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Course Information

  
Instructor:Jonathan Templin
email:jonathan-templin@uiowa.edu
Course website:https://jonathantemplin.com/bayesian-psychometric-modeling-spring-2019/
Office:224B1 Lindquist Center South
Office Phone:319-335-6429
Classroom:N203 Lindquist Center
Course Meeting Time:Fridays 9:30am-12:00pm
Course Office Hours:Mondays 11am-1pm or by appointment
Course GitHub Repositoryhttps://github.com/jonathantemplin/BayesianPsychometricModeling

Course Textbook

The course will follow the chapters of Bayesian Psychometric Modeling (2017) by Roy Levy and Bob Mislevy. The University of Iowa Library has electronic access to the book at https://ebookcentral.proquest.com/lib/uiowa/detail.action?docID=4532358.

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 classical test theory, confirmatory factor analysis (CFA), item response theory (IRT), latent class analysis, diagnostic classification models, and Bayesian networks. The course will follow the chapters of the Levy and Mislevy (2017) text and may feature some additional references when necessary.

Course Materials

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

  • Class Lecture Videos: Forthcoming

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

In-Class Lecture Materials:

In Class Lecture Videos, Notes, and Additional Files (if any):

  • Class Lecture Videos:
    • Part 1: Forthcoming
    • Part 2: Forthcoming

Tentative Course Schedule (Note: Links to Course Materials Will Appear Here Each Week)

DateTopicReading(s)Homework Assigned
January 18 Course IntroductionNoneHW1
January 25No Class
February 1Introduction to Bayesian Methods; Conceptual Issues; JAGSChs. 1, 2, 3
February 8, 15Bayesian Linear ModelsChs. 4, 5, 6HW2 (due March 1)
February 22MCMC AlgorithmsCh 5.
March 1CFA Models/Model Fit EvaluationChs. 9, 10
March 8CFA Models/Model Fit Evaluation
March 15CFA Models/Model Fit EvaluationHW3
March 22No Class: Spring Break
March 29CFA Models/Reliability in Bayesian CFA Models
April 5No Class: Conferences
April 12IRT Models for Binary DataCh. 11HW 4
April 19IRT Models for Polytomous DataCh. 11
April 26Missing Data/Latent Class ModelsCh. 12, 13HW 5
May 3Bayesian Inference NetworksChs. 14, 15

Course Website/Technology

This course will not use ICON for lecture materials. Instead, we will use freely available commercial software for communication and dissemination of course materials. Course lecture slides, lecture examples, video files, assignments, and information are available on the website, https://jonathantemplin.com/bayesian-psychometric-modeling-spring-2019/.

All lectures will be streamed and archived on YouTube (my YouTube channel is https://www.youtube.com/channel/UC6WctsOhVfGW1D9NZUH1xFg?view_as=subscriber). ICON will be used for storing your individual grades only.

Course Materials

All course materials will be based in R, R Notebooks, and R Markdown and will be available using the course Git repository at: https://github.com/jonathantemplin/BayesianPsychometricModeling. We will be using Git to enable each of us to make changes to documents whenever mistakes are made or whenever other materials may be needed.

Further, all homework assignments will be turned in as R Markdown documents, weaving text with analysis syntax. I am attempting to use GitHub Classroom to allow you to submit your materials that way.

Statistical Computing

The course will use the R statistical package with the R Studio development suite along with a set of packages for using R with Non-R Bayesian Inference Software. Additionally, we will be using JAGS for all analyses. All assignments must be completed in R, using R Markdown. For all other specific information regarding general course policies, course evaluation rubrics, and grading systems, please see the course syllabus at the link below.

R, R Studio, and JAGS are available for free from the following websites:

R and R Studio work with JAGS by using a series of downloadable packages. Further, additional R packages may be used within the course as needed.

Course Structure and Student Evaluation

Student evaluation will be made based homework grades only. There will be at most seven homework assignments, with no less than two weeks time to submit answers. All homework and answers must be from each student’s own work and not be copied or paraphrased from anyone else’s answers. After each homework (but the last) has been graded, students can revise and resubmit their homework for a better grade. Homework revisions will have a unique deadline for submission. Each homework will be worth approximately 15 points, for 105 total points that can be earned.

Course Grading System

Point TotalLetter Grade
100 and AboveA+
99-93A
92-90A-
89-87B+
86-83B
82-80B-
79-77C+
76-73C
72-70C-
69-60D
Below 60F

Late Homework Assignments:

In order to be able to provide the entire class with prompt feedback, late homework assignments will incur a 5-point penalty. However, extensions may be granted as needed for extenuating circumstances (e.g., conferences, family obligations) if requested at least three weeks in advance of the due date.

Planned Homework Assignments

HomeworkTopicDate Assigned
Homework 1R/RStudio/JAGSJan 18
Homework 2Linear ModelsFeb 15
Homework 3CFA ModelsMar 15
Homework 4IRT ModelsMar 29
Homework 5MIRT ModelsApr 12
Homework 6LCA ModelsApr 25

Additional Information

Students with Disabilities

I ask any students who are in need of any accomodations, have any emergency medical information of whichI should be aware, or need alternate arrangements in the event the building must be evacuated to schedule a meeting with me as soon as possible.

Respect for Diversity

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit. It is my intent to present materials and activities that are respectful of diversity: gender, sexual orientation, disability, age, socioeconomic status, ethnicity, race, culture, perspective, and other background characteristics. Your suggestions about how to improve the value of diversity in this course are encouraged and appreciated. Please let me know ways to improve the effectiveness of the course for you personally or for other students or student groups.

In addition, in scheduling this course, I have attempted to avoid conflicts with major religious holidays. If, however, I have inadvertently scheduled a deadline that creates a conflict with your religious observances, please let me know as soon as possible so that we can make other arrangements.