A guided tutorial on linear mixed-effects models for the analysis of accuracies and response times in experiments with fully crossed design

Experiments with fully crossed designs are often used in experimental psychology spanning several fields, from cognitive psychology to social cognition. These experiments consist in the presentation of stimuli representing super-ordinate categories, which have to be sorted into the correct category in two contrasting conditions. This tutorial presents a linear mixed-effects model approach for obtaining Rasch-like parameterizations of response times and accuracies of fully crossed design data. The modeling framework for the analysis of fully crossed design data is outlined along with a step-by-step guide of its application, which is further illustrated with two practical examples based on empirical data. The first example regards a cognitive psychology experiment and pertains to the evaluation of a spatial–numerical association of response codes effect. The second one is based on a social cognition experiment for the implicit evaluation of racial attitudes. A fully commented R script for reproducing the analyses illustrated in the examples is available in the online supplemental materials.