Awards
In 2019 I received the Collegiate Teaching Award for excellence in instruction from the College of Liberal Arts & Sciences at UIowa.
Undergraduate Courses
Poli 1500: 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 2019 Syllabus
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 2019 Syllabus
Poli 3000: Analyzing Political Data
Note: Poli 3000 changed in 2017, splitting the research design and analysis components into separate courses. Analyzing Political Data is the newer version.
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. While Poli 2000 is not a prerequisite, this course will focus on the various statistical tools that can be used to evaluate our
theories; theory generation will mainly be left to other courses. We will 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 statistical software (Stata), providing 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 presented by others. Spring 2021 Syllabus and Course Calendar.
Note: Poli 3000 changed in 2017, splitting the research design and analysis components into separate courses. Analyzing Political Data is the newer version.
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. While Poli 2000 is not a prerequisite, this course will focus on the various statistical tools that can be used to evaluate our
theories; theory generation will mainly be left to other courses. We will 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 statistical software (Stata), providing 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 presented by others. Spring 2021 Syllabus and Course Calendar.
Poli 3522: 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.
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
Poli 5001: 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 2020 Syllabus.
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 2020 Syllabus.
Poli 7002: Special Topics in Methodology 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. Spring 2020 Syllabus and Calendar.
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. Spring 2020 Syllabus and Calendar.
Poli 7002: Special Topics in Methodology 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. 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 will be emphasized. Spring 2021 Syllabus and Course Schedule.
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. 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 will be emphasized. Spring 2021 Syllabus and Course Schedule.
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.
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.
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.