This paper examines both theoretically and empirically whether using ordinary least squares (OLS) multivariate regression models to estimate average treatment effects under experimental designs is justified by the Neyman model for causal inference. The paper finds that estimated standard errors...

This paper 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...

Finds that, a year or more after enrolling in the program, 2-year-olds perform significantly better on a range of measures of cognitive, language, and social-emotional development, when compared with a randomly assigned control group. EHS families are also more likely to attend school or job...

This article develops a new approach for calculating appropriate sample sizes for school-based randomized controlled trials (RCTs) with binary outcomes using logit models with and without baseline covariates. The theoretical analysis develops sample size formulas for clustered designs in which...

Statistical procedures that correct for multiple testing typically result in hypothesis tests with reduced statistical power because adjustment methods reduce the likelihood of identifying real differences between contrasted groups. There is disagreement among researchers about the use of...

Describes the characteristics of eligible applicants and compares them with the broader national population of disadvantaged youth.

This paper, commissioned by the National Center for Education Research, provides readers with an introduction to PN-RCTs and ways to design and analyze the results from them. This paper was written primarily for applied education researchers with introductory knowledge of quantitative impact...

This paper examines the estimation of two-stage clustered randomized controlled trial designs (RCTs) 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...