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  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
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