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.
Lessons from the BIAS Project
In child support programs, parents must often make complicated decisions with little information in a highly emotional context. The BIAS project, which applied behavioral insights to human services programs, worked with three states to design eight tests focusing on child support order modifications and collection of payments.
Laying the Groundwork for Long-Term Follow-Up in the Mother and Infant Home Visiting Program Evaluation (MIHOPE)
Home visiting provides information, resources, and support to expectant low-income parents and low-income families with young children. This brief summarizes evidence from existing studies on the impact of early childhood home visiting on children 5 and older for four national models of home visiting.
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.
New Approaches to Serving the Lowest-Skilled Students at Community Colleges in Texas and Beyond
Faced with many applicants with very low math skills, community colleges are responding with a variety of reforms, including restricting developmental courses to students with high-school-level skills. This brief provides context for the policy changes and describes the alternatives two colleges offer to those who don’t make the cut.
Three-Year Impacts from the WorkAdvance Demonstration
WorkAdvance offers training and placement services to help prepare individuals for quality jobs in sectors that have strong local demand and advancement opportunities. In this update on employment and earnings only, the most experienced provider continued to produce substantial impacts on both; one other provider increased earnings for late enrollees.
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.
Introducing ExCEL P-3, a Study from the Expanding Children’s Early Learning Network
The ExCEL Network, a collaboration of researchers, preschool providers, and local officials, is exploring how benefits of early childhood interventions persist. The ExCEL P-3 project examines whether one preschool program, reinforced by a system-wide alignment of instruction into elementary school, has impacts on a range of skills through third grade.
Using Data as a Performance Management Tool
The NYC Change Capital Fund, a donor collaborative, invests in community organizations to help build their data capacity. This brief outlines the challenges and benefits of creating a data infrastructure and the need to help staff members go beyond standard funder reporting practices and begin using data to improve programs.
Building a School Choice Architecture
As school choice systems expand, district enrollment offices are striving to make the choice process accessible and clear for families. This practitioner brief offers lessons for supporting families through the sequence of decisions involved as they engage in the process, search for information, and compare and select schools.