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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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de Alteriis, Martin – American Journal of Evaluation, 2020
This article examines factors that could have influenced whether evaluations of U.S. government--funded foreign assistance programs completed in 2015 had considered unintended consequences. Logit regression models indicate that the odds of considering unintended consequences were increased when all or most of seven standard data collection methods…
Descriptors: Federal Programs, International Programs, Program Evaluation, Influences
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Moulton, Shawn R.; Peck, Laura R.; Greeney, Adam – American Journal of Evaluation, 2018
In experimental evaluations of health and social programs, the role of dosage is rarely explored because researchers cannot usually randomize individuals to experience varying dosage levels. Instead, such evaluations reveal the average effects of exposure to an intervention, although program exposure may vary widely. This article compares three…
Descriptors: Marriage, Intervention, Prediction, Program Effectiveness
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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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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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Mulvey, Kevin P.; Atkinson, Donna Durant; Avula, Deepa; Luckey, J. William – American Journal of Evaluation, 2005
The Internet and World Wide Web are increasingly used to accelerate progress in a variety of fields. These applications go beyond the traditional technology- or computer-related fields and have expanded to nontechnical fields, which have benefited greatly from the use of the Web as a data-gathering and management support tool. The purpose of this…
Descriptors: Program Effectiveness, Measurement Techniques, Program Evaluation, Evaluation Methods
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Renger, Ralph – American Journal of Evaluation, 2006
The Office of Management and Budget has recommended the termination of numerous federal programs, citing a lack of program results as the primary reason for this decision. In response to this recommendation, several federal agencies have turned to logic modeling to demonstrate that programs are on the path to results accountability. However,…
Descriptors: Federal Programs, Models, Accountability, Program Evaluation