TY  - JOUR
AB  - Blocked cluster randomized trials (CRTs) are widely used to evaluate educational interventions. In such trials, researchers face critical choices about how to define and estimate the average treatment effect (ATE). These choices—both the estimand (e.g., person- vs. cluster-weighted) and the estimator (e.g., regression models, multilevel models)—can influence study conclusions. This paper provides an applied guide to estimands and estimators for blocked CRTs and empirically examines their performance using 26 large-scale blocked CRTs. We estimate ATEs and standard errors for 50 outcomes using 19 estimators and compare results across and within estimands. Findings show that estimator choice can substantially affect point estimates and precision: ranges in ATE estimates exceed 0.10 SD in 18% of cases, and standard error estimates differ by more than 50% in 16% of cases. Some estimators fail or produce unstable results in common real-world designs. These findings underscore the importance of pre-specifying estimation strategies in a public pre-analysis plan. We provide practical guidance for researchers planning and analyzing blocked CRTs.
AU  - Miratrix, Luke
AU  - Weiss, Michael J.
AU  - Litschwartz, Sophie
AU  - Hill, Colin
AU  - Warner, Kayla 
PY  - 2026
ST  - An Applied Researchers’ Guide to Estimating Effects from Blocked Cluster Randomized Trials: Estimands, Estimators, and Estimates
TI  - An Applied Researchers’ Guide to Estimating Effects from Blocked Cluster Randomized Trials: Estimands, Estimators, and Estimates
UR  - http://www.edworkingpapers.com/ai26-1537
ER  -