Value-Added Modeling
Also known as: VAM
Value-Added Modeling (VAM) is a method for assessing the contribution of schools or teachers to student achievement growth, developed by Sanders and Horn (1998). VAM isolates the effect of a teacher or school by comparing student gains (value added) while controlling for prior achievement and student characteristics.
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When to use it
Apply VAM when evaluating teacher or school effectiveness using growth-based measures, when you want to account for student composition differences across teachers, or when predicting student outcomes under different teachers. Most common in K-12 education policy and evaluation.
Strengths & limitations
- Isolates teacher effect: attempts to separate teacher contribution from student selection
- Uses growth: measures learning gains rather than absolute achievement levels
- Accounts for baseline: controls for students' starting points
- Policy-relevant: directly addresses whether specific teachers improve student outcomes
- Specification sensitivity: results depend heavily on which controls are included; different models yield different rankings
- Bias from unmeasured confounds: cannot control for unmeasured factors (motivation, parental involvement) that affect both sorting and learning
- Instability: teacher rankings can fluctuate dramatically year-to-year due to random variation
- Fairness concerns: teachers in challenging contexts may appear less effective even if working as hard as others
Frequently asked
What should I include as controls in VAM?
Include student demographics (poverty, race, language, special education status), prior achievement, and family factors. Do not include factors directly caused by the teacher (current grades, attendance, behavior referrals). Sensitivity to controls is a major VAM limitation.
What is the standard error of a VAM estimate?
VAM estimates have large standard errors, especially for teachers with small classes or high student turnover. A teacher's 'true' value-added is usually 2-4 times the estimated standard error away from the point estimate.
Can VAM account for peer effects?
Standard VAM cannot. If student A's classmates are high-achieving, A learns more from peer effects, which may be attributed to the teacher. Some advanced models attempt to control for peer composition.
Is VAM appropriate for non-standardized tests?
VAM requires comparable outcome measures across years. Works best with standardized tests. Open-ended assessments or portfolio-based outcomes are harder to use in VAM.
How do I communicate VAM results fairly?
Avoid rankings. Report confidence intervals. Note that VAM estimates have substantial year-to-year volatility. Use as one input among multiple sources (observations, surveys, student growth) for evaluations.
Sources
- Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us about teacher effectiveness? Evidence from New York City. Economics of Education Review, 27(6), 615-631. DOI: 10.1016/j.econedurev.2007.05.005 ↗
- Sanders, W. L., & Horn, S. P. (1998). Research findings on classroom heterogeneity and achievement. Journal of Educational Research, 91(5), 294-303. link ↗
- Koedel, C., Mihaly, K., & Rockoff, J. E. (2015). Value-added modeling: A review. Economics of Education Review, 47, 180-195. DOI: 10.1016/j.econedurev.2015.01.006 ↗
How to cite this page
ScholarGate. (2026, June 3). Value-Added Modeling. ScholarGate. https://scholargate.app/en/psychometrics/value-added-modeling
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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