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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
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Wing, Coady; Bello-Gomez, Ricardo A. – American Journal of Evaluation, 2018
Treatment effect estimates from a "regression discontinuity design" (RDD) have high internal validity. However, the arguments that support the design apply to a subpopulation that is narrower and usually different from the population of substantive interest in evaluation research. The disconnect between RDD population and the…
Descriptors: Regression (Statistics), Research Design, Validity, Evaluation Methods
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Nicole Bohme Carnegie; Masataka Harada; Jennifer L. Hill – Journal of Research on Educational Effectiveness, 2016
A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the…
Descriptors: Randomized Controlled Trials, Simulation, Evidence Based Practice, Barriers
Steiner, Peter M. – Society for Research on Educational Effectiveness, 2011
Given the different possibilities of matching in the context of multilevel data and the lack of research on corresponding matching strategies, the author investigates two main research questions. The first research question investigates the advantages and disadvantages of different matching strategies that can be pursued with multilevel data…
Descriptors: Educational Research, Research Methodology, Observation, Causal Models