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Sarah E. Robertson; Jon A. Steingrimsson; Issa J. Dahabreh – Evaluation Review, 2024
When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…
Descriptors: Randomized Controlled Trials, Generalization, Inferences, Hierarchical Linear Modeling
Freedman, David A.; Berk, Richard A. – Evaluation Review, 2008
Regressions can be weighted by propensity scores in order to reduce bias. However, weighting is likely to increase random error in the estimates, and to bias the estimated standard errors downward, even when selection mechanisms are well understood. Moreover, in some cases, weighting will increase the bias in estimated causal parameters. If…
Descriptors: Causal Models, Weighted Scores, Error of Measurement, Case Studies
Hicks, Darrin; Larson, Carl; Nelson, Christopher; Olds, David L.; Johnston, Erik – Evaluation Review, 2008
Though collaboration is often required in community initiatives, little evidence documents relationships between collaboration and program success. The authors contend that clarification of the construct collaboration is necessary for investigating its contribution to the success of community initiatives. After respecifying collaboration, they…
Descriptors: Participant Characteristics, Community Health Services, Nurses, Home Visits
Freedman, David A. – Evaluation Review, 2004
This article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional…
Descriptors: Prior Learning, Identification, Causal Models, Regression (Statistics)