Course Descriptions

MSSP Course Descriptions

Policy analysis requires an understanding of social problems/social issues and the processes by which policy is developed and implemented. Critical skills in many policy frameworks include: problem definition and analysis, review of relevant research, identification of possible actions, implementation and evaluation, and fiscal analysis. Competency in written and oral communication is also essential. To develop these and related skills, this course utilizes as a base a dynamic social problem analysis framework that addresses issues of equity, equality and adequacy. It also examines multiple theoretical and analytical perspectives. Through the review of contemporary and historical social policy debates and provisions, selected case examples and policy briefs, this course provides students with an understanding of the policy roles of the legislative and executive branches of government, including goal setting, policy rulemaking and enactment, allocation of resources, financing, regulation, and implementation. The policy process at state and local levels of government will also be addressed. The primary focus is on U.S. policy although global policies will be discussed when relevant.

Research & Evaluation Design introduces social research methods in the context of social policy and program evaluation. The course provides a conceptual and practical understanding in the design of experimental, quasi-experimental, and non-experimental research and in the application of quantitative and qualitative methods. Students learn about the application of the research process and skills in all phases of assessing a social policy and developing a social program, including needs assessment, implementation analysis, and evaluation of policy or program effectiveness. Students learn to be critical and informed consumers of research and to apply guidelines of research ethics in social policy settings.

What do these numbers mean? Do they confirm my theory? And what is a theory anyway? In this class, we will be exploring simple theories and how to test them using data. We will also look into how data can give us clues to formulate our theories. We will discuss how to plot data
to understand its contents and potential problems. Once we understand what is in the data, we can start testing some simple theories. For example, can we say that more educated people earn more than less educated people? And how confident can we be about this statement? Even if more educated people do earn more than less educated people, does this mean that increasing education will be causing people to earn more? Or is it simply that more educated people are smarter to begin with? We will see how data can allow us to solve this kind of question and advise policy makers on the benefits of increased education.

This course introduces students to the basics of the American legal system, focusing on the interplay between litigation and social policy. Students will learn how law, and particularly case law, is made, how to read case law and evaluate precedent, legal reasoning and argument. This course will utilize various teaching methods including introduction to the “Socratic” lecturing method which is frequently utilized in the study of law. Students will study the structure of court systems at both state and federal levels as well as the litigation process and the role of law and courts in shaping and addressing social policy issues. Students will also learn the basics of several areas of substantive law, with an eye toward consideration of how that law has been, and can be, used to effect social change.

The focus of Capstone I: Policy Communications (.5 CU) is three-fold:

  1. To enhance student integration of the theory and practice of social policy analysis;
  2. To enhance the student’s competencies in the written and oral communication processes and procedures necessary for the policy world; and
  3. To ensure basic knowledge about federal budget processes, stakeholder roles, and inter-organizational collaboration.

Capstone II: Policy Internship (.5 CU) consists of an intensive, multi-week policy internship that is selected through a consultative process involving the student, internship coordinator, advisor, and mentors/supervisors at potential sites.

The volume and complexity of data continues to increase in the world around us, including science, business, medicine, social media and everyday human activity. This course aims to expose students to visual representation methods and techniques that increase the understanding of complex data. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making. In this course, students will learn about the fundamentals of perception, the theory of visualization, and good design practices for visualization.

The course will also provide hands-on experience on the process of data communication, from initial data analysis, to identifying appropriate visualization techniques, to crafting informative visualizations.

Economics allows us to determine the costs and benefits of social policies like cash benefits, unemployment insurance, health insurance, pensions, education, etc. Policies typically affect the behavior of agents like individuals, families and firms, and we have to take these reactions into account when analyzing policy. Economics allows us to predict how policy is likely to affect behavior by understanding how the policy changes the agents’ decisions, and what collective outcomes these myriad individual decisions bring about.  For example, unemployment insurance allows individuals to sustain themselves and their families when they are out of a job. At the same time, unemployment insurance provides an incentive for people to search less
hard for a job, and this ultimately increases the time they spend unemployed. When all of the unemployed behave this way, the unemployment rate in the economy tends to increase. Policy makers have to take these phenomena into account in order to design a good unemployment insurance system.

With the advent of digital technologies and the increasing power of computational analytics, the proliferation and ubiquity of data production has increased at exponential rates enabling new possibilities for social analysis. This course will examine the emergence of democratizing data – the movement to make government and other data more widely or publicly available and its potential enabling for democratic possibilities. The types of data being made available, through various analytic systems, and the ways in which their accessibility and inaccessibility is contributing to reconfigured power relations, will be described. The paradigmatic tensions and shifts that have emerged in the debates on “Big Data,” such as deductive versus inductive reasoning and the challenges posed to statistical sampling theory, will be interrogated. The appropriation of machine learning and predictive analytic algorithms for social analysis will be critically explored. Issues related to the ethical and legal use of administrative data, particularly data related to patient, client, student, and taxpayer information will be considered, as well as from internet-based sources including social media. Potential solutions to data security challenges will be additionally considered.

Methods for web-scraping of data, analysis of web traffic data, and the use of social networking data in the modeling of social phenomena and public opinion will be examined. Students will learn how to make results accessible to non-technical audiences via data visualization tools, such as web-based data dashboards and web-based maps. These topics will be discussed for the analysis of health, education, and social policy as well as their implications for questions pertaining to race, gender, class, sexuality, dis/abilities, age, and youth culture. This course will develop students’ knowledge of computational and data analytics and its applications for social policy analysis.

Gender and Social Policy develops an advanced understanding of social policies through a focus on social issues and conditions through the lens of gender, economic, and critical theory. The specialized focus on gender and social policy provides students with the opportunity to develop more specialized knowledge about how market dynamics and government policies respond to the needs and risks faced by women. Specific emphasis is placed on utilizing theoretical frameworks to evaluate the intersection between social policy, history, and social science in relationship to gender issues. Students are also expected to conduct a policy analysis that includes an evaluation of how current and former social movements surrounding gender issues shaped their policy of interest.

In this course we will explore ways to provide women with practical, “real world” skills that will enable them to achieve meaningful political and advocacy participation. The course is designed to give you the theoretical background and tools to put together a meaningful international training such as those sponsored by Women’s Campaign International. The course will also focus on political and community organizing, communications, fundraising, advocacy and media experience, which will aid women politically, economically and civically in the life of their communities. Students will not only gain experience from working with Women’s Campaign International’s trainers, but will also learn how to develop training strategies for specific countries – addressing the particular challenges within countries that women face as they determine their political and economic involvement in emerging democracies.

Social constructions of “difference” permeate the institutions, spaces, and assumptions of our society. These social constructions include but are not limited to the racialized, gendered, sexed, classed, and dis/abled constructions of the body. By leaning on postmodern thinkers such as Iris Marion Young, Pierre Bourdieu, Judith Butler, Jacques Derrida, Ernesto Laclau, and Michel Foucault, this seminar course will begin by engaging the questions of what is “difference” and how is “difference” discursively constructed and reproduced in society. Using a postmodern lens, the remainder two-thirds of the course will engage various social science text that deal with the varieties of “difference” (i.e. race, gender, class, sexuality) and the explicit and/or implicit policy implications of these works. Thus, we will critically engage policies such as welfare, affirmative action, economic policies of taxation, and same-gender marriage among others. The underlying questions throughout the course will be to what extent does social policy enable the possibilities of freedom, justice, and democracy for the “Other”, the deviant, the abject, the marginalized, those of assumed “difference”? And, to what extent does policy constrain those possibilities at the same time?

This course deals with the underlying assumptions and applications of the general linear model with social science, education, and social policy related questions/data. The first half of the course begins by covering simple linear regression and the assumptions of the general linear model, assumption diagnostics, consequences of violation, and how to correct for violated assumptions. This will also include methods of incomplete case analysis (i.e. missing data analysis). Then various aspects of regression analysis with multiple independent variables will be covered including categorical explanatory variables (e.g. to estimate group differences), interaction effects, mediating effects (e.g. to estimate the indirect effect of social processes), and non-linear effects. The course will then cover some of the applications of the general(ized) linear model including logistic regression, some elements of path modeling (structural equation modeling), multilevel analysis (hierarchical linear modeling ), and longitudinal modeling (growth modeling). The course will be taught using SAS, but students are welcome to use any statistical package of comfort. Pre-requisite: Introductory Graduate Statistics.