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Matuschek, et al. (2017). Balancing Type I Error and Power in Linear Mixed Models
Hannes Matuschek, Reinhold Kliegl, Shravan Vasishth, Harald Baayen, Douglas BatesAbstract: Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. The advantages of LMMs over ANOVAs, however, come at a cost: Setting up an LMM is not as straightforward as running an ANOVA. One simple option, when numerically possible, is to fit the full variance-covariance structure of random effects (the maximal model; Barr et al., 2013), presumably to keep Type I error down to the nominal $\alpha$ in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, models with a random effect structure that is supported by the data have optimal Type I error and power properties.
Also available at arXiv.
 MatuschekPreprint2015.zip | [ ] | [ ] | 18307 Kb | 16/02/2017 15:02 |
 manuscript.pdf | [ ] | [ ] | 1945 Kb | 28/07/2016 14:56 |
