MAc (version 1.1.1)

macatC: Direct Categorical Moderator Comparison

Description

Function for a planned comparison between two levels of a moderator under a fixed or random effects model.

Usage

macatC(x1, x2, es, var, mod, data, method= "random", type= "post.hoc", ztor = FALSE)

Arguments

x1

One level of categorical moderator.

x2

Comparison level of same categorical moderator.

es

r or z' effect size.

var

Variance of es.

mod

Categorical moderator variable used for moderator analysis.

method

Default is random. For fixed effects, use fixed.

type

post.hoc assumes the comparison was not planned prior to conducting the meta analysis. The a priori option, planned, assumes the researcher planned to conduct the analysis a priori. Default is post.hoc using the Scheffe post hoc statistical method.

ztor

Default is FALSE. If TRUE, this assumes z' (Fisher's z) was used in the es argument and the analyist would like z' to be converted to r (for interpretive purposes) after analyzing in z'.

data

data.frame with values above.

Value

diff

Mean difference between the two levels.

var.diff

Variance of diff.

p

Significance level.

Details

See Konstantopoulos & Hedges (2009; pp. 280-288) for the computations used in this function.

References

Konstantopoulos & Hedges (2009). Analyzing effect sizes: Fixed-effects models. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta analysis (pp. 279-293). New York: Russell Sage Foundation.

Shadish & Haddock (2009). Analyzing effect sizes: Fixed-effects models. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta analysis (pp. 257-278). New York: Russell Sage Foundation.

See Also

macat,

Examples

# NOT RUN {
id<-c(1:20)
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<-c(1,2,3,4,1,2,8,7,5,3,9,7,5,4,3,2,3,5,7,1)
dat<-data.frame(id,n,r,mod1)
dat$var.r <- var_r(dat$r, dat$n) # MAc function to derive variance
dat$z <- r_to_z(dat$r)  # MAc function to convert to Fisher's z (z')
dat$var.z <- var_z(dat$n)  # MAc function for variance of z'
dat$mods2 <- factor(rep(1:4, 5))
dat

# Example
macatC(1, 2, es=r, var=var.r, mod=mods2, data=dat,  method= "random", 
  type= "post.hoc", ztor = FALSE) 


# }