Description Usage Arguments Value Author(s) References Examples
Computes fixed and random effects omnibus effect size for correlations.
1 |
es |
r or z' effect size. |
var |
Variance of es. |
type |
|
method |
Default is |
ztor |
Default is FALSE. If TRUE, this assumes z' (Fisher's z) was used in the |
data |
|
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 systematic differences between effect sizes rather than by chance (see Shadish & Haddock, 2009; pp. 263). |
AC Del Re & William T. Hoyt
Maintainer: AC Del Re acdelre@gmail.com
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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))
# Example
omni(es = z, var = var.z, data = dat, type="weighted", method = "random", ztor = TRUE)
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