rgr.ols: Random Group Resampling OLS Regression

In multilevel: Multilevel Functions

Random Group Resampling OLS Regression

Description

Uses Random Group Resampling (RGR) within an Ordinary Least Square (OLS) framework to contrast actual group results with pseudo group results. This specific function performs an RGR on an OLS hierarchical OLS model with two predictors as in Bliese & Halverson (2002). To run this analysis on data with more predictors, the function would have to be modified.

Usage

rgr.ols(xdat1,xdat2,ydata,grpid,nreps)

Arguments

xdat1	The first predictor.
xdat2	The second predictor.
ydata	The outcome.
grpid	The group identifier.
nreps	The number of pseudo groups to create.

Value

A matrix containing mean squares. Each row provides mean square values for a single pseudo group iteration

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.

See Also

mix.data

Examples

data(lq2002)
RGROUT<-rgr.ols(lq2002$LEAD,lq2002$TSIG,lq2002$HOSTILE,lq2002$COMPID,100)

#Compare values to those reported on p.62 in Bliese & Halverson (2002)
summary(RGROUT)  

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