I mostly teach research design and quantitative methods classes (along with international political economy). I believe strongly that there's no such thing as a "math person" and that anyone can succeed in mastering quantitative methods. Descriptions of the courses I currently offer are listed below. Syllabi are available upon request, since I am terrible at keeping the links on my website up-to-date.

I also love working with students. If you're an undergrad who's interested in learning about quantitative research, please get in touch! I work with students through UROP, DURIP, and DFRACS, in addition to hiring students independently.

I also love working with students. If you're an undergrad who's interested in learning about quantitative research, please get in touch! I work with students through UROP, DURIP, and DFRACS, in addition to hiring students independently.

**Undergraduate Courses**

POL 3085: Quantitative Analysis in Political Science

(Taught Spring 2019, Fall 2017, Fall 2016)

POL 3085 teaches students how to study politics scientifically and introduces them to how to use quantitative analysis to answer political questions. The first part of the class covers how to formulate a theory (a possible answer to a question), specify testable hypotheses (what you would see if the theory is correct or incorrect), and set up a research design to test those hypotheses. In the second part of the class, we cover quantitative data analysis, beginning from preliminary statistical analysis to multivariate linear regression. There is no mathematical or statistical background required for this course. By the end of the class, students should be able to ask and answer political questions using quantitative data and fluently evaluate statistical analyses of political phenomena in the media and many academic articles.

**POL 4085: Advanced Political Data Analysis**

(Taught Spring 2018 and as POL 4010 in Spring 2017)

(Taught Spring 2018 and as POL 4010 in Spring 2017)

In this course, students learn how to use statistical methods to answer a wide variety of questions in political science. More specifically, students will focus on how to test hypotheses where the dependent variable is dichotomous, ordered categories, unordered categories, counts, and more. The course covers advanced topics in linear regression, including time series data, multilevel modeling, and interaction terms. Assignments focus on how to convey statistical results in many different ways, ranging from technical reports, to blog posts, to personal communication. Students will learn and improve their skills in the R statistical software package. Prior knowledge of R is not required. This class is especially recommended for students completing an undergraduate thesis with a quantitative component as well as students who want to pursue graduate studies in political science.

**POL 3833: The United States and the Global Economy**

(Taught Spring 2018, Spring 2017)

(Taught Spring 2018, Spring 2017)

POL 3833 teaches students about the politics of the global economy with a focus on the role the United States plays within it. The class covers a variety of topics in international political economy, including international trade, international investment, and international finance. Students will learn about the factors that drive politicians' decision-making, interest-group stances, and citizens' preferences over such salient issues as tariffs and other forms of trade protection, trade and investment agreements, central banking, interest rates, international migration, and more. No background in economics is required or assumed.

**Graduate Courses**

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**Maximum Likelihood Estimation**

(Taught Fall 2017, Fall 2016)

(Taught Fall 2017, Fall 2016)

This course presents an overview of the likelihood theory of statistical inference, and its wide range of uses in applied quantitative political science. When dependent variables take the form of ordered or unordered categories, event counts, or otherwise violate the traditional assumptions of the linear regression model, models estimated by maximum likelihood provide an essential alternative. Topics covered include binary, multinomial, and ordered logit/probit, Poisson regression, and multilevel models. We will rely heavily on computational methods of analysis using the R statistical computing environment, and instruction on how to use R for applied research will be provided throughout the length of the course.

**R Camp**

(Taught Summer 2018, Summer 2017)

(Taught Summer 2018, Summer 2017)

R Camp is a week-long introduction to R. R Camp takes students from having no knowledge of R to, by the end of the week, being able to input and manage data, produce summary statistics and basic data visualizations, run ordinary least squares (OLS) linear models and diagnostics, implement basic linear algebra, and understand some of the fundamentals of programming, including for loops and conditional statements. More concisely: by the end of one week, students should be able to reproduce most if not all of the work they did in their first year of graduate school using R. R Camp is intended for graduate students and faculty with little to no experience in R or computer programming, although those with more advanced knowledge are welcome.