multilevel: rwg.sim – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

rwg.sim

Simulate rwg values from a random null distribution

Description

This function is based on the work of Dunlap, Burke & Smith-Crowe (2003). The function draws data from a random uniform null distribution, and calculates the within group agreement measure rwg for single item measures as described in James, Demaree & Wolf (1984). By repeatedly drawing random samples, a distribution of the rwg is generated. The sampling distribution can be used to calculate confidence intervals for different combinations of group sizes and number of response options (A).

Usage

rwg.sim(gsize, nresp, nrep)

Arguments

`gsize`	Group size upon which to base the rwg simulation.
`nresp`	The number of response options (e.g., there would be 5 response options if using Strongly Disagree, Disagree, Neither, Agree, Strongly Agree).
`nrep`	The number of rwg values to simulate. This will generally be 10,000 or more, although the examples use nrep of 1000 to make the calculations fast.

Value

`rwg`	rwg value from each simulation.
`gsize`	Group size used in the rwg simulation.
`nresp`	Simulated number of response options.
`nitems`	Will always be 1 for an rwg estimate.
`rwg.95`	Estimated 95 percent confidence interval. Values greater than or equal to rwg.95 are considered significant, p<.05.

Author(s)

Paul Bliese paul.bliese@moore.sc.edu

References

Cohen, A., Doveh, E., & Eick, U. (2001). Statistical properties of the rwg(j) index of agreement. Psychological Methods, 6, 297-310.

Dunlap, W. P., Burke, M. J., & Smith-Crowe, K. (2003). Accurate tests of statistical significance for rwg and average deviation interrater agreement indices. Journal of Applied Psychology, 88, 356-362.

James, L.R., Demaree, R.G., & Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85-98.

Examples

#An example from Dunlap et al. (2003).  The estimate from Dunlap
#et al. Table 2 is 0.53 (p=.05)
RWG.OUT<-rwg.sim(gsize=10,nresp=5,nrep=1000)
summary(RWG.OUT)
quantile(RWG.OUT, c(.95,.99))

multilevel

Multilevel Functions

v2.6

GPL (>= 2)

Authors

Paul Bliese

Initial release

2016-07-26