PowerUpR (version 0.1.2)

mdes.cra3r3: Model 3.2: MDES Calculator for 3-Level Cluster Random Assignment Designs, Treatment at Level 3

Description

mdes.cra3r3 calculates minimum detectable effect size (MDES) for designs with 3-levels where level 3 units are randomly assigned to treatment and control groups.

Usage

mdes.cra3r3(power=.80, alpha=.05, two.tail=TRUE, rho2, rho3, P=.50, g3=0, R12=0, R22=0, R32=0, n, J, K, ...)

Arguments

power

statistical power (1 - type II error).

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.

rho3

proportion of variance in the outcome explained by level 3 units.

P

proportion of level 3 units randomly assigned to treatment.

g3

number of covariates at level 3.

R12

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

R22

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

R32

proportion of level 3 variance in the outcome explained by level 3 covariates.

n

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

J

harmonic mean of level 2 units across level 3 units (or simple average).

K

level 3 sample size.

...

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

Value

Details

MDES formula and 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.

See Also

power.cra3r3, mrss.cra3r3, optimal.cra3r3

Examples

## Not run: 
# 
#  mdes.cra3r3(rho3=.13, rho2=.10, omega3=.40,
#              n=10, J=6, K=24)
# 
#   ## End(Not run)