A simulated data example from Aguinis and Culpepper (in press) 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.

1 |

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.

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

Steven Andrew Culpepper, Herman Aguinis

Maintainer: Steven Andrew Culpepper <sculpepp@illinois.edu>

Aguinis, H., & Culpepper, S.A. (in press). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. *Organizational Research Methods*. Available at: http://mypage.iu.edu/~haguinis/pubs.html

`lmer`

, `model.matrix`

, `VarCorr`

, `LRTSim`

, `Hofmann`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## Not run:
data(simICCdata)
require(lme4)
#computing icca
vy = var(simICCdata$Y)
lmm0 <- lmer(Y ~ (1|l2id),data=simICCdata,REML=F)
VarCorr(lmm0)$l2id[1,1]/vy
#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=F)
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
## End(Not run)
``` |

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