PowerUpR (version 0.1.3)

mdes.cra4r4: Model 3.3: MDES Calculator for 4-Level Cluster Random Assignment Designs, Treatment at Level 4

Description

mdes.cra4r4 calculates minimum detectable effect size (MDES) for designs with 4-levels where level 4 units are randomly assigned to treatment and control groups.

Usage

mdes.cra4r4(power=.80, alpha=.05, two.tail=TRUE,
       rho2, rho3, rho4,
       P=.50, R12=0, R22=0, R32=0, R42=0, g4=0,
       n, J, K, L, ...)

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.

rho4

proportion of variance in the outcome explained by level 4 units.

P

proportion of level 4 units randomly assigned to treatment.

g4

number of covariates at level 4.

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.

R42

proportion of level 4 variance in the outcome explained by level 4 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

harmonic mean of level 3 units across level 4 units (or simple average).

L

number of level 4 units.

...

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

Value

fun

function name.

par

list of parameters used in MDES calculation.

df

degrees of freedom

M

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

mdes

minimum detectable effect size and 95% lower and upper confidence limits.

Details

MDES formula and 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.

See Also

power.cra4r4, mrss.cra4r4, optimal.cra4r4

Examples

## Not run: ------------------------------------
# 
#   mdes.cra4r4(rho4=.05, rho3=.05, rho2=.10,
#               n=10, J=2, K=3, L=20)
#   
## ---------------------------------------------