This course covers research methods for assessing the nature and extent of needs for social intervention, evaluating the success or failure of existing social welfare policies, and determining the anticipated consequences of alternative policies and interventions. Also considered will be values and assumptions underlying policies and research, similarities and differences between methods for developing social policy knowledge and those for basic knowledge development, strategies to promote utilization and dissemination of research results, and methods of studying community, regional, national, and comparative international policies. Possible topics will be: community needs assessment techniques; subjective and objective measures of program and policy consequences; aggregation problems within and across communities, regions, or countries; analysis of time series data; archival and other historical methods of research; case study techniques; analysis of cross‐sectional, panel, and comparative international data as natural experiments; the design and analysis of formal social experiments; meta‐analysis of existing research results; and benefit‐cost analysis and other related methods.
Students with less than doctoral standing are expected to seek permission of the course instructor prior to enrolling.
Multilevel models have become a standard statistical tool for quantitative research on neighborhoods, communities and schools. The cross-sectional multilevel model is appropriate for situations in which respondents are clustered inside larger social or geographic units e.g. people in geographic areas, residents in neighborhoods, or children in classrooms and/or schools. This course requires a solid understanding of ordinary least squares regression (OLS) as a starting point. The first part of the course is all about the idea of “nesting”: e.g. students nested in classrooms, residents nested in neighborhoods, study participants nested in cities. The second part of the course extends these ideas to longitudinal data, thinking about situations where we have repeated measures on an outcome of interest, like anxiety, depression or substance abuse. Course assignments focus on individually focused student data driven projects, so for students considering the course, it is useful to have an available data set has both some kind of “nesting” as well as repeated measures of some outcome of interest.
Other SW832 Offerings
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