Course Information
| Instructor: | Jonathan Templin |
| email: | jonathan-templin@uiowa.edu |
| Office: | S210B Lindquist Center |
| Office Phone: | 319-335-6429 |
| Classroom: | S302 Lindquist Center |
| Meeting Time: | W & F 12:30-13:45 |
| Office Hours: | W 14:00-16:00 or by appointment |
| GitHub Repository | https://github.com/jonathantemplin/Bayesian-Psychometric-Modeling-Course-Fall2022 |
| Syllabus | https://github.com/jonathantemplin/Bayesian-Psychometric-Modeling-Course-Fall2022/raw/main/syllabus/bpm22_syllabus.pdf |
Course Objectives, and Prerequisites
In this course, a unified Bayesian modeling approach will be presented across traditionally separate families of psychometric models. Focusing more directly how to use Bayesian methods in psychometrics, this course will to cover Bayesian theory along with applied treatments of popular psychometric models, including confirmatory factor analysis (CFA), item response theory (IRT), latent class analysis, diagnostic classification models, and Bayesian networks. The focus of this course will be on model building directly in Bayesian programs (i.e., stan and JAGS) rather than the use of packages that build such code automatically.
Time permitting, multilevel models and multilevel psychometric models will be presented.