Sean Reardon. 200 million test scores and what do we know? Income, race, and the geography of educational opportunity in the U.S.

Monday, January 23, 2017 - 2:00pm to 3:30pm
Blumer Room - 402 Barrows Hall

200 million test scores and what do we know? Income, race, and the geography of educational opportunity in the U.S.

We test students a great deal in the United States. In grades three through eight alone, U.S. students take roughly 50 million standardized state accountability tests each year. Their scores on these tests, aggregated within geographic school districts and student subgroups, provide a useful proxy measure of the sum total of educational opportunities available to children in different communities and groups. In this talk, I will describe the construction and use of a population-level data set (the Stanford Education Data Archive) based on over 200 million test scores from 2009-2013. Using these data, I will describe the patterns and correlates of academic performance and racial/ethnic achievement gaps at an unprecedented level of detail, with a particular focus on the role of socioeconomic context and segregation patterns in shaping opportunity. These data reveal a great deal about patterns of educational opportunity in the United States.

Sean Reardon is the Stanford University Endowed Professor of Poverty and Inequality in the School of Education at Stanford University. He has a courtesy appointment in Sociology and is the Director of the Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis. Professor Reardon's research investigates the causes, patterns, trends, and consequences of social and educational inequality. In particular, he studies residential and school segregation and its relationship to racial/ethnic and socioeconomic disparities in academic achievement and educational success. In addition, his work involves quantitative methods of measuring social and educational inequality (including the measurement of segregation and achievement gaps) and methods of causal inference in educational and social science research.