This doctoral course covers the design, implementation and interpretation of empirical methods used for evaluating causal relationships in educational research. We will read and discuss applied methodological texts as well as journal articles using advanced causal methods. We will cover instrumental variables (IV), randomized experiments (RCT), natural experiments, differences-in-differences (DID), regression discontinuity (RD), propensity score matching (PSM), and hierarchical linear model (HLM). Goals of the course are for students to understand the conceptual underpinnings of each type of study design; to be able to critically evaluate particular studies utilizing each approach; to gain first-hand experience in formulating causal questions and implementing a causal method; and to develop skills in communicating research designs and findings (in both written and presentation form). The course is designed for doctoral students in education who are interested in conducting research using quantitative methods.
Overview
Syllabus
- Weeks 1 & 2 Basic Econometrics
- Weeks 3 & 4: Instrumental Variable
- Weeks 5 & 6: Randomized Experiments - Class Size, Career Academies
- Weeks 7 & 8: Natural experiment and DID
- Weeks 9 & 10: Regression discontinuity
- Weeks 11&12: Propensity Score Matching
- Weeks 13&14: HLM
- final exam
Taught by
Yu Zhang