This easy-to-use resource provides a clear explanation of mixed modeling techniques and theories and demonstrates the use of popular statistical software procedures for fitting linear mixed models (LMMs). The authors fit LMMs based on both general and hierarchical model specifications, develop the model-building process step-by-step, and demonstrate the estimation, testing, and interpretation of fixed-effect parameters and covariance parameters associated with random effects. These concepts are illustrated through examples of real-world data sets using R, SAS, SPSS, HLM, and Stata that enable comparison of model fitting options and results across the software procedures. A supporting website provides software code and the data sets discussed in the book.