Statistical findings summary

Why numeric analysis was needed

Dashboard charts and maps reveal broad patterns, but key factors are correlated with each other. Income, adult education, attendance, and school-level poverty often move together, so visual inspection alone cannot cleanly separate their contributions.

Core methodology

• Weighted correlations and regression models; 2024-25 achievement models use students with reported Level 1-4 results as weights.
• Joint models to compare relative signal strength under controls.
• Dual-spec checks: income-based continuity models and poverty-aware companion models.
• Interaction checks to test whether one factor's association changes at different levels of another.
• Split-sample stability checks to verify findings are not driven by a narrow subset.
• Focused exploratory models for spending and class size under collinearity-aware settings.

Main findings to date

• The strongest school-context signals are student poverty concentration, attendance, and adult BA+ context. Which one leads depends on subject and model specification.
• Attendance adds substantial explanatory power and remains a top-tier factor after SES controls.
• ODE Students Experiencing Poverty is a first-tier factor in 2024-25 models and often rivals or exceeds adult BA+ in explanatory strength.
• Income remains useful as a community-context measure, but is usually smaller after controls.
• Funding and class-size signals are weaker and less stable than the leading context signals: student poverty concentration, attendance, and adult BA+.
• Some interaction terms (especially education x attendance) improve fit, suggesting conditional effects.

What appears robust

• Education-over-income ordering persisted in repeated split checks and in historical comparisons performed so far.
• In 2024-25 analyses, poverty-aware models generally outperform income-only analogs, and school poverty often rivals or exceeds adult BA+ in explanatory strength.
• Attendance remains a major co-varying factor and should be interpreted alongside SES, not as a side note.

Limits

• Analyses are school-level aggregates; they do not identify individual-level causal pathways.
• Cross-sectional design limits causal interpretation, especially for spending/class-size effects.
• Collinearity between spending measures requires caution when reading raw coefficient signs.
• Poverty-aware findings apply to 2024-25 models; 2018-19 comparisons remain income/education-based.

Detailed reports and source scripts

Open the grouped Evidence Lab index for model-by-model summaries, source scripts, machine-readable outputs, figures, and full-text exports.

Open reports and source scripts

Explore the evidence

These reports provide focused reads on the main findings summarized above.

• Adult BA+ and student poverty two-factor check
• SES explorations summary
• Spending and class-size findings memo