PowerUpR (version 0.1.2)

power.bira2c1: Model 2.1: Statistical Power Calculator for 2-Level Constant Effects Blocked Individual Random Assignment Designs, Treatment at Level 1

Description

power.bira2c1 calculates statistical power for designs with 2-levels where level 1 units are randomly assigned to treatment and control groups within level 2 units (school intercepts only).

Usage

power.bira2c1(mdes=.25, alpha=.05, two.tail=TRUE, P=.50, g1=0, R12=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.

P

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

g1

number of covariates at level 1.

R12

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

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 & 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/.

See Also

mdes.bira2c1, mrss.bira2c1, optimal.bira2c1

Examples

## Not run: 
# 
#    power.bira2c1(n=55, J=3)
# 
#   ## End(Not run)