macat: Categorical Moderator Analysis

In MAc: Meta-Analysis with Correlations

Description

Computes single predictor categorical moderator analysis under a fixed or random effects model.

Usage

macat(es, var, mod, data, method= "random", ztor = FALSE)

Arguments

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.
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

mod	Level of the categorical moderator.
k	Number of studies for each level of the moderator.
estimate	Mean effect size of each level of the moderator.
ci.l	Lower 95% confidence interval.
ci.u	Upper 95% confidence interval.
z	z-score (standardized value).
p	Significance level.
var	Variance of effect size.
se	Square root of variance.
Q	Q-statistic (measure of homogeneity).
df	Degrees of freedom for Q-statistic.
p.h	p-value for homogeneity within that level of the moderator.
I2	Proportion of total variation in effect size that is due to heterogeneity rather than chance (see Shadish & Haddock, 2009; pp. 263).
Q	Q-statistic overall. Note: Whether fixed or random effects analyses are conducted, the Q statistic reported is for the fixed effect model. Therefore, Qb + Qw != Q in the random effects output.
Qw	Q-within (or error). Measure of within-group heterogeneity.
Qw.df	Degrees of freedom for Q-within.
Qw.p	Q-within p-value (for homogeneity).
Qb	Q-between (or model). Measure of model fit.
Qb.df	Degrees of freedom for Q-between.
Qb.p	Q-between p-value (for homogeneity). Qb and Qb.p provide the test of whether the moderator variable(s) account for significant variance among effect sizes.

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

plotcat, wd

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(1:2, 10))
dat

# Example

# Random effects
macat(es = z, var= var.z, mod = mods2, data = dat, ztor = TRUE, method= "random")

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