DOI: 10.25740/db586ns4974
title: Stanford Education Data Archive (SEDA)

description: Racial, socioeconomic, and gender disparities in academic performance and educational attainment are stubborn features of the U.S. educational system. These disparities are neither inevitable nor immutable, however. They have been produced by—and so may also be reduced by—a welter of social and economic policies, social norms and patterns of interaction, and the organization of American schooling.

The Stanford Education Data Archive (SEDA) is an initiative aimed at harnessing data to help us—scholars, policymakers, educators, parents—learn how to improve educational opportunity for all children. We are making the data files public so that anyone who is interested can obtain detailed information about American schools, communities, and student success. We hope that researchers will use these to generate evidence about what policies and contexts are most effective at increasing educational opportunity, and that such evidence will inform educational policy and practices.

SEDA includes a range of detailed data on educational conditions, contexts, and outcomes in schools and school districts across the United States. It includes data at a range of institutional and geographic levels of aggregation, including schools, districts, counties, commuting zones, metropolitan areas, and states. It includes measures of academic achievement, achievement gaps, school and neighborhood racial and socioeconomic composition, school and neighborhood racial and socioeconomic segregation patterns, and other features of the schooling system. The data will be regularly updated as more data become available.

The SEDA 2022 dataset is unique from other versions of SEDA. The most recent version, SEDA 2022 2.0, includes district-level data for a larger subset of states on the average math and reading achievement in 2019 and 2022 respectively, and the change in math and reading achievement between 2019 and 2022 relative to the national average in grades 3-8 in 2019. 

DATA USE AGREEMENT:

You agree not to use the data sets for commercial advantage, or in the course of for-profit activities. Commercial entities wishing to use this Service should contact Stanford University’s Office of Technology Licensing (info@otlmail.stanford.edu).
You agree that you will not use these data to identify or to otherwise infringe the privacy or confidentiality rights of individuals.

THE DATA SETS ARE PROVIDED “AS IS” AND STANFORD MAKES NO REPRESENTATIONS AND EXTENDS NO WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED. STANFORD SHALL NOT BE LIABLE FOR ANY CLAIMS OR DAMAGES WITH RESPECT TO ANY LOSS OR OTHER CLAIM BY YOU OR ANY THIRD PARTY ON ACCOUNT OF, OR ARISING FROM THE USE OF THE DATA SETS.

You agree that this Agreement and any dispute arising under it is governed by the laws of the State of California of the United States of America, applicable to agreements negotiated, executed, and performed within California.

You agree to acknowledge the Stanford Education Data Archive as the source of these data. In publications, please cite the data as:

Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D., Jang, H., & Chavez, B. (2021). Stanford Education Data Archive (Version 4.1). Retrieved from http://purl.stanford.edu/db586ns4974.

Reardon, S. F., Fahle, E. M., Ho, A. D., Shear, B. R., Kalogrides, D., Saliba, J., & Kane, T. J. (2023). Stanford Education Data Archive (Version SEDA2022 2.0). Retrieved from http://purl.stanford.edu/db586ns4974.

Subject to your compliance with the terms and conditions set forth in this Agreement, Stanford grants you a revocable, non-exclusive, non-transferable right to access and make use of the Data Sets.