Examine literacy screening data for intersectional differences not apparent from simple disaggregation and consider student and school characteristics together when targeting supports¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description¶

The article recommends that educators and policymakers analyze screening data at the intersection of multiple background characteristics rather than disaggregating to single categories, because overlapping identities affect outcomes in ways simple disaggregation hides. It also recommends considering student and school characteristics in combination when targeting resources and interventions such as EC programming. As the brief states, educators "may need to examine literacy screening data carefully for differences that are not apparent from simple disaggregation."

Design Implications¶

Context¶

Requirements¶

• Screening assessment data linked to student background information, such as district-provided screening data and SIMS collection data

Constraints¶

• The analysis cannot identify the causes for the findings; additional research may be needed to determine how and why student and school factors interact

Target Learners¶

• K–3 students, particularly those from historically marginalized groups

Target Learning Goals¶

• Early literacy: identifying students at risk of reading difficulty and targeting support effectively

Claims¶

• The likelihood of being identified as significantly below benchmark in early literacy screening increases as historically underserved background characteristics intersect [+W]
• 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 [+W]

Related Strategies¶

• Examine data across subgroups and account for diversity within subgroup categories before interpreting score differences
• Disaggregate participation data intersectionally by race and gender to surface disparities hidden in aggregate analyses
• Use multiple, diverse data sources selected to offer distinct yet complementary information
• Select evidence-based mindfulness programs matched to school needs assessment data
• Apply a set of dos and don'ts when examining school performance data to decide school support options

Examples¶

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Key Sources¶

• Lemke, M., Murphy, D., Soo Ping Chow, A., & Acuña, A. (2024). Intersections: Student background and early literacy performance. WestEd. https://eric.ed.gov/?id=ED663858