2009-2010 ITV Course Schedule
Fall 2009:
"Questionnaire Design" -
Joanne Miller
Fridays: 12-2 EST, 11am-1pm CST
Email: jmiller@polisci.umn.edu
Dates:
Sept 25
Oct. 2, 9, 16
Oct. 23 – Break, No Class
Oct. 30
Nov. 6, 13
Spring 2010:
"Time Series - 2 modules" -
Box-Steffensmeier, Freeman, and Pevehouse,
Fridays: 12-2pm EST, 11am-1pm CST
Email: steffensmeier.2@polisci.osu.edu
Dates:
Jan 22, 29
Feb 5, 12, 19, 26
March 5
March 12
March 19, 26 – Break, No Class
April 2
April 9, 16
April 23 – MPSA
April 30
May 7
"Topics in Statistical Graphics and Visualization" (Early Spring) - Franklin
Wednesdays: 2:30-4:30pm EST, 1:30-3:30pm CST
Email: franklin@polisci.wisc.edu
Dates:
Jan. 20, 27
Feb. 3, 10
Feb. 17 – Break, No Class
Feb. 24
March 3, 10
"Nonparametric and Robust Estimation" (Late Spring) - Keele,
Wednesdays: 2:30-4:45pm EST, 1:30-3:45pm CST
Email: keele.4@polisci.osu.edu
Dates:
March 31
April 7, 14, 21, 28
May 5
Course Descriptions
Questionnaire Design
Fall 2009
Instructor: Joanne Miller, University of Illinois
Fridays: 12-2pm EST, 11am-1pm CST
Email: jmiller@polisci.umn.edu
Dates:
Description: This course offers a review of some of the major theoretical and empirical issues associated with survey questionnaire design and prepares students in the fundamental skill areas necessary to design their own surveys and critique existing questionnaires.
Time Series - 2 modules
Spring 2010
Instructor:
Box-Steffensmeier, Freeman, and Pevehouse, University of Illinois
Email:
steffensmeier.2@polisci.osu.edu
Fridays: 12-2pm EST, 11am-1pm CST
Dates:
Description: This course considers statistical techniques to evaluate social processes occurring through time. The course introduces students to time series methods and to the applications of these methods in political science. After a brief review of the calculus of finite differences and other estimation techniques, we study stationary ARMA models. In the next section of the course, we examine a number of important topics in time series analysis including "reduced form" methods (granger causality and vector autogression), unit root tests, near-integration, fractional integration, cointegration, and error correction models. Time series regression is also discussed (including pooling cross-sectional and time series data). We learn not only how to construct these models but also how to use them in policy analysis. We expect students to have a firm grounding in probability and regression analysis and to bring to the course some interesting questions about the dynamics of political processes. The emphasis throughout the course will be on application, rather than on statistical theory. However, the focus of most lectures will be statistical theory. Homework will revolve as much as possible around the time series you are interested in understanding. To that end, students will need to gather time series data for analysis during the first week of class (this data need not be used throughout the term, though that would make your life easier). The length of the series should be at least 40 time points; longer series are better than shorter ones.
Topics in Statistical Graphics and Visualization
Spring
I 2010
Instructor:
Charles Franklin, University of Wisconsin
Email:
franklin@polisci.wisc.edu
Wednesdays:
2:30-4:30pm EST, 1:30-3:30pm CST
Dates:
Description: Data visualization has become increasingly important in all areas of statistics, business, science and the social sciences. Yet it is hardly touched upon in standard statistics courses and few opportunities exist for an entire course devoted to the subject. We aim to change that. This course will focus on developing visualizations for 1-d, 2-d, 3-d, 4-d and (yes!) 5-d data and models. We will use R as the visualization tool along with the Lattice and other packages plus develop some original visualization techniques. We will also read widely in the growing visualization field, from Tufte's classics to modern computer science techniques. The goal of the course is to develop methods that allow us to understand our data and models better, and to communicate that understanding to readers through graphical techniques. Previous experience with R is highly recommended along with a good understanding of standard statistical modeling such as linear models and models for limited dependent variables. The "product" of the course will be weekly applications in which students develop original displays of interesting datasets and models. A final paper should delve more deeply into a particular visualization challenge such as high dimensional data, or discrete data or animation techniques.
Nonparametric and Robust Estimation
Spring
II 2010
Instructor:
Luke
Keele, Ohio State University
Email:
keele.4@polisci.osu.edu
Wednesdays:
2:30-4:30pm EST, 1:30-3:30pm CST
Dates:
Course Website: http://www.polisci.ohio-state.edu/faculty/lkeele/itv.html
Description: This course is designed to introduce graduate students to a variety of advanced and computationally intensive methods that are starting to be used regularly in the field. The class will familiarize students with such topics as bootstrapping, non-parametric and semi-parametric estimation, and robust estimation. The topics in this class are not standard fare in the typical methods sequence, but are appearing with increasing frequency in applied work, and in many cases should be used more often. The course will be run more along the lines of a workshop, and it is hoped that there will be extensive interaction during class as we review the methods covered here.
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