PSCI 236 / PPE 312 Public Policy Process
Marc Meredith (MW 10:00 am - 11:00 am)
This course introduces students to the theories and practice of the policy-making process. There are four primary learning objectives. First, understanding how the structure of political institutions matter for the policies that they produce. Second, recognizing the constraints that policy makers face when making decisions on behalf of the public. Third, identifying the strategies that can be used to overcome these constraints. Fourth, knowing the toolbox that is available to participants in the policy-making process to help get their preferred strategies implemented. While our focus will primarily be on American political institutions, many of the ideas and topics discussed in the class apply broadly to other democratic systems of government. (Counts toward Core Competency 1: American Politics)
PSCI 333 / COMM 393 Political Polling
David Dutwin (R 3:00 pm - 6:00 pm)
Political polls are a central feature of elections and are ubiquitously employed to understand and explain voter intentions and public opinion. This course will examine political polling by focusing on four main areas of consideration. First, what is the role of political polls in a functioning democracy? This area will explore the theoretical justifications for polling as a representation of public opinion. Second, the course will explore the business and use of political polling, including media coverage of polls, use by politicians for political strategy and messaging, and the impact polls have on elections specifically and politics more broadly. The third area will focus on the nuts and bolts of election and political polls, specifically with regard to exploring traditional questions and scales used for political measurement; the construction and considerations of likely voter models; measurement of the horserace; and samples and modes used for election polls. The course will additionally cover a fourth area of special topics, which will include exit polling, prediction markets, polling aggregation, and other topics. It is not necessary for students to have any specialized mathematical or statistical background for this course. (Counts toward Core Competency 3: Survey Research)
PSCI 243 Dilemmas of Immigration
Michael Jones-Correa (TR 3:00 pm - 4:30 pm)
Beneath the daily headlines about refugees blocked entry, and undocumented migrants deported there is a set of hard questions which deserve closer attention: Should countries have borders? If countries have borders, how should they decide who is kept out and who is allowed in? How many immigrants is 'enough'? Are immigrants equally desirable? What kinds of obligations do immigrants have to their receiving society? What kinds of obligations do host societies have to immigrants? Should there be 'pathways' to citizenship? Should citizenship be automatic? Can citizenship be earned? This course explores these and other dilemmas raised by immigration.
PSCI 247 Campaigns and Elections
Andrew Gooch (TR 4:30 pm - 6:00 pm)
This lecture course will teach students about American campaigns and elections, from the local level to the presidential level. We will cover as many topics as possible including: the nominating process, the general campaign, campaign strategy, turnout, campaign finance, the role of issues, the importance of the economy, the power of party identification, and the role of data analysis used by campaign professionals. We will also consider how these factors matter in terms of who wins the election. In addition to the literature on campaigns and election, this lecture will put minor focus on the most recent 2016 presidential election relative to what the literature would have predicted. After the first part of the course about presidential elections, the second part will focus on Congressional elections (and a bit about state and local elections). Lastly, the third part of the course will examine how data analytics that originated in political science are now being used by campaign practitioners to win elections.
COMM 125 Introduction to Communication Behavior
Michael Delli Carpini (MW 10:00 am - 11:00 am)
This course introduces students to social science research regarding the influence of mediated communication on individual and collective attitudes, beliefs, and behaviors. Throughout the semester we explore the impacts of various types of mediated content (e.g., violence, gender and sexuality, race and ethnicity, politics and activism, health and wellbeing); genres (e.g., news, entertainment, educational, marketing); and mediums (e.g., television, film, social media) on what we think and how we act. The aim of the course is to provide students with (1) a general understanding of both the positive and negative effects of mediated communication on people's personal, professional, social, and civic lives; and (2) the basic conceptual tools needed to evaluate the assumptions, theories, methods, and empirical evidence supporting these presumed effects. Class meets twice a week (MW) as a lecture and once a week (F) in smaller discussion groups led by graduate teaching fellows. In addition to a midterm exam and occasional short assignments, students have the option of producing a multi-media capstone project or a final term paper on a media-effects topic of their choice. Group projects or final papers are permitted, with approval of the instructor. In addition to fulfilling General Education Curriculum Sector 1 Requirement (Society), this course fulfills one of the two introductory-level courses required of Communication majors or prospective majors.
COMM 290 Intro to Data Analysis for Communications
Jin Woo Kim (TR 10:30 am - 12:00 pm)
In this course, we will learn the basic tools of data analysis and apply them to answer various questions in communication science. Can reluctant parents be convinced to vaccinate their children? Can get-out-the-vote mailings mobilize voters? Does the diffusion of political rumors affect public opinion? Are toxic comments more likely to go viral on Facebook? These are examples of the questions that we will answer using pre-existing datasets as well as a new online experiment that we will run together as part of the course. There is no official prerequisite for this class and students are not expected to have any familiarity with statistical programming. Students will be given step-by-step instructions and we will work together to analyze the datasets. For the final project, each student will write a research note based on their analysis of the new experimental data. At the end of this course, students will be able to use quantitative data to extract statistical patterns and answer empirical questions. These skills will be extremely useful in various settings, from academia to the media and tech industry and more.
COMM 313 Computational Text Analysis for Communication Research
Matthew O’Donnell (TR 12:00 pm - 1:30 pm)
In this 'big data' era, presidents and popes tweet daily. Anyone can broadcast their thoughts and experiences through social media. Speeches, debates and events are recorded in online text archives. The resulting explosion of available textual data means that journalists and marketers summarize ideas and events by visualizing the results of textual analysis (the ubiquitous 'word cloud' just scratches the surface of what is possible). Automated text analysis reveals similarities and differences between groups of people and ideological positions. In this hands-on course students will learn how to manage large textual datasets (e.g. Twitter, YouTube, news stories) to investigate research questions. They will work through a series of steps to collect, organize, analyze and present textual data by using automated tools toward a final project of relevant interest. The course will cover linguistic theory and techniques that can be applied to textual data (particularly from the fields of corpus linguistics and natural language processing). No prior programming experience is required. Through this course students will gain skills writing Python programs to handle large amounts of textual data and become familiar with one of the key techniques used by data scientists, which is currently one of the most in-demand jobs.
GAFL 531 Data Science for Public Policy
Samantha Sangenito (MW 10:30 am - 11:50 am)
In the 21st century, Big Data surround us. Data are being collected about all aspects of our daily lives. To improve transparency and accountability an increasing number of public organizations are sharing their data with the public. But data are not information. You need good information to make sound decisions. To be an effective public leader, you will need to learn how to harness information from available data. This course will introduce you to key elements of data science, including data transformation, analysis, visualization, and presentation. An emphasis is placed on manipulating data to create informative and compelling analyses that provide valuable evidence in public policy debates. We will teach you how to present information using interactive apps that feature software packages. As in all courses at Fels, we will concentrate on more practical skills than theoretical concepts behind the techniques. This course is designed to expand upon core concepts in data management and analysis that you learned in GAFL 640: Program Evaluation and Data Analysis. This is a graduate level course and while GAFL 640 is not a pre-requisite, students are expected to have a foundation of data management and analysis before beginning this course.