In RCTs of interventions to improve student test scores, estimators including late pretests will typically be preferred to estimators excluding them or using other baseline data¶
Claim · All claims
Evidence · 1 study · theoreticalr3·q2
Currency · newest study 2008 · checked against its sources 2026-10-09 (automated, 1 of 1 entry) · not reviewed by a person
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? For RCTs of interventions to improve student test scores, estimators that include late pretests will typically be preferred to estimators that exclude them or that instead include uncontaminated baseline test score data from other sources. → Peter Z. Schochet 2008
Evidence¶
Peter Z. Schochet 2008¶
Peter Z. Schochet. (2008). The Late Pretest Problem in Randomized Control Trials of Education Interventions. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. https://ies.ed.gov/ncee
q2 · i? · theoretical · r3
Theoretical and empirical analysis using a loss function approach compares estimator classes: those including late pretests versus those excluding them or using "uncontaminated baseline test score data from other sources". The comparison is hedged as "typically" preferred; no effect size is printed.
"For RCTs of interventions to improve student test scores, estimators that include late pretests will typically be preferred to estimators that exclude them or that instead include uncontaminated baseline test score data from other sources."
Discussion¶
Related Claims¶
- The variance-bias trade-off for late pretests is addressed both theoretically and empirically for several commonly used impact estimators — a broader claim this one bears on
- Including late pretest data in RCT analyses can bias post-test impact estimates because pretests are collected after random assignment — related
- The late-pretest estimator preference holds as long as test score impacts do not grow very quickly early in the school year — a narrower finding that bears on this claim
- Deciding whether to collect and use late pretest data in RCTs involves a variance-bias trade-off — a broader claim this one bears on
- Empirical examples using real-world education RCT data demonstrate the design-based grouped-data theory — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
late-pretest-estimators-typically-preferred |
title |
In RCTs of interventions to improve student test scores, estimators including late pretests will typically be preferred to estimators excluding them or using other baseline data |
description |
In RCTs of interventions to improve student test scores, estimators including late pretests will typically be preferred to estimators excluding them or using other baseline data |
status |
review |
evidence_strength |
weak |
newest_evidence |
2008 |
source_checked |
2026-10-09 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — peter-z-schochet-2008 |
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