|Title:||Linking Inequities in Educational Opportunities to Inequality in Educational Outcomes: An Exploratory Analysis in New York State|
|Principal Investigator:||Reardon, Sean||Awardee:||Stanford University|
|Program:||Improving Education Systems [Program Details]|
|Award Period:||4 years (09/01/2022 – 08/31/2026)||Award Amount:||$1,700,000|
Co-Principal Investigators: Armour-Garb, Allison; Fahle, Erin; LeRoy, Rose
Purpose: The research team will conduct population-based descriptive and exploratory research to examine the extent of, and connections among, racial/ethnic and economic segregation, inequities in students' access to opportunities to learn, and disparities in academic outcomes in New York State.
Project Activities: Using state longitudinal data, the team will construct potential measures of disparities in educational opportunities and systemic inequity in those opportunities at the school and district levels. They will then map the distributions of opportunities and experiences within schools, between schools, and between districts. They will conduct a set of exploratory analyses investigating the linkages among inequalities in schooling experiences; and expand existing knowledge on how segregation relates to opportunities and experiences in schools and to disparities in student outcomes.
Products: The project team will disseminate results to three audiences: the academic community, the New York State Education Department, and other state officials and education stakeholders. To accomplish this goal, they will produce a variety of research products, including conference presentations, publications, workshops and webinars, and data files with technical documentation.
Setting: The project includes the 4,413 regular public and 354 charter schools in New York from 2009–2025. Each year, New York state educates approximately 2.6 million PK-12 students in public and charter schools, and employs approximately 200,000 teachers, 5,000 principals, 7,000 counselors, and 3,000 counselors, among other staff.
Sample: The project team will use New York state longitudinal data for all students, teachers, and staff from kindergarten through high school graduation, from 2009-2025, across all New York public schools and districts. The team will draw on population-based student, teacher, staff data from the New York state longitudinal data system. The student population is extremely racially, ethnically, economically, and linguistically diverse. While not geographically representative of all U.S. students, the student population in New York schools is demographically similar to the overall U.S. population, with the exception of having a larger proportion of students enrolled in urban schools and proportionately larger Asian and smaller White student populations
Factors: The project team will study: (A) the extent of between-school and between-district segregation in New York; (B) the extent of racial and economic disparities in students' access to qualified and effective teachers, principals, and staff; access to AP/IB course offerings; school discipline; disability identification; peer composition; and school funding; and (C) how the collection of these factors relates to disparities in student outcomes, including achievement, learning, and graduation.
Research Design and Methods: The team will analyze the secondary data using a series of regression models, including hierarchical linear models and fixed effects models, to explore variation in each measure and to explore associations among the measures of segregation, opportunities and experiences, and outcomes.
Control Condition: The research team will examine a number of factors that vary widely among schools and districts. Because there is not a single "treatment" being studied; there is no corresponding "control condition." Rather, the study will contrast settings where disparities in educational opportunities and experiences are large with settings where they are smaller.
Key Measures: The primary measures are between-group disparities in educational opportunities, experiences, and outcomes, rather than on individual student-level measures and outcomes. This reflects the focus of the research questions on equity in the educational system. Because equity is, by definition, a feature of the educational system, not of an individual, the measures are disparities in opportunities, experiences, and outcomes. The team will construct measures of (1) between-school and between-district racial and economic segregation; (2) between-group disparities in access to specific resources (e.g., highly qualified teachers, staff, funding, and advanced courses) and experiences (e.g., discipline and identification for special education); and (3) disparities in outcomes (e.g., achievement, learning, and graduation). The primary disparities of interest in the study will be racial/ethnic disparities (White-Black, and White-Hispanic), and economic disparities (between students eligible and not eligible for free- and reduced-price-lunch).
Data Analytic Strategy: The research team will use descriptive analysis methods and decomposition analyses to describe the variation and patterns of disparities in educational opportunities, experiences, and outcomes. These analyses will describe the extent to which inequities in access to educational opportunities are due to between-school processes (e.g., between-school segregation and the unequal distribution of resources among schools) or within-school processes (e.g., within-school tracking and between-class segregation, the unequal distribution of teachers and resources among classes, and differential treatment of students by race/ethnicity and income). The team will use varieties of regression analyses, including fixed effects models and hierarchical linear models, to study the associations among measures of segregation, disparities in opportunity and experience, and group differences in average test scores, learning rates, and graduation rates across grades and over time.
Related IES Projects: The Effects of Racial School Segregation on the Black-White Achievement Gap (R305A070377); Addressing Practical Problems in Achievement Gap Estimation: Nonparametric Methods for Censored Data (R305D110018)