H. van den Bergh
- Examples of mixed-effects modeling with crossed random effects and with binomial data
- Journal of Memory and Language
- Volume | Issue number
- 59 | 4
- Pages (from-to)
- Document type
- Faculty of Social and Behavioural Sciences (FMG)
- Research Institute of Child Development and Education (RICDE)
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.
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