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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 Repositoryhttps://github.com/jonathantemplin/Bayesian-Psychometric-Modeling-Course-Fall2022
Syllabushttps://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.

Course Schedule and Content

DateTopicReading(s)Homework Assigned
24 Aug 2022Course Introduction (see syllabus)SyllabusNone
26 Aug 2022