Fall 2009 Courses
  • Center for Applied Statistics 430: Multilevel Models in Quantitative Resesearch
    Thursday, 4:00-6:00PM, in CRTC, Wohl Building (Washington University School of Medicine).
    • 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.

    • 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. Knowledge of matrix algebra is convenient but not required. The course will make extensive use of the R statistical language and we will be offering a 2.5-day "R Boot Camp" training in late summer. Tentative dates are August 12-14 or August 19-21, with Wednesday and Thursday being full days (9-5) and Friday being one half day (9-1). Tentative schedule is as follows:
      1. Wednesday 9-12, 1-5: Basics of data handling and data analysis
      2. Thursday 9-12, 1-5: running models: linear, log-linear, tabular, and dichotomous outcomes
      3. Friday 9-1: Advanced topics for those who are interested: setting up basic simulations, model diagnostics, nonparametrics/smoothing


    • Syllabus.
    • Homework Answers, Ch2-6.
    • Google's R Style Guide.
    • Warfarin Analysis 1.
    • Warfarin Analysis 2.
    • Warfarin Analysis 3.
    • WinBUGS Example.

  • Political Science 213: Quantitative Analysis in Political Science II
    Monday, 1:00-3:00PM in Seigle 16.
    • 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.
    • Missing Data Lecture.
    • Poisson Models.
    • Tabular Models.

  • University of Texas at Dallas, School of Economic, Political, and Policy Sciences.
    Autumn 2009 Research Methods Short Course: "Multilevel Models in Quantitative Research: Bayesian Perspectives on Inference and Computation. Information.