This article discusses estimation of average treatment effects for randomized controlled trials (RCTs) using grouped administrative data to help improve data access. The focus is on design-based estimators, derived using the building blocks of experiments, that are conducive to grouped data for a wide range of RCT designs, including clustered and…

This article introduces an alternative impact parameter for group-based RCTs with student mobility--the survivor average causal effect ("SACE")--that pertains to the subpopulation of original cohort students who would remain in their baseline study schools in either the treatment or control condition. The "SACE" parameter has a clear…

This article examines theoretical and empirical issues related to the statistical power of impact estimates under clustered regression discontinuity (RD) designs. The theory is grounded in the causal inference and hierarchical linear modeling literature, and the empirical work focuses on common designs used in education research to test…

Pretest-posttest experimental designs are often used in randomized control trials (RCTs) in the education field to improve the precision of the estimated treatment effects. For logistic reasons, however, pretest data are often collected after random assignment, so that including them in the analysis could bias the posttest impact estimates. Thus,…

This report examines theoretical and empirical issues related to the statistical power of impact estimates under clustered regression discontinuity (RD) designs. The theory is grounded in the causal inference and HLM modeling literature, and the empirical work focuses on commonly-used designs in education research to test intervention effects on…

This article examines theoretical and empirical issues related to the statistical power of impact estimates for experimental evaluations of education programs. The author considers designs where random assignment is conducted at the school, classroom, or student level, and employs a unified analytic framework using statistical methods from the…

This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…