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.
A Primer for Researchers Working with Education Data
Predictive modeling estimates individuals’ probabilities of future outcomes by building and testing a model using data on similar individuals whose outcomes are already known. The method offers benefits for continuous improvement efforts and efficient allocation of resources. This paper explains MDRC’s framework for using predictive modeling in education.
An Empirical Assessment Based on Four Recent Evaluations
This reference report, prepared for the National Center for Education Evaluation and Regional Assistance of the Institute of Education Sciences (IES), uses data from four recent IES-funded experimental design studies that measured student achievement using both state tests and a study-administered test.
No universal guideline exists for judging the practical importance of a standardized effect size, a measure of the magnitude of an intervention’s effects. This working paper argues that effect sizes should be interpreted using empirical benchmarks — and presents three types in the context of education research.
In his testimony before the House Ways and Means Subcommittee on Income Security and Family Support, MDRC President Gordon Berlin argues that the most direct way to alleviate poverty is to tackle the legacy of falling wages, particularly for men with less education.
Presented Before the Subcommittee on Federalism and the Census, House Committee on Government Reform
MDRC’s study of Jobs-Plus, an employment program for public housing residents, offered the first hard evidence that a work-focused intervention based in public housing can effectively boost residents’ earnings and promote their self-sufficiency. Congress may wish to consider introducing Jobs-Plus in additional housing developments across the country.
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.
Relying on 427 classroom observations conducted over a three-year period, this study traces changes in teachers’ instructional practices in the First Things First schools.
Presented Before the Science, Technology and Space Subcommittee of the Committee on Commerce, Science, and Transportation, United States Senate