Donna Harrington writes:

I will be teaching a new multilevel models course in the fall and am currently reading your text, /Data Analysis Using Regression and Multilevel/Hierarchical Models/ as I prepare. I am enjoying the book and am considering adopting it for use in the course.

Would you be willing to share the syllabus you have used for your Applied Regression and Multilevel Models course? I am particularly interested in seeing how much of the book you use in a one semester course.

My reply:

I have to admit that, over the years, I’ve made my syllabuses less and less detailed as I’ve focused more and more on writing the books. For a multilevel modeling course, I suggested the following:

– chapters 3,4,5: linear and logistic regression
– chapter 7: basics of simulation
– chapter 9: basics of causal inference
– chapters 11-14: multilevel linear and logistic regression (up to and including varying-intercept, varying-slope models)
– chapter 18: all the theory that they’ll need.

For a one-semester introductory course, my usual strategy for a one-semester course is to focus chapters 2-10: that is, cover everything except multilevel modeling. Linear regression, logistic, glm, computation, and causal inference. Then for the last part of the course, I can choose among some options, including: intro to multilevel models, sample size and power calculations, and missing data imputation.

P.S. To those of you who haven’t had the opportunity to take a course from me: Don’t worry about it. I’m better at writing than teaching. Maybe you’re better off learning out of one of my books with somebody else actually teaching the class.