Focusing on mixed-effects models, this book offers a comprehensive guide to analysing experiments across diverse fields, including behavioural, agricultural, and medical sciences. The text opens with a traditional analysis of variance and then ranges from linear fixed-effects models to generalised linear mixed-effects models. It covers the most common experimental designs, such as factorial, hierarchical, between-subject, within-subject, cross-over, two-factor mixed, and split-plot designs, before studying analysis of covariance, models with group-specific error variances and models for repeated-measures analysis. Frequently drawing on real-life experiments, the book offers 69 examples and 134 exercises. Readers are supported with digital supplements, comprising the solutions to exercises, the datasets and R code and SAS code for all examples requiring software computation. This is an essential resource for students, practitioners conducting experiments and applied statisticians wishing to use mixed-effects models for the analysis of experiments.