Students will curate administrative data to conduct Sequence Analysis (SA), a technique used to analyze patterns in sets of categorical sequences over time. Traditional education reports often rely on cross-sectional data (e.g., proportion of students under economic disadvantage), and in tend to overlook the chronic exposure to disadvantages that longitudinal data can reveal. SA allows for analysis of individual trajectories, such as transitions in economic disadvantage, school events (e.g., disciplinary infractions), absenteeism, residential and school mobility, and course-sequencing, by chronologically ordering these data points. It also allows us to examine the best predictors of specific trajectories, such as high-school drop-out and college admission.
Project Lead: Marcos Rangel, Public Policy
