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.
Howard Bloom’s Remarks on Accepting the Peter H. Rossi Award
In a speech before the Association for Public Policy Analysis and Management Conference on November 5, 2010, Howard Bloom, MDRC’s Chief Social Scientist, accepted the Peter H. Rossi Award for Contributions to the Theory or Practice of Program Evaluation.
Strategies for Interpreting and Reporting Intervention Effects on Subgroups
This revised paper examines strategies for interpreting and reporting estimates of intervention effects for subgroups of a study sample. Specifically, the paper considers: why and how subgroup findings are important for applied research, the importance of prespecifying subgroups before analyses are conducted, and the importance of using existing theory and prior research to distinguish between subgroups for which study findings are confirmatory, as opposed to exploratory.
This paper is the first step in a study of instrumental variables analysis with randomized trials to estimate the effects of settings on individuals. The goal of the study is to examine the strengths and weaknesses of the approach and present them in ways that are broadly accessible to applied quantitative social scientists.
In some experimental evaluations of classroom- or school-level interventions, random assignment is conducted at the student level and the program is delivered at the higher level. This paper clarifies the correct causal interpretation of “program impacts” when this study design is used and discusses the implications and limitations of this research design. A real example is used to demonstrate the paper’s key points.
This paper illustrates how to design an experimental sample for measuring the effects of educational programs when whole schools are randomized to a program and control group. It addresses such issues as what number of schools should be randomized, how many students per school are needed, and what is the best mix of program and control schools.