Center for Applied Statistics 430: Multilevel Modeling Seminar
Thursday, 4:00-6:00 PM in the Clinical Research Training Center (CRTC)
Classroom (Medical School, Wohl Building Second Floor).
- Course Description:
This course covers statistical model development with explicitly defined hierarchies. Such multilevel
specifications allow researchers to account for different structures in the data and provide for the
modeling of variation between defined groups. The course begins with simple nested linear models and
proceeds on to non-nested models, multilevel models with dichotomous outcomes, and multilevel
generalized linear models. In each case, a Bayesian perspective on inference and computation is
featured. The focus on the course will be practical steps for specifying, fitting, and checking
multilevel models with much time spent on the details of computation in the R and bugs environments.
- Prerequisite Details:
This course assumes only a knowledge of basic statistics as taught in a first year graduate sequence.
Topices should include: probability, cross-tabulation, basic statistical summaries, and linear
regression in either scalar or matrix form. Knowledge of matrix algebra is convenient but not required.
- Course Grade:
The final grade will be based on three components: weekly attendance and participation (20%), homework (60%)
and a review exam on December 10 (20%). Readings should be completed before class.
- Office Hours: Thursday 9-11, and by appointment.
(email Sue Tuhro to schedule)
- Incompletes: Due to the scheduled nature of the course, no incompletes will be given.
- Teaching Assistant: Tsung-han Tsai. Office Hours Thursday 2:30-4:00 in CRTC.
- Required Reading: Gelman and Hill, "Data Analysis Using Regression and Multilevel/Hierarchical
Models (Cambridge University Press 2007). Some papers will be available at jstor.org or distributed by the
instructor.
- Topics (subject to minor change):
- August 27: Introduction To the Course and Motivation
- Reading: Gelman & Hill, Chapters 1 and 2
- Exercises: Gelman & Hill 2.2, 2.3.
- September 3: No Meeting
- Reading: Gelman & Hill, Chapters 3 and 4. R handout.
- Exercises: Gelman & Hill 3.4, 4.4.
- September 10: Review of Linear and Generalized Linear Regression
- Reading: Gelman & Hill, Chapters 5 and 6
- Exercises: Gelman & Hill 5.4, 6.1.
- September 17: Multilevel Structures and Multilevel Linear Models: the Basics
- Reading Gelman & Hill, Chapters 11 and 12
- Exercises: Gelman & Hill 11.4, 12.2, 12.5.
- September 24: Multilevel Linear Models: Varying Slopes, Non-Nested Models and Other Complexities
- Reading Gelman & Hill, Chapter 13
- Exercises: Gelman & Hill 13.2, 13.5.
- October 1: Multilevel Logistic Regression
- Reading Gelman & Hill, Chapter 14 (skip Section 14.3)
- Exercises: Gelman & Hill 14.5, 14.6.
- October 8: Multilevel Modeling in Bugs and R: the Basics
- Reading Gelman & Hill, Chapter 16
- Exercises: Gelman & Hill 16.1, 16.3.
- October 15: Fitting Multilevel Linear and Generalized Linear Models in Bugs and R
- Reading Gelman & Hill, Chapter 17
- Exercises: Gelman & Hill Rerun 16.3 using the instructions in 17.2 and 17.3.
- October 22: Likelihood and Bayesian Inference and Computation, Part 1
- Reading Gelman & Hill, Chapter 18
- Exercises: Handount.
- October 29: Likelihood and Bayesian Inference and Computation
- Reading Gelman & Hill, Chapter 18
- Exercises: Handount.
- November 5: Sample Size and Power Calculations
- Reading Gelman & Hill, Chapter 20
- Exercises: 20.1, 20.3.
- November 12: Understanding and Summarizing the Fitted Models
- Reading Gelman & Hill, Chapter 21
- Exercises: Handount: 21.1, 21.3, 21.4.
- November 19: Analysis of Variance
- Reading Gelman & Hill, Chapter 22
- Exercises: Handount: 22.1.
- November 26: No meeting (Thanksgiving)
- December 3: Missing Data Imputation
- Reading Gelman & Hill, Chapter 25
- Exercises: Handout.
- December 10: Summary Exam
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