Package 'multilevel'

Description

Calculates the average deviation of the mean or median as a measure of within-group agreement as proposed by Burke, Finkelstein and Dusig (1999). A basic rule for interpreting whether or not the results display practically significant levels of agreement is whether the AD value is smaller than A/6 where A represents the number of response options. For instance, A would be 5 on a five-point response option format of strongly disagree, disagree, neither, agree, strongly agree (see Dunlap, Burke & Smith-Crowe, 2003). To estimate statistical significance see the ad.m.sim function and help files.

Usage

ad.m(x, grpid, type="mean")

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

Burke, M. J., Finkelstein, L. M., & Dusig, M. S. (1999). On average deviation indices for estimating interrater agreement. Organizational Research Methods, 2, 49-68.

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.

See Also

ad.m.sim awg rwg rwg.j rgr.agree rwg.sim rwg.j.sim

Examples

data(bhr2000)

#Examples for multiple item scales
AD.VAL<-ad.m(bhr2000[,2:12],bhr2000$GRP)
AD.VAL[1:5,]
summary(AD.VAL)
summary(ad.m(bhr2000[,2:12],bhr2000$GRP,type="median"))

#Example for single item measure
summary(ad.m(bhr2000$HRS,bhr2000$GRP))

Description

Uses procedures detailed in Dunlap, Burke, and Smith-Crowe (2003) and Cohen, Doveh, and Nahum-Shani (2009) to estimate the significance of the average deviation of the mean or median (AD.M). Dunlap et al. proposed a strategy to use Monte Carlo techniques to estimate the significane of single item AD.M measures. Cohen et al., (2009) expanded these ideas to cover multiple item scales, ADM(J), and account for correlations among items. The ad.m.sim function is flexible and covers single item or multiple item measures. In the case of multiple item measures, correlations among items can be included (preferred method) or excluded. In the Monte Carlo simulations conducted by both Dunlap et al. (2003) and Cohen et al., (2009), 100,000 repetitions were used. In practice, it will require considerable time to perform 100,000 repetitions and 10,000 should suffice. The examples use 500 repetitions simply to speed up the process.

Usage

ad.m.sim(gsize, nitems=1, nresp, itemcors=NULL, type="mean",nrep)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

Cohen, A., Doveh, E., & Nahum-Shani, I. (2009). Testing agreement for multi-item scales with the indices rwg(j) and adm(j). Organizational Research Methods, 12, 148-164.

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.

See Also

ad.m rgr.agree rwg.sim rwg.j.sim

Examples

#Example from Dunlap et al. (2003), Table 3.  The listed significance
#value (p=.05) for a group of size 5 with a 7-item response format is
#0.64 or less. Use more than 500 iterations in practice.
 
SIMOUT<-ad.m.sim(gsize=5, nitems=1, nresp=7, itemcors=NULL,
        type="mean", nrep=500) 
summary(SIMOUT)

#Example with a multiple item scale basing item correlations on observed
#correlations among 11 leadership items in the lq2002 data set. Estimate
#in Cohen et al., (2009) is 0.99

data(lq2002)
SIMOUT<-ad.m.sim(gsize=10, nresp=5, itemcors=cor(lq2002[,3:13]), 
        type="mean", nrep=500) 
summary(SIMOUT)
quantile(SIMOUT,c(.05,.10))

Description

Calculates the awg index proposed by Brown and Hauenstein (2005). The awg agreement index can be applied to either a single item vector or a multiple item matrix representing a scale. The awg is an analogue to Cohen's kappa. Brown and Hauenstein (pages 177-178) recommend interpreting the awg similarly to how the rwg (James et al., 1984) is commonly interpreted with values of .70 indicating acceptable agreement; values between .60 and .69 as reasonable agreement, and values less than .60 as unacceptable levels of agreement.

Usage

awg(x, grpid, range=c(1,5))

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

Brown, R. D. & Hauenstein, N. M. A. (2005). Interrater Agreement Reconsidered: An Alternative to the rwg Indices. Organizational Research Methods, 8, 165-184.

Wagner, S. M., Rau, C., & Lindemann, E. (2010). Multiple informant methodology: A critical review and recommendations. Sociological Methods and Research, 38, 582-618.

See Also

rwg rwg.j ad.m

Examples

data(lq2002)

#Examples for multiple item scales
awg.out<-awg(lq2002[,3:13],lq2002$COMPID,range=c(1,5))
summary(awg.out)

#Example for single item measure
awg.out<-awg(lq2002$LEAD05,lq2002$COMPID,range=c(1,5))
summary(awg.out)

Description

The complete data used in Bliese and Halverson (1996). The dataset contains 4 variables: Cohesion (COHES), Leadership Climate (LEAD), Well-Being (WBEING) and Work Hours (HRS). Each of these variables has two variants - a group mean version that replicates each group mean for every individual in the group, and a within-group version where the group mean is subtracted from each individual response (i.e., a group-mean centered or demeaned variable). The group mean version is designated with a G. (e.g., G.HRS), and the within-group version is designated with a W. (e.g., W.HRS).

Usage

data(bh1996)

Format

A data frame with 13 columns and 7,382 observations from 99 groups

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.

Description

The complete data used in Bliese, Halverson and Rothberg (2000). Contains 14 variables referencing individual ratings of US Army Company leadership, work hours, and the degree to which individuals find comfort from religion. The leadership and workhours variables are subsets of the Bliese and Halveson (1996) data (bh1996); however, in the case of leadership, the data set contains the 11 scale items whereas the bh1996 data set contains only the scale score. Most items are on a strongly disagree to strongly agree scale. The RELIG item is on a never to always scale.

Usage

data(bhr2000)

Format

A data frame with 14 columns and 5,400 observations from 99 groups

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., & Rothberg, J. (2000). Using random group resampling (RGR) to estimate within-group agreement with examples using the statistical language R.

Description

An experimental function that implements a 2-level bootstrap to estimate non-parametric bootstrap confidence intervals of the ICC1 using the percentile method. The bootstrap first draws a sample of level-2 units with replacement, and in a second stage draws a sample of level-1 observations with replacement from the level-2 units. Following each bootstrap replication, the ICC(1) is estimated using the lme function (default) or the ANOVA method.

Usage

boot.icc(x, grpid, nboot, aov.est=FALSE)

Arguments

Value

Provides ICC(1) estimates for each bootstrap draw.

Author(s)

Paul Bliese [email protected]

References

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.

See Also

ICC1 ICC2 sim.icc sim.mlcor

Examples

## Not run: 
data(bh1996)
ICC.OUT<-boot.icc(bh1996$WBEING,bh1996$GRP,100)
quantile(ICC.OUT,c(.025,.975))

## End(Not run)

Description

Contains the complete data used in Chen (2005). Chen (2005) examined newcomer adaptation in 65 project teams. The level of analysis was the team. In the study, team leaders assessed the initial team performance (TMPRF) at time 1 and then assessed newcomer performance over three additional time points (NCPRF.T1, NCPRF.T2, NCPRF.T3). Initial team expectations (TMEXP) and initial newcomer empowerment (NCEMP) were also assessed and modeled, but were not analyzed as repeated measures. To specify Table 2 model in Chen (2005), these data need to be converted to univariate or stacked form (see the make.univ function). Using the default values of make.univ and creating a dataframe called chen2005.univ, the specific lme model for Table 2 in Chen (2005) is:

lme(MULTDV~NCEMP*TIME+TMEXP*TIME+TMPRF*TIME,random=~TIME|ID,chen2005.univ)

Usage

data(chen2005)

Format

A data frame with 7 columns and 65 team-level observations

References

Chen, G.(2005). Newcomer adaptation in teams: Multilevel antecedents and outcomes. Academy of Management Journal, 48, 101-116.

Description

Contains five cohesion measures provided by 11 individuals. The individuals providing the measures are members of four platoons further nested within two larger units. This data file is used for demonstative purposes in the document "Multilevel Modeling in R" that accompanies this package.

Usage

data(cohesion)

Format

A data frame with 7 columns and 11 observations

Description

Tests for statistical differences between two independent correlations using the formula provided on page 54 of Cohen & Cohen (1983). The function returns a z-score estimate.

Usage

cordif(rvalue1,rvalue2,n1,n2)

Arguments

Value

Produces a single value, the z-score for the differences between the correlations.

Author(s)

Paul Bliese [email protected]

References

Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

See Also

rtoz cordif.dep

Examples

cordif(rvalue1=.51,rvalue2=.71,n1=123,n2=305)

Description

Tests for statistical differences between two dependent correlations using the formula provided on page 56 of Cohen & Cohen (1983). The function returns a t-value, the DF, and the p-value.

Usage

cordif.dep(r.x1y,r.x2y,r.x1x2,n)

Arguments

Value

Returns three values. A t-value, DF and p-value.

Author(s)

Paul Bliese [email protected]

References

Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.). Hillsdale, nJ: Lawrence Erlbaum Associates.

See Also

cordif

Examples

cordif.dep(r.x1y=.30,r.x2y=.60,r.x1x2=.10,n=305)

Description

Calculates Cronbach's alpha estimate of reliability for a multi-item scale.

Usage

cronbach(items)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

Cronbach L. J. (1951) Coefficient Alpha and the internal structure of tests. Psychometrika, 16,297-334

See Also

cronbach

Examples

data(bhr2000)
   cronbach(bhr2000[,2:11])

Description

Creates time-varying covariates for estimating a discontinuous growth model (DGM). Creating time-varying covariates requires only simple recoding of a time vector when data are balanced and the discontinuity event occurs at the same time for each group. When data are not balanced and one or more events occurs at different times for each group, coding the time-varying covariates is complex. For instance, if the event of interest was employee turnover in a store in a given month, it is likely that stores would differ on how many months of data were available and on the specific months when turnover occurred. With these irregularly-timed events and different time windows for each store, it would be challenging to create time-varying covariates for the DGM specific to each store's circumstances.

Usage

dgm.code(grp,time,event,n.events=FALSE,first.obs=FALSE)

Arguments

Value

Produces a data.frame with the columns

[, c("grp","time","event","trans1","post1","time.a","tot.events","event.first")]

If numerous events are coded, the data.frame will contain more trans and post values (trans2, post2, etc.) corresponding to the maximum number of events experienced by a group unless n.events is set to limit the number of events. The data.frame must be merged back to the original data.frame for subsequent analyses. See examples.

Author(s)

Paul Bliese [email protected]

References

Bliese, P. D., & Lang, J. W. B. (2016). Understanding relative and absolute change in discontinuous growth models: Coding alternatives and implications for hypothesis testing. Organizational Research Methods, 19, 562-592.

Bliese, P. D., Kautz, J., & Lang, J. W. (2020). Discontinuous growth models: Illustrations, recommendations, and an R function for generating the design matrix. In Y. Griep & S. D. Hansen (Eds.), Handbook on the Temporal Dynamics of Organizational Behavior (pp. 319-350). Northampton, MA: Edward Elgar Publishers, Inc.

Examples

##########
# Example 1: Coding trans, post and time.a in data where
# every event occurs at the same time for each person
##########

# Read data from library
data(tankdat)

# Add a marker (1 or 0) indicating an event
tankdat$taskchange<-0
tankdat$taskchange[tankdat$TIME==6]<-1

# Run function with defaults
OUT<-with(tankdat,dgm.code(ID,TIME,taskchange))
names(OUT)
names(tankdat)

# Merge original data and dgm codes and reorder
tankdat.dgm<-merge(tankdat,OUT,by.x=c("ID","TIME"),by.y=c("grp","time"))
tankdat.dgm<-tankdat.dgm[order(tankdat.dgm$ID,tankdat.dgm$TIME),]

# Examine data
tankdat.dgm[1:12,]

##########
# Example 2: Coding trans, post and time.a in data where every transition
# event occurs at the different times for each person
##########
# Read data from library
data(tankdat)

# Add a marker (1 or 0) indicating an event at random
set.seed(343227)
tankdat$taskchange<-rbinom(nrow(tankdat),1,prob=.1)
tankdat[1:24,] #ID 1 had one event at TIME 10. ID 2 had 3 events

# Run function with defaults
## Not run: 
OUT<-with(tankdat,dgm.code(ID,TIME,taskchange))
## End(Not run) 
# returns an error showing the 24 groups that started with an event.
# Either drop these groups or change the first.obs option to TRUE
# which changes these first events to 0 (non-events)

OUT<-with(tankdat,dgm.code(ID,TIME,taskchange,first.obs=TRUE))
OUT[1:24,]
OUT[OUT$grp==9,]
#Notice the event.first value of 1 for group 9 indicating that the
#first value was present and recoded.


# In the default setting, one ID had 4 events. It may be preferable 
# to restrict the number of events to 3 or more and code accordingly

OUT<-with(tankdat,dgm.code(ID,TIME,taskchange,n.events=3,first.obs=TRUE))
OUT[1:24,]

Description

Calculates the group-mean reliability from a linear mixed effects (lme) model. If group sizes are identical, the group-mean reliabilty estimate equals the ICC(2) estimate from an ANOVA model. When group sizes differ, however, a group-mean reliability estimate is calculated for each group based on the Spearman-Brown formula, the overall ICC, and group size for each group.

Usage

gmeanrel(object)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

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.

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

See Also

ICC1 ICC2 lme

Examples

data(bh1996)
library(nlme)
tmod<-lme(WBEING~1,random=~1|GRP,data=bh1996)
gmeanrel(tmod)

Description

Uses random group resampling (RGR) to create a distribution of pseudo group means. Pseudo group means are then contrasted with actual group means to provide a visualization of the group-level properties of the data. It is, in essense, a way of visualizing the ICC1 or an F-Value from an ANOVA model.

Usage

graph.ran.mean(x, grpid, nreps, limits, graph=TRUE, bootci=FALSE)

Arguments

Value

Produces either a plot (graph=TRUE) or a data.frame (graph=FALSE)

Author(s)

Paul Bliese [email protected]

References

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

See Also

ICC1 mix.data

Examples

data(bh1996)

# with the bootci=TRUE option, nreps should be 1000 or more.  The value
# of 25 is used in the example to reduce computation time

with(bh1996,graph.ran.mean(HRS,GRP,limits=c(8,16),nreps=25, bootci=TRUE))

GRAPH.DAT<-graph.ran.mean(bh1996$HRS,bh1996$GRP,limits=c(8,16),nreps=25,
           graph=FALSE)

Description

Calculates the Intraclass Correlation Coefficient 1 or ICC(1) from an ANOVA model. This value is equivalent to the ICC discussed in the random coefficient modeling literature, and represents the amount of individual-level variance that can be "explained" by group membership.

Usage

ICC1(object)

Arguments

Value

Provides an estimate of ICC(1) for the sample.

Author(s)

Paul Bliese [email protected]

References

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.

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

See Also

ICC2 aov sim.icc sim.mlcor

Examples

data(bh1996)
   hrs.mod<-aov(HRS~as.factor(GRP),data=bh1996)
   ICC1(hrs.mod)

Description

Calculates the Intraclass Correlation Coefficient 2 or ICC(2) from an ANOVA model. This value represents the reliability of the group means.

Usage

ICC2(object)

Arguments

Value

Provides an estimate of ICC(2) for the sample.

Author(s)

Paul Bliese [email protected]

References

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.

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

See Also

ICC1 aov sim.icc sim.mlcor

Examples

data(bh1996)
   hrs.mod<-aov(HRS~as.factor(GRP),data=bh1996)
   ICC2(hrs.mod)

Description

Calculates item-total correlations in multi-item scales.

Usage

item.total(items)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

Cronbach L. J. (1951) Coefficient Alpha and the internal structure of tests. Psychometrika, 16,297-334

See Also

cronbach

Examples

data(bhr2000)
   item.total(bhr2000[,2:11])

Description

Contains the complete data used in Klein et al. (2000). The Klein et al. chapter uses this simulated data set to compare and contrast WABA, HLM, and Cross-Level Operator Analyses (CLOP). The simulated data set was created by Paul Bliese.

Usage

data(klein2000)

Format

A data frame with 9 columns and 750 observations from 50 groups

References

Klein, K. J., Bliese, P.D., Kozlowski, S. W. J, Dansereau, F., Gavin, M. B., Griffin, M. A., Hofmann, D. A., James, L. R., Yammarino, F. J., & Bligh, M. C. (2000). Multilevel analytical techniques: Commonalities, differences, and continuing questions. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations (pp. 512-553). San Francisco, CA: Jossey-Bass, Inc

Description

Contains the complete data used in a special issue of Leadership Quarterly edited by Paul Bliese, Ronald Halverson and Chet Schriesheim in 2002 (Vol 13). Researchers from several universities analyzed this common dataset using various multilevel techniques. The three scales used in the analyses are Leadership Climate (LEAD), Task Significance (TSIG) and Hostility (HOSTILE). The data set contains each item making up these scales. These items were additionally used by Cohen, Doveh and Nahum-Shani (2009).

Usage

data(lq2002)

Format

A data frame with 27 columns and 2,042 observations from 49 groups

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., & Schriesheim, C. A. (2002). Benchmarking multilevel methods: Comparing HLM, WABA, SEM, and RGR. Leadership Quarterly, 13, 3-14.

Cohen, A., Doveh, E., & Nahum-Shani, I. (2009). Testing agreement for multi-item scales with the indices rwg(j) and adm(j). Organizational Research Methods, 12, 148-164.

Description

Longitudinal data is often stored in multivariate or wide form. In multivariate form, each row contains data from one subject, and repeated measures variables are indexed by different names (e.g., OUTCOME.T1, OUTCOME.T2, OUTCOME.T3). In repeated measures designs and growth modeling, data needs to be converted to univariate or stacked form where each row represents one of the repeated measures indexed by a TIME variable nested within subject. In univariate form, each subject has as many rows of data as there are time points. R has several functions to convert data from wide to long formats and vice versa including reshape. The code used in make.univ borrows heavily from code provided in Chambers and Hastie (1991).

Usage

make.univ(x,dvs,tname="TIME", outname="MULTDV")

Arguments

Value

Returns a dataframe in univariate (i.e., stacked) form with a TIME variable representing the repeated observations, and a variable named MULTDV representing the time-indexed variable. The TIME variable begins with 0.

Author(s)

Paul Bliese [email protected]

References

Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing and illustrations. Organizational Research Methods, 5, 362-387.

Chambers, J. M., & Hastie, T. J. (1991). Statistical models in S. CRC Press, Inc..

See Also

mult.make.univ reshape

Examples

data(univbct)  #a dataframe in univariate form for job satisfaction
TEMP<-univbct[3*1:495,c(22,1:17)]  #convert back to multivariate form

#Transform data to univariate form
TEMP2<-make.univ(x=TEMP,dvs=TEMP[,c(10,13,16)]) 

#Same as above, but renaming repeated variable
TEMP3<-make.univ(x=TEMP,dvs=TEMP[,c(10,13,16)],outname="JOBSAT")

Description

Called by graph.ran.mean (and potentially other functions) to randomly mix data and create new pseudo group ID variables. Pseudo group IDs match real group IDs in terms of size.

Usage

mix.data(x,grpid)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

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

See Also

graph.ran.mean

Examples

data(bh1996)
mix.data(x=bh1996[c(1:10,200:210,300:310),2:3],
         grpid=bh1996$GRP[c(1:10,200:210,300:310)])

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

Value

Author(s)

Paul Bliese [email protected]

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

library(nlme)
data(bh1996)
mult.icc(bh1996[,c("HRS","LEAD","COHES")],grpid=bh1996$GRP)

Description

Longitudinal data is often stored in multivariate or wide form. In multivariate form, each row contains data from one subject, and repeated measures variables are indexed by different names (e.g., OUTCOME.T1, OUTCOME.T2, OUTCOME.T3). In the case of repeated measures designs and growth modeling, it is necessary to convert the data to univariate or stacked form where each row represents one of the repeated measures indexed by a TIME variable and nested within subject. In univariate form, each subject has as many rows of data as there are time points. The make.univ function in the multilevel library will convert a single item to univariate form while the mult.make.univ function converts two or more variables to univariate form. The mult.make.univ function was developed by Patrick Downes at the University of Iowa, and was included in the multilevel library in January of 2013. R also has built-in function such as reshape to perform the same data manipulation.

Usage

mult.make.univ(x,dvlist,tname="TIME", outname="MULTDV")

Arguments

Value

Returns a dataframe in univariate (i.e., stacked) form with a TIME variable representing the repeated observations, and new variables representing the time-indexed variables (MULTDV1, MULTDV2, etc.). The TIME variable begins with 0.

Author(s)

Patrick Downes [email protected] Paul Bliese [email protected]

References

Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing and illustrations. Organizational Research Methods, 5, 362-387.

See Also

make.univ reshape

Examples

data(univbct) #a dataframe in univariate form for job sat
TEMP<-univbct[3*1:495,c(22,1:17)]  #convert back to multivariate form
names(TEMP) #use the column names to find the column numbers

#Create a list of DV's - each DV should have the same number of obs
dvlist <- list(c(10,13,16),c(11,14,17))
names(dvlist) <- c("JOBSAT","COMMIT") #names for univariate output

#Transform the data into univariate form with multiple level-1 variables
mldata <- mult.make.univ(x=TEMP,dvlist=dvlist)

Description

Contains a random sample of firm-level data from 196 firms for the years 2007 to 2011 based on data from COMPUSTAT and MSCI. The data are balanced in that each firm provided five years of data. The data contains a time variable and time varying covariates for the discontinuous growth model along with within-firm predictors related to the composition of boards of directors. The discontinuity was indexed to 2009 as the first year following the great recession. In addition to illustrating variants of growth models, the panel data is useful for contrasting econometric fixed-effect models and mixed-effect models (Bliese et al., 2020).

Usage

data(paneldat)

Format

A data frame with 14 columns and 960 observations

References

Bliese, P. D., Schepker, D. J., Essman, S. M., & Ployhart, R. E. (2020). Bridging methodological divides between macro- and microresearch: Endogeneity and methods for panel data. Journal of Management, 46, 70-99.

Description

Provides a concise quantile summary of objects created using the functions rwg.sim and rwg.j.sim. The simulation functions for rwg and rwg.j return a limited number of estimated values. Consequently, the normal quantile methods are biased. The quantile methods incorporated in this function produce unbiased estimates.

Usage

## S3 method for class 'agree.sim'
quantile(x,confint,...)

Arguments

Value

A dataframe with two columns. The first column contains the quantile value and the second contains the estimate based on the object.

Author(s)

Paul Bliese [email protected]

See Also

rwg.sim rwg.j.sim

Examples

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

Description

Provides a concise quantile summary of objects created using the function ad.m.sim. The simulation functions for the average deviation of the mean (or median) return a limited number of estimated values. Consequently, the normal quantile methods are biased. The quantile methods incorporated in this function produce unbiased estimates.

Usage

## S3 method for class 'disagree.sim'
quantile(x,confint,...)

Arguments

Value

A dataframe with two columns. The first column contains the quantile value and the second contains the estimate based on the object.

Author(s)

Paul Bliese [email protected]

See Also

ad.m.sim

Examples

#Example from Dunlap et al. (2003), Table 3.  The listed significance
#value (p=.05) for a group of size 5 with a 7-item response format is
#0.64 or less.

SIMOUT<-ad.m.sim(gsize=5, nitems=1, nresp=7, itemcors=NULL,
        type="mean", nrep=1000) 
quantile(SIMOUT, c(.05,.01))

Description

Provides a concise quantile summary of objects created using the function rgr.waba.

Usage

## S3 method for class 'rgr.waba'
quantile(x,confint,...)

Arguments

Value

A dataframe containing the confidence intervals for each parameter in the rgr.waba model.

Author(s)

Paul Bliese [email protected]

See Also

rgr.waba

Examples

data(bh1996)

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

#create 100 pseudo group runs and summarize        
RWABA<-rgr.waba(bh1996$HRS,bh1996$WBEING,bh1996$GRP,100)  
quantile(RWABA,confint=c(.025,.975))

Description

Called by rgr.agree (and potentially other functions). The ran.group function randomly mixes data and applies a function to the pseudo groups. Pseudo group IDs match real group IDs in terms of size.

Usage

ran.group(x,grpid,fun,...)

Arguments

Value

A vector containing the results of applying the function to each random group.

Author(s)

Paul Bliese [email protected]

References

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

See Also

rgr.agree

Examples

data(bh1996)
   ran.group(bh1996$HRS,bh1996$GRP,mean)

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

Value

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

Author(s)

Paul Bliese [email protected]

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)

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

Value

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

Author(s)

Paul Bliese [email protected]

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)

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

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 [email protected]

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))

Description

When large SPSS datasets are imported into R, non-numeric fields frequently have numerous blank spaces prior to the text. The blank spaces make it difficult to summarize non-numeric text. The function is applied to an entire dataframe and removes the blank spaces.

Usage

rmv.blanks(object)

Arguments

Value

Returns a new dataframe without preceeding

Author(s)

Paul Bliese [email protected]

See Also

read.spss

Examples

## Not run: library(foreign)
   mydata<-read.spss(file.choose(),to.data.frame=T,use.value.labels=F)
   mydata<-rmv.blanks(mydata)
## End(Not run)

Description

Transforms a correlation (r) to a z variate using the formula provided on page 53 of Cohen & Cohen (1983). The formula is z=.5*((log(1+r))-(log(1-r))) where r is the correlation.

Usage

rtoz(rvalue)

Arguments

Value

The z transformation.

Author(s)

Paul Bliese [email protected]

References

Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

See Also

cordif

Examples

rtoz(.84)

Description

Calculates the within group agreement measure rwg for single item measures as described in James, Demaree and Wolf (1984). The rwg is calculated as:

rwg = 1-(Observed Group Variance/Expected Random Variance)

James et al. (1984) recommend truncating the Observed Group Variance to the Expected Random Variance in cases where the Observed Group Variance was larger than the Expected Random Variance. This truncation results in an rwg value of 0 (no agreement) for groups with large variances.

Usage

rwg(x, grpid, ranvar=2)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

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.

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.

See Also

ad.m awg rwg.j rwg.sim rgr.agree rwg.j.lindell

Examples

data(lq2002)
RWGOUT<-rwg(lq2002$LEAD,lq2002$COMPID)
RWGOUT[1:10,]
summary(RWGOUT)

Description

Calculates the within group agreement measure rwg(j) for multiple item measures as described in James, Demaree and Wolf (1984). James et al. (1984) recommend truncating the Observed Group Variance to the Expected Random Variance in cases where the Observed Group Variance was larger than the Expected Random Variance. This truncation results in an rwg.j value of 0 (no agreement) for groups with large variances.

Usage

rwg.j(x, grpid, ranvar=2)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

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.

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.

See Also

ad.m awg rwg rgr.agree rwg.j.lindell rwg.j.sim

Examples

data(lq2002)
RWGOUT<-rwg.j(lq2002[,3:13],lq2002$COMPID)
RWGOUT[1:10,]
summary(RWGOUT)

Description

Calculates the Lindell et al r*wg(j) within-group agreement index for multiple item measures. The r*wg(j) is similar to the James, Demaree and Wolf (1984) rwg and rwg(j) indices. The r*wg(j) index is calculated by taking the average item variability as the Observed Group Variance, and using the average item variability in the numerator of the rwg formula (rwg=1-(Observed Group Variance/ Expected Random Variance)). In practice, this means that the r*wg(j) does not increase as the number of items in the scale increases as does the rwg(j). Additionally, the r*wg(j) allows Observed Group Variances to be larger than Expected Random Variances. In practice this means that r*wg(j) values can be negative.

Usage

rwg.j.lindell(x, grpid, ranvar=2)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

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.

Lindell, M. K. & Brandt, C. J. (1999). Assessing interrater agreement on the job relevance of a test: A comparison of CVI, T, rWG(J), and r*WG(J) indexes. Journal of Applied Psychology, 84, 640-647.

See Also

ad.m awg rwg rwg.j rgr.agree

Examples

data(lq2002)
RWGOUT<-rwg.j.lindell(lq2002[,3:13],lq2002$COMPID)
RWGOUT[1:10,]
summary(RWGOUT)

Description

Based on the work of Cohen, Doveh and Eick (2001) and Cohen, Doveh and Nahum-Shani (2009). Draws data from a random uniform null distribution and calculates the James, Demaree and Wolf (1984) within group agreement measure rwg(j) for multiple item scales. By repeatedly drawing random samples, a null distribution of the rwg(j) is generated. The null sampling distribution can be used to calculate confidence intervals for different combinations of group sizes and number of items (J). Users provide the number of scale response options (A) and the number of random samples. By default, items (J) drawn in the simulation are independent (non-correlated); however, an optional argument (itemcors) allows the user to specify a correlation matrix with relationships among items. Cohen et al. (2001) show that values of rwg(j) are primarily a function of the number of items and the group size and are not strongly influenced by correlations among items; nonetheless, assuming correlations among items is more realistic and thereby is a preferred model (see Cohen et al., 2009).

Usage

rwg.j.sim(gsize, nitems, nresp, itemcors=NULL, nrep)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

Cohen, A., Doveh, E., & Nahum-Shani, I. (2009). Testing agreement for multi-item scales with the indices rwg(j) and adm(j). Organizational Research Methods, 12, 148-164.

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

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.

See Also

rwg.j rwg rwg.sim rwg.j.lindell rgr.agree

Examples

#An example assuming independent items
RWG.J.OUT<-rwg.j.sim(gsize=10,nitems=6,nresp=5,nrep=500)
summary(RWG.J.OUT)
quantile(RWG.J.OUT, c(.95,.99))

#A more realistic example assuming correlated items.  The 
#estimate in Cohen et al. (2006) is .61.

data(lq2002)
RWG.J.OUT<-rwg.j.sim(gsize=10,nresp=5,
  itemcors=cor(lq2002[,c("TSIG01","TSIG02","TSIG03")]),
  nrep=500)
summary(RWG.J.OUT)
quantile(RWG.J.OUT,c(.95,.99))

Description

Based on the work of Dunlap, Burke and Smith-Crowe (2003). 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 and& Wolf (1984). By repeatedly drawing random samples, a null distribution of the rwg is generated. The null 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

Value

Author(s)

Paul Bliese [email protected]

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.

See Also

ad.m awg rwg.j rwg rwg.j.sim rgr.agree

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=500)
summary(RWG.OUT)
quantile(RWG.OUT, c(.95,.99))

Description

Generate a sample vector (y) with a known population correlation to a given vector (x). The degree of correlation between x and y is determined by rho (the population correlation). Observed sample correlations between x and y will vary around rho, but this variation will decrease as the number of x observations increase.

Usage

sam.cor(x,rho)

Arguments

Value

The function prints the sample correlation for the specific set of numbers generated.

Author(s)

Paul Bliese [email protected]

See Also

sim.mlcor

Examples

data(bh1996)
   NEWVAR<-sam.cor(x=bh1996$LEAD,rho=.30)
   cor(bh1996$LEAD,NEWVAR)

Description

Contains estimates of movement length (in inches) of a light in a completely dark room. Eight groups of three individuals provided three estimates for a total of 72 observations. In four of the groups, participants first made estimates alone prior to providing estimates as a group. In the other four groups participants started as groups. Lang and Bliese (2019) used these data to illustrate how variance functions in mixed-effects models (lme) could be used to test whether groups displayed consensus emergence. Data were obtained from

https://brocku.ca/MeadProject/Sherif/Sherif_1935a/Sherif_1935a_3.html

Usage

data(sherifdat)

Format

A dataframe with 5 columns and 72 observations

References

Sherif, M. (1935). A study of some social factors in perception: Chapter 3. Archives of Psychology, 27, 23- 46.

https://brocku.ca/MeadProject/Sherif/Sherif_1935a/Sherif_1935a_3.html

Lang, J. W. B., & Bliese, P. D., (2019). A Temporal Perspective on Emergence: Using 3-level Mixed Effects Models to Track Consensus Emergence in Groups. In S. E. Humphrey & J. M. LeBreton (Eds.), The Handbook for Multilevel Theory, Measurement, and Analysis. Washington, DC: American Psychological Association

Lang, J. W. B., Bliese, P. D., & Adler, A. B. (2019). Opening the Black Box: A Multilevel Framework for Studying Group Processes. Advances in Methods and Practices in Psychological Science, 2, 271-287.

Lang, J. W. B., Bliese, P. D., & de Voogt, A. (2018). Modeling Consensus Emergence in Groups Using Longitudinal Multilevel Methods. Personnel Psychology, 71, 255-281.

Lang, J. W. B., Bliese, P. D., & Runge, J. M. (2021). Detecting consensus emergence in organizational multilevel data: Power simulations. Organizational Research Methods, 24(2), 319-341.

Description

ICC(1) values play an important role influencing the form of relationships among variables in nested data. This simulation allows one to create data with known ICC(1) values. Multiple variables can be created both with and without level-1 correlation.

Usage

sim.icc(gsize, ngrp, icc1, nitems=1, item.cor=FALSE)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

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

ICC1 sim.mlcor

Examples

## Not run: 
set.seed(1535324)
ICC.SIM<-sim.icc(gsize=10,ngrp=100,icc1=.15)
ICC1(aov(VAR1~as.factor(GRP), ICC.SIM))

# 4 items with no level-1 correlation
set.seed(15324)
#items with no level-1 (within) correlation
ICC.SIM<-sim.icc(gsize=10,ngrp=100,icc1=.15,nitems=4) 
mult.icc(ICC.SIM[,2:5],ICC.SIM$GRP)
with(ICC.SIM,waba(VAR1,VAR2,GRP))$Cov.Theorem  #Examine CorrW 

# 4 items with a level-1 (within) correlation of .30
set.seed(15324)
ICC.SIM<-sim.icc(gsize=10,ngrp=100,icc1=.15,nitems=4, item.cor=.3) 
mult.icc(ICC.SIM[,2:5],ICC.SIM$GRP)
with(ICC.SIM,waba(VAR1,VAR2,GRP))$Cov.Theorem  #Examine CorrW 

## End(Not run)

Description

In multilevel or hierarchical nested data, correlations at the within and between levels often differ in magnitude and/or sign. For instance, Bliese and Halverson (1996) showed that the within correlation between individual reports of work hours and individual well-being was -.11. When these same data were mean-aggregated to the group-level, the between correlation based on group means was -.71. A necessary, but not sufficient, condition for differences across levels is a non-zero ICC1 value for both variables (Bliese, 2000). This simulation creates a dataset with a group ID and an X and Y variable for any combination of group size, number of groups, within and between correlations, ICC1 values and reliability (alpha).

Usage

sim.mlcor(gsize,ngrp,gcor,wcor,icc1x,icc1y,alphax=1,alphay=1)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

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. & 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., 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

ICC1 sim.icc

Examples

## Not run: 
#
# Examine the multilevel properties of work hours and well-being
# in the bh1996 data
#
data(bh1996)
with(bh1996,waba(HRS,WBEING,GRP))
mult.icc(bh1996[,c("HRS","WBEING")],bh1996$GRP)
#
#Estimate true group-mean correlation by adding ICC2 adjusted incremental
#correlation back to within correlation. For value of -.8256
#
-0.110703+(-0.7121729--0.110703)/(sqrt(0.9171286*0.771756))
#
# Simulate data with same properties assuming no measurement error
#
set.seed(578323)
SIM.ML.COR<-sim.mlcor(gsize=75,ngrp=99,gcor=-.8256,wcor=-.1107,
                 icc1x=0.04338,icc1y=0.12924,alphax=1,alphay=1)
#
# Compare Simulated results to results (above) from bh1996
#
with(SIM.ML.COR,waba(X,Y,GRP))
mult.icc(SIM.ML.COR[,c("X","Y")],SIM.ML.COR$GRP)

## End(Not run)

Description

Non-independence due to groups is a common characteristic of applied data. In non-independent data, responses from members of the same group are more similar to each other than would be expected by chance. Non-independence is typically measured using the Intraclass Correlation Coefficient 1 or ICC(1). When non-independent data is treated as though it is independent, standard errors will be biased and power can decrease. The simbias simulation allows one to estimate the bias and loss of statistical power that occurs when non-independent data is treated as though it is independent. The simulation contrasts a simple Ordinary Least Squares (OLS) model that fails to account for non-independence with a random coefficient model that accounts for non-independence. The simulation assumes that both the outcome (y) and the predictor (x) vary among individuals in the same group (i.e., are within variables).

Usage

simbias(corr,gsize,ngrp,icc1x,icc1y,nrep)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

Bliese, P. D. & Hanges, P. J. (2004). Being both too liberal and too conservative: The perils of treating grouped data as though they were independent. Organizational Research Methods, 7, 400-417.

See Also

ICC1

Examples

library(nlme)
set.seed(15)
SIM.OUTPUT<-simbias(corr=.15,gsize=10,ngrp=50,icc1x=0.05,
            icc1y=0.35, nrep=100)
apply(SIM.OUTPUT,2,mean)
1-pnorm(1.96-3.39) #Power of the lme model (two-tailed, alpha=.05)
1-pnorm(1.96-2.95) #Power of the OLS model (two-tailed, alpha=.05)

Description

Estimate Sobel's (1982) indirect test for mediation. The function provides an estimate of the magnitude of the indirect effect, Sobel's first-order estimate of the standard error associated with the indirect effect, and the corresponding z-value. The estimates are based upon three models as detailed on page 84 of MacKinnon, Lockwood, Hoffman, West and Sheets (2002).

Usage

sobel(pred,med,out)

Arguments

Value

Author(s)

Paul Bliese [email protected]

References

MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.

Sobel, M. E., (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociological Methodology 1982 (pp. 290-312). Washington, DC: American Sociological Association.

Examples

data(bh1996)

#A small but significant indirect effect indicates leadership mediates
#the relationship between work hours and well-being.
sobel(pred=bh1996$HRS,med=bh1996$LEAD,out=bh1996$WBEING)

Description

Provides a concise summary of objects created using the functions rwg.sim and rwg.j.sim.

Usage

## S3 method for class 'agree.sim'
summary(object,...)

Arguments

Value

A summary of all the output elements in the agree.sim class object.

Author(s)

Paul Bliese [email protected]

See Also

rwg.sim rwg.j.sim

Examples

#An example from Dunlap et al. (2003).  The estimate from Dunlap et al.
#Table 2 is 0.53. Increase replications in actual use.
RWG.OUT<-rwg.sim(gsize=10,nresp=5,nrep=500)
summary(RWG.OUT)

Description

Provides a concise summary of objects created using the function ad.m.sim.

Usage

## S3 method for class 'disagree.sim'
summary(object,...)

Arguments

Value

A summary of all the output elements in the disagree.sim class object.

Author(s)

Paul Bliese [email protected]

See Also

ad.m.sim

Examples

#Example from Dunlap et al. (2003), Table 3.  The listed significance
#value for a group of size 5 with a 7-item response format is 0.64 or less.
#Increase nrep in actual use
SIMOUT<-ad.m.sim(gsize=5, nitems=1, nresp=7, itemcors=NULL,
        type="mean", nrep=500) 
summary(SIMOUT)

Description

Provides a concise summary of objects created using the function rgr.agree.

Usage

## S3 method for class 'rgr.agree'
summary(object,...)

Arguments

Value

Author(s)

Paul Bliese [email protected]

See Also

rgr.agree

Examples

# Example with a small number of replications (500). Increase in actual use.
data(bh1996)
RGROUT<-rgr.agree(bh1996$HRS,bh1996$GRP,500)
summary(RGROUT)

Description

Provides a concise summary of objects created using the function rgr.waba.

Usage

## S3 method for class 'rgr.waba'
summary(object,...)

Arguments

Value

A dataframe containing summary statistics in the form of number of repetitions (NRep), Mean and Standard Deviations (SD) for each parameter in the rgr.waba model.

Author(s)

Paul Bliese [email protected]

See Also

rgr.waba

Examples

data(bh1996)

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

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

Description

A partial sample of data collected by Lang and reported in Lang and Bliese (2009). The tankdat sub-sample was used as an example of discontinuous growth modeling in Bliese and Lang (2016). The data set is in long (univariate) format, and contains performance data from 184 participants over 12 repeated measures on a complex tank simulation task. In the research paradigm, the task was unexpectedly changed after the first six performance episodes. Discontinuous growth models were used to examine participants' reactions to the unexpected change. The data set contains the person-level predictor of conscientiousness.

Usage

data(tankdat)

Format

A dataframe with 4 columns and 2208 observations

References

Bliese, P. D., & Lang, J. W. B. (2016). Understanding relative and absolute change in discontinuous growth models: Coding alternatives and implications for hypothesis testing. Organizational Research Methods, 19, 562-592.

Lang, J. W. B., & Bliese, P. D. (2009). General mental ability and two types of adaptation to unforeseen change: Applying discontinuous growth models to the task-change paradigm. Journal of Applied Psychology, 92, 411-428.

Description

Contains the complete data set used in Bliese and Ployhart (2002) focused on Job Satisfation. The data is in univariate (i.e., stacked) form. Data were collected at three time points.

Usage

data(univbct)

Format

A data frame with 22 columns and 1485 observations from 495 individuals

References

Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing and illustrations. Organizational Research Methods, 5, 362-387.

Description

Performs the covariance theorem decomposition of a raw correlation in situations where lower-level entities (individuals) are nested in higher-level groups (see Robinson, 1950). Dansereau, Alutto and Yammarino (1984) refer to the variance decomposition as "Within-And-Between-Analysis II" or "WABA II". The waba function decomposes a raw correlation from a two-level nested design into 6 components. These components are (1) eta-between value for X, (2) eta-between value for Y, (3) the group-size weighted group-mean correlation, (4) the within-eta value for X, (5) the within-eta value for Y, and (6) the within-group correlation between X and Y. The last value represents the correlation between X and Y after each variable has been group-mean centered (demeaned).

The program is designed to automatically perform listwise deletion on missing values; consequently, users should pay attention to the diagnostic information (Number of Groups and Number of Observations) provided as part of the output.

Note that Within-And-Between-Analysis proposed by Dansereau et al. involves more than covariance theorem decomposition of correlations. Specifically, WABA involves decision rules based on eta-values. These are not replicated in the R multilevel library because the eta based decision rules have been shown to be highly related to group size (Bliese, 2000; Bliese & Halverson, 1998), a factor not accounted for in the complete Within-And-Between-Analysis methodology.

Usage

waba(x, y, grpid)

Arguments

Value

Returns a list with three elements.

Author(s)

Paul Bliese [email protected]

References

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., & Halverson, R. R. (1998). Group size and measures of group-level properties: An examination of eta-squared and ICC values. Journal of Management, 24, 157-172.

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

rgr.waba

Examples

data(bh1996)
waba(bh1996$HRS,bh1996$WBEING,bh1996$GRP)