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Bayesian Psychometric Modeling (Fall 2024)

Course Information
Instructor:Jonathan Templin
email:jonathan-templin@uiowa.edu
Office:S300A Lindquist Center
Office Phone:319-335-6429
Classroom:Zoom Only ( https://uiowa.zoom.us/j/91009790282?pwd=K4T1Da6Nb9iTl1Vfaox1Mki3tq0GUl.1)
Meeting Time:T & Th 9:30am-10:45am
Office Hours:W 10am-12pm on Zoom Only ( https://uiowa.zoom.us/my/jonathantemplinuiowa)
Course YouTube Playlisthttps://www.youtube.com/playlist?list=PLSmMs4UgmSMhG7GYKRpSftbdxTCPFUsBZ
IDAS Notebooks Sitehttps://notebooks.hpc.uiowa.edu/fall2024-psqf-7375-0exw/hub/home
GitHub Repositoryhttps://github.com/jonathantemplin/Bayesian-Psychometric-Modeling-Course-Fall-2024
Syllabushttps://jonathantemplin.github.io/Bayesian-Psychometric-Modeling-Course-Fall-2024/syllabus/bpm24_syllabus.pdf
Teaching Assistant:Bladimir Padilla
Teaching Assistant email:geraldo-padilla@uiowa.edu
Teaching Assistant Office HoursW 9am-10am and F 10am-12pm in N476LC and on Zoom at https://uiowa.zoom.us/my/bladimirpadilla

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 AssignedFormative Assessment
27 AugCourse Introduction (see syllabus )

SyllabusNoneNone
29 AugIntroduction to Bayesian Concepts (Lecture 01)

LM (2016): Chs. 2 & 3 None
2 SepFA 1 (Via Icon)
3 SepIntroduction to Psychometric Models (Lecture 02)

McDonald (1999): Chapters 1 & 2 (via ICON)
5 Sep
9 SepFA 2 (Via Icon)
10 SepMCMC and Stan (Lecture 03a)




LM (2016): Ch. 5
12 Sep
17 SepExample Bayesian Linear Model (Lecture 03a Example)

FA 3 (Via Icon)
19 SepNo Live Course (JT at a conference)

Listen to the Learning Bayesian Statistics Podcast where I appeared:

Link on my main website page
24 SepNo Live Course (JT at a conference)

Work on HW 2
26 Sep No Live Course (JT at a conference)

Work on HW 2
1 OctExample Bayesian Linear Model (Lecture 03a Example)
3 OctEfficient Stan Code and Generated Quantities (Lecture 03b)
8 OctNote: No live lecture!

Bayesian Model Fit and Comparisons (Lecture 03c)
10 Oct
15 OctGeneralized Measurement Models (Lecture 04a)
17 OctModeling Observed Data (Lecture 04b)
22 OctFA 6 (Via Icon)
24 OctModeling Observed Dichotomous Data (Lecture 04c)
29 OctFA 7 (Via Icon)
31 Oct
5 NovModeling Observed Polytomous Data (Lecture 04d)
7 NovNote: No "live" lecture--please see video above (marked Nov 7) for end of the lecture on polytomous data models
12 NovModeling Multidimensional Latent Variables (Lecture 04e)



FA 8 (Via Icon)
14 NovNo class
19 NovBayesian Psychometric Model Fit (Lecture 04f)



FA 9 (Via Icon)
21 NovMissing Data (Lecture 04g)
3 DecEmpirical Priors (Lecture 04h)
3 and 5 Dec
Scale Identification (Lecture 04i)

References

Levy, R., & Mislevy, R.J. (2016). Bayesian Psychometric Modeling (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315374604

McDonald, R. P. (1999). Test theory: A unified treatment. Erlbaum.