Course Materials
(Subject to minor change; make sure to check out the latest version)
- August 26
Introducing Bayesian Inference - September 2
Linear Model Theory Review - September 9
Multilevel Structures and Multilevel Linear Models: the Basics - September 16
Multilevel Linear Models: Varying Slopes, Non-Nested Models and Other Complexities - September 23
Multilevel Logistic Regression, Multilevel Generalized Linear Models - September 30
Multilevel Modeling in Bugs and R: the Basics, MCMC Theory. Part 1 - October 7
Causal Inference. Guest lecture by Dr. Ryan Moore - October 14
Multilevel Modeling in Bugs and R: the Basics, MCMC Theory. Part 2 - October 21
Fitting Multilevel Linear and Generalized Linear Models in Bugs and R, MCMC Coding - October 28
Understanding and Summarizing the Fitted Models, Multilevel Analysis of Variance - November 4
Model Checking and Comparison - November 11
Treatment of Missing Data - November 18
Sample Size and Power Calculations - November 25
Online Wrap Up and Discussion
Calendar
August 26
Introducing Bayesian Inference
Reading:
- R For Beginners (to make sure you are fluent on the basics)
- Gelman & Hill, Chapters 1 and 2
- MLE Review
Code/Data: Intro code
Slides: Introduction to Bayesian methods (This is the slides for the lecture.); Bayesian mechanics slides; Preview of multilevel models.
Exercise: Exercise 1
September 2
Linear Model Theory Review
Exercise 1 due
Reading:
- Gelman & Hill, Chapters 3 and 4
Code/Data: Chapter 3-4 code, Binomial PMF likelihood grid search, Anaemia data, Tweed data, clx.R
Slides: Linear model
Exercise: Exercise 2
September 9
Multilevel Structures and Multilevel Linear Models: the Basics
Exercise 2 due
Reading:
- Gelman & Hill, Chapters 11 and 12
- Introductory Chapter (Gill and Womack, from the SAGE Handbook of Multilevel Modeling)
Code/Data: chapter 11-12 code, Radon data, Uranium data, Smoking data
Slides: Intro to hierarchical modelling
Exercise: Exercise 3
September 16
Multilevel Linear Models: Varying Slopes, Non-Nested Models and Other Complexities
Exercise 3 due
Reading:
- Gelman & Hill, Chapter 13
Code/Data: Chapter 13 code
Slides: Multilevel linear models
Exercise: Exercise 4
September 23
Multilevel Logistic Regression, Multilevel Generalized Linear Models
Exercise 4 due
Reading:
- Gelman & Hill, Chapter 14 (skip Section 14.3), Chapter 15
Code/Data: Chapter 14 code, polls.dta file (remove .txt appendix, load with foreign library), cheney.asia.sub.txt, police_stops_data.txt.
Slides: Multilevel logistic regression
Exercise: Exercise 5 (see data inside)
September 30
Multilevel Modeling in Bugs and R: the Basics, MCMC Theory. Part 1
Exercise 5 due
Reading:
- Bayesian Estimation Case Study (Gill and Witko 2012)
Code/Data: R to JAGS code, data
Slides: MCMC methods
Exercise: Exercise 6
October 7
Causal Inference. Guest lecture by Dr. Ryan Moore
Exercise 6 due
Reading:
- Gelman & Hill Chapters 9 and 10
Code/Data:
Slides:
Exercise: Exercise 7
October 14
Multilevel Modeling in Bugs and R: the Basics, MCMC Theory. Part 2
Exercise 7 due
Reading:
- Gelman & Hill Chapter 16
Code/Data: Chapter 16 code
Slides: BUGS Modeling Language
Exercise: Exercise 8
October 21
Fitting Multilevel Linear and Generalized Linear Models in Bugs and R, MCMC Coding
Exercise 8 due
Reading:
- Gelman & Hill, Chapter 17
Code/Data: Chapter 17 code
Slides: BUGS Modeling Language
Exercise: Exercise 9
October 28
Understanding and Summarizing the Fitted Models, Multilevel Analysis of Variance
Exercise 9 due
Reading:
- Gelman & Hill, Chapter 21
Code/Data: Chapter 21 code, ANOVA, CD4 data, Caesarian data, Bypass data, Depression data.
Slides: Understanding and summarizing models, ANOVA
Exercise: Exercise 10
November 4
Model Checking and Comparison
Exercise 10 due
Reading:
- Gelman & Hill, Chapter 24
Code/Data: Chapter 24 code
Slides: Model checking
Exercise: Exercise 11
November 11
Treatment of Missing Data
Exercise 11 due
Reading:
- Gelman & Hill, Chapter 25
- Paper by van Buuren and Groothuis-Oudshoorn
Code/Data: Chapter 25 code
Slides: Missing data
Exercise: Exercise 12 (see data inside)
November 18
Sample Size and Power Calculations
All remaining homework and the project due this day
Reading:
- Gelman & Hill, Chapter 20
Code/Data: None.
Slides: Sample size
Exercise: None.