Bayesian Psychometric Modeling (Spring 2019)

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 Repository	https://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

Lecture 1: Introduction to Bayesian Psychometric Modeling (Lecture Date: 18 Jan 2019)

In-Class Lecture Materials:

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

Part 1: https://youtu.be/7utulVrp1Mo
Part 2: Unavailable due to technical error

Subscribe to my YouTube channel for easier access to class videos: https://www.youtube.com/channel/UC6WctsOhVfGW1D9NZUH1xFg?view_as=subscriber

Lecture 2: Git, Rstudio, and R Markdown

In-Class Lecture Materials:

Lecture GitHub Page: https://jonathantemplin.github.io/BayesianPsychometricModeling/Lectures/02-Git-RStudio-R-Markdown/bpm19psqf7375_Lecture02.html
Lecture Repo Files: https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/02-Git-RStudio-R-Markdown

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

Class Lecture Videos: https://youtu.be/6xlhuMe1xBc

Lecture 3: Chapter 1 of Levy and Mislevy (2017)

In-Class Lecture Materials:

Lecture Page: https://jonathantemplin.github.io/BayesianPsychometricModeling/Lectures/03-Chapter01/bpm19psqf7375_Lecture03.html
Lecture Repo Files: https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/03-Chapter01

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

Class Lecture Videos: https://www.youtube.com/watch?v=6xlhuMe1xBc&t=48m07s

Lecture 4: Chapter 2 of Levy and Mislevy (2017)

In-Class Lecture Materials:

Lecture Page: https://jonathantemplin.github.io/BayesianPsychometricModeling/Lectures/04-Chapter02/bpm19psqf7375_Lecture04.html
Lecture Repo Files: https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/04-Chapter02

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

Class Lecture Videos: https://youtu.be/_4zdZrcl5fU

Lecture 5: Introduction to JAGS

In-Class Lecture Materials:

Lecture Page: https://jonathantemplin.github.io/BayesianPsychometricModeling/Lectures/05-JAGS/bpm18psqf7375_Lecture05.html
Lecture Repo Files: https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/05-JAGS

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

Class Lecture Videos: Forthcoming

Lecture 6: Chapter 3 of Levy and Mislevy (2017)

In-Class Lecture Materials:

Lecture Page: https://jonathantemplin.github.io/BayesianPsychometricModeling/Lectures/06-Chapter03/bpm19psqf7375_Lecture06.html
Lecture Repo Files: https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/06-Chapter03

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

Class Lecture Videos: Forthcoming

Lecture 7: Bayesian Linear Models

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/07-Intro-to-MCMC

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

Class Lecture Videos:
- Part 1: https://youtu.be/vDHAxEzvOP4
- Part 2: https://youtu.be/wY-ykuifHeI
- Part 3: https://youtu.be/rb5HMZxmD2c
- Part 4: https://youtu.be/WmS_jEkb7Xw
- Part 5: https://youtu.be/Q5pcUI2o090
- Part 6: https://youtu.be/3Rx5cSEMFnw

Lecture 8: MCMC Algorithms

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/08-MCMC-Algorithms

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

Class Lecture Videos:
- Part 1: https://youtu.be/X5hH4euXXjg

Lecture 9: Confirmatory Factor Analysis

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/09-Confirmatory-Factor-Analysis

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

Class Lecture Videos:
- Part 1: https://youtu.be/8oRAlm2vIec
- Part 2: https://youtu.be/xxSVJQk___U
- Part 3: https://youtu.be/1KHms_od22k
- Part 4: https://youtu.be/TBT-RqfCL_Y
- Part 5: https://youtu.be/bC1j7CAo4e4
- Part 6: https://youtu.be/BW2YaGobQJ4
- Part 7: https://youtu.be/CodasHE4A1Y

Lecture 10: Reliability in Bayesian Confirmatory Factor Analysis

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/10-Reliability-Estimation

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

Class Lecture Videos:
- Part 1: https://youtu.be/hUAlp5hUjPE

Lecture 11: Binary Item Response Theory Models

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/11-Item-Response-Models

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

Class Lecture Videos:
- Part 1: https://youtu.be/hUAlp5hUjPE
- Part 2: https://youtu.be/Jw_GYYUGIpY

Lecture 12: Polytomous Item Response Theory Models

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/12-Polytomous-IRT-Models

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

Class Lecture Videos:
- Part 1: https://youtu.be/ERBRlVdPMdc
- Part 2: https://youtu.be/Jw_GYYUGIpY

Lecture 13: Missing Data

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/13-Missing%20Data

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

Class Lecture Videos:
- https://youtu.be/bBtCyShjRWo

Lecture 14: Finite Mixture (Latent Class) Models

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/12-Polytomous-IRT-Models

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

Class Lecture Videos:
- Part 1: https://youtu.be/bBtCyShjRWo (Starts at 1:04:00)
- Part 2: https://youtu.be/NsEIJ_HFXlQ

Lecture 15: Multilevel Structural Equation Models (AKA nearly everything else multilevel)

In-Class Lecture Materials:

Lecture Repo Files:
- https://github.com/jonathantemplin/BayesianPsychometricModeling/tree/master/Lectures/15-Multilevel-Models-Multilevel-SEM

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)

Date	Topic	Reading(s)	Homework Assigned
January 18	Course Introduction	None	HW1
January 25	No Class
February 1	Introduction to Bayesian Methods; Conceptual Issues; JAGS	Chs. 1, 2, 3
February 8, 15	Bayesian Linear Models	Chs. 4, 5, 6	HW2 (due March 1)
February 22	MCMC Algorithms	Ch 5.
March 1	CFA Models/Model Fit Evaluation	Chs. 9, 10
March 8	CFA Models/Model Fit Evaluation
March 15	CFA Models/Model Fit Evaluation		HW3
March 22	No Class: Spring Break
March 29	CFA Models/Reliability in Bayesian CFA Models
April 5	No Class: Conferences
April 12	IRT Models for Binary Data	Ch. 11	HW 4
April 19	IRT Models for Polytomous Data	Ch. 11
April 26	Missing Data/Latent Class Models	Ch. 12, 13	HW 5
May 3	Bayesian Inference Networks	Chs. 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: http://www.r-project.org/
R Studio:http://www.rstudio.com/
JAGS: http://mcmc-jags.sourceforge.net
Git Client (for Windows; MacOS and Linux have one installed): https://gitforwindows.org

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

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

Homework	Topic	Date Assigned
Homework 1	R/RStudio/JAGS	Jan 18
Homework 2	Linear Models	Feb 15
Homework 3	CFA Models	Mar 15
Homework 4	IRT Models	Mar 29
Homework 5	MIRT Models	Apr 12
Homework 6	LCA Models	Apr 25

Additional Information

Name of Department: Psychological and Quantitative Foundations
Location of Departmental Office: 361 Lindwquist Center
College policy on student complaints and dispute resolution: https://education.uiowa.edu/services/office-dean/policies/student-complaint-procedure
Preferred Contact Information for the DEO: saba-ali@uiowa.edu
College policy on student academic misconduct (plagiarism and cheating): https://education.uiowa.edu/coe-policies/student-academic-misconduct
University Policy on Sexual Harassment: http://opsmanual.uiowa.edu/community-policies/sexual-harassment

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.

Jonathan Templin

University of Iowa

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