The present paper, which is intended for a diverse audience of evaluation researchers, applied social scientists, and research funders, provides a broad overview of the conceptual and statistical issues involved in using multisite randomized trials to learn "about" and "from" variation in program effects across…

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…

In a rigorous evaluation of ASSISTments as an online homework support conducted in the state of Maine, SRI International reported that "the intervention significantly increased student scores on an end-of-the-year standardized mathematics assessment as compared with a control group that continued with existing homework practices."…

In a pretest-posttest cluster-randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores that ignores measurement error in the…

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…

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…

Multisite trials can clarify the average impact of a new program and the heterogeneity of impacts across sites. Unfortunately, in many applications, compliance with treatment assignment is imperfect. For these applications, we propose an instrumental variable (IV) model with person-specific and site-specific random coefficients. Site-specific IV…

A multilevel latent transition analysis (LTA) with a mixture IRT measurement model (MixIRTM) is described for investigating the effectiveness of an intervention. The addition of a MixIRTM to the multilevel LTA permits consideration of both potential heterogeneity in students' response to instructional intervention as well as a methodology for…