omni: Omnibus Effect Size (Fixed and Random Effects)

In MAd: Meta-Analysis with Mean Differences

Omnibus Effect Size (Fixed and Random Effects)

Description

Computes fixed and random effects omnibus effect size for correlations.

Usage

omni(g, var, data, type="weighted", method = "random")

Arguments

g	Hedges g (unbiased estimate of d) effect size.
var	Vaiance of g.
type	weighted or unweighted. Default is weighted. Use the unweighted variance method only if Q is rejected and is very large relative to the number of studies in the meta-analysis.
method	Default is random. For fixed effects, use fixed.
data	data.frame with above values.

Details

Depricated function. Use mareg(es~1, var, data) instead.

Value

Fixed and random effects:

k	Number of studies in the meta-analysis.
estimate	Unstandardized regression coefficient estimate.
se	Standard error of the estimate coefficient.
z	z-value.
ci.l	Lower 95% confidence interval.
ci.u	Upper 95% confidence interval.
p	Significance level.
Q	Q-statistic (measure of homogeneity).
df.Q	Degrees of freedom for Q-statistic.
Qp	Q-statistic p-value (assesses overall homogeneity between studies).
I2	Proportion of total variation in effect size that is due to heterogeneity rather than chance (see Shadish & Haddock, 2009; pp. 263).

Author(s)

AC Del Re & William T. Hoyt

Maintainer: AC Del Re [e-mail]

References

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.

Examples

id<-c(1:20)
n.1<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)
n.2 <- c(11,22,10,20,25,12,12,36,19,11,34,75,33,120,37,14,40,16,10,21)
g <- c(.68,.56,.23,.64,.49,-.04,1.49,1.33,.58,1.18,-.11,1.27,.26,.40,.49,
.51,.40,.34,.42,1.16)
var.g <- c(.08,.06,.03,.04,.09,.04,.009,.033,.0058,.018,.011,.027,.026,.0040,
.049,.0051,.040,.034,.0042,.016)
mod<-factor(c(rep(c(1,1,2,3),5)))
df<-data.frame(id, n.1,n.2, g, var.g,mod)

# Example

omni(g = g, var = var.g, data = df, type="weighted", method = "random")

MAd documentation built on Aug. 7, 2022, 1:05 a.m.