Course Overview
Statistical models are necessary for analyzing the type of multivariate (often large) datasets that are usually encountered in data science and statistical science, and hierarchical models often play a vital role in many of those applications. This is a graduate-level course that introduces students to the building blocks of hierarchical modeling and provides students with the tools needed to build, fit and interpret hierarchical models.
Hierarchical or multilevel models provide a principled way to model naturally grouped or clustered data, in a way that takes advantage of the relationship between observations in the same group, but also allows for borrowing of information across the groups. In this course, you will be introduced to these models, with particular emphasis on the theoretical and conceptual foundations, as well as implementation, model fitting, and interpretation of the results.
This course emphasizes the mathematical theory behind hierarchical models, as well as real data analyses, including interpretation of results. All students must have the theoretical background covered in the prerequisites (in particular, in the context of Bayesian statistics) to be able to keep up with and understand the materials.
Learning Objectives
By the end of this course, students should be able to
- Understand the foundations and general structure of both classical and Bayesian hierarchical models.
- Specify and fit hierarchical models to various types of grouped or clustered data.
- Use the models covered in class to analyze real data sets.
- Assess the adequacy of hierarchical models to any given data and make a decision on what to do in cases when certain models are not appropriate for a given dataset.
Course Info
Meeting Times
Wednesdays and Fridays (10:15am - 11:30am)
Zoom Meeting ID: See Sakai.
Labs
Section 01:
Mondays (12:00pm - 1:15pm)
Zoom Meeting ID: See Sakai
Section 02:
Mondays (7:00pm - 8:15pm)
Zoom Meeting ID: See Sakai
Recordings will be made available afterwards for students who are unable not to attend the live sessions.
Playposit
To gain access to the pre-recorded lecture videos, you will have to create a Playposit account. There are participation quizzes embedded within the videos. These quizzes make up a part of your final grade (see: course policies) so take them seriously. To join the class on Playposit, you first need to create a new account as a student here. Next, you will use the class link, which I will send out via email, to join the class site. While you need not create an account with your Duke email, I strongly suggest you do.
Zoom meetings
The easiest way for you to join the different Zoom meetings is to log in to Sakai, go to the "Zoom meetings" tab, and click "Upcoming Meetings". For the recordings (for lab and discussion sessions), also log in to Sakai, go to the "Zoom meetings" tab, and click "Cloud Recordings". Those will be available few minutes after the sessions.
Teaching Team and Office Hours
| Instructor | Dr. Olanrewaju Michael Akande | Tuesdays: 6:00pm - 7:00pm Fridays: 9:00am - 10:00am |
Zoom Meeting ID: See Sakai | |
| TA | Jiurui Tang | Mondays: 9:00am - 10:00am Wednesdays: 5:00pm - 7:00pm |
Zoom Meeting ID: See Sakai | |
| Meng (Amy) Xie | Tuesdays: 4:00pm - 5:00pm Thursdays: 3:00pm - 5:00pm |
Zoom Meeting ID: See Sakai |
Texts
| Lecture Notes on Hierarchical Modeling | Peter D. Hoff | Required (available on Sakai; not to be shared publicly) |
| Data Analysis Using Regression and Multilevel/Hierarchical Models | Gelman A., and Hill, J. | Recommended |
Materials
Lecture notes and slides, lab exercises and assigned readings will be posted on the course website, while lecture and lab videos will be posted on Sakai. White boards will also be used frequently in the lecture videos, so please pay special attention to those. Finally, we will closely follow the main text so students should make sure to always read the corresponding chapters in the assigned readings.
Important Dates
| Wed, January 20 | Classes begin |
| Tue, February 2 | Drop/Add ends |
| Fri, February 26 | Exam I (tentative) |
| Tue - Wed, March 9 - 10 | No classes held |
| Mon, April 12 | Wellness day |
| Fri, April 16 | Exam II (tentative) |
| Fri, April 23 | Classes end |
Wellness day
In lieu of a traditional class meeting on April 12, 2021, please use our regular class time to engage in reflection and wellness endeavors. A list of wellness strategies and programs is available at https://studentaffairs.duke.edu/duwell/wellness-day-2021.
Although the goal of Wellness Day 2021 is to provide time and space to engage in activities that enhance your well-being, please remember that wellness isn’t achieved in one day. Learning to balance your personal, professional, and academic commitments is a skill that should be practiced regularly and over time.
Green Classroom
This course has achieved Duke’s Green Classroom Certification. The certification indicates that the faculty member teaching this course has taken significant steps to green the delivery of this course. Your faculty member has completed a checklist indicating their common practices in areas of this course that have an environmental impact, such as paper and energy consumption. Some common practices implemented by faculty to reduce the environmental impact of their course include allowing electronic submission of assignments, providing online readings and turning off lights and electronics in the classroom when they are not in use. The eco-friendly aspects of course delivery may vary by faculty, by course and throughout the semester. Learn more at https://sustainability.duke.edu/action/certification.
Acknowledgement
This web page contains materials such as lecture slides, homework assignments, and datasets developed or adapted by Dr. Amy H Herring.