iccbeta (version 1.0)

Hofmann: A multilevel dataset from Hofmann, Griffin, and Gavin (2000).

Description

A multilevel dataset from Hofmann, Griffin, and Gavin (2000).

Usage

data(Hofmann)

Format

A data frame with 1,000 observations and 7 variables.

id

a numeric vector of group ids.

helping

a numeric vector of the helping outcome variable construct.

mood

a level 1 mood predictor.

mood_grp_mn

a level 2 variable of the group mean of mood.

cohesion

a level 2 covariate measuring cohesion.

mood_grp_cent

group-mean centered mood predictor.

mood_grd_cent

grand-mean centered mood predictor.

Source

Hofmann, D.A., Griffin, M.A., & Gavin, M.B. (2000). The application of hierarchical linear modeling to management research. In K.J. Klein, & S.W.J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 467-511). Hoboken, NJ: Jossey-Bass.

References

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

See Also

lmer, model.matrix, VarCorr, LRTSim, simICCdata

Examples

## Not run: 
# data(Hofmann)
#   require(lme4)
# 
#   #Random-Intercepts Model
#   lmmHofmann0 = lmer(helping ~ (1|id),data=Hofmann)
#   vy_Hofmann = var(Hofmann[,'helping'])
#   #computing icca
#   VarCorr(lmmHofmann0)$id[1,1]/vy_Hofmann
# 
#   #Estimating Group-Mean Centered Random Slopes Model, no level 2 variables
#   lmmHofmann1  <- lmer(helping ~ mood_grp_cent + (mood_grp_cent |id),data=Hofmann,REML=F)
#   X_Hofmann = model.matrix(lmmHofmann1)
#   P = ncol(X_Hofmann)
#   T1_Hofmann  = VarCorr(lmmHofmann1)$id[1:P,1:P]
#   #computing iccb
#   icc_beta(X_Hofmann,Hofmann[,'id'],T1_Hofmann,vy_Hofmann)$rho_beta
#   
#   #Performing LR test
#   #Need to install 'RLRsim' package
#   library('RLRsim')
#   lmmHofmann1a  <- lmer(helping ~ mood_grp_cent + (1 |id),data=Hofmann,REML=F)
#   obs.LRT <- 2*(logLik(lmmHofmann1)-logLik(lmmHofmann1a))[1]
#   X <- getME(lmmHofmann1,"X")
#   Z <- t(as.matrix(getME(lmmHofmann1,"Zt")))
#   sim.LRT <- LRTSim(X, Z, 0, diag(ncol(Z)))
#   (pval <- mean(sim.LRT > obs.LRT))
# ## End(Not run)