As the first major effort to use a behavioral economics lens to examine human services programs that serve poor and vulnerable families in the United States, the BIAS project demonstrated the value of applying behavioral insights to improve the efficacy of human services programs.
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.
WorkAdvance connects low-income job seekers to high-demand sectors that offer quality jobs with strong career pathways. This infographic describes the program model and its implementation in four locations and presents encouraging evidence of WorkAdvance’s effects on boosting earnings.
Jobs-Plus – a “place-based,” workforce-development model proven to help public housing residents find employment – is about to be replicated across the country. This infographics depicts the program model, its effects on earnings, and the history of its development over the past 20 years.
A Guide for Researchers
Conducting multiple statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) counteract this problem but can substantially change statistical power. This paper presents methods for estimating multiple definitions of power and presents empirical findings on how power is affected by the use of MTPs.
This infographic describes the Improving Contraceptive Options Now (ICON) demonstration, which is assisting primary care health clinics to better serve patients’ family planning needs by offering women a broader range of effective contraceptive options, including long-acting reversible contraception.
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.