PowerUpR (version 0.1.3)

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

Description

mdes.bira2c1 calculates minimum detectable effect size (MDES) 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

mdes.bira2c1(power=.80, alpha=.05, two.tail=TRUE,
              P=.50, g1=0, R12=0,
              n, J, ...)

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.

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

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.bira2c1, mrss.bira2c1, optimal.bira2c1

Examples

## Not run: ------------------------------------
# 
#  mdes.bira2c1(n=55, J=3)
# 
#   
## ---------------------------------------------