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The content of the workshop is aimed at scientists who already have basic knowledge of R and have been carrying out traditional statistical analyses in this environment. This workshop does not offer an introduction to R. Visualization of individual differences and item differences in (quasi-) experimental effects (Kliegl et al., 2011).Computation and visualization of LMM partial effects with the remef() function (Hohenstein &.Confidence intervals for model parameters.Model selection through model comparison (LRT, AIC, BIC).Specification of fixed effects, variance components, correlation parameters.LMMs (Kliegl et al., 2011 Hohenstein & Kliegl, 2014 Masson & Kliegl, 2013).Contrast specification of fixed effects (Kliegl & Vasishth, 2013).Transformation of dependent variable (Kliegl et al., 2010).The workshop is structured into four sections: The goal of this workshop is to teach how LMMs go beyond the usual rmAOV and rmMR analyses, using the lmer()-function of the lme4 package (Version 1.1-4 Bates, Maechler, Bolker, & Walker, 2014) in the R environment (R Core Team, 2013). This workshop focuses on analyses of these two types of dependent variable. Linear Mixed Models (LMMs) are increasingly replacing traditional F1/F2-mixed-model/repeated-measure ANOVAs (rmAOVs i.e., designs with both between- and within-subject/item factors, e.g., Kliegl, Risse & Laubrock, 2007) or repeated-measures multiple regressions (rmMRs e.g., Kliegl, 2007) for statistical inference about experimental effects on fixation durations or response latencies. To register for this workshop (open to the academic community having some experience with R): please go to