The variance-bias trade-off for late pretests is addressed both theoretically and empirically for several commonly used impact estimators¶

Claim · All claims
Evidence · 1 study · theoretical r? · q2
Currency · newest study 2010 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? The article provides both theoretical and empirical analysis of the late pretest issue across several commonly used impact estimators. → Peter Z. Schochet 2010

Evidence¶

Peter Z. Schochet 2010¶

Peter Z. Schochet. (2010). The Late Pretest Problem in Randomized Control Trials of Education Interventions. Journal of Educational and Behavioral Statistics, vol. 35, no. 4. https://www.mathematica.org/publications/journal-article-the-late-pretest-problem-in-randomized-control-trials-of-education-interventions

q2 · i? · theoretical · r?

The article's stated scope: the late pretest variance-bias issue is addressed "both theoretically and empirically for several commonly used impact estimators" within a loss function framework.

"This article addresses this issue both theoretically and empirically for several commonly used impact estimators, using a loss function approach grounded in the causal inference literature."

Discussion¶

Related Claims¶

• Deciding whether to collect and use late pretest data in RCTs involves a variance-bias trade-off — related
• Including late pretest data in RCT analyses can bias post-test impact estimates because pretests are collected after random assignment — related
• 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 — a narrower finding that bears on this claim
• 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
• Empirical examples using real-world education RCT data demonstrate the design-based grouped-data theory — related