Balancing Type I Error and Power in Linear Mixed Models

@article{Matuschek2017BalancingTI,
  title={Balancing Type I Error and Power in Linear Mixed Models},
  author={Hannes Matuschek and Reinhold Kliegl and Shravan Vasishth and Harald Baayen and Douglas Bates},
  journal={Journal of Memory and Language},
  year={2017},
  volume={94},
  pages={305-315}
}
  • Hannes Matuschek, Reinhold Kliegl, +2 authors Douglas Bates
  • Published 2017
  • Psychology, Mathematics
  • Journal of Memory and Language
  • Abstract Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance-covariance structure of random effects (the maximal model; Barr, Levy, Scheepers & Tily, 2013), presumably to keep Type I error down to the… CONTINUE READING

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