2008-2009 ITV Course Schedule
Fall:
"Multilevel Modeling" - Tom Rudolph,
broadcasting from the Univ. of IL
Fridays: 12-2pm EST, 11am-1pm CST
Email: rudolph@uiuc.edu
Spring:
"Spatial
Modeling" - Jude Hays, broadcasting from
the Univ. of IL
Fridays: 12-2pm EST, 11am-1pm CST
Email:
jchays@uiuc.edu
"Introduction to Bayesian Methods" - Charles Franklin, broadcasting
from the Univ. of Wisc.
Wednesdays: 2:30-4:30pm EST, 1:30-3:30pm CST
Email: franklin@polisci.wisc.edu
"Advanced Bayesian Methods" - Charles Franklin, broadcasting from
the Univ. of Wisc.
Wednesdays: 2:30-4:30pm EST, 1:30-3:30pm CST
Email: franklin@polisci.wisc.edu
Course Descriptions
Multilevel Modeling
Instructor: Tom Rudolph, University of Illinois
Fridays: 12-2pm EST, 11am-1pm CST
Email: rudolph@uiuc.edu
Dates:
Sept. 26
Oct. 3
Oct. 10
Oct. 17
NO CLASS on Oct. 24
Oct. 31
Nov. 7
Nov. 14
Description:
Social science data frequently have a hierarchical or multilevel structure. In survey research, for example, we collect individual-level data on each respondent. We may have information about respondents’ party identification, race, education, and voting behavior. Depending on the design of the survey, these respondents can often be grouped into a larger unit such as a county, state, or nation. We may have data concerning the characteristics of these higher-order units such as racial diversity, income inequality, or type of government institutions. While multilevel data present great theoretical opportunities, they also pose some statistical challenges. Hierarchical linear models are designed to meet these challenges and enable the analyst to exploit multilevel data structures for theoretical gain.
This course provides an introduction to the use of hierarchical or multilevel models. The purpose of the course is to introduce students to the basic principles and applications of hierarchical linear modeling in political science research. Topics covered include an introduction to multilevel analyses, random intercept models, random slope models, hypothesis testing, hierarchical models for limited dependent variables, model fitting, and three-level models.
Spatial Modeling
Instructor:
Jude Hays, University of Illinois
Email:
jchays@uiuc.edu
Fridays: 12-2pm EST, 11am-1pm CST
Dates:
Jan. 23
Jan. 30
Feb. 6
Feb. 13
Feb. 20 -- NO Class -- just take a week off
Feb. 27
March 6
March 13
Description:
Spatial interdependence is ubiquitous in politics. The likelihood and outcomes of demonstrations, riots, coups, and revolutions in one country almost certainly depend on such occurrences in other countries. Election outcomes, candidate qualities or strategies in some contests surely depend on those in others, and individual legislators' votes certainly depend on others' votes or expected votes. In micro-behavioral research, contextual or network effects usually refer to the effects on each individual's behavior or opinion from sets of other individuals' opinions or behavior. States' entry decisions in wars, alliances, and international organizations depend on how many and who enter and how. Globalization implies strategic and non-strategic interdependence in national-level macroeconomic policymaking. This course provides an introduction to spatial and spatio-temporal models for continuous and limited dependent variables with an emphasis on political science applications. Participants will learn how to estimate the structural parameters of spatial and spatio-temporal regression models, calculate and present the implied spatial and spatio-temporal effects, and use spatial modeling to evaluate the nature of interdependence (e.g., strategic free-riding behaviour, learning, coercion) among their units of observation.
Introduction to Bayesian Analysis (Spring I)
Instructor:
Charles Franklin, University of Wisconsin
Email: franklin@polisci.wisc.edu
Wednesdays:
2:30-4:30pm EST, 1:30-3:30pm CST
Dates:
Jan. 21
Jan. 28
Feb. 4
Feb. 11
Feb. 18
Feb. 25
March 4
Description:
This course introduces Bayesian methods for data analysis in the social sciences. Bayesian methods provide a flexible and powerful approach to complex statistical models and have a theoretical elegance and clarity that is impressive. Bayesian models inherently recognize and incorporate subjective judgments of the researcher, which is the source of both their great power the controversy surrounding their use. We will discuss some of the epistemological issues raised by Bayesian methods as well as their application. We will cover the basic concepts of Bayesian statistical inference. There are several sets of tools needed to do applied Bayesian modeling. First we need to review some probability theory. Second, we will develop the fundamental notion of Bayes theorem as a foundation for statistical inference. We'll also see how likelihood is incorporated within the Bayesian framework. Finally, we'll explore the world of applied Bayesian modeling. This will include learning some new software tools using WinBugs and S-Plus/R. The main focus of the course will be application of Bayesian models to cutting edge issues in social science modeling. This will include a number of applied readings. We'll take time to discuss these applications in class in order to develop a feel for what research that takes a Bayesian approach "feels" like.
The goal of the class is to develop the necessary theoretical understanding to correctly apply Bayesian models using modern software. A second goal is to reach a level of understanding that will support further reading and learning on your own. The syllabus points the way to further reading throughout in the "Advanced Topics" sections.
Advanced Bayesian Analysis (Spring II)
Instructor:
Charles Franklin, University of Wisconsin
Email: franklin@polisci.wisc.edu
Wednesdays:
2:30-4:30pm EST, 1:30-3:30pm CST
Dates:
April 1
April 8
April 15
April 22
April 29
May 6
Description:
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