multilevel (version 2.7)

mult.icc: Multiple ICCs from a dataset

Description

Estimates ICC(1) and ICC(2) values for each column given a data frame and a group identifier. Uses a mixed-effects estimate of the ICC, so ICC values cannot be negative. In cases where ICC values are assumed to be zero or negative, the ANOVA-based formulas should be used (see the ICC1 and ICC2 functions). The mult.icc function only works with one level of nesting.

Usage

mult.icc(x, grpid)

Arguments

x

A data frame containing the variables of interest in each column.

grpid

A vector identifying the groups from which the variables originated.

Value

Variable

The variable name.

ICC1

Intraclass correlation coefficient 1.

ICC2

Group mean reliability or intraclass correlation coefficient 2.

References

Bartko, J.J. (1976). On various intraclass correlation reliability coefficients. Psychological Bulletin, 83, 762-765.

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.

Bliese, P. D., Maltarich, M. A., Hendricks, J. L., Hofmann, D. A., & Adler, A. B. (2019). Improving the measurement of group-level constructs by optimizing between-group differentiation. Journal of Applied Psychology, 104, 293-302.

See Also

ICC2 ICC1 sim.icc

Examples

# NOT RUN {
library(nlme)
data(bh1996)
mult.icc(bh1996[,c("HRS","LEAD","COHES")],grpid=bh1996$GRP)

# }