Shrinkage could differentially affect consequences for teachers of fewer students or students with hard-to-predict achievement¶
Claim · All claims
Evidence · 1 study · associationalr?·q1
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.
q1 i? Because less precise estimates arise from fewer students and hard-to-predict achievement, shrinkage could differentially affect the probability that such teachers' estimates lead to consequences. → 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
q1 · i? · associational · r?
This is the article's motivating analytical argument, not a tested result: it states that the shrinkage procedure "could have differential impacts on the probability" that teachers of fewer or hard-to-predict students are assigned consequences.
"Because value-added estimates based on fewer students and students with “hard-to-predict” achievement will be less precise, the procedure could have differential impacts on the probability that the teachers of fewer students or students with hard-to-predict achievement will be assigned consequences."
Discussion¶
Related Claims¶
- Teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates — related
- Shrinkage had no statistically significant effect on the relative probability that teachers of hard-to-predict students received consequences — related
- Teachers of students with hard-to-predict achievement levels tend to have less precise value-added estimates than teachers of other students — related
- 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
- Shrinkage has no statistically significant effect on the relative probability that teachers of hard-to-predict students receive extreme value-added ratings — related
- Empirical Bayes shrinkage could be used to address bias in proxy pre-test value-added estimates — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
shrinkage-differential-impact-concern |
title |
Shrinkage could differentially affect consequences for teachers of fewer students or students with hard-to-predict achievement |
description |
Shrinkage could differentially affect consequences for teachers of fewer students or students with hard-to-predict achievement |
status |
review |
evidence_strength |
weak |
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.