Journal of Memory and Language· 2017Q1
Balancing Type I error and power in linear mixed models
- 1,997citations
- Q1SCImago
- 2017year
Short summary
Fitting maximal linear mixed-effects models (LMMs) to psycholinguistic data can significantly reduce statistical power without fully controlling Type I error, compared to using model selection criteria.
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Key points
- Maximal LMMs, while intended to control Type I error, can significantly reduce statistical power.
- Fitting only random intercepts in LMMs may increase Type I error.
- Model selection criteria offer a way to achieve higher power without inflating Type I error in LMMs.
- Simulations demonstrate the trade-offs between model complexity, Type I error, and power in LMMs.
AI-generated from the title and abstract; the full text is not read.
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 nominal α 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, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data.
The authors' abstract, as published at the source. Journal of Memory and Language, 2017 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics