This article addresses likely error rates for measuring teacher and school performance in the upper elementary grades using value-added models applied to student test score gain data. Using formulas based on ordinary least squares and empirical Bayes estimators, error rates for comparing a...

In randomized control trials (RCTs) in the education field, the complier average causal effect (CACE) parameter is often of policy interest, because it pertains to intervention effects for students who receive a meaningful dose of treatment services. This article uses a causal inference and...

In randomized control trials (RCTs) in the education field, the complier average causal effect (CACE) parameter is often of policy interest, because it pertains to intervention effects for students who receive a meaningful dose of treatment services. This article uses a causal inference and...

This article addresses likely error rates for measuring teacher and school performance in the upper elementary grades using value-added models applied to student test score gain data. Using formulas based on ordinary least squares and empirical Bayes estimators, error rates for comparing a...

This article addresses likely error rates for measuring teacher and school performance in the upper elementary grades using value-added models applied to student test score gain data. Using a realistic performance measurement system scheme based on hypothesis testing, the authors develop error...

For randomized controlled trials (RCTs) of education interventions, estimates of associations between student and mediating teacher practice outcomes can help examine the extent to which the data support the study's conceptual model. They can also identify mediators most associated with student...

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 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...

In social policy evaluations, the multiple testing problem occurs due to the many hypothesis tests typically conducted across multiple outcomes and subgroups, which can lead to spurious impact findings. This article discusses a framework that balances Types I and II errors for addressing this...

Performance management systems may improve service delivery and participant outcomes, but they do not necessarily provide information on a program's causal effects. This article develops a statistical model that describes the conditions under which regression adjustment improves the...