# 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.