rgr.agree: Random Group Resampling for Within-group Agreement

In multilevel: Multilevel Functions

Random Group Resampling for Within-group Agreement

Description

Uses random group resampling (RGR) to estimate within group agreement. RGR agreement compares within group variances from actual groups to within group variances from pseudo groups. Evidence of significant agreement is inferred when variances from the actual groups are significantly smaller than variances from pseudo groups. RGR agreement methods are rarely reported, but provide another way to consider group level properties in data.

Usage

rgr.agree(x, grpid, nrangrps)

Arguments

x	A vector upon which to estimate agreement.
grpid	A vector identifying the groups from which x originated (actual group membership).
nrangrps	A number representing the number of random groups to generate. Note that the number of random groups created must be directly divisible by the number of actual groups to ensure that group sizes of pseudo groups and actual groups are identical. The rgr.agree routine will generate the number of pseudo groups that most closely approximates nrangrps given the group size characteristics of the data.

Value

An object of class 'rgr.agree' with the following components:

NRanGrp	The number of random groups created.
AvRGRVar	The average within-group variance of the random groups.
SDRGRVar	Standard deviation of random group variances used in the z-score estimate.
zvalue	Z-score difference between the actual group and random group variances.
RGRVARS	The random group variances.

Author(s)

Paul Bliese [e-mail]

References

Bliese, P. D., & Halverson, R. R. (2002). Using random group resampling in multilevel research. Leadership Quarterly, 13, 53-68.

Bliese, P.D., Halverson, R. R., & Rothberg, J. (2000). Using random group resampling (RGR) to estimate within-group agreement with examples using the statistical language R. Walter Reed Army Institute of Research.

Ludtke, O. & Robitzsch, A. (2009). Assessing within-group agreement: A critical examination of a random-group resampling approach. Organizational Research Methods, 12, 461-487.

See Also

rwg rwg.j

Examples

data(bh1996)
RGROUT<-rgr.agree(bh1996$HRS,bh1996$GRP,1000)
summary(RGROUT)

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