School and district factors relate to risk identification: low income concentration correlates with repeated risk identification, and segregation modestly increases it, though some schools outperform model predictions¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

Subclaims¶

q2 i? School-level factors like higher concentrations of students from low income backgrounds correlated with increased likelihood of repeated risk identification, and historically underserved students in more racially segregated districts were slightly more likely to be identified as significantly below benchmark multiple times. → Lemke 2025

Evidence¶

Lemke 2025¶

Lemke, M., Faulkner-Bond, M., LeBeau, B., Soo Ping Chow, A., & Quesen, S. (2025). Early literacy performance in Massachusetts: Results of ongoing analysis of literacy screening assessments (2023/24 data). WestEd. https://eric.ed.gov/?id=ED677571

q2 · i? · associational · r2

New district-level analysis using Segregation Tracking Project measures of poverty and racial/ethnic concentration, related to repeated risk identification. The report describes a slight increase in likelihood in more racially segregated contexts; correlational, no effect size printed.

"Zooming out to the district level this year— through an analysis of district-level school segregation measures— showed that historically underserved students in more racially segregated contexts were slightly more likely to be identified as significantly below benchmark multiple times."

Discussion¶

Related Claims¶

• The likelihood of being identified as significantly below benchmark in early literacy screening increases as historically underserved background characteristics intersect — related
• The likelihood of being identified as at risk increases as historically underserved background characteristics intersect, with variation by gender and race/ethnicity, and school characteristics also matter — related
• Students from low income, English learner, special education, and several racial/ethnic groups are more likely to be repeatedly identified as significantly below benchmark, with low income students 2.5 times as likely — related
• School-level concentration of low income students and racial isolation are associated with higher early literacy risk rates — possibly the same claim (merge candidate)
• School improvement rates varied by community type: Latino, racially diverse, and integrated schools improved disproportionately, while stagnation concentrated in predominantly African-American low-income communities — related
• Overperforming schools were more likely predominantly White but not concentrated in higher-income areas, specific locales, or lower proportions of far-behind students — related
• Overperforming schools in Grades 1-2 mathematics are more often White-dominant but are not concentrated in higher-income areas or specific locales — related
• School characteristics—percentage of low income students, teacher retention, and attendance—are associated with students' likelihood of being identified as at significant risk in early literacy screening — related
• District support for principals’ continuous improvement varies by school poverty and racial composition — related
• Universal PreK school-based classrooms are about as diverse as first-grade classrooms, while means-tested and community-based classrooms are more segregated — related
• Students' risk of suspension is more strongly determined by which school they attend than by their individual backgrounds — related