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Bell, Stephen H.; Stapleton, David C.; Wood, Michelle; Gubits, Daniel – American Journal of Evaluation, 2023
A randomized experiment that measures the impact of a social policy in a sample of the population reveals whether the policy will work on average with universal application. An experiment that includes only the subset of the population that volunteers for the intervention generates narrower "proof-of-concept" evidence of whether the…
Descriptors: Public Policy, Policy Formation, Federal Programs, Social Services
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Stapleton, David C.; Bell, Stephen H.; Hoffman, Denise; Wood, Michelle – American Journal of Evaluation, 2020
The Benefit Offset National Demonstration (BOND) tested a $1 reduction in benefits per $2 earnings increase above the level at which Social Security Disability Insurance benefits drop from full to zero under current law. BOND included a rare and large "population-representative" experiment: It applied the rule to a nationwide, random…
Descriptors: Federal Programs, Public Policy, Experiments, Comparative Analysis
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Zandniapour, Lily; Deterding, Nicole M. – American Journal of Evaluation, 2018
Tiered evidence initiatives are an important federal strategy to incentivize and accelerate the use of rigorous evidence in planning, implementing, and assessing social service investments. The Social Innovation Fund (SIF), a program of the Corporation for National and Community Service, adopted a public-private partnership approach to tiered…
Descriptors: Program Effectiveness, Program Evaluation, Research Needs, Evidence
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Kidwell, Kelley M.; Hyde, Luke W. – American Journal of Evaluation, 2016
Heterogeneity between and within people necessitates the need for sequential personalized interventions to optimize individual outcomes. Personalized or adaptive interventions (AIs) are relevant for diseases and maladaptive behavioral trajectories when one intervention is not curative and success of a subsequent intervention may depend on…
Descriptors: Intervention, Individualized Programs, Child Behavior, Behavior Problems
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Dong, Nianbo – American Journal of Evaluation, 2015
Researchers have become increasingly interested in programs' main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin's causal model to…
Descriptors: Probability, Statistical Analysis, Research Design, Causal Models
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Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen – American Journal of Evaluation, 2016
Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…
Descriptors: Intervention, Multivariate Analysis, Mixed Methods Research, Models