PSY 646: Bayesian Statistics for Psychological Science
Fall 2016
Days/times: Tuesday, Thursday / 9:00 am - 10:30 am
Location: PRCE 255 (will have computers with appropriate software installed)
Notes:
- In class, there was a request for access to the t-test simulation that explores robustness. It is at IntroStats Online, an online statistics textbook. To log in use ID Greg99-0 and password 12345678. When the page loads, it first shows 7 questions. Just guess on each question and then the simulation will appear. At some point the questions come back, but you can ignore them.
- Some one in class pointed out to me that the textbook is on sale at the publisher. If you have not yet bought the textbook, this might be a good opportunity.
- I activated the Blackboard site for this class, and I think I set up a forum to discuss the textbook homework problems (but you can use it for other purposes too). Let me know if it works, or not.
Instructor:
Please contact me (email is best) if you cannot visit during office hours to schedule an alternative time to meet.
Materials (lectures, readings):
- PPT slides for 23 August.
- PPT slides for 25 August.
- PPT slides for 30 August and 01 September.
- PPT slides for 01 and 06 September.
- PPT slides for 08 September.
- PPT slides for 13 September, Shrinkage.R, ShrinkagePrediction.R.
- PPT slides for 15 September, VisualSearch.R, VisualSearch.csv.
- AgeOfAcquisition.csv.
- PPT slides for 22 September, VisualSearch2.R, VisualSearch3.R (slides updated on September 18).
- PPT slides for 27 September (updated September 21), Related code: AIC.R, AIC2.R VisualSearch4.R. Read up through Chapter 6 of the textbook.
- PPT slides for 29 September, Related code: FacialFeedback.R, FacialFeedback.csv
- PPT slides for 04 October, Related code: Zenner1.R (updated 04 October with a better approach to extracting coefficients from the models), ZennerCards.csv
- PPT slides for 06 October (updated 05 October), Related code: VisualSearch5.R, VisualSearch5c.R, VisualSearch5e.R, VisualSearch5f.R.
- PPT slides for 13 October , Related code: FacialFeedback2.R, (corrected)FacialFeedback.csv, Zenner3.R.
- Analysis assignment 1 (Due October 27) , Related files: LevelsProcessing.csv.
- PPT slides for 25 October , Related code: MapSearch3.R, MapSearch.csv.
- PPT slides for 03 November (a bit of a mess).
- PPT slides for 08 November (Bayes Factors).
- PPT slides for 10 November (Bayes Factors). Updated 11 November to include a few cautionary slides at the end.
- Analysis assignment 2 (Due November 29) , Related file: DecisionMaking.csv.
- PPT slides for 15 November (Bayes Factors). Related files: DecisionMaking1.R, Related file: SerialPosition.csv, SerialPosition.R.
- PPT slides for 17 November . Related files: DecisionMaking1.R, Related files: SternbergSearch.csv, OneSubject.csv, SternbergSearch.R, SternbergSearch2.R, SternbergSearch3.R.
- PPT slides for 22 November (Decision Making) .
- Given the discussion in class on November 22, you might be interested in a discussion about how many quarterS fit into a one quart mason jar. We had a one pint jar, so divide the answer there by two.
- PPT slides for 29 November (Model convergence) . Related files: SternbergSearch11.R, Related files: SternbergSearch.csv.
- Analysis assignment 3 (Due December 15) , Related file: WordLength.csv.
- PPT slides for 01 December (Model checking) . Related files: SternbergSearch8.R, SternbergSearch12.R, Related files: SternbergSearch.csv, SSmodelInteractionWithCauchy.Rpd, SSmodelInteractionOnlyWithCauchy.Rpd, SSmodelAdditiveWithCauchy.Rpd.
- In case you do not have the textbook, Chapter 1 of the textbook is on-line.
Text:
Kruschke, J. Doing Bayesian Data Analysis, 2nd Edition. The text is available as an e-Book (PDF), print, or Kindle text. Details are at the book web site.
Sorry for the last minute change, but late in the summer I found a book that covers exactly what I want the course to focus on:
 |
McElreath, R Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Ordering information and code examples are at the book web site. |
General plan: The course will explain why you might want to use Bayesian methods instead of frequentist methods (such as t-tests, ANOVA, or regression). The general plan is to:
- Explain some problems/difficulties with frequentist methods: Publication bias, optional stopping, questionable research practices.
- Discuss differences between hypothesis testing and prediction: mean squared error, shrinkage.
- Discuss methods for prediction: likelihood, AIC, BIC, cross-validation, lasso.
- Explain the basic ideas of Bayesian methods: non-informative priors, informative priors.
- Provide hands-on examples of applying Bayesian methods: Bayes Factors, hierarchical models.
- Discuss ways to make decisions: utility.
Throughout, we will be using computer programs to demonstrate the ideas. There will not be any proofs.
Class home page: The home page for this course is http://www.psych.purdue.edu/~gfrancis/Classes/PSY646/indexF16.html From this page you can download lecture notes, view the class schedule, view current grades, and connect to the various homework laboratory assignments.
Homework: Assignments will be due approximately every two weeks. The intention is to use the homework assignments as a way of practicing the concepts we discuss in class. They will be graded, but only to insure that students actively participate.
Project: In the last two weeks, students will present a Bayesian (or related) analysis of some of their own data. If you do not happen to have a data set, we will get one for you.
Assumed background:
- It would be nice, but not necessary, if you had some previous exposure to calculus.
- Doing any kind of Bayesian analysis requires some programming. We will be using the free R studio program. You do not need to be an expert programmer, but if you have little programming experience, you will have some catching up to do.
- Students should have experience with typical statistical methods (t-test, ANOVA, regression).