**Undergraduate Courses**

**Understanding Political Research**

This course is designed to achieve two objectives: (1) introduce you to research and quantitative analysis in political science, and (2) help you become critical (but not cynical) consumers of quantitative analysis used in political and policy-oriented reporting. Throughout the course we'll discuss the complexities of generating a good research design, starting with theory building and operationalizing concepts of interest to political scientists. We'll discuss the challenges and limitations of gathering good data to test these theories as well as various statistical tools that can be used to evaluate our theories. We'll tackle the challenge of what conclusions we can draw from these analyses, trying to disentangle cause and effect from observed correlations. To help our pursuit of these goals, we'll use computing software (Stata), also providing you an introduction to statistical computing. Throughout the course we'll use what we've learned to think critically about the use of quantitative research and the inferences drawn from that research by analysts, reporters, politicians, and policy advocates. As such, not only will you be learning to do your own analysis this semester, but also learning to evaluate such information when it's presented in the media. Spring 2017 Syllabus.

**Introduction to International Relations**

This course is designed to achieve two objectives: (1) to introduce you to some of the most important topics and puzzles in the study of international relations, and (2) to provide you with some analytic concepts that can be used to study world politics. The course is designed to teach you how to think about politics in the global area and to prepare you for more advanced courses in international relations. There are no prerequisites for the course. We will frequently refer to important historical and current events as examples and applications of the theories and concepts taught in this course, so if you do not already have your favorite source of news or history, now is a good time to find one. Fall 2016 Syllabus.

**Ending Wars & Keeping Peace**

When are international and civil wars “ripe for resolution”? What determines intervention strategies for third parties, and why do attempts at conflict resolution so frequently fail? In this course we will investigate these questions. We will consider the process by which conflicts reach cease-fires and peace agreements, why some agreements last when others do not, and what can be done to make peace more durable. We will conclude by considering post-conflict societies and the lingering problems they face. Specific topics include peacekeeping, mediation, the role of regional (and international) organizations, and post-conflict justice.

**Graduate Courses**

**Bayesian Statistics**

This course covers the theoretical and applied foundations of Bayesian statistical analysis First, we will begin with discussing the Bayesian approach and how it differs from Frequentist analyses, learning how to estimate simple Bayesian models. Second, we will discuss model checking, assessment, and comparison, with an emphasis on computational approaches. Third, the course will cover Bayesian stochastic simulation (Markov chain Monte Carlo) in depth with an orientation towards deriving important properties of the Gibbs sampler and the Metropolis Hastings algorithms. Extensions and hybrids will be discussed. The fourth section will focus on applications of Bayesian statistics in social science data analysis. The topics could include Bayesian Hierarchical models for cross-sections and panel data, factor analysis models, IRT and other measurement models, and latent space models. Throughout the course, estimation with modern programming software (R and Jags) will be emphasized.

Class meetings will typically have two of the following three components: (1) lecture on the main technical points of the weekly reading (often statistical/mathematical), (2) computational demonstration using software such as packages in R, (3) discussion of theoretical and substantive applications. Initial readings are listed in the schedule below, although additional articles may be added. I will make any changes well in advance of the class meeting and notify you of changes or additions by email. When working with statistical software in class, I strongly encourage you to bring your laptops so you can write (and annotate) your own code. Spring 2017 Syllabus.

**Introductory Methodology**

This course is an introduction to statistical analysis, the second in our four-course research methods sequence. The purpose of the class is to (1) provide you with an understanding of some of the concepts that underlie statistical analysis, (2) introduce you to some basic statistical techniques, (3) learn basic math skills for social scientists and (4) develop your own capacity to do quantitative analysis. We will cover a broad range of topics including descriptive statistics, probability distributions, sampling distributions, point and interval estimation, hypothesis testing, and regression analysis. Fall 2016 Syllabus.

**Network Analysis**

This course is a comprehensive introduction to analyzing network data. We will cover network data collection and

management, the formulation and expression of network theory, network visualization and description, and models for

the statistical analysis of networks. The course will integrate theoretical discussions with practical examples. Fall 2015 Syllabus.

**ICPSR**

**Advanced Bayesian Models for the Social Sciences**

This course, taught at the ICPSR summer program in Ann Arbor, MI, is an intensive exploration of Bayesian models commonly used in Political Science, starting with linear models and then moving to more complex specifications including hierarchical models and seemingly unrelated regression.

**I3 Data Science Institute**

**Network Analysis using R**

Introduction to Social Network Analysis using R is a one day workshop that introduces attendees to concepts of social network analysis by illustration. The course walks through R code, learning what the code does and introducing network concepts along the way. Attendees leave with knowledge of commonly used R packages useful for network analysis. At least a small amount of prior experience with R is recommended.

**More information on the workshops can be found here.**