PSY 646: Bayesian Statistics for Psychological Science
Days/times: Tuesday, Thursday / 9:00 am - 10:30 am
Location: PRCE 255 (will have computers with appropriate software installed)
- 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.
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.
- 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.
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.
- 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).