Harnessing data to enhance youth wellbeing: Quantitative research for THESIS (Thriving and Health Equity through Social Inclusion in Schools)
Sean Darling-Hammond, Professor
Public Health
Applications for Spring 2026 are closed for this project.
The THESIS Lab (Thriving and Health Equity through Social Inclusion in Schools) features a vibrant team of postdoctoral scholars / staff, PhD students, Masters students, and undergraduate students. Led by Dr. Sean Darling-Hammond with support from Dr. Stephanie Guinosso, we are conducting an array of quantitative methods projects using data from the California Healthy Kids Survey, the California Health Interview Survey, and other sources that we have unique access to. Our goal is to identify actionable insights that can improve school practices and, as a result, enhance child wellbeing.
Role: We aim to hire a student who will serve as the "second coder" on a variety of quantitative projects. This means you and a "primary coder" will write code to achieve the same goal and then compare results. This approach ensures code is accurate and streamlined. Once we feel you are ready, you could graduate to the "primary coder" role on appropriate projects. Whether you are the primary or secondary coder, the code you write will be designed achieve one or more of the following ends:
- Cleaning data
- Merging data
- Summarizing samples
- Running a variety of analyses
- Producing tables and visualizations
The learning outcome of this work is to become extremely adept at managing and working with data "in the wild." Classroom projects provide a helpful sandbox to practice working with data tools, but real social science typically presents novel (and exciting) challenges. Working in a coding team allows you to learn, practice, refine, and master a variety of extremely practical and marketable data analysis skills. To put it bluntly, few hire for the skills you learn in class. Manny hire for the skills you learn in practice. This is practical learning in every sense of the word.
Qualifications: Students must already have a *strong* command of STATA, data cleaning, data architecture (merging / appending), summarizing samples, conducting multivariate regression analyses, and presenting the results of these (and other) analyses in tables and figures. This opportunity is not designed to help students learn the basics, but to help them apply the basics to achieve meaningful mastery.
Day-to-day supervisor for this project: Sean Darling-Hammond
Hours: 6-8 hrs
Social Sciences Education, Cognition & Psychology