macat | R Documentation |
Computes single predictor categorical moderator analysis under a fixed or random effects model.
macat(g, var, mod, data, method= "random")
g |
Hedges g (unbiased estimate of d) effect size. |
var |
Vaiance of g. |
mod |
Categorical moderator variable used for moderator analysis. |
method |
Default is |
data |
|
See Konstantopoulos & Hedges (2009; pp. 280-288) for the computations used in this function.
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. |
AC Del Re & William T. Hoyt
Maintainer: AC Del Re acdelre@gmail.com
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.
plotcat
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 # Random effects macat(g = g, var= var.g, mod = mod, data = df, method= "random")
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