cra3: Three-level Cluster-randomized Trials to Detect Main,...
In PowerUpR: Power Analysis Tools for Multilevel Randomized Experiments
Description
Usage
Arguments
Value
References
Examples
Description
For main treatment effects, use mdes.cra3() to calculate the minimum detectable effect size, power.cra3() to calculate the statistical power, mrss.cra3() to calculate the minimum required sample size (number of clusters).
For moderator at level 1, use mdesd.mod331() to calculate the minimum detectable effect size, power.mod331() to calculate the statistical power, mrss.mod331() to calculate the minimum required sample size (number of clusters).
For moderator at level 2, use mdesd.mod332() to calculate the minimum detectable effect size, power.mod332() to calculate the statistical power, mrss.mod332() to calculate the minimum required sample size (number of clusters).
For moderator at level 3, use mdesd.mod333() to calculate the minimum detectable effect size, power.mod333() to calculate the statistical power, mrss.mod333() to calculate the minimum required sample size (number of clusters).
For mediator at level 3, use power.med331(), for mediator at level 2, use power.med321(), for mediator at level 1, use power.med311() to calculate the statistical power.
For cluster-randomized block designs (treatment at level 3, with fixed effects across level 4 blocks), use mdes.bcra4f3() to calculate the minimum detectable effect size, power.bcra4f3() to calculate the statistical power, and mrss.bcra4f3() to calculate the minimum required sample size (number of clusters per block).
Usage
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mdes.cra3(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0,
n, J, K)
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K)
mdesd.mod332(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K)
mdesd.mod333(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K)
power.cra3(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, g3=0, r21=0, r22=0, r23=0,
p=.50, n, J, K)
power.mod331(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K)
power.mod332(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K)
power.mod333(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K)
power.med331(esa, esB, two.tailed=TRUE, alpha=.05,
mc=TRUE, nsims=1000, ndraws=1000,
rho2, rho3, gm3=4, r2m3=0, r21=0, r22=0,
g3=5, r23=0, p=.50, n, J, K)
power.med321(esa, esB, two.tailed=TRUE, alpha=.05,
mc=TRUE, nsims=1000, ndraws=1000,
rhom3, rho2, rho3, r2m2=0,
gm3=4, r2m3=0, r21=0, r22=0, g3=5, r23=0,
p=.50, n, J, K)
power.med311(esa, esB, two.tailed=TRUE, alpha=.05,
mc=TRUE, nsims=1000, ndraws=1000,
rhom2, rhom3, rho2, rho3,
r2m1=0, r2m2=0, gm3=4, r2m3=0,
r21=0, r22=0, g3=5, r23=0,
p=.50, n, J, K)
mrss.cra3(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J, K0=10, tol=.10,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0)
mrss.mod331(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K0=10, tol=.10)
mrss.mod332(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K0=10, tol=.10)
mrss.mod333(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K0=10, tol=.10)
mdes.bcra4f3(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, r21=0, r22=0, r23=0, g3=0,
n, J, K, L)
power.bcra4f3(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, r21=0, r22=0, r23=0, g3=0,
n, J, K, L)
mrss.bcra4f3(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J, L, K0=10, tol=.10,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0)
Arguments
power
statistical power (1-β).
es, esa, esB
effect size for main/moderator effects, or for path coefficients a (treatment - mediator), or B (overall mediator - outcome) in the mediation model.
alpha
probability of type I error.
two.tailed
logical; TRUE for two-tailed hypothesis testing, FALSE for one-tailed hypothesis testing.
rho2
proportion of variance in the outcome between level 2 units (unconditional ICC2).
rho3
proportion of variance in the outcome between level 3 units (unconditional ICC3).
rhom2
proportion of variance in the mediator between level 2 units.
rhom3
proportion of variance in the mediator between level 3 units.
omegam2
ratio of the unconditional variance in the moderator effect that is between level 2 units to the residual variance between level 2 units in the null model.
omegam3
ratio of the unconditional variance in the moderator effect that is between level 3 units to the residual variance between level 3 units in the null model.
p
proportion of level 3 units randomly assigned to treatment.
q
proportion of level 1, level 2, or level 3 units in the moderator subgroup.
g3
number of covariates at level 3.
gm3
number of covariates at level 3 for the mediation model.
r21
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.
r23
proportion of level 3 variance in the outcome explained by level 3 covariates.
r2m1
proportion of mediator variance at level 1 explained by level 1 predictors.
r2m2
proportion of variance in the mediator explained by level 2 predictors.
r2m3
proportion of variance in the moderator effect that is explained by level 3 predictors. For the mediation model, proportion of variance in the mediator explained by level 3 predictors.
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.
L
level 4 sample size (blocks).
K0
starting value for K.
tol
tolerance to end iterative process for finding K.
mc
logical; TRUE for monte carlo simulation based power.
nsims
number of replications, if mc = TRUE.
ndraws
number of draws from the distribution of the path coefficients for each replication, if mc = TRUE.
Value
fun
function name.
parms
list of parameters used in power calculation.
df
degrees of freedom.
ncp
noncentrality parameter.
power
statistical power (1-β).
mdes
minimum detectable effect size.
K
number of level 3 units.
References
Spybrook, J., Kelcey, B., \& Dong, N. (2016). Power for detecting treatment by moderator effects in two-and three-level cluster randomized trials. Journal of Educational and Behavioral Statistics, 41(6), 605-627. doi: 10.3102/1076998616655442
Dong, N., Kelcey, B., \& Spybrook, J. (2018). Power analyses for moderator effects in three-level cluster randomized trials. The Journal of Experimental Education, 86(3), 489-514. doi: 10.1080/00220973.2017.1315714
Dong, N., \& Maynard, R. (2013). PowerUp!: A tool for calculating minimum detectable effect sizes and minimum required sample sizes for experimental and quasi-experimental design studies. Journal of Research on Educational Effectiveness, 6(1), 24-67. doi: 10.1080/19345747.2012.673143
Kelcey, B., Xie, Y., Spybrook, J., \& Dong, N. (2020). Power and sample size determination for multilevel mediation in three-Level cluster-randomized trials. Multivariate Behavioral Research. Advance online publication. doi: 10.1080/00273171.2020.1738910
Kelcey, B., Spybrook, J., Dong, N., \& Bai, F. (2020). Cross-level mediation in school-randomized studies of teacher development: Experimental design and power. Journal of Research on Educational Effectiveness. Advance online publication. doi: 10.1080/19345747.2020.1726540
Examples
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# cross-checks for the main effect
mdes.cra3(rho3=.06, rho2=.17, n=15, J=3, K=60)
power.cra3(es=.269, rho3=.06, rho2=.17, n=15, J=3, K=60)
mrss.cra3(es=.269, rho3=.06, rho2=.17, n=15, J=3)
# cross-checks for the randomly varying cont. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=NULL, n=15, J=3, K=60)
power.mod331(es=0.1248, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=NULL, n=15, J=3, K=60)
mrss.mod331(es=0.1248, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=NULL, n=15, J=3)
# cross-checks for the non-randomly varying cont. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=NULL, n=15, J=3, K=60)
power.mod331(es=.0946, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=NULL, n=15, J=3, K=60)
mrss.mod331(es=.0946, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=NULL, n=15, J=3)
# cross-checks for the randomly varying bin. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=.50, n=15, J=3, K=60)
power.mod331(es=.2082, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=.50, n=15, J=3, K=60)
mrss.mod331(es=.2082, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=.50, n=15, J=3)
# cross-checks for the non-randomly varying bin. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=.50, n=15, J=3, K=60)
power.mod331(es=.1893, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=.50, n=15, J=3, K=60)
mrss.mod331(es=.1893, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=.50, n=15, J=3)
# cross-checks for the randomly varying bin. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=.05,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.2244, rho3=.1, rho2=.1, omegam3=.05,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.2244, rho3=.1, rho2=.1, omegam3=.05,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4)
# cross-checks for the randomly varying cont. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=.05,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.1209, rho3=.1, rho2=.1, omegam3=.05,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.1209, rho3=.1, rho2=.1, omegam3=.05,
r21=.30, r22=.4, r2m3=0, n=20, J=4)
# cross-checks for the non-randomly varying bin. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=0,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.2157, rho3=.1, rho2=.1, omegam3=0,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.2157, rho3=.1, rho2=.1, omegam3=0,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# cross-checks for the non-randomly varying cont. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=0,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.1079, rho3=.1, rho2=.1, omegam3=0,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.1079, rho3=.1, rho2=.1, omegam3=0,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# cross-checks for the randomly varying bin. L3 moderator effect
mdesd.mod333(rho3=.1, rho2=.1, q=.5,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
power.mod333(es=.4128, rho3=.1, rho2=.1, q=.5,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
mrss.mod333(es=.4128, rho3=.1, rho2=.1, q=.5,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
# cross-checks for the randomly varying cont. L3 moderator effect
mdesd.mod333(rho3=.1, rho2=.1,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
power.mod333(es=.2064, rho3=.1, rho2=.1,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
mrss.mod333(es=.2064, rho3=.1, rho2=.1,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
# 3-3-1 mediation
power.med331(esa= .50, esB = .30, rho2 = .15, rho3 = .15,
r21 = .20, r22 = .20, g3 = 4,
n = 20, J = 4, K = 80, p = .5)
# 3-2-1 mediation
power.med321(esa= .51, esB = .30, rhom3 = 0.27, rho2 = .15, rho3 = .19,
r2m2 = .07, gm3 = 4, r2m3 = .16,
r21 = .02, r22 = .41, g3 = 5, r23 = .38,
p = .50, n = 20, J = 4, K = 60)
# 3-1-1 mediation
power.med311(esa= .49 , esB = .30,
rhom2 = .05, rhom3 = .26, rho2 = .15, rho3 = .20,
r2m1 = .10, r2m2 = .07, r2m3 = .17,
r21 = .02, r22 = .41, r23 = .38,
p = .50, n = 20, J = 4, K = 30)
# cross-checks for cluster-randomized block design
# treatment at level 3, with fixed effects across level 4 blocks
mdes.bcra4f3(rho3=.15, rho2=.15,
n=10, J=4, K=23, L=15)
power.bcra4f3(es=0.137, rho3=.15, rho2=.15,
n=10, J=4, K=33, L=15)
mrss.bcra4f3(es=0.137, rho3=.15, rho2=.15,
n=10, J=4, L=15)
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Description Usage Arguments Value References Examples
Description
For main treatment effects, use mdes.cra3() to calculate the minimum detectable effect size, power.cra3() to calculate the statistical power, mrss.cra3() to calculate the minimum required sample size (number of clusters).
For moderator at level 1, use mdesd.mod331() to calculate the minimum detectable effect size, power.mod331() to calculate the statistical power, mrss.mod331() to calculate the minimum required sample size (number of clusters).
For moderator at level 2, use mdesd.mod332() to calculate the minimum detectable effect size, power.mod332() to calculate the statistical power, mrss.mod332() to calculate the minimum required sample size (number of clusters).
For moderator at level 3, use mdesd.mod333() to calculate the minimum detectable effect size, power.mod333() to calculate the statistical power, mrss.mod333() to calculate the minimum required sample size (number of clusters).
For mediator at level 3, use power.med331(), for mediator at level 2, use power.med321(), for mediator at level 1, use power.med311() to calculate the statistical power.
For cluster-randomized block designs (treatment at level 3, with fixed effects across level 4 blocks), use mdes.bcra4f3() to calculate the minimum detectable effect size, power.bcra4f3() to calculate the statistical power, and mrss.bcra4f3() to calculate the minimum required sample size (number of clusters per block).
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | mdes.cra3(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0,
n, J, K)
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K)
mdesd.mod332(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K)
mdesd.mod333(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K)
power.cra3(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, g3=0, r21=0, r22=0, r23=0,
p=.50, n, J, K)
power.mod331(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K)
power.mod332(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K)
power.mod333(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K)
power.med331(esa, esB, two.tailed=TRUE, alpha=.05,
mc=TRUE, nsims=1000, ndraws=1000,
rho2, rho3, gm3=4, r2m3=0, r21=0, r22=0,
g3=5, r23=0, p=.50, n, J, K)
power.med321(esa, esB, two.tailed=TRUE, alpha=.05,
mc=TRUE, nsims=1000, ndraws=1000,
rhom3, rho2, rho3, r2m2=0,
gm3=4, r2m3=0, r21=0, r22=0, g3=5, r23=0,
p=.50, n, J, K)
power.med311(esa, esB, two.tailed=TRUE, alpha=.05,
mc=TRUE, nsims=1000, ndraws=1000,
rhom2, rhom3, rho2, rho3,
r2m1=0, r2m2=0, gm3=4, r2m3=0,
r21=0, r22=0, g3=5, r23=0,
p=.50, n, J, K)
mrss.cra3(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J, K0=10, tol=.10,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0)
mrss.mod331(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K0=10, tol=.10)
mrss.mod332(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K0=10, tol=.10)
mrss.mod333(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K0=10, tol=.10)
mdes.bcra4f3(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, r21=0, r22=0, r23=0, g3=0,
n, J, K, L)
power.bcra4f3(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, r21=0, r22=0, r23=0, g3=0,
n, J, K, L)
mrss.bcra4f3(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J, L, K0=10, tol=.10,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0)
|
Arguments
power |
statistical power (1-β). |
es, esa, esB |
effect size for main/moderator effects, or for path coefficients a (treatment - mediator), or B (overall mediator - outcome) in the mediation model. |
alpha |
probability of type I error. |
two.tailed |
logical; |
rho2 |
proportion of variance in the outcome between level 2 units (unconditional ICC2). |
rho3 |
proportion of variance in the outcome between level 3 units (unconditional ICC3). |
rhom2 |
proportion of variance in the mediator between level 2 units. |
rhom3 |
proportion of variance in the mediator between level 3 units. |
omegam2 |
ratio of the unconditional variance in the moderator effect that is between level 2 units to the residual variance between level 2 units in the null model. |
omegam3 |
ratio of the unconditional variance in the moderator effect that is between level 3 units to the residual variance between level 3 units in the null model. |
p |
proportion of level 3 units randomly assigned to treatment. |
q |
proportion of level 1, level 2, or level 3 units in the moderator subgroup. |
g3 |
number of covariates at level 3. |
gm3 |
number of covariates at level 3 for the mediation model. |
r21 |
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. |
r23 |
proportion of level 3 variance in the outcome explained by level 3 covariates. |
r2m1 |
proportion of mediator variance at level 1 explained by level 1 predictors. |
r2m2 |
proportion of variance in the mediator explained by level 2 predictors. |
r2m3 |
proportion of variance in the moderator effect that is explained by level 3 predictors. For the mediation model, proportion of variance in the mediator explained by level 3 predictors. |
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. |
L |
level 4 sample size (blocks). |
K0 |
starting value for |
tol |
tolerance to end iterative process for finding |
mc |
logical; |
nsims |
number of replications, if |
ndraws |
number of draws from the distribution of the path coefficients for each replication, if |
Value
fun |
function name. |
parms |
list of parameters used in power calculation. |
df |
degrees of freedom. |
ncp |
noncentrality parameter. |
power |
statistical power (1-β). |
mdes |
minimum detectable effect size. |
K |
number of level 3 units. |
References
Spybrook, J., Kelcey, B., \& Dong, N. (2016). Power for detecting treatment by moderator effects in two-and three-level cluster randomized trials. Journal of Educational and Behavioral Statistics, 41(6), 605-627. doi: 10.3102/1076998616655442
Dong, N., Kelcey, B., \& Spybrook, J. (2018). Power analyses for moderator effects in three-level cluster randomized trials. The Journal of Experimental Education, 86(3), 489-514. doi: 10.1080/00220973.2017.1315714
Dong, N., \& Maynard, R. (2013). PowerUp!: A tool for calculating minimum detectable effect sizes and minimum required sample sizes for experimental and quasi-experimental design studies. Journal of Research on Educational Effectiveness, 6(1), 24-67. doi: 10.1080/19345747.2012.673143
Kelcey, B., Xie, Y., Spybrook, J., \& Dong, N. (2020). Power and sample size determination for multilevel mediation in three-Level cluster-randomized trials. Multivariate Behavioral Research. Advance online publication. doi: 10.1080/00273171.2020.1738910
Kelcey, B., Spybrook, J., Dong, N., \& Bai, F. (2020). Cross-level mediation in school-randomized studies of teacher development: Experimental design and power. Journal of Research on Educational Effectiveness. Advance online publication. doi: 10.1080/19345747.2020.1726540
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | # cross-checks for the main effect
mdes.cra3(rho3=.06, rho2=.17, n=15, J=3, K=60)
power.cra3(es=.269, rho3=.06, rho2=.17, n=15, J=3, K=60)
mrss.cra3(es=.269, rho3=.06, rho2=.17, n=15, J=3)
# cross-checks for the randomly varying cont. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=NULL, n=15, J=3, K=60)
power.mod331(es=0.1248, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=NULL, n=15, J=3, K=60)
mrss.mod331(es=0.1248, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=NULL, n=15, J=3)
# cross-checks for the non-randomly varying cont. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=NULL, n=15, J=3, K=60)
power.mod331(es=.0946, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=NULL, n=15, J=3, K=60)
mrss.mod331(es=.0946, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=NULL, n=15, J=3)
# cross-checks for the randomly varying bin. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=.50, n=15, J=3, K=60)
power.mod331(es=.2082, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=.50, n=15, J=3, K=60)
mrss.mod331(es=.2082, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=.10, omegam3=.10,
q=.50, n=15, J=3)
# cross-checks for the non-randomly varying bin. L1 moderator effect
mdesd.mod331(power=.80, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=.50, n=15, J=3, K=60)
power.mod331(es=.1893, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=.50, n=15, J=3, K=60)
mrss.mod331(es=.1893, alpha=.05, two.tailed=TRUE,
rho2=.17, rho3=.06, omegam2=0, omegam3=0,
q=.50, n=15, J=3)
# cross-checks for the randomly varying bin. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=.05,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.2244, rho3=.1, rho2=.1, omegam3=.05,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.2244, rho3=.1, rho2=.1, omegam3=.05,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4)
# cross-checks for the randomly varying cont. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=.05,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.1209, rho3=.1, rho2=.1, omegam3=.05,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.1209, rho3=.1, rho2=.1, omegam3=.05,
r21=.30, r22=.4, r2m3=0, n=20, J=4)
# cross-checks for the non-randomly varying bin. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=0,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.2157, rho3=.1, rho2=.1, omegam3=0,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.2157, rho3=.1, rho2=.1, omegam3=0,
q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# cross-checks for the non-randomly varying cont. L2 moderator effect
mdesd.mod332(rho3=.1, rho2=.1, omegam3=0,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod332(es=.1079, rho3=.1, rho2=.1, omegam3=0,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mrss.mod332(es=.1079, rho3=.1, rho2=.1, omegam3=0,
r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# cross-checks for the randomly varying bin. L3 moderator effect
mdesd.mod333(rho3=.1, rho2=.1, q=.5,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
power.mod333(es=.4128, rho3=.1, rho2=.1, q=.5,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
mrss.mod333(es=.4128, rho3=.1, rho2=.1, q=.5,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
# cross-checks for the randomly varying cont. L3 moderator effect
mdesd.mod333(rho3=.1, rho2=.1,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
power.mod333(es=.2064, rho3=.1, rho2=.1,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
mrss.mod333(es=.2064, rho3=.1, rho2=.1,
r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
# 3-3-1 mediation
power.med331(esa= .50, esB = .30, rho2 = .15, rho3 = .15,
r21 = .20, r22 = .20, g3 = 4,
n = 20, J = 4, K = 80, p = .5)
# 3-2-1 mediation
power.med321(esa= .51, esB = .30, rhom3 = 0.27, rho2 = .15, rho3 = .19,
r2m2 = .07, gm3 = 4, r2m3 = .16,
r21 = .02, r22 = .41, g3 = 5, r23 = .38,
p = .50, n = 20, J = 4, K = 60)
# 3-1-1 mediation
power.med311(esa= .49 , esB = .30,
rhom2 = .05, rhom3 = .26, rho2 = .15, rho3 = .20,
r2m1 = .10, r2m2 = .07, r2m3 = .17,
r21 = .02, r22 = .41, r23 = .38,
p = .50, n = 20, J = 4, K = 30)
# cross-checks for cluster-randomized block design
# treatment at level 3, with fixed effects across level 4 blocks
mdes.bcra4f3(rho3=.15, rho2=.15,
n=10, J=4, K=23, L=15)
power.bcra4f3(es=0.137, rho3=.15, rho2=.15,
n=10, J=4, K=33, L=15)
mrss.bcra4f3(es=0.137, rho3=.15, rho2=.15,
n=10, J=4, L=15)
|
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