Formula-based estimation of value-added classification error rates using ordinary least squares and empirical Bayes estimators¶
Research Method · All research methods
Evidence · 4 claims (3 for, 1 mixed) · 2 studies (1 causal, 1 associational),q2· 0 of 2 report an effect size · 4 claims rest on one study
Description¶
The article's methodological framework estimates likely error rates for value-added performance classifications using "formulas based on ordinary least squares and empirical Bayes estimators." It applies these formulas to student test score gain data to quantify how often teachers or schools would be misclassified relative to the average, and how often overall false positive and false negative classifications occur. The framework links the amount of available data (one versus three years) to expected misclassification rates, providing a tool for anticipating system error before high-stakes use.
Accounts¶
- Formula-based estimation of value-added classification error rates using ordinary least squares and empirical Bayes estimators: The article's methodological framework estimates likely error rates for value-added performance classifications using "formulas based on ordinary least squares and empirical Bayes estimators." It applies these formulas to student test score gain data to quantify how often teachers or schools would be misclassified relative to the average, and how often overall false positive and false negative classifications occur. The framework links the amount of available data (one versus three years) to expected misclassification rates, providing a tool for anticipating system error before high-stakes use. (Peter Z. Schochet (2013))
Claims¶
- Error rates for comparing a teacher's performance to the average are about 25 percent with three years of data and 35 percent with one year of data [+M]
- Value-added error rates apply to measuring both teacher and school performance in the upper elementary grades using student test score gain data [+W]
- Overall false positive error rates are about 10 percent and false negative error rates about 20 percent for value-added performance classifications [+W]
- Shrinkage has no statistically significant effect on the relative probability that teachers of hard-to-predict students receive extreme value-added ratings [~W]
Related Research Methods¶
-
Key Sources¶
- Peter Z. Schochet, Hanley S. Chiang. (2013). What Are Error Rates for Classifying Teacher and School Performance Using Value-Added Models? Journal of Educational and Behavioral Statistics, vol. 38, no. 2. https://www.mathematica.org/publications/what-are-error-rates-for-classifying-teacher-and-school-performance-using-value-added-models
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
formula-based-estimation-of-value-added-classification-error-rates-using-ordinary-least-sq |
title |
Formula-based estimation of value-added classification error rates using ordinary least squares and empirical Bayes estimators |
description |
The article's methodological framework estimates likely error rates for value-added performance classifications using "formulas based on ordinary least squares and empirical Bayes estimators." It applies these formulas to student test score gain data to quantify how often teachers or schools would b |
status |
review |
moved_from |
theory/ols-empirical-bayes-error-rate-estimation-framework |
generated |
by process:sweep-kinds on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — peter-z-schochet-2013 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.