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.
Testimony Before the New York City Council Committee on Higher Education
In the City University of New York’s innovative program, CUNY’s least prepared students delay matriculation, beginning instead with noncredit, time-intensive instruction aimed at eliminating developmental needs after one semester, preparing participants for college courses, and improving academic outcomes. An independent evaluation will help determine CUNY Start’s effect on academic success.
In a speech before the Association for Public Policy Analysis and Management Conference on November 7, 2008, Judith M. Gueron, President Emerita and Scholar in Residence at MDRC, accepted the Peter H. Rossi Award for Contributions to the Theory or Practice of Program Evaluation.
This MDRC working paper on research methodology explores two complementary approaches to developing empirical benchmarks for achievement effect sizes in educational interventions.
This MDRC working paper on research methodology provides practical guidance for researchers who are designing studies that randomize groups to measure the impacts of interventions on children.
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.