multilevel (version 2.7)

boot.icc: Bootstrap ICC values in 2-level data

Description

An experimental function that implements a 2-level bootstrap to estimate non-parametric bootstrap confidence intervals of the ICC1 using the percentile method. The bootstrap first draws a sample of level-2 units with replacement, and in a second stage draws a sample of level-1 observations with replacement from the level-2 units. Following each bootstrap replication, the ICC(1) is estimated using the lme function (default) or the ANOVA method.

Usage

boot.icc(x, grpid, nboot, aov.est=FALSE)

Arguments

x

A vector representing the variable upon which to estimate the ICC values.

grpid

A vector representing the level-2 unit identifier.

nboot

The number of bootstrap iterations. Computational demands underlying a 2-level bootstrap are heavy, so the examples use 100; however, the number of interations should generally be 10,000.

aov.est

An option to estimate the ICC values using aov.

Value

Provides ICC(1) estimates for each bootstrap draw.

References

Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations (pp. 349-381). San Francisco, CA: Jossey-Bass, Inc.

See Also

ICC1 ICC2 sim.icc sim.mlcor

Examples

# NOT RUN {
data(bh1996)
ICC.OUT<-boot.icc(bh1996$WBEING,bh1996$GRP,100)
quantile(ICC.OUT,c(.025,.975))
# }