Courses

This course examines the basic methods data analysis and statistics that political scientists use in quantitative research that attempts to make causal inferences about how the political world works. The same methods apply to other kinds of problems about cause and effect relationships more generally. The course will provide students with extensive experience in analyzing data and in writing (and thus reading) research papers about testable theories and hypotheses. It will cover basic data analysis and statistical methods, from univariate and bivariate descriptive and inferential statistics through multivariate regression analysis. Computer applications will be emphasized. The course will focus largely on observational data used in cross-sectional statistical analysis, but it will consider issues of research design more broadly as well. It will assume that students have no mathematical background beyond high school algebra and no experience using computers for data analysis.

This class aims to introduce students to the logic of social scientific inquiry and research design. Although it is a course in political science, our emphasis will be on the science part rather than the political part — we’ll be reading about interesting substantive topics, but only insofar as they can teach us something about ways we can do systematic research. This class will introduce students to a medley of different methods to conduct social scientific research.

This class aims to introduce students to the logic of social scientific inquiry and research design. Although it is a course in political science, our emphasis will be on the science part rather than the political part — we’ll be reading about interesting substantive topics, but only insofar as they can teach us something about ways we can do systematic research. This class will introduce students to a medley of different methods to conduct social scientific research.

This course explores techniques to harness the power of ``big data'' to answer questions related to political science and/or American politics. Students will learn how to use R---a popular open-source programming language---to obtain, clean, analyze, and visualize data. No previous knowledge of R is required.

We will focus on applied problems using real data wherever possible, using R's ``Tidyverse.'' In total, in this course we will cover concepts such as reading data in various formats (including ``cracking'' atypical government data sources and pdf documents); web scraping; data joins; data manipulation and cleaning (including string variables and regular expressions); data mining; making effective data visualizations; using data to make informed prediction, and basic text analysis. We will also cover programming basics including writing functions and loops in R. Finally, we will discuss how to use R Markdown to communicate our results effectively to outside audiences. Class sessions are applied in nature, and our exercises are designed around practical problems: Predicting election outcomes, determining the author of anonymous texts, and cleaning up messy government data so we can use it. 

This course explores techniques to harness the power of ``big data'' to answer questions related to political science and/or American politics. Students will learn how to use R---a popular open-source programming language---to obtain, clean, analyze, and visualize data. No previous knowledge of R is required.

We will focus on applied problems using real data wherever possible, using R's ``Tidyverse.'' In total, in this course we will cover concepts such as reading data in various formats (including ``cracking'' atypical government data sources and pdf documents); web scraping; data joins; data manipulation and cleaning (including string variables and regular expressions); data mining; making effective data visualizations; using data to make informed prediction, and basic text analysis. We will also cover programming basics including writing functions and loops in R. Finally, we will discuss how to use R Markdown to communicate our results effectively to outside audiences. Class sessions are applied in nature, and our exercises are designed around practical problems: Predicting election outcomes, determining the author of anonymous texts, and cleaning up messy government data so we can use it. 

Interpretations of civil society and the foundations of political order according to the two main traditions of political thought--contraction and Aristotelian. Readings include works by Hobbes, Spinoza, Locke, Montesquieu, Hume, Rousseau, Kant, Hegel, Saint-Simon, Tocqueville, Marx, and Mill.

Prerequisites: POLS W4710 or the equivalent. 

This course will intensively examine some of the data analysis methods which deal with problems occurring in the use of multiple regression analysis.  It will stress computer applications and cover, as needed, data coding and data processing. Emphasis will also be placed on research design and writing research reports. 

The course assumes that students are familiar with basic statistics, inference, and multiple regression analysis and have analyzed data using computer software (e.g., any standard statistical programs on micro-computers or larger machines -- Stata, “R”, SPSS, SAS, etc.). Students will be instructed on the use of the microcomputers and the R and Stata statistical software program(s) available as freeware (R) or in the CUIT computer labs (Stata; several campus locations) or through SIPA. The lectures and required discussion section will emphasize the use of “R.” Students may use whatever computer programs they prefer for all data analysis for the course. There may be an additional fee for classroom instructional materials.