Fall 2011 Courses Political Science 430: Multilevel Models in Quantitative Resesearch Thursday, 4:00-6:00PM, location Seigle 016. Course Description: This course covers statistical model development with explicitly defined hierarchies. Such multilevel specifications allow researchers to account for different structures in the data and provide for the modeling of variation between defined groups. The course begins with simple nested linear models and proceeds on to non-nested models, multilevel models with dichotomous outcomes, and multilevel generalized linear models. In each case, a Bayesian perspective on inference and computation is featured. The focus on the course will be practical steps for specifying, fitting, and checking multilevel models with much time spent on the details of computation in the R and bugs environments. Eligibility: All graduate and professional students meeting the prerequisites (below) are eligible. Previous attendees have included residents, medical students, fellows, Arts & Sciences Ph.D. students, Brown School Ph.D. students, and practicing researchers in the medical school. Prerequisite Details: This course assumes only a knowledge of basic statistics as taught in a first year graduate sequence. Topices should include: probability, cross-tabulation, basic statistical summaries, and linear regression in either scalar or matrix form. Very basic knowledge of matrix algebra and calculus is convenient but not required. The course will make extensive use of the R statistical language. Syllabus. Political Science 582: Quantitative Analysis in Political Science II Friday, 10:00-12:00, location Seigle 016. Course Description: More advanced topics in the use of statistical methods, with emphasis on political applications. Topics include: properties of least squares estimates, problems in multiple regression, and advanced topics (probit analysis, simultaneous models, time-series analysis, etc." What this really means.... This course extends what you did in the linear models course by focusing more on nonlinear model forms. These are typically called "generalized linear models," although for historical reasons people in political science call them "maximum likelihood models." The principle we will care about is how to modify the standard linear model that you know so that a broader class of outcome variables can be accomodated. These include: counts, dichotomous outcomes, bounded variables, and more. The second aspect of the course is focused on the statistical package *R* Prerequisite Details: The only official prerequisite for this course is a course on linear models. For political science graduate students, Political Science 581 is adequate. However, each student should be familiar with: basic probability theory, statistical inference, hypothesis testing, and least squares estimation. The course will also assume a working knowledge of calculus and linear algebra at the level of Essential Mathematics for Political and Social Research. Jeff Gill, 2006, Cambridge University Press. Since students come to the course with varying levels of experience with statistical packages like R, some may spend quite a bit of time learning basic programming skills. If you suspect that you are in this group, it will pay to spend some time with a basic text such as An R and S-Plus Companion to Applied Regression. John Fox, 2002, Sage. Syllabus. Methods Electives for Political Science Ph.D. Students Political Science 430 Multilevel Models in Quantitative Resesearch B54 MEC 660 Bayesian Inferences Math 408 Nonparametric Statistics Math 429 Linear Algebra Math 434 Survival Analysis Math 459 Bayesian Statistics Math 493 Probability Math 494 Mathematical Statistics Math 495 Stochastic Processes Econ 4151 Applied Econometrics