macatC: Direct Categorical Moderator Comparison

In MAc: Meta-Analysis with Correlations

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.

Details

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

Value

diff	Mean difference between the two levels.
var.diff	Variance of diff.
p	Significance level.

Author(s)

AC Del Re & William T. Hoyt

Maintainer: AC Del Re [e-mail]

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

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) 

MAc documentation built on May 1, 2019, 10:55 p.m.