PB AF 599
Study and analysis of special topics in public affairs. Topics vary each quarter depending on curricular needs and interests of students and faculty.
This course is designed to prepare students with advanced applied work with multivariate methods especially in program evaluation and policy analysis. The course will be run as a workshop: students will choose a major course project that will result in a professional quality product. In addition, students will read examples of professional and academic policy analyses and program evaluations, and be responsible for presenting the methods and results to the class. Guest speakers will present additional examples of analysis and provide information on the context of analysis.
Student learning goals
Further skills in assessing data quality, research design, and presentation methods for multivariate analyses.
Gain experience in the application of multivariate modeling and enhance skills in data manipulation.
Become familiar with advanced analysis methods such as limited dependent variable models (e.g., logit, probit, tobit models), multiple equation models, multilevel methods, multiple imputation of missing data, and selection bias correction models.
ain additional exposure to the use of multivariate methods in program evaluation and policy analysis
General method of instruction
The course will be run as a workshop: students will choose a major course project that would result in a professional quality product. In addition, students will read sophisticated examples of professional and academic policy analyses and program evaluations and be responsible for presenting the methods and results to the class. Guest speakers from policy shops at the federal (GAO), state (JLARC, WSIPP) and local levels will add information on the institutional context for analysis.
Previous coursework in multivariate regression
Class assignments and grading
Expected Student assignments:
· Read complex empirical reports and assess the quality of research design, data collection methods, data analysis, and conclusions. · Lead class discussion of 1 academic article or professional report. · Produce a professional quality report using multivariate analysis
Assignments and class participation