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Mean value, median value, and random sample imputation
Linear regression
Predictive mean matching, classification and regression trees, random
forest imputation
Bayes predictive mean matching
Bootstrap expectation maximization
Non-parametric random forest
Additive regression and bootstrap predictive mean matching
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Deviation increases approximately linearly with the percentage of
missing data, most strongly for missing SDs and for Fisher’s z.
Unbiased below ca. 80% of missing data.
Unbiased below ca. 40-60% of missing data, then with increasing
deviation and volatile with the percentage of missing data
Unbiased below ca. 50% of missing SDs in the log response ratio, ca.
80% of missing SDS in Hedges’ d and 80% in Fisher’s z.
Unbiased until a threshold of ca. 50% of missing data, above which the
algorithm frequently failed to converge.
Unbiased below ca. 70-80% of missing SDs in the log response ratio,
nearly linear increase in deviation with the percentage of missing SDs
in Hedges’ d and missing SSs in Fisher’s z.
Unbiased until a threshold of ca. 50-70% of missing SDs in the log
response ratio and in Hedges’ d, above which the algorithm
frequently failed to converge. Unbiased below ca. 60-70% of missing
data in Fisher’s z.
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Unbiased, except smaller CI for Hedges’ d.
Unbiased, except larger above ca. 70% of missing both, SDs and SSs.
Unbiased, except larger above ca. 70% of missing both, SDs and SSs.
Unbiased, except larger above ca. 70% of missing both, SDs and SSs.
Unbiased.
Unbiased.
Unbiased.
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