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

Hofmann {iccbeta} | R Documentation

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

### Description

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

### Usage

    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. (2015). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods. Available at: http://hermanaguinis.com/pubs.html

### See Also

`lmer`, `model.matrix`, `VarCorr`, `LRTSim`, `simICCdata`

### Examples

    ## Not run:

    if(requireNamespace("lme4") && requireNamespace("RLRsim")){
    data(Hofmann)
    library("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 = FALSE)
    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 = FALSE)
    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))
    } else {
     stop("Please install packages `RLRsim` and `lme4` to run the above example.")
    }

    ## End(Not run)

[Package iccbeta version 1.2.0 Index]