# Overview

P533 is a tutorial introduction to doing Bayesian data analysis. The course is intended to make advanced Bayesian methods genuinely accessible to graduate students in the social sciences. Students from all fields are welcome and encouraged to enroll, and the course uses examples from a variety of disciplines. The course covers all the fundamental concepts of Bayesian methods, and works from the simplest models up through hierarchical models (a.k.a. multilevel models) applied to various types of data. More details about content are provided in the Schedule, below.

# Prerequisites

This is not a mathematical statistics course, but some math is unavoidable. If you understand basic summation notation like $$\sum_i x_i$$ and integral notation like $$\int x \, dx$$, then you’re in good shape. We will not be using much math, especially after the first few weeks.

We will, however, be doing a lot of computer programming in a language called R. R is free and can be installed on any computer. The textbook includes an introductory chapter on R and there are lots of resources online.

A previous course in traditional statistics or probability can be helpful as background, but is not essential. P533 proceeds independently of traditional (“null hypothesis significance testing”) statistical methods.

# Information online: Canvas

Our online hub for the course is IU Canvas. If you do not have access to Canvas, please notify Prof. Kruschke immediately.

# Contact

## Instructor

Prof. John Kruschke (pronounced “crush-kee”), . Office hours by appointment; please do ask.

## Assistant

See Canvas for info.

# Required textbook

Doing Bayesian Data Analysis, 2nd Edition: A Tutorial with R, JAGS, and Stan, by John K. Kruschke. The book is available to students online through the IU Library; a link will be posted in Canvas.

# Schedule

Spring 2020: Tu/Th 9:30-10:45am, room PY 111

The exact dates of some topics might flex, but the order of topics will be as shown below.

Week Day Chapter
1 Tu 2. Introduction: Credibility, models, and parameters. Strongly recommended article: Bayesian data analysis for newcomers
1 Th 3. The R programming language. Instructions for installation of software are here
2 Tu 4. Probability.
2 Th 5. Bayes rule.
3 Tu 6. Inferring a probability via mathematical analysis.
3 Th 7. Markov chain Monte Carlo (MCMC).
4 Tu 8. JAGS.
4 Th 8, continued.
5 Tu 9. Hierarchical (a.k.a. multi-level) models.
5 Th 9, continued. 10. Model comparison.
6 Tu 10, continued. 11. Null hypothesis significance testing (NHST). Strongly recommended article: The Bayesian New Statistics
6 Th 11. NHST, continued.
7 Tu 12. Bayesian null assessment. Strongly recommended article: Rejecting or accepting paramter values in Bayesian estimation and its important supplement.
7 Th 12, continued.
8 Tu 13. Goals, power, and sample size. See also this video.
8 Th 13, continued.
9 Tu 15. The generalized linear model. 16. Metric predicted variable, 1 or 2 group predictor variable.
9 Th 16, continued. Also power analysis applied to two groups. See article, Bayesian estimation supersedes the t test.
Break
10 Tu Extended Spring break due to corona virus disruption.
(Previously: 17. Metric predicted variable, metric predictor variable.)
10 Th Extended Spring break due to corona virus disruption.
(Previously: 17, continued. 18. Metric predicted variable, metric predictor variables. See also, The time has come: Bayesian methods for data analysis in the organizational sciences.)
11 Tu 17. Metric predicted variable, metric predictor variable.
(Previously: 18, continued.)
11 Th 17, continued. 18. Metric predicted variable, metric predictor variables. See also, The time has come: Bayesian methods for data analysis in the organizational sciences.
(Previously: 19. Metric predicted variable, nominal predictor variable.)
12 Tu 18, continued.
(Previously: 19, continued. 20. Metric predicted variable, nominal predictor variables.)
12 Th 19. Metric predicted variable, nominal predictor variable.
(Previously: 20, continued.)
13 Tu 19, continued. 20. Metric predicted variable, nominal predictor variables.
(Previously: 21. Dichotomous predicted variable (logistic regression).)
13 Th 20, continued.
(Previously: 22. Nominal predicted variable (softmax regression). For an applied example of hierarchical conditional logistic regression, see Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type.)
14 Tu 21. Dichotomous predicted variable (logistic regression).
(Previously: 22, continued. 23. Ordinal predicted variable (ordered probit regression). Recommended article: Analyzing ordinal data with metric models: What could possibly go wrong?.)
14 Th 22. Nominal predicted variable (softmax regression). For an applied example of hierarchical conditional logistic regression, see Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type.
(Previously: No class, to attend Conference of Midwestern Psych Association, but that was canceled.)
15 Tu 22, continued. 23. Ordinal predicted variable (ordered probit regression). Recommended article: Analyzing ordinal data with metric models: What could possibly go wrong?.
(Previously: 23, continued.)
15 Th 23, continued.
(Previously: 24. Count predicted variable.)
Finals No final exam, but last homework assignment is due.

All assignments are mandatory. Late homework is exponentially penalized with a half-life of one week, meaning that after one week 50% is the maximum possible score. (The R program for the exponential decay is in the Canvas files; see LatePenaltyCalculator.R.) No homework may be turned in more than three weeks later than its due date, and no homework may be turned in after 12:00 noon of Wednesday of finals week. There are two reasons for this policy: First, the course moves quickly and the material is cumulative, so the late penalty acts as an extra incentive to keep up. Second, the assistant, who will be grading the homework, must not be given a flood of late homework papers at the end of the semester. In recognition of the fact that “life happens” (e.g., short-term illness, personal turmoil, overwhelming confluence of deadlines, etc.), your two worst late penalties will be dropped. In other words, for every homework we will record the scores with and without a late penalty. The two homeworks with the largest difference between with- and without- late penalty will have their late penalty dropped. Note, therefore, that any homework not turned in will count as zero.

Grading is based on your total homework score, as a percentile relative to the class. There are no exams and no projects. N.B.: Scores tend to be very high, so do not think that, say, 96% must be a grade of A because it could end up being an A- if, say, two thirds of the class does better than 96%. Typically the late penalties turn out to be a bigger deduction than points missed due to errors, so don’t fall behind. As this is a graduate course, grades are typically in the A to high B range, and only rarely is a C or less assigned.

# Disclaimer

All information in this document is subject to change. Changes will be announced in class.