Nonparametric estimation of heterogeneity variance for the standardised difference used in meta-analysis
By U. Malzahn, Dankmar Böhning, Heinz Holling. 2000
59 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4acd2f2db2fd72c9b9b120e04060b3e7fc41f55d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Georgia Salanti
- Julian P. T. Higgins
- Dan Jackson
- W. Viechtbauer
- Dean Langan
- A. Veroniki
- J. Bowden
- Jeffrey N. Jonkman
In Statistics and Probability
- W. Viechtbauer
- Georgia Salanti
- Julian P. T. Higgins
- Dan Jackson
- Andrew L. Rukhin
- J. Bowden
- Jeffrey N. Jonkman
- Kurex Sidik
Citations in the corpus
- Methods to estimate the between‐study variance and its uncertainty in meta‐analysis† (2015, methodology): “…not have the properties assumed for them, such as the chi-square distribution for the Q-statistic under the null hypothesis of homogeneity (Kulinskaya et al., 2011a, 2011b), and some attempts have been made to account for the uncertainty in the weights (Böhning et al., 2002; Malzahn et al., 2000).”
- Simple heterogeneity variance estimation for meta‐analysis (2005, methodology): “The estimator of Hedges and Olkin (1985), equation (10), page 194, which we denote by HO, is (see also Malzahn et al. (2000)) τ̂2HO = ( 1 k −1 ) k∑ i=1 .di − d̄/2 − 1 k k∑ i=1 ( Ni mini +ψid2i ) , where di is the unbiased standardized mean difference estimate in the ith study, d̄ =Σki=1di=k, ψi = 8.”
- On Constructing Confidence Intervals for a Standardized Mean Difference in Meta-analysis (2003, methodology): “Note that v̂ 2 i is the study-specific variance estimate 1194 Sidik and Jonkman of di in the ith study and is given by (see Malzahn et al. (2000)) v̂2i ¼ Ni 2 qiðNi 4Þ½GððNi 2Þ=2Þ 2 þ Ni 2 ðNi 4Þ½GððNi 2Þ=2Þ 2 1 d2i : ð2:2Þ For the standardized mean difference in a random effects model, Hedges and…”
- Estimating heterogeneity variance in meta‐analysis (2013, background): “This fact may explain the large mean-squared error of σ̃2DL which was observed in many simulations (Malzahn et al., 2000; Jackson et al., 2010) and which is discussed further.”