Teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates¶
Claim · All claims
Evidence · 1 study · associationalr?·q2
Currency · newest study 2016 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person · overdue for a recheck since 2019: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Teachers of students with low prior achievement and free lunch recipients tend to have less precise value-added estimates. → Mariesa Herrmann 2016
Evidence¶
Mariesa Herrmann 2016¶
Mariesa Herrmann, Elias Walsh, Eric Isenberg. (2016). Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels. Statistics and Public Policy, vol. 3, issue 1. https://www.mathematica.org/publications/ja-shrinkage-of-valueadded-estimates-and-characteristics-of-students-with-hardtopredict
q2 · i? · associational · r?
The article reports from its analysis of teacher value-added estimates that "teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates"; no effect size or test statistic is printed on the page.
"We found that teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates."
Discussion¶
Related Claims¶
- Shrinkage could differentially affect consequences for teachers of fewer students or students with hard-to-predict achievement — related
- Teachers of students with hard-to-predict achievement levels tend to have less precise value-added estimates than teachers of other students — possibly the same claim (merge candidate)
- Empirical Bayes shrinkage increases the precision of value-added estimates and reduces their absolute value for teachers of hard-to-predict students — related
- Shrinkage does not change accountability consequences for teachers of hard-to-predict students, since extreme-rating probabilities are unaffected — related
- Empirical Bayes shrinkage could be used to address bias in proxy pre-test value-added estimates — related
- The report synthesizes 21 empirical studies on value-added model key issues and findings — related
- Shrinkage has no statistically significant effect on the relative probability that teachers of hard-to-predict students receive extreme value-added ratings — related
- Shrinkage had no statistically significant effect on the relative probability that teachers of hard-to-predict students received consequences — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
low-prior-achievement-free-lunch-teachers-less-precise-va |
title |
Teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates |
description |
Teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2016 |
source_checked |
2026-10-20 |
recheck_by |
2019 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — mariesa-herrmann-2016 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.