Simulated data example from Aguinis and Culpepper (2015).

A simulated data example from Aguinis and Culpepper (2015) to demonstrate the icc_beta function for computing the proportion of variance in the outcome variable that is attributed to heterogeneity in slopes due to higher-order processes/units.

Usage

simICCdata

Format

A data frame with 900 observations (i.e., 30 observations nested within 30 groups) on the following 6 variables.

l1id: A within group ID variable.
l2id: A group ID variable.
one: A column of 1's for the intercept.
X1: A simulated level 1 predictor.
X2: A simulated level 1 predictor.
Y: A simulated outcome variable.

Source

Aguinis, H., & Culpepper, S.A. (2015). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods. Available at: http://www.hermanaguinis.com/pubs.html

Details

See Aguinis and Culpepper (2015) for the model used to simulate the dataset.

Examples

if (FALSE) { # \dontrun{
data(simICCdata)
if(requireNamespace("lme4")){ 
library("lme4")

# computing icca
vy <- var(simICCdata$Y)
lmm0 <- lmer(Y ~ (1|l2id), data = simICCdata, REML = FALSE)
VarCorr(lmm0)$l2id[1,1]/vy

# Create simICCdata2
grp_means = aggregate(simICCdata[c('X1','X2')], simICCdata['l2id'],mean)
colnames(grp_means)[2:3] = c('m_X1','m_X2')
simICCdata2 = merge(simICCdata, grp_means, by='l2id')

# Estimating random slopes model
lmm1  <- lmer(Y ~ I(X1-m_X1) + I(X2-m_X2) + (I(X1-m_X1) + I(X2-m_X2) | l2id),
              data = simICCdata2, REML = FALSE)
X <- model.matrix(lmm1)
p <- ncol(X)
T1 <- VarCorr(lmm1) $l2id[1:p,1:p]
# computing iccb
# Notice '+1' because icc_beta assumes l2ids are from 1 to 30.
icc_beta(X, simICCdata2$l2id+1, T1, vy)$rho_beta
} else {
 stop("Please install `lme4` to run the above example.")
}
} # }