rgr.waba: Random Group Resampling of Covariance Theorem Decomposition

In multilevel: Multilevel Functions

Random Group Resampling of Covariance Theorem Decomposition

Description

Performs the covariance theorem decomposition of a raw correlation in situations where lower-level entities (individuals) are nested in higher-level groups (see Dansereau, Alutto & Yammarino, 1984; Robinson, 1950). Builds upon previous work by incorporating Random Group Resampling or RGR. RGR is used to randomly assign individuals to pseudo groups and create a sampling distributions of the covariance theorem components. The sampling distribution provides a way to contrast actual group covariance components to pseudo group covariance components.

Note that rgr.waba is computationally intensive.

Usage

rgr.waba(x, y, grpid, nrep)

Arguments

x	A vector representing one variable for the correlation.
y	A vector representing the other variable for the correlation.
grpid	A vector identifying the groups from which X and Y originated.
nrep	The number of times that the entire data set is reassigned to pseudo groups

Value

Returns an object of class rgr.waba. The object is a list containing each random run for each component of the covariance theorem.

Author(s)

Paul Bliese [e-mail]

References

Bliese, P. D. & Halverson, R. R. (1996). Individual and nomothetic models of job stress: An examination of work hours, cohesion, and well- being. Journal of Applied Social Psychology, 26, 1171-1189.

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

Dansereau, F., Alutto, J. A., & Yammarino, F. J. (1984). Theory testing in organizational behavior: The varient approach. Englewood Cliffs, NJ: Prentice-Hall.

Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351-357.

See Also

waba

Examples

# This example is from Bliese & Halverson (1996). Notice that all of the
# values from the RGR analysis differ from the values based on actual
# group membership. Confidence intervals for individual components can
# be estimated using the quantile command. In practice, the nrep option
# should be more than 100

data(bh1996)

#estimate the actual group model
waba(bh1996$HRS,bh1996$WBEING,bh1996$GRP)

#create 100 pseudo group runs and summarize the model        
RWABA<-rgr.waba(bh1996$HRS,bh1996$WBEING,bh1996$GRP,nrep=100)  
summary(RWABA)

#Estimate 95th percentile confidence intervals (p=.05)                     
quantile(RWABA,c(.025,.975))       

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