MAc (version 1.1.1)

agg: Meta-Analysis Aggregation

Description

This fuction will simultaneously aggregate all within-study effect sizes while taking into account the correlations among the within-study outcome measures (Hunter and Schmidt, 2004). The default imputed correlation between within-study effect sizes is set at .50 (Wampold et al., 1997) and will compute an aggregated effect size for each study. This default of .50 is adjustable.

Usage

agg(id, r, n, cor = .50, mod=NULL, data)

Arguments

id

Study id with multiple rows of same id.

r

Correlation coefficient.

n

Sample size.

cor

Estimated correlation among within-study outcome variables. Default is .50 based on Wampold et al. (1997) recommended procedure.

mod

Default is NULL. To aggregate by id and one moderator. If there are multiple levels of a categorical moderator within study and one can in derive seperate effect size estimates for each level within and between studies. However, there will be dependency issues and one way to resolve is shown below in the examples.

data

data.frame with above values.

Value

Outputs a data.frame with aggregated effect sizes where each study is reduced to one row per study (unless aggregated by a moderator) by a weighted average formula. This formula is based on Hunter and Schmidt's (2004) approach to aggregation of dependent effect sizes (see chapter 10, pp. 435-8).

References

Hunter, J. E., Schmidt, F. L. (2004). Methods of meta-analysis (2nd edition). Thousand Oaks, CA: Sage.

Examples

# NOT RUN {
id<-rep(1:5, 4)
n<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
r<-c(.68,.56,.23,.64,.49,-.04,.49,.33,.58,.18,-.11,.27,.26,.40,.49,
.51,.40,.34,.42,.16)
mod1<-factor(rep(1:2, 10))
dat<-data.frame(id,n,r,mod1)

# Examples

# aggregate to 1 id per study (independent sample)
agg(id = id, r = r, n = n, data=dat) 

# aggregate by id & a moderator (non-independent sample)
temp <- agg(id = id, r = r, n = n, mod = mod1, data=dat) 

temp

# This function below will randomly select one within
# study level of the moderator (if there are more than one) and output an
# independent sample. Replace temp with the name of your data.
do.call(rbind, lapply(split(temp, temp$id), 
          function(.data) .data[sample(nrow(.data), 1),]))


# }