With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come
to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive …

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 …

Abstract The use of Linear Mixed-effects Models (LMMs) is set to dominate statistical
analyses in psychological science and may become the default approach to analyzing …

Linear mixed-effects models have increasingly replaced mixed-model analyses of variance
for statistical inference in factorial psycholinguistic experiments. Although LMMs have many …

The analysis of experimental data with mixed-effects models requires decisions about the
specification of the appropriate random-effects structure. Recently, Barr, Levy, Scheepers …

This article introduces two sophisticated statistical modeling techniques that allow
researchers to analyze systematicity, individual variation, and nonlinearity in second …

Proficient bilinguals use two languages actively, but the contexts in which they do so may
differ dramatically. The present study asked what consequences the contexts of language …

Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly
prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical …

The current study examined the extent to which pupillary responses (both pretrial baseline
and phasic responses) would accurately track lapses of attention as predicted by theories of …

Generalized additive mixed models are introduced as an extension of the generalized linear
mixed model which makes it possible to deal with temporal autocorrelational structure in …