PhD Resources

This course provides an introduction to statistical inference. We will learn the fundamental tools of data science and apply them to a wide range of social science and policy-oriented questions. The objective of the course is to develop two broad skill sets: (1) an understanding of the conceptual foundations for why we might manage or analyze data in one way versus another, and (2) learning the computing and programming tools (using R) to manage, visualize, and analyze data. The topics covered in the course include descriptive statistics, measure of association for categorical and continuous variables, introduction to t-tests, ANOVA and linear regression, research design (e.g., sampling, measurement, and causal inference), and the language of data analysis. Students will learn how to apply statistical tools to data sources, to design research studies, to test hypotheses, and to interpret the results of quantitative studies. *Note: Teaching for R programming occurs in lab sections (sign up separately for your weekly lab section). Lectures focus, instead, on the conceptual foundations of statistical inference.

Also Offered As: MSSP 6300

1 Course Unit

This course deals with how to critically and responsibly model real-world data to answer social science, education, and social policy-related questions, using the framework of the general linear model. Linear modeling (which, in statistics, is synonymous with regression analysis) is the workhorse of much of quantitative social science and, despite its enormous flaws and powerful limitations (which this course will also cover!), it remains an important tool to understand and be able to use. The course builds up multiple regression from correlation and bi-variate regression, and then covers categorical independent variables, nonlinear transformations and polynomial terms, diagnostic checks, model-building and model iteration, interaction effects, mediation analysis, and logistic regression. Mathematical (e.g., Gauss-Markov) assumptions are covered but the emphasis is on deeper epistemic assumptions and more immediate practical limitations. While not covered in detail, pointers will be given to techniques for specific types of data (especially multilevel modeling for nested data) and to important modern developments (especially structural causal modeling, non-parametrics, and machine learning). Throughout, the course will return to and emphasize critiques of linear modeling, to encourage students to be able to use (or choose not to use and oppose) regression analysis rigorously, critically, and responsibly. The course will be taught using R. This course includes an introduction to R. Background in R or in programming is not strictly necessary for this course but it is helpful. Prerequisite: MSSP 6300 Quantitative Reasoning/Social Statistics, or another Introductory graduate statistics course.

1 Course Unit

The course discusses the emergence of the welfare state and its development, the structural and ideological forces that have shaped and challenged it. We will examine the historical contexts and discuss how the different philosophical and ideological positions are influenced by diverse class, gender racial, ethnic, and cultural perspectives. Integrated throughout the discussions long standing conflicts and tensions in the field such as the role of social responsibility vs. social control vs. social exclusion based on race, ethnicity, and gender, how needs are recognized and determined, the nature of helping, perspectives on social justice and charity, the professional role of social workers, and organizational arrangements for social work and social welfare. To understand the US welfare state, we will also use a comparative, cross-national and multicultural lens. The course is to help students gain a critical understanding of social welfare taking a historical lens.

1 Course Unit

Course reading consists of the original works of theorists who offer classical, contemporary, and postmodern perspectives on social thought, social interaction, and issues germane to social welfare. Through intensive examination of multiple theoretical frameworks, students are expected to increase their analytical and critical orientation to theory. This more nuanced understanding about epistemology, underlying assumptions, and theory construction can then be used to inform the student’s substantive field of study and methodological orientation to research. This course is conducted in mixed lecture-seminar format. Students have the opportunity to practice pedagogical techniques and exercise class leadership.

This course is designed to provide the students with knowledge about the scientific method, theory construction, and the relationship of theory to research. Students will learn the steps involved in designing a research proposal, including problem formulation, the development of conceptual frameworks, researchable questions, hypothesis development, justification and significance of the research study, research designs, instrument construction, measurement issues, sampling procedures, and ethical issues involved in research. Various types of experimental, quasi-experimental, and non-experimental designs will be presented. Specific methods such as survey research, single system design, and evaluation research will be covered. The course will deal with issues related to the feasibility and practicality of designing, implementing, and conducting proposed research. Students will understand the connection between this course and their required statistics, theory, and policy analysis courses. By the end of the course, students will know how to design a sound scientific research study that is ethical, practical, and feasible.

1 Course Unit

Prerequisite: SOCW 8520

The course is intended to augment and fine tune what students already know about the design, development, and execution of sound scientific practices. Emphasis will be placed on greater depth in areas of sampling, measurement, proposal writing and special topics. Topics that will be included are practical sampling design, question formation, questionnaire/interview construction and format, scaling, critiquing proposals, budget development, budget justification, timelines, etc. In addition, special topics to be covered will be randomized clinical trials (RCTs), fidelity assessment, treatment manuals, meta-analysis and systematic reviews, evidence-based practice, comparative effectiveness, translational & implementation science, community based participatory research, qualitative methods, and mixed methodology. Class discussion will involve critiquing students’ own research proposals, NIH funded research proposals, and published literature in peer reviewed journals. The class will focus on the application of research methodology to increase the student’s understanding of the tradeoffs and limitations of various designs within the parameters of naturalistic settings. The importance of balancing rigorous scientific methods with ethics, practicality, and feasibility to answer important questions for social work practice and social welfare policy will be emphasized. This course will be conducted in a seminar format (not a lecture) where each student is expected to discuss the topic for the week. One student will take responsibility for leading the discussion on the topic(s) for a given class and will also have half a class session devoted to their own research topic to seek classmates input.

1 Course Unit

This course examines alternative models of policy development and applies them to current issues in social welfare. It emphasizes frameworks for policy research and secondary analysis of governmental data. Topics include race, class, and sex in policy formulation; the budget process and policy outcomes; major social welfare programs; and the design, implementation, and evaluation of social service systems.

1 Course Unit

Four terms of Proseminar—once weekly, 90-minute (.5 course unit) courses—are required of all students, to be taken (usually) during the first two years of the program. The overall purpose of the proseminar is the facilitation of doctoral students’ socialization to academic and research careers. The specific content of the course—a mix of sessions designed to enhance the development of a variety of skills and capacities necessary for students’ success as scholars, teachers, and colleagues— will vary across the four semesters.

0.5 Course Units