Using mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects. This book takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. It examines what is known and unknown about mixed linear models and identifies research opportunities. Supporting materials are available on the author's website.
