University of Michigan
Postdoctoral Training Program in Experimental and Quasi-Experimental Methods for Education Research
»The program will be based at the Gerald R. Ford School of Public Policy and led by faculty with appointments at the School of Public Policy, School of Education, and Department of Economics. The program faculty are engaged in research projects that will expose fellows to a broad array of topics and methods in education research. Researchers will train four postdoctoral fellows, who will each spend 2 years in the program. Fellows will receive close mentorship from program faculty, attend courses and specialized training institutes on quantitative methods, participate in seminars and workshops devoted to causal inference in education research, and assist in research projects that will develop skills in experimental and quasi-experimental methods for causal inference. Training will emphasize the use of state longitudinal data systems using techniques that allow for robust causal inference. Fellows will collect, compile, and analyze data; design surveys; participate in research planning; write papers; present results at seminars and professional meetings; and supervise research assistants. Fellows will gain substantive knowledge of policy-relevant topics such as charter schools, online learning, school choice, high school standards, graduation requirements, teacher effectiveness, and postsecondary attainment. IES topic areas addressed by these projects include Analysis of Longitudinal Data to Support State and Local Education Reform, Middle and High School Reform, Education Policy, Finance and Systems, Mathematics and Science Education, Organization and Management of Schools and Districts, Education Technology, Postsecondary Education, and Teacher Quality. Fellows will learn a variety of specific methodological techniques including randomized controlled trials, instrumental variables, fixed effects, comparative interrupted time series, regression discontinuity analysis, and matching estimators.
Principal Investigator: Susan Dynarski