Mixed-Effects Location Scale Models for Joint Modeling School Value-Added Effects on the Mean and Variance of Student Achievement
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View abstract on PubMed
Summary
This summary is machine-generated.New school value-added models analyze achievement variability, not just average scores. This helps identify schools with unusually high or low student learning spread, improving accountability.
Area Of Science
- Educational Measurement and Statistics
- Quantitative Educational Research
Background
- School value-added models (VAMs) are standard for evaluating school performance based on student learning gains.
- Traditional VAMs use mixed-effects models to estimate average student achievement, controlling for prior achievement and background factors.
- These models provide a 'school value-added score' representing mean student achievement differences.
Purpose Of The Study
- To extend traditional school value-added models by analyzing the variance of student achievement within schools.
- To identify schools with unusually high or low variability in student achievement, beyond average performance.
- To offer new insights for school accountability systems and educational research.
Main Methods
- Fitting mixed-effects location scale models.
- Extending traditional mixed-effects linear regression VAMs to simultaneously model the mean and variance of student achievement.
- Analyzing student achievement data to estimate both central tendency and dispersion at the school level.
Main Results
- The proposed method allows for the estimation and study of school-specific achievement variance.
- Identified schools exhibiting statistically significant high or low variability in student achievement, independent of average VAM scores.
- Demonstrated the feasibility of applying location-scale models to VAMs for richer school performance analysis.
Conclusions
- Analyzing achievement variance in addition to the mean provides a more comprehensive understanding of school effectiveness.
- This approach can identify schools with distinct achievement patterns (e.g., high consistency vs. high variability).
- Findings have significant implications for refining school accountability metrics and guiding educational interventions.
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