Suchergebnisse

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also helpful to anticipate the benefits, in terms of statistical power, of collecting household data, testing students at baseline, or relying on administrative data on previous cohorts from the same school. We use data from multiple cluster-randomized trials in four Latin American countries to provide information on the intraclass correlations in early grade literacy outcomes. We also describe the proportion of variance explained by different types of covariates. These parameters will help future researchers conduct statistical power analysis, estimate the required sample size, and determine the necessity of collecting different types of baseline data such as child assessments, administrative data at the school level, or household surveys.

To assess nonexperimental (NX) evaluation methods in the context of welfare, job training, and employment services programs, the authors reexamined the results of twelve case studies intended to replicate impact estimates from an experimental evaluation by using NX methods. They found that the NX methods sometimes came close to replicating experimentally derived results but often produced estimates that differed by policy-relevant margins, which the authors interpret as estimates of bias. Although the authors identified several study design factors associated with smaller discrepancies, no combination of factors would consistently eliminate discrepancies. Even with a large number of impact estimates, the positive and negative bias estimates did not always cancel each other out. Thus, it was difficult to identify an aggregation strategy that consistently removed bias while answering a focused question about earnings impacts of a program. They conclude that although the empirical evidence from this literature can be used in the context of training and welfare programs to improve NX research designs, it cannot on its own justify the use of such designs.

To assess nonexperimental (NX) evaluation methods in the context of welfare, job training, & employment services programs, the authors reexamined the results of twelve case studies intended to replicate impact estimates from an experimental evaluation by using NX methods. They found that the NX methods sometimes came close to replicating experimentally derived results but often produced estimates that differed by policy relevant margins, which the authors interpret as estimates of bias. Although the authors identified several study design factors associated with smaller discrepancies, no combination of factors would consistently eliminate discrepancies. Even with a large number of impact estimates, the positive & negative bias estimates did not always cancel each other out. Thus, it was difficult to identify an aggregation strategy that consistently removed bias while answering a focused question about earnings impacts of a program. They conclude that although the empirical evidence from this literature can be used in the context of training & welfare programs to improve NX research designs, it cannot on its own justify the use of such designs. [Copyright 2003 Sage Publications, Inc.]