Computes fixed and random effects omnibus effect size for correlations.
omni(es, var, data, type="weighted", method = "random", ztor = FALSE)r or z' effect size.
Variance of es.
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.
Default is random. For fixed effects, use fixed.
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.frame with above values.
Fixed and random effects:
Number of studies in the meta-analysis.
Unstandardized regression coefficient estimate.
Standard error of the estimate coefficient.
z-value.
Lower 95% confidence interval.
Upper 95% confidence interval.
Significance level.
Q-statistic (measure of homogeneity).
Degrees of freedom for Q-statistic.
Q-statistic p-value (assesses overall homogeneity between studies).
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).
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.
# 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(1:2, 10))
# Example
omni(es = z, var = var.z, data = dat, type="weighted", method = "random", ztor = TRUE)
# }
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