Courses and Workshops

Political Science 584, Mathematics 584C, Public Health M19-530 (FALL): Multilevel Models in Quantitative Research

Wednesday, 3:00-5:00 PM, Location: Simon 020.

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: Generalized Linear Models (FALL):

Friday, 1:00-3:00, location Seigle 205.

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.

Political Science 555: Longitudinal and Event History Models for the Social and Political Sciences (SPRING):

MOnday 2-4PM, location: Cupples II, 200.

Description: This course will cover the statistical concepts and techniques that are used to model social and political events over time, including basic time-series and event history (survival) data. Such data routinely occurs in both the social sciences and public health sciences. Lectures will introduce: second order stationary time series, autoregressive structures, spectrum and linear filtering theory, autocorrelation consistent (HAC) variance estimation, survival functions, hazard rates, types of censoring and truncation.  Modes of inference for regression models will be provided. All applied work will be in the R software environment for statistical computing and graphics. Students will be able to identify and classify data problems in longitudinal analysis, define the appropriate function accounting for time as well as summarize and interpret analyses of such data using various estimators. In addition, participants will able to formulate research questions related to longitudinal data and the appropriate associated regression models or other approach.

Syllabus. Week 1 Slides. Colon cancer data. Week 2 Slides. Week 3 Slides. Week 4 Slides. Week 5 Slides. Week 6 Slides. Week 7 Slides. Week 8 Slides. Week 9 Slides.

M19-513, Division of Public Health Sciences Biostat R Primer (SUMMER 2017):

8:30-5:00 (lunch break 12-1:30) July 6-8, location: Taylor Avenue Building, Doll and Hill Room (second floor).

Description: This Summer course for graduate students, fellows and residents is designed to introduce the R statistical language and environment from the very beginning. The purpose is to familiarize you with the R language and environment to allow substantive work in the program without further instruction on the basics.

Syllabus.

AAPOR May 2016 Workshop: Modern Bayesian Methods and Computing for Survey Research

Resources: R Code, writeDatafile.R, abortion_cluster.sav, slides
 

ASU January 2017 Workshop: Non-parametric Statistical Tools for the Social Sciences

Resources: lab assignment 1, Deindustrial dataset, lab assignment 2, Africa dataset.