APPLIED BAYESIAN STATISTICS FOR EDUCATION RESEARCH
Covers the basic elements of Bayesian statistics through lecture, discussion and practice. Focuses on why the Bayesian perspective provides a powerful alternative to the frequentist perspective. Topics to be covered include; Bayes' theorem, Markov chain Monte Carlo sampling and the "rjags" program, Bayesian hypothesis testing, Bayesian model building and evaluation, Bayesian approaches to missing data, Bayesian model averaging, Bayesian multilevel models and latent variable models, and philosophical debates within the Bayesian paradigm of statistics.
Prerequisites & course information
Requisites: ED PSYCH 763 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Understand, analyze and write up an empirical study using methods of Bayesian statistical inference. Audience: Graduate 2. Analyze and properly interpret data from a Bayesian perspective. Audience: Graduate Learning Outcomes: 1. Understand, analyze and write up an empirical study using methods of Bayesian statistical inference. Audience: Graduate 2. Analyze and properly interpret data from a Bayesian perspective. Audience: Graduate
Sources
- Course catalogue ↗2026-2027