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PowerUpR (version 0.1.3)

mrss.bcra3f2: Model 4.1: MRSS Calculator for 3-Level Fixed Effects Blocked Cluster Random Assignment Designs, Treatment at Level 2

Description

mrss.bcra3f2 calculates minimum required sample size (MRSS) for designs with 3-levels where level 2 units are randomly assigned to treatment and control groups within level 3 units (fixed blocks).

Usage

mrss.bcra3f2(mdes=.25, power=.80, alpha=.05, two.tail=TRUE,
              gm=2, ncase=10, constrain="power",
              n=NULL, J=NULL, K=NULL, K0=10, J0=10, tol=.10,
              rho2,
              P=.50, g2=0, R12=0, R22=0)

Arguments

mdes
minimum detectable effect size.
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.
gm
grid multiplier to increase the range of sample size search for each level.
ncase
number of cases to show in the output.
constrain
parameter to contrain; "cost", "power", or "mdes".
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.
J0
starting value for estimating number of level 2 units.
K0
starting value for estimating number of level 3 units.
tol
tolerance to stop the search algorithm.
rho2
proportion of variance in the outcome explained by level 2 units.
P
average proportion of level 2 units randomly assigned to treatment within level 3 units.
g2
number of covariates at level 2.
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.

Value

fun
function name.
par
list of parameters used in MRSS calculation.
round.mrss
solution after rounding.
integer.mrss
best integer solutions around round.mrss solution.

Details

Level 2 and level 3 sample sizes (J and K) are calculated using an iterative procedure described in Dong & Maynard (2013) due to degrees of freedom dependency on J and K. For other levels (n) MRSS calculation is simply solving for the unknown. MRSS calculator returns values that are not integer. Rounding may produce MDES and power values different from what was specified, therefore an integer solution is approximated using brute force (See Value section). Integer solution to MRSS for an omitted level assumes that specified sample sizes for remaining levels may subject to some changes. 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

mdes.bcra3f2, power.bcra3f2, optimal.bcra3f2

Examples

Run this code
## Not run: ------------------------------------
# 
#      mrss.bcra3f2(rho3=.06, rho2=.17,
#                  n=15, J=3)
# 
#   
## ---------------------------------------------

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