Assessing an intervention’s effects on multiple outcomes increases the risk of false positives. Procedures that make adjustments to address this risk can reduce power, or the probability of detecting effects that do exist. MDRC’s Reflections on Methodology discusses how to estimate power when making adjustments as well as alternative definitions of power.
To improve outcomes among high-interest borrowers, policymakers need to understand what is driving usage. This second post in MDRC’s Reflections on Methodology series discusses how a data discovery process revealed clusters of borrowers who differed greatly in the kinds of loans and lenders they used and in their loan outcomes.
Machine learning algorithms, when combined with the contextual knowledge of researchers and practitioners, offer service providers nuanced estimates of risk and opportunities to refine their efforts. The first post of a new series, Reflections on Methodology, discusses how MDRC helps organizations make the most of predictive modeling tools.
This paper examines the properties of two nonexperimental study designs that can be used in educational evaluation: the comparative interrupted time series (CITS) design and the difference-in-difference (DD) design. The paper looks at the internal validity and precision of these two designs, using the example of the federal Reading First program as implemented in a midwestern state.
This paper presents a conceptual framework for designing and interpreting research on variation in program effects. The framework categorizes the sources of program effect variation and helps researchers integrate the study of variation in program effectiveness and program implementation.
Empirical Guidance for Studies That Randomize Schools to Measure the Impacts of Educational Interventions
This paper examines how controlling statistically for baseline covariates (especially pretests) improves the precision of studies that randomize schools to measure the impacts of educational interventions on student achievement.