power.cra4r4
calculates statistical power for designs with 4-levels
where level 4 units are randomly assigned to treatment and control groups.
power.cra4r4(mdes=.25, alpha=.05, two.tail=TRUE, rho2, rho3, rho4, P=.50, R12=0, R22=0, R32=0, R42=0, g4=0, n, J, K, L, ...)
TRUE
for two-tailed hypothesis testing, FALSE
for one-tailed hypothesis testing. Power formula was derived within power analysis framework descibed by Hedges & Rhoads (2009). Further definition of design parameters can be found in Dong & Maynard (2013).
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.
Hedges, L. & Rhoads, C.(2009). Statistical Power Analysis in Education Research (NCSER 2010-3006). Washington, DC: National Center for Special Education Research, Institute of Education Sciences, U.S. Department of Education. This report is available on the IES website at http://ies.ed.gov/ncser/.
mdes.cra4r4, mrss.cra4r4, optimal.cra4r4
## Not run:
#
# power.cra4r4(rho4=.05, rho3=.05, rho2=.10,
# n=10, J=2, K=3, L=20)
# ## End(Not run)
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