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Rhoads, Christopher – Society for Research on Educational Effectiveness, 2016
Current practice for conducting power analyses in hierarchical trials using survey based ICC and effect size estimates may be misestimating power because ICCs are not being adjusted to account for treatment effect heterogeneity. Results presented in Table 1 show that the necessary adjustments can be quite large or quite small. Furthermore, power…
Descriptors: Statistical Analysis, Correlation, Effect Size, Surveys
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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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Rhoads, Christopher – Journal of Research on Educational Effectiveness, 2014
Recent publications have drawn attention to the idea of utilizing prior information about the correlation structure to improve statistical power in cluster randomized experiments. Because power in cluster randomized designs is a function of many different parameters, it has been difficult for applied researchers to discern a simple rule explaining…
Descriptors: Correlation, Statistical Analysis, Multivariate Analysis, Research Design
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Hedges, Larry V.; Rhoads, Christopher – National Center for Special Education Research, 2010
This paper provides a guide to calculating statistical power for the complex multilevel designs that are used in most field studies in education research. For multilevel evaluation studies in the field of education, it is important to account for the impact of clustering on the standard errors of estimates of treatment effects. Using ideas from…
Descriptors: Research Design, Field Studies, Computers, Effect Size