Skip Navigation
Funding Opportunities | Search Funded Research Grants and Contracts

IES Grant

Title: The Educational Benefits of Attending High-Performing High Schools
Center: NCER Year: 2012
Principal Investigator: Allensworth, Elaine Awardee: University of Chicago
Program: Improving Education Systems      [Program Details]
Award Period: 2 years (7/1/2012–6/30/2014) Award Amount: $693,432
Type: Exploration Award Number: R305A120136
Description:

Co-Principal Investigator: Marisa de la Torre

Purpose: In school districts nationwide, policymakers are implementing reforms that rest on a simple assumption: students do better when they attend schools with high achievement levels. Vouchers, school closings, and school choice policies all aim to improve student outcomes by shifting students from lower-performing to higher-performing schools. Schools are often deemed high performing based on statistics reported about their students’ academic attainment: average test scores, percentage of students, scoring at proficiency levels, and graduation rates. Yet, these measures reflect the qualities of the students who attend a particular school as much as the effectiveness of the school educating its students. The goal of this study is to examine whether, how, and under what circumstances students benefit from attending high schools with high reported performance levels. Researchers will also explore which high school metrics best predict high school outcomes for students with different incoming skills and backgrounds.

Project Activities: Researchers from the Consortium on Chicago School Research at the University of Chicago will focus on determining the types of schools at which students with different backgrounds and preparation do best on an array of student outcomes. Specifically, researchers will use an extensive data set of Chicago Public School (CPS) students (which includes longitudinal student-level records on parental support, teacher-student relationships, middle school choice, middle grade achievement, middle grade learning trajectories, and characteristics of default neighborhood schools) to predict the effects of attending high performing schools on students’ test scores, grades, absences, graduation, college enrollment, and several social-behavioral outcomes.

Products: The products of this project will be preliminary evidence of how students benefit from attending high schools with high achievement levels and which factors best predict high school outcomes. Peer reviewed publications will also be produced.

Structured Abstract

Setting: This study uses a large dataset of public school information from Chicago, Illinois.

Sample: This study examines an extensive dataset of CPS students and schools between 2000 and 2012. CPS is a predominantly low-income minority district, with 86 percent of students eligible for free/reduced lunch, and 86 percent racial-ethnic minorities.

Intervention: This exploratory project will answer questions about the types of schools at which students with different backgrounds and preparation do best on an array of student outcomes. Research questions include: (1) What are the effects of attending a high achieving school on students’ later outcomes? (2) To what extent are factors other than school achievement level predictive of later outcomes, including school climate (safety, teacher support, curricular rigor), teacher quality, test score gains, value added indicators, and racial match? (3) How do high school factors together predict student outcomes, and are there interactions among them?

Research Design and Methods: This study takes advantage of an extensive dataset on CPS students and schools and uses a propensity score analysis to address selection bias. Propensity scores rely heavily on observed variables available to select a counterfactual group of students. Data available on pre-high school factors includes longitudinal, student-level records on parental support, teacher-student relationships, middle school choice, middle grade achievement, middle grade learning trajectories, and characteristics of default neighborhood schools.

Control Condition: Due to the nature of the research design, there is no control condition.

Key Measures: This study includes measures of students’ backgrounds and characteristics including complete administrative records for each student, course transcripts of high school students, elementary/middle and high school achievement test scores, U.S. Census data, Chicago crime data, and survey data on the school experiences of 6th-12th grade students.

Data Analytic Strategy: This study will estimate the effects of attending high-performing schools on students’ test scores, grades, absences, graduation, college enrollment, sense of belonging, safety, discipline infractions, and study behaviors. Researchers will conduct propensity score analyses separately by cohort. First, researchers will use propensity score analyses to uncover the effect of attending schools with different school achievement levels. These analyses have five steps: estimating the propensity score for each student, matching the students using the propensity score, assessing the quality of the match, and estimating the impact and its standard error. A final step will be to do a sensitivity analysis to examine the robustness of the results. Then, researchers will use the strata formed in the previous analysis and include these other factors in the models predicting outcomes, along with the treatment (attended a high achieving school) as a predictor, to estimate whether these other factors have an effect beyond any relationship with school achievement. Finally, researchers will cross-nest students within their middle school and their high school to estimate high school effects controlling for middle school characteristics and student backgrounds.

Publications

Journal article, monograph, or newsletter

Allensworth, E. M., Moore, P. T., Sartain, L., and de la Torre, M. (2016). The Educational Benefits of Attending Higher Performing Schools: Evidence from Chicago High Schools. Educational Evaluation and Policy Analysis, 39 (2): 175–197.


Back