Description Author(s) References
PowerRTCRT
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Jessaca Spybrook ???
Bloom, H. S., Richburg- Hayes, L. & Black, A. R. (2007). Using Covariates to Improve Precision for Studies that Randomize Schools to Evaluate Educational Interventions. Educational Evaluation and Policy Analysis, 29(1), 0-59.
Deke, John, Dragoset, Lisa, and Moore, Ravaris (2010). Precision Gains from Publically Available School Proficiency Measures Compared to Study-Collected Test Scores in Education Cluster-Randomized Trials (NCEE 2010-4003). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. http://ies.ed.gov/ncee/pubs/20104003/
Dong & Maynard (2013). PowerUp!: A Tool for Calculating Minum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies,Journal of Research on Educational Effectiveness, 6(1), 24-6.
Dong, N., Reinke, W. M., Herman, K. C., Bradshaw, C. P., & Murray, D. W. (2016). Meaningful effect sizes, intraclass correlations, and proportions of variance explained by covariates for panning two-and three-level cluster randomized trials of social and behavioral outcomes. Evaluation Review. doi: 10.1177/0193841X16671283
Hedges, L. V., & Borenstein, M. (2014). Conditional Optimal Design in Three- and Four-Level Experiments. Journal of Educational and Behavioral Statistics, 39(4), 257-281
Hedberg, E., & Hedges, L. V.(2014). Reference Values of Within-District Intraclass Correlations of Academic Achivement by District Characteristics: Results From a Meta-Analysis of District-Specified Values. Evaluation Review, 38(6), 546-582.
Hedges, L. V., & Hedberg, E. (2007). Interclass correlation values for planning group-randomized trials in education. Educational Evaluation and Policy Analysis, 29(1), 60-87.
Hedges, L. V., & Hedberg, E. (2013). Interclass Correlations and Covariate Outcome Correlations for Planning Two- and Three-Level Cluster-Randomized Experiments in Education. Evaluation Review, 37(6), 445-489.
Hedges, L. & Rhoads, C.(2009). Statistical Power Analysis in Education Research (NCSER 2010-3006). Washington, DC: National Center for Special Education Researc , Institute of Education Sciences, U.S. Department of Education. This report is available on the IES website at http://ies.ed.gov/ncser/.
Kelcey, B., & Phelps, G. (2013). Strategies for improving power in school randomized studies of professional development. Evaluation Review, 37(6), 520-554.
R Core Team (2016). R: A language and environment for statistical computin . R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.
Raudenbush, S. W. (1997). Statistical analysis and optimal design for cluster randomized trials. Psychological Methods, 2, 173-185.
Raudenbush, S. W., & Liu, X. (2000). Statistical power and optimal design for multisite trials. Psychological Methods, 5, 199-213.
Schochet, P. Z. (2008). Statistical Power for Random Assignment Evaluations of Education Programs. Journal of Educational and Behavioral Statistics, 33(1), 62-87
Spybrook, J., Westine, C. D., & Taylor, J. A. (2016). Design Parameters for Impact Research in Science Education: A Multisite Anlaysis. AERA Open, 2(1), 1-15.
Westine, C. D., Spybrook, J., & Taylor, J. A. (2013). An Empirical Investigation of Variance Design Parameters for Planning Cluster-Randomized Trials of Science Achievement. Evaluation Review, 37(6), 490-519.
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