Test the Homogeneity of Effect Sizes
It tests the homogeneity of univariate and multivariate effect sizes.
homoStat(y, v)
y |
A vector of effect size for univariate meta-analysis or a k x p matrix of effect sizes for multivariate meta-analysis where k is the number of studies and p is the number of effect sizes. |
v |
A vector of the sampling variance of the effect size for univariate
meta-analysis or a k x p* matrix of the sampling
covariance matrix of the effect sizes for multivariate meta-analysis
where p* = p(p+1)/2. It is arranged by column
major as used by |
A list of
Q |
Q statistic on the null hypothesis of homogeneity of effect sizes. It has an approximate chi-square distribution under the null hypothesis. |
Q.df |
Degrees of freedom of the Q statistic |
pval |
p-value on the test of homogeneity of effect sizes |
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
Becker, B. J. (1992). Using results from replicated studies to estimate linear models. Journal of Educational Statistics, 17, 341-362.
Cheung, M. W.-L. (2010). Fixed-effects meta-analyses as multiple-group structural equation models. Structural Equation Modeling, 17, 481-509.
Cochran, W. G. (1954). The combination of estimates from different experiments. Biometrics, 10, 101-129.
with( Hox02, homoStat(yi, vi) )
with( HedgesOlkin85, homoStat(y=cbind(d_att, d_ach),
v=cbind(var_att, cov_att_ach, var_ach)) )Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.