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

In multilevel: Multilevel Functions

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.

Author(s)

Paul Bliese [e-mail]

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))

## End(Not run)

multilevel documentation built on March 18, 2022, 5:47 p.m.