Substantive research interests include political economy, especially the politics of distribution and the manipulation of budgets around elections. Methodological research interests include time series, pooled time series, compositional data, machine learning and the graphical presentation of results.
political budget cycles, distributive politics, political economy, time series, pooled time series, compositional data, graphical presentation of results, machine learning
PSCI 2075 - Quantitative Research Methods
Fall 2018 / Spring 2020
Introduces quantitative research methods used in political science. Focuses on basic tools of analysis: data collection, processing, and evaluation, with special attention to survey techniques. Includes elite and case study analysis; aggregate, cluster, and content analysis; and the use of computers in political research.
PSCI 3075 - Applied Political Science Research
Introduces the types of research design and quantitative methodology used in applied political science research. Directly builds on the data analysis performed in Quantitative Research Methods (PSCI 2075).
PSCI 7095 - Advanced Political Data Analysis
Spring 2018 / Spring 2019 / Spring 2020
Provides advanced training in the statistical modeling of political relationships. Focuses on the properties and assumptions of the ordinary least squares regression model, building on material covered in PSCI 7085: Introduction to Political Science Data Analysis.
PSCI 7108 - Special Topics
Various topics not normally offered in the curriculum. Topics vary each semester. May be repeated up to 12 total credit hours.
PSCI 7155 - Maximum Likelihood Estimation and Generalized Linear Models
Introduces maximum likelihood estimation and extends the linear model to several generalized linear models. Provides students with the skills to analyze and understand a broad class of outcome variables and data structures such as dichotomous outcomes, counts, ordered and unordered categorical outcomes and bounded variables. Also examines several special topics such as multilevel models, causal inference and missing data.