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Shen, Zuchao; Kelcey, Benjamin – Journal of Educational and Behavioral Statistics, 2020
Conventional optimal design frameworks consider a narrow range of sampling cost structures that thereby constrict their capacity to identify the most powerful and efficient designs. We relax several constraints of previous optimal design frameworks by allowing for variable sampling costs in cluster-randomized trials. The proposed framework…
Descriptors: Sampling, Research Design, Randomized Controlled Trials, Statistical Analysis
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Rhoads, Christopher – Journal of Educational and Behavioral Statistics, 2017
Researchers designing multisite and cluster randomized trials of educational interventions will usually conduct a power analysis in the planning stage of the study. To conduct the power analysis, researchers often use estimates of intracluster correlation coefficients and effect sizes derived from an analysis of survey data. When there is…
Descriptors: Statistical Analysis, Hierarchical Linear Modeling, Surveys, Effect Size
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VanHoudnos, Nathan M.; Greenhouse, Joel B. – Journal of Educational and Behavioral Statistics, 2016
When cluster randomized experiments are analyzed as if units were independent, test statistics for treatment effects can be anticonservative. Hedges proposed a correction for such tests by scaling them to control their Type I error rate. This article generalizes the Hedges correction from a posttest-only experimental design to more common designs…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Error of Measurement, Scaling
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Pustejovsky, James E.; Hedges, Larry V.; Shadish, William R. – Journal of Educational and Behavioral Statistics, 2014
In single-case research, the multiple baseline design is a widely used approach for evaluating the effects of interventions on individuals. Multiple baseline designs involve repeated measurement of outcomes over time and the controlled introduction of a treatment at different times for different individuals. This article outlines a general…
Descriptors: Hierarchical Linear Modeling, Effect Size, Maximum Likelihood Statistics, Computation