Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis. Stephan Kambach. Helge Bruelheide. Katharina Gerstner. Jessica Gurevitch. Michael Beckmann. Ralf Seppelt. Abstract Meta-analyses often encounter studies with incompletely reported variance measures e. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta-analyses showing that the majority of meta-analyses encountered incompletely reported studies. Performance was thereby assessed using results from fully informed weighted analyses on hypothetically complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size-based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta-analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta-analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness. Hide details.
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