PowerUpR (version 0.1.3)

power.bira2r1: Model 2.3: Statistical Power Calculator for 2-Level Random Effects Blocked Individual Random Assignment Designs, Individuals Randomized within Blocks

Description

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).

Usage

power.bira2r1(mdes=.25, alpha=.05, two.tail=TRUE,
               rho2, omega2,
               g2=0, P=.50, R12=0, RT22=0,
               n, J, ...)

Arguments

mdes

minimum detectable effect size.

alpha

probability of type I error.

two.tail

logical; TRUE for two-tailed hypothesis testing, FALSE for one-tailed hypothesis testing.

rho2

proportion of variance in the outcome explained by level 2 units.

omega2

treatment effect heterogeneity as ratio of treatment effect variance among level 2 units to the residual variance at level 2.

P

average proportion of level 1 units randomly assigned to treatment within level 2 units.

g2

number of covariates at level 2.

R12

proportion of level 1 variance in the outcome explained by level 1 covariates.

RT22

proportion of treatment effect variance among level 2 units explained by level 2 covariates.

n

harmonic mean of level 1 units across level 2 units (or simple average).

J

level 2 sample size.

...

to handle extra parameters passed from other functions, do not define any additional parameters.

Value

fun

function name.

par

list of parameters used in power calculation.

df

degrees of freedom

M

multiplier for MDES calcuation given degrees of freedom, \(\alpha\) and \(\beta\) (1-power).

power

statistical power (1 - type II error).

Details

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).

References

Dong, N., & Maynard, R. A. (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/.

See Also

mdes.bira2r1, mrss.bira2r1, optimal.bira2r1

Examples

## Not run: ------------------------------------
# 
#    power.bira2r1(rho2=.35, omega2=.10,
#                n=83, J=480)
# 
#   
## ---------------------------------------------