9.5 Further reading

Barr et al. (2013) is an important reference on the topic of Type I error inflation through overly simplified specification of the random effects structure of the linear mixed model, although also see Matuschek et al. (2017) and Bates et al. (2015). von der Malsburg and Angele (2017) investigate the problem of Type I error through multiple comparisons in linear mixed models, focusing on eyetracking reading data.

References

Barr, Dale J, Roger Levy, Christoph Scheepers, and Harry J Tily. 2013. “Random Effects Structure for Confirmatory Hypothesis Testing: Keep It Maximal.” Journal of Memory and Language 68 (3): 255–78.

Bates, Douglas M., Reinhold Kliegl, Shravan Vasishth, and Harald Baayen. 2015. “Parsimonious Mixed Models.”

Matuschek, Hannes, Reinhold Kliegl, Shravan Vasishth, R. Harald Baayen, and Douglas M. Bates. 2017. “Balancing Type I Error and Power in Linear Mixed Models.” Journal of Memory and Language 94: 305–15. https://doi.org/10.1016/j.jml.2017.01.001.

von der Malsburg, Titus, and Bernhard Angele. 2017. “False Positives and Other Statistical Errors in Standard Analyses of Eye Movements in Reading.” Journal of Memory and Language 94: 119–33.