The Research Assistant will work under the direction of Professor Susanna Loeb and Dr. Xiaoyang Ye to support research on Effects of High School Math and Science Coursetaking on Students' Long-term Outcomes. In this project, we will be using large-scale nationally representative survey data to examine how high schools choose which math and science courses, when to take them, in what order as well as how these courestaking behaviors predict long-term outcomes in high school and college. In addition to descriptive analysis and causal effect estimation, we will also apply machine learning models to build the prediction models.
Primary responsibilities include data cleaning and analysis, as well as analysis and writing. We seek a student who is interested in quantitative education policy research and aims to produce high-quality research that can inform policy-making and be submitted for academic publications.
Desired qualifications include experience with data analysis using Stata (or R, Python) and knowledge in applied econometrics. Experience with quantitative education policy research is a big plus.
To apply: Applicants should log into Workday, navigate to "Find Student Jobs" and search for REQ169747.