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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).