Adrian Raftery | CSSS | Statistics Department | Sociology Department | University of Washington

CS&SS/STAT/SOC 560 - Hierarchical Modeling for the Social Sciences

Spring Quarter 2011

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: Jennifer Laird, Department of Sociology. Jenn's office is Savery 272, her phone number is 646-369-0964 and her email address is jdlaird at uw dot edu. Jenn will run the weekly quiz section.

Office hours: I will hold office hours on Tuesdays from 9:00-10:20am 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.

Jenn Laird will hold office hours on Mondays 3:30-4:30pm and Fridays 4:00-5:00pm in Savery 251.

Here is a summary of the general course schedule and office hours.

CS&SS/STAT/SOC 560: Course Schedule and Office Hours
Time Monday Tuesday Wednesday Thursday Friday
9:00-10:20 Adrian office hours PDL C-313
...
2:30-3:20 Class: Homework due Class Quiz section Class
3:30-4:00 Jenn office hour SAV 251
4:00-4:30 Jenn office hour SAV 251 Jenn office hour SAV 251
4:30-5:00 Jenn office hour SAV 251

Prerequisites: One of the following:

Broadly speaking, before taking this class you should have a good grounding in basic probability and statistics, including linear regression (at the level of SOC 506 or STAT 504), and basic mathematics including basic calculus and matrix algebra (at the level of CS&SS 505 or the CSSS Math Camp).

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.

Homework will be assigned most weeks, and will be due in class on the Monday of the following week at 2:30pm. The schedule is designed so that homework can be corrected and returned to you quickly, usually at the Thursday quiz section, where it will be discussed. To enable us to do this we will not be able to accept late homework. Many 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 introductory documentation is also available. Jennifer Laird will give a lecture introducing R and the lmer R package that will be used in the class on April 7.

Class mailing list: There will be a class mailing list. Your uw.edu address is automatically part of the mailing list, and you may post to it from your uw.edu email address. Please feel free to post to the class mailing list.

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.

Text:
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.

  1. Multilevel data: GH 11.
  2. Hierarchical linear models: GH 12.
  3. Varying coefficient models: GH 13.
  4. Hierarchical logistic regression: GH 14.
  5. Bayesian hierarchical models: GH 16, 17.1-17.4.

Last updated May 26, 2011