Research Methods for Social Policy Analysis

SW832, Section 001

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.

Topic Description / Additional Information

Focus on Multilevel and Longitudinal Data Analysis

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 units e.g. residents in neighborhoods, or children in classrooms and/or schools. Perhaps surprisingly, the multilevel model for cross-sectional data can easily accommodate longitudinal data where study participants are observed repeatedly over time. Further, while this is sometimes not recognized, multilevel models for longitudinal data are closely related to other important longitudinal data models, such as fixed effects regression, an important technique for controlling for unobserved variables. Multilevel models are also closely related to statistical models for meta-analysis of multiple studies.

This course focuses on the use of multilevel and longitudinal data analysis for social work and social science research. The course is conceptualized as covering the following topics:

1) The multilevel model for cross-sectional data.
2) The extension of multilevel modeling to longitudinal research (i.e. growth trajectory models).
3) Other panel data models such as fixed effects and random effects models.
4) Possible additional topics based on student interest (models for dichotomous outcomes; multiple imputation for missing data in multilevel models; meta-analysis)

The models discussed in this course are primarily intended, and best developed, for continuous outcomes, so are most appropriate for students with research questions that can in some way be conceptualized as continuous outcomes.

The first half of the semester will be devoted to a discussion of cross-sectional and clustered data. The second half of the semester will focus on data with repeated measures on at least the dependent variable. Thus, it is very beneficial for students taking the course to locate suitable data for each half of the course prior to the beginning of the semester.

The course is intended for students who have some familiarity with ordinary least squares regression, since the OLS model will be used as a starting point for discussion.

The treatment of these topics involves discussion of the underlying statistical theory, but is also focused on the application of these models with real data. STATA is intended to be the primary statistical software used in this course. Depending upon student interest, some reference may be made to the analysis of these models in other statistical packages such as R, or SAS.

Instruction is a mixture of in class lecture and discussion and time spent in the computer lab working through actual data problems with statistical software.