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.


Section 01:

  Jiurui Tang

  Mondays (12:00pm - 1:15pm)

  Zoom Meeting ID: See Sakai

Section 02:

  Meng (Amy) Xie

  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.


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


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


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

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


This web page contains materials such as lecture slides, homework assignments, and datasets developed or adapted by Dr. Amy H Herring.