Quantitative Methods for Evaluating Educational Policies and Programs
This advanced master’s course addresses a key issue in evaluating education programs and policies: determining whether a policy causes an impact on student trajectories that would not have occurred in absence of the policy. The course will cover experimental and quasi-experimental techniques used to attribute causal relationships between educational programs and student outcomes. Students will become sophisticated consumers of quantitative educational research and will practice statistical techniques in problems sets. There will be an exam and a ﬁnal project. Prerequisites: Successful completion of 4002 and 5002 or equivalent and familiarity with the Stata statistical software package. No prior exposure to causal inference methods is expected.
Data Analysis for Policy and Decision Making II
This is an intermediate‑level course in non‑experimental quantitative research methods, especially those related to education policy. The class examines such topics as residual analysis, modeling non‑linear relationships and interactions using regression, logistic regression, missing data analyses, multilevel models, and principal components analysis. Prerequisite: Students should have completed at least one graduate‑level course in applied statistics or data analysis (e.g., EDPA 4002) and have experience with Stata software.