Background: The data revolution in education has led to more data collection, more randomized controlled trials (RCTs), and more data collection within RCTs. Often following IES recommendations, researchers studying program effectiveness gather data on how the intervention was implemented. Educational implementation data can be complex, including…

This study examined bias reduction in the eight nonequivalent comparison group designs (NECGDs) that result from combining (a) choice of a local versus non-local comparison group, and analytic use or not of (b) a pretest measure of the study outcome and (c) a rich set of other covariates. Bias was estimated as the difference in causal estimate…

Design-based methods have recently been developed as a way to analyze randomized controlled trial (RCT) data for designs with a single treatment and control group. This article builds on this framework to develop design-based estimators for evaluations with multiple research groups. Results are provided for a wide range of designs used in…

Design-based methods have recently been developed as a way to analyze data from impact evaluations of interventions, programs, and policies (Freedman, 2008; Lin, 2013; Imbens and Rubin, 2015; Schochet, 2013, 2016; Yang and Tsiatis, 2001). The non-parametric estimators are derived using the building blocks of experimental designs with minimal…

In basic regression discontinuity (RD) designs, causal inference is limited to the local area near a single cutoff. To strengthen the generality of the RD treatment estimate, a design with multiple cutoffs along the assignment variable continuum can be applied. The availability of multiple cutoffs allows estimation of a pooled average treatment…

In basic regression discontinuity (RD) designs, causal inference is limited to the local area near a single cutoff. To strengthen the generality of the RD treatment estimate, a design with multiple cutoffs along the assignment variable continuum can be applied. The availability of multiple cutoffs allows estimation of a pooled average treatment…

The present article provides a synthesis of the conceptual and statistical issues involved in using multisite randomized trials to learn about and from a distribution of heterogeneous program impacts across individuals and/or program sites. Learning "about" such a distribution involves estimating its mean value, detecting and quantifying…

Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous…

Working memory capacities are associated with mathematical development. Many studies have tried to improve working memory abilities through training. Furthermore, the central executive has been shown to be the component of working memory, which is the most strongly related to numerical and arithmetical skills. Therefore, we developed a training…

Design-based methods have recently been developed as a way to analyze data from impact evaluations of interventions, programs, and policies. The impact estimators are derived using the building blocks of experimental designs with minimal assumptions, and have good statistical properties. The methods apply to randomized controlled trials (RCTs) and…

A new design-based theory has recently been developed to estimate impacts for randomized controlled trials (RCTs) and basic quasi-experimental designs (QEDs) for a wide range of designs used in social policy research (Imbens & Rubin, 2015; Schochet, 2016). These methods use the potential outcomes framework and known features of study designs…

Design-based methods have recently been developed as a way to analyze data from impact evaluations of interventions, programs, and policies (Imbens and Rubin, 2015; Schochet, 2015, 2016). The estimators are derived using the building blocks of experimental designs with minimal assumptions, and are unbiased and normally distributed in large samples…

This study examined the efficacy of a kindergarten mathematics intervention program, ROOTS, focused on developing whole-number understanding in the areas of counting and cardinality and operations and algebraic thinking for students at risk in mathematics. The study utilized a randomized block design with students within classrooms randomly…

Policy-makers are frequently interested in understanding how effective a particular intervention may be for a specific (and often broad) population. In many fields, particularly education and social welfare, the ideal form of these evaluations is a large-scale randomized experiment. Recent research has highlighted that sites in these large-scale…

Interest in variation in program impacts--How big is it? What might explain it?--has inspired recent work on the analysis of data from multi-site experiments. One critical aspect of this problem involves the use of random or fixed effect estimates to visualize the distribution of impact estimates across a sample of sites. Unfortunately, unless the…