This Tutorial serves as both an approachable theoretical introduction to mixed-effects
modeling and a practical introduction to how to implement mixed-effects models in R. The …

This tutorial serves as a quick boot camp to jump-start your own analyses with linear mixed
effects models. This text is different from other introductions by being decidedly conceptual; I …

R is a freely available implementation of John Chambers' award-winning S language for
computing with data. It is “Open Source” software for which the user can, if she wishes …

Experimental designs that sample both subjects and stimuli from a larger population need to
account for random effects of both subjects and stimuli using mixed-effects models …

This chapter describes a class of statistical model that is able to account for most of the
cases of nonindependence that are typically encountered in psychological experiments …

Mixed-effects models are being used ever more frequently in the analysis of experimental
data. However, in the lme4 package in R the standards for evaluating significance of fixed …

Many common statistical models can be expressed as linear models that incorporate both
fixed effects, which are parameters associated with an entire population or with certain …

Well-designed computing tools play a fundamental role in the creation and sustenance of
widespread interest in the application of statistical models involving fi xed and random …

Mixed-effects models are a powerful tool for modeling fixed and random effects
simultaneously, but do not offer a feasible analytic solution for estimating the probability that …

Linear mixed-effects models (LMMs) are increasingly being used for data analysis in
cognitive neuroscience and experimental psychology, where within-participant designs are …