This monograph is written assuming familiarity with linear models and matrix algebra and some exposure to mixed models and logistic regression. References are given to more standard texts that cover some of the basic material in more depth. The monograph begins with an extended example that introduces all the main ideas. Chapters 2 and 3 briefly review linear mixed and generalized linear models and Chapter 4 defines and introduces GLMMs. Chapter 5 illustrates the breadth of inferential goals possible with GLMMs. One of my main attractions in conducting research on this class of models was the wide variety of practical applications. Chapters 6 through 9 contain the "meat" and tackle the difficult aspects fitting these models to data. The monograph is organized along the lines of the CBMS lectures.