2006-2007 ITV Course Schedule
Fall:
"Questionnaire Design" - Joanne Miller
Fridays: 11:00- 1:00 CST
Email: jmiller@polisci.umn.edu
"Multilevel Modeling" - Tom Rudolph
Wednesdays: 1:30- 3:30 CST
Email: rudolph@uiuc.edu
Spring:
"Bayesian Analysis" - Charles Franklin
Wednesdays: 1:30- 3:30 CST
Email: franklin@polisci.wisc.edu
"Event History" - Jan Box-Steffensmeier
Fridays: 11:00-1:00 CST
Email: steffensmeier.2@osu.edu
Course Descriptions
Survey Questionnaire Design
Instructor: Joanne Miller, University of Minnesota
Email: jmiller@polisci.umn.edu
Times: 11:00am - 1:00pm CST, Fridays
Dates:
Sept 29, Oct.
6, Oct. 13, Oct. 20th, Oct. 27, Nov. 3rd, Nov. 17th (Note that
Nov. 10th is a day without class. OSU is closed that day
(Veteran's Day I believe). Fridays 11:00- 1:00 CST,
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.
Required Textbook:
Schuman, Howard, and Stanley Presser. 1996. Questions and Answers in Attitude Surveys. CA: Sage. ISBN: 0-7619-0359-3
Multilevel Modeling
Instructor: Tom Rudolph, University of Illinois
Email: rudolph@uiuc.edu
Times: 1:30pm - 3:30pm CST, Wednesdays
Dates:
Sept. 27, Oct.
4, Oct. 11, Oct. 18, Nov. 1, Nov. 8, Nov. 15 (Note that I have
Oct. 25 as a day without class. the 7 weeks flies by and we
have generally found it useful to take a deep breath a bit over
midway in the course. ) Wednesdays 1:30- 3:30 CST,
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.
Required Textbook:
Snijders, Tom, and Roel Bosker. 1999. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Sage Publications.
Event History
Instructor: Jan Box-Steffensmeier, Ohio State University
Email: steffensmeier.2@osu.edu
Times: 11:00am - 1:00pm CST, Fridays
Dates:
Feb. 9, Feb.
16, Feb. 23, March 2, March 9, March 30, April 6. Fridays
11:00-1:00 CST,
Description:
Social science theories are increasingly focused on change processes and temporal data are becoming widely available. Event history methods are ideal for studying temporal change. They address not only whether an event occurred, but when the event occurred. For many research questions in social science, the timing or history of social change is at least as interesting as understanding the culminating event. Research designs incorporating "history" into the analysis promises greater analytical leverage than other designs.
Event history analysis is longitudinal and involves the statistical examination of longitudinal data collected on a set of observations. While a wide variety of statistical models may be constructed for event history data, at the most basic level, all event history models have some common features. The dependent variable measures the duration of time that units spend in a state before experiencing some event. Generally, a researcher knows when the observations enter the process, i.e., when the history begins, and when, and whether or not, the process ends (with the occurrence or nonoccurrence of some event). Analysts are typically interested in the relationship between the length of the observed duration and independent variables, or covariates, of theoretical interest. A statistical model can then be constructed to link the dependent variable to the covariates. Inferences can be made regarding the influence of the covariates on the length of the duration and the occurrence (or nonoccurrence) of some event.
These methods have many advantages and allow new questions to be addressed. Event history data are becoming more available in all areas of empirically oriented political science. Applications include the duration of peace, the duration of unemployment, the length of time a cabinet is in place, when a challenger enters a congressional race, the duration of congressional careers, when a policy is likely to be adopted by the states, or how long it takes to complete a dissertation. The course will thoroughly describe different models for different kinds of duration data, document the assumptions underlying these different models, and consider goodness-of-fit indices and diagnostic techniques, i.e., residual and specification analysis.
Bayesian Analysis
Instructor: Charles Franklin, University of Wisconsin
Email: franklin@polisci.wisc.edu
Times: 1:30-3:30, Wednesdays
Dates:
Feb 7, Feb. 14,
Feb. 21, Feb. 28, March 7, March 28, April 4 (The dates off are
March 14 and 21 due to spring breaks at the various schools.
MPSA is the following week, April 12-15, too.) Wednesdays 1:30-
3:30 CST,
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.
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