Determinants of Between-School Variation in Student Achievement: Results From U.S. Population Data

• In Event: Advanced Techniques in Multilevel Modeling and Treatment Effect Analysis

Abstract

School-level achievement data from the Stanford Education Data Archive (SEDA) are used to estimate between-school within-state variation in achievement and how this is related to school characteristics. We estimate the proportion of variation in test scores that is between schools within states (the intraclass correlation coefficient, ICC), for grades 3-8 math and reading language arts (RLA) achievement test data from 2009-2019 across all 50 states and the District of Columbia. Across 6,021 state-grade-year-subject estimates, the average math ICC is 0.194 and the average RLA ICC is 0.168. We explore the ICCs from these analyses, as well as their relationship to state-level school characteristics as an entry point to illuminating the contexts in which variation among schools is largest.

Authors

• Benjamin R. Shear, University of Colorado - Boulder

• Joseph Taylor, University of Colorado - Colorado Springs/AIR

• Erin M. Fahle, Stanford University