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Course Materials

(Subject to minor change; make sure to check out the latest version)

  1. August 26
    Introducing Bayesian Inference
  2. September 2
    Linear Model Theory Review
  3. September 9
    Multilevel Structures and Multilevel Linear Models: the Basics
  4. September 16
    Multilevel Linear Models: Varying Slopes, Non-Nested Models and Other Complexities
  5. September 23
    Multilevel Logistic Regression, Multilevel Generalized Linear Models
  6. September 30
    Multilevel Modeling in Bugs and R: the Basics, MCMC Theory. Part 1
  7. October 7
    Causal Inference. Guest lecture by Dr. Ryan Moore
  8. October 14
    Multilevel Modeling in Bugs and R: the Basics, MCMC Theory. Part 2
  9. October 21
    Fitting Multilevel Linear and Generalized Linear Models in Bugs and R, MCMC Coding
  10. October 28
    Understanding and Summarizing the Fitted Models, Multilevel Analysis of Variance
  11. November 4
    Model Checking and Comparison
  12. November 11
    Treatment of Missing Data
  13. November 18
    Sample Size and Power Calculations
  14. November 25
    Online Wrap Up and Discussion

Calendar

August 26
Introducing Bayesian Inference


Reading:

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:

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.


November 25
Online Wrap Up and Discussion