Instructor: Adrian Raftery, Departments of Statistics and Sociology. My office is room C-313, Padelford Hall. My phone number is 206-543-4505, which I will answer during office hours. My email address is raftery AT uw DOT edu; email is the best way to contact me outside office hours.
Teaching Assistant: Nathan Welch, Department of Statistics. Nathan's office is room C-312, Padelford Hall, his office phone number is 206-685-8969, and his email address is nwelch at uw dot edu. Nathan will run the weekly quiz section, for which material will be posted on Canvas. Please check the Canvas course modules web page regularly. The weekly quiz section will take place on Fridays, 2:30-3:20pm.
Office hours: I will hold office hours on Mondays from 4:00-6:00pm and Wednesdays from 4:00-5:00pm in Thompson Hall (initially either in or close to the classroom, THO 202) and then in Padelford C-313, or by appointment. Please do not hesitate to come and see me if you have a problem or if you just want to discuss issues arising in the class.
I will also hold "electronic office hours" by responding to email questions, with a target response time of one working day. If it seems appropriate to me, and if you don't ask me not to, I will send the response to the class mailing list (see below), after removing your name and identifying information, and it will be posted automatically on the class archive accessible from the course home page.
Nathan Welch will hold office hours on Tuesdays from 3:00-5:00pm in the Statistics Study and Tutor Center in the basement of the Communications Building (CMU B023). Nathan will run the course Canvas page, on which there will be a discussion board. You may send questions and comments to either the class email list or the class Canvas discussion board.
Here is a summary of the general course schedule and office hours.
|3:00-3:30||Class||Nathan office hour||Class||Quiz section|
|3:30-4:00||Class||Nathan office hour||Class|
|4:00-4:30||Adrian office hour||Nathan office hour||Adrian office hour|
|4:30-5:00||Adrian office hour||Nathan office hour||Adrian office hour|
|5:00-5:30||Adrian office hour|
|5:30-6:00||Adrian office hour|
Prerequisites: One of the following:
Registration: Please register for the course for credit; auditing is not allowed. If you are not a registered student but are a UW employee, you may be eligible to take this class tuition-free via the UW Tuition Exemption Benefit. In any event, all students must register. See the registration instructions for students, UW employees and non-UW individuals.
Requirements: Your course grade will be based on homework assignments (55%), quiz section participation (5%), a group project (35%), and participation in the project presentation sessions (5%). Participation in the project presentation sessions is required, even if you are not presenting yourself. If you propose a project topic and dataset that are used, your final course grade will be increased by 0.1. There may also be an optional poster session for presenting your project; if this takes place and you present an acceptable poster in it, your project grade will be increased by 5%.
Homework will be assigned most weeks, and will be due electronically on Canvas on the Wednesday of the following week at 2:00pm. This schedule is designed so that homework can be corrected and returned to you quickly, usually at the Friday quiz section, where it will be discussed. To enable us to meet such a tight schedule for returning your homework to you, we will not be able to accept late homework. Most of the homeworks will involve computing.
Computing: Most of the homework assignments will involve computing. The preferred software for the class is R, and you may use this on any platform that you wish, including your own PC (it runs under Windows, Mac OS X and Linux). R can be downloaded for free at CRAN where good tutorial and introductory documentation is also available. Nathan Welch will give a lecture introducing R, the lmer R package that will be used in the class, and the Rmarkdown report writing software on Friday January 10.
Catalog Course description: Explores ways in which data are hierarchically organized, such as voters nested within electoral districts that are in turn nested within states. Provides a basic theoretical understanding and practical knowledge of models for clustered data and a set of tools to help make accurate inferences. Prerequisite: SOC 504-505-506 or equivalent; recommended: CS&SS 505-506 or equivalent.
Andrew Gelman and Jennifer Hill (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
We will focus on chapters 11, 12, 13, 14, 16 and 17.1-17.4.
Course outline: Note: GHa refers to chapter a of the Gelman and Hill text, and GHa.b refers to section a.b of the Gelman and Hill text.
Last updated January 31, 2020