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ERIC Number: EJ818420
Record Type: Journal
Publication Date: 2008-Nov
Pages: 13
Abstractor: As Provided
Reference Count: 0
ISSN: ISSN-0749-596X
Examples of Mixed-Effects Modeling with Crossed Random Effects and with Binomial Data
Quene, Hugo; van den Bergh, Huub
Journal of Memory and Language, v59 n4 p413-425 Nov 2008
Psycholinguistic data are often analyzed with repeated-measures analyses of variance (ANOVA), but this paper argues that mixed-effects (multilevel) models provide a better alternative method. First, models are discussed in which the two random factors of participants and items are crossed, and not nested. Traditional ANOVAs are compared against these crossed mixed-effects models, for simulated and real data. Results indicate that the mixed-effects method has a lower risk of capitalization on chance (Type I error). Second, mixed-effects models of logistic regression (generalized linear mixed models, GLMM) are discussed and demonstrated with simulated binomial data. Mixed-effects models effectively solve the "language-as-fixed-effect-fallacy", and have several other advantages. In conclusion, mixed-effects models provide a superior method for analyzing psycholinguistic data. (Contains 4 tables and 1 figure.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A