PSY 626: Bayesian Statistics for Psychological Science
Fall 2020
Days/times: Tuesday, Thursday / 10:30 am - 11:45 am
Location: Online
Update
- August 25: I changed the link to the WebEx meeting, below. This might work better.
- August 25: A link to the recording of today's lecture is now available, below.
- August 27: The TA set up some instructions for installing software we will (eventually) use on a Scholar cluster. It also includes some tips for other systems. If you have problems, please contact mkon@purdue.edu.
- September 1: ITP may still be setting up student access to the Scholar cluster. If you do not get it to work by Thursday, let me know and I will reach out to them.
- September 9: Homework 1 is available, below.
- September 22: Despite the problems we had this morning with WebEx, we will try to use it again on Thursday. Skype will be our backup plan.
- October 12: I forgot to put a time for submitting homework 2. Send it to Maria by midnight on Friday, October 16.
- October 28: Each student will prepare and present a "final project" for PSY 626. Using a data set, you will run a Bayesian analysis (exactly how is up to you). Ideally, you have your own data set to use for this investigation, but if you need a data set, I can provide one for you. Although the end of the semester is several weeks away, it seems prudent to start scheduling the student presentations. I plan to have two presentations each class period. Please sign up for a day at a Google doc by putting your name next to the date you are willing to present in the Speaker 1 or Speaker 2 column.
- November 5: In response to a question after lecture, I mentioned a statistics focused blog hosted by Andrew Gelman. It is Statistical Modeling, Causal Inference, and Social Science. It discusses a variety of statistical-related topics. Gelman is a prominent Bayesian. Many of the discussions are quite interesting.
Instructor:
Office hours: Virtual office hours will be held 2:30-3:30 pm (US Eastern time) via WebEx. If WebEx asks for a meeting ID, use: gfrancis.
Materials (lectures, readings, datasets, code):
- PPT slides for Lecture 1, Francis (2019).
- PPT slides for Lecture 2, Francis (2012).
- PPT slides for Lecture 3, Francis (2014).
- PPT slides for Lecture 4.
- PPT slides for Lecture 5, Shrinkage.R, ShrinkagePrediction.R.
- PPT slides for Lecture 6, VisualSearch.csv, VisualSearch.R.
- PPT slides for Lecture 7, VisualSearch2.R, VisualSearch3.R.
- PPT slides for Lecture 8, VisualSearch4.R.
- PPT slides for Lecture 9, SmilesLeniency.csv, SmilesLeniency1.R.
- PPT slides for Lecture 10, WeaponPrime.csv, WeaponPrime1.R, FacialFeedback.csv, FacialFeedback.R.
- PPT slides for Lecture 11, DecisionMaking.csv, DecisionMaking.R, ZennerCards.csv, Zenner1.R.
- PPT slides for Lecture 12, VisualSearch.csv, VisualSearch5.R, VisualSearch5c.R, VisualSearch5e.R, VisualSearch5f.R.
- PPT slides for Lecture 13, FacialFeedback.csv, FacialFeedback2.R, ZennerCards.csv, Zenner3.R.
- PPT slides for Lecture 14, SmilesLeniency.csv, SmilesLeniency2.R, ADHDTreatment.csv, ADHDTreatment1.R.
- PPT slides for Lecture 15.
- PPT slides for Lecture 16.
- PPT slides for Lecture 17.
- PPT slides for Lecture 18, OneSubject.csv, SternbergSearch0.R, SternbergSearch1.R, SternbergSearch.csv, SternbergSearch2.R, SternbergSearch2b.R, SternbergSearch3.R.
- PPT slides for Lecture 19.
- PPT slides for Lecture 20, MoonIllusion.zip (data and analysis files).
- PPT slides for Lecture 21, PhysiciansWeight.csv, PhysiciansWeight1.R.
Class recordings:
- August 25, 2020.
- August 27, 2020.
- September 1, 2020. (The recording starts 5-10 minutes in the lecture; I just forgot to start recording until then.)
- September 3, 2020.
- September 8, 2020.
- September 10, 2020.
- September 15, 2020.
- September 17, 2020.
- September 22, 2020.
- September 24, 2020. (Sorry, the lecture did not get recorded. I thought I had pressed the record button, but apparently, I had not.)
- September 29, 2020.
- October 1, 2020.
- October 6, 2020.
- October 8, 2020.
- October 13, 2020.
- October 15, 2020.
- October 20, 2020.
- October 22, 2020.
- October 27, 2020.
- October 29, 2020.
- November 3, 2020.
- November 5, 2020.
- November 10, 2020.
- November 12, 2020.
Text:
|
McElreath, R Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Try to get the second edition. Ordering information and code examples are at the book web site. |
In case you do not yet have the textbook, Chapters 1 and 2 of the textbook are on-line.
Lectures:
Lectures will happen online via via WebEx. If WebEx asks for a meeting ID, use: gfrancis. I will record the class meetings and make them available. Try to attend the (online) class if possible, so that we can address questions.
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.
- Explain the basic ideas of Bayesian methods: non-informative priors, informative priors.
- Provide hands-on examples of applying Bayesian methods: Bayes Factors, hierarchical models.
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/PSY626/indexF20.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.
- Homework 1: as PDF, as MS Word, ComputePower.R,
- Homework 2: as PDF, as MS Word, SleepySubjects.csv,
- Homework 3: as PDF, as MS Word, LookDontType.csv,
Project: In the last two weeks, students will present (document updated 29 October 20202) 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 software. Many people like the 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).
Teaching Assistant:
Please contact the TA if you cannot meet during office hours to schedule an alternative time.