Bias, not precision loss, drives the difference in proxy pre-test value-added estimates¶
Claim · All claims
Evidence · 1 study · associationalr2·q2
Currency · newest study 2018 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2021: 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? The greater impact of omitting same-subject pre-tests appears to result from more bias in proxy pre-test estimates rather than from reduced precision. → Elias Walsh 2018
Evidence¶
Elias Walsh 2018¶
Elias Walsh, Dallas Dotter, Albert Y. Liu. (2018). Can More Teachers Be Covered? The Accuracy, Credibility, and Precision of Value-Added Estimates with Proxy Pre-Tests. Working Paper 64. Princeton, NJ: Mathematica Policy Research. https://www.mathematica.org/publications/can-more-teachers-be-covered-the-accuracy-credibility-and-precision-of-value-added-estimates
q2 · i? · associational · r2
Diagnostic finding from the Oklahoma statewide data analysis: the larger effect of omitting same-subject pre-tests "appears to be a result of more bias in the proxy pre-test estimates rather than less precision." The article hedges this as an apparent result.
"This difference appears to be a result of more bias in the proxy pre-test estimates rather than less precision."
Discussion¶
Related Claims¶
- Empirical Bayes shrinkage could be used to address bias in proxy pre-test value-added estimates — related
- Proxy pre-test value-added estimates show bias, but may still carry information about teacher effectiveness — related
- The working paper examines the sensitivity and precision of teacher value-added estimates under specifications differing in student-level, peer-level, and double-lagged achievement controls — related
- Omitting same-subject pre-tests affects value-added estimates more than excluding other student background characteristics — related
- Adding a second pretest wave eliminates selection bias in two additional instances beyond those cured by a single wave — related
- Multiple sources of student information can be accumulated to estimate teacher effectiveness — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
proxy-pretest-difference-driven-by-bias-not-precision |
title |
Bias, not precision loss, drives the difference in proxy pre-test value-added estimates |
description |
Bias, not precision loss, drives the difference in proxy pre-test value-added estimates |
status |
review |
evidence_strength |
weak |
newest_evidence |
2018 |
recheck_by |
2021 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — elias-walsh-2018 |
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