo
ORCID: Quené, Hugo
Venue: Journal of Memory and Language
Publisher: Elsevier BV
Published: 2008-11
Volume: 59
Issue: 4
Pages: 413-425
ISSN: 0749-596X (ISSN-L)
DOI: 10.1016/j.jml.2008.02.002
OpenAlex: W1988055263
Cited by: 534
References: 49
Citations by year: 2026: 11 · 2025: 14 · 2024: 17 · 2023: 19 · 2022: 17 · 2021: 44 · 2020: 38 · 2019: 44 · 2018: 44 · 2017: 41 · 2016: 23 · 2015: 28 · 2014: 48 · 2013: 46 · 2012: 33
FWCI: 20.57
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Memory and Language (publishedVersion)
UvA-DARE (University of Amsterdam) (publishedVersion)
UvA-DARE (University of Amsterdam) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: Universiteit Utrecht
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
Keywords: mixed models, crossed random effects, binomial data, generalized linear mixed models, repeated measures ANOVA, type I error

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