Estimates of heterogeneity (I2) can be biased in small meta-analyses
By Paul T. von Hippel. 2014
221 citations recorded by the source corpus.
Estimated effects vary across studies, partly because of random sampling error and partly because of heterogeneity. In meta-analysis, the fraction of variance that is due to heterogeneity is known as I 2 . We show that the usual estimator of I 2 is biased. The bias is largest when a meta-analysis has few studies and little heterogeneity. For example, with 7 studies and the true value of I 2 at 0, the average estimate of I 2 is .124. Estimates of I 2 should be interpreted cautiously when the meta-analysis is small and the null hypothesis of homogeneity ( I 2 =0) has not been rejected. In small meta-analyses, confidence intervals may be preferable to point estimates for I 2
Subfields: Endocrinology, Diabetes and Metabolism, Neurology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1e8e895f027695e88923ad0bee5bef0de4651497
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In Endocrinology, Diabetes and Metabolism
In Neurology
In Statistics and Probability
Citations in the corpus
- The Relationship between Visuospatial Working Memory and Mathematical Performance in School-Aged Children: a Systematic Review (2019, background): “Following findings by Von Hippel (2015), it is important to consider the I2 statistic in relation to the number of studies included in the meta-analysis; however, 35 studies should be sufficient to mitigate the potential for bias with a small number of studies.”