power.bira2r1
calculates statistical power for designs with 2-levels
where level 1 units are randomly assigned to treatment and control groups within level 2 units (random blocks).
power.bira2r1(mdes=.25, alpha=.05, two.tail=TRUE, rho2, omega2, g2=0, P=.50, R12=0, RT22=0, n, J, ...)
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.bira2r1, mrss.bira2r1, optimal.bira2r1
## Not run:
#
# power.bira2r1(rho2=.35, omega2=.10,
# n=83, J=480)
#
# ## End(Not run)
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