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People also ask
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What is the problem with imputation?
Problem #1: Mean imputation does not preserve the relationships among variables. True, imputing the mean preserves the mean of the observed data. So if the data are missing completely at random, the estimate of the mean remains unbiased.
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When to not use multiple imputation?
Multiple imputation is not appropriate when: The proportion of missing data is small (≤5% as a general rule). ... Only the outcome variable has missing values, and not covariate (independent) variables. The data are MCAR (rare). The proportion of missing data is quite large (≥40% as a general rule).
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When should you not use multiple imputation?
Multiple imputation should only be used when too few cases are left after listwise deletion. If results from statistical analyses obtained from multiple imputation differ from those of listwise deletion, the results of multiple imputation must be wrong. Outcome variables must not be imputed.
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What are the limits of multiple imputation?
There are limits, though. No matter how many imputations you use, multiple imputation estimates can never be more precise or replicable than maximum likelihood estimates. And beyond a certain number of imputations, any improvement in precision and replicability becomes negligible.
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How multiple imputation is a valid way of dealing with missing data?
Multiple imputation is one of the most robust and widely used statistical techniques for dealing with missing data. In multiple imputation, the distribution of observed data is used to estimate a set of plausible values for missing data.
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What is multiple imputation used for?
Multiple imputation fills in missing values by generating plausible numbers derived from distributions of and relationships among observed variables in the data set.
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What are the disadvantages of multiple imputation?
The major danger of the technique is that it may provide nonsensical or even misleading results if applied without appropriate care or insight. Multiple imputation is not a simple technical fix for the missing data.
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What is the problem with multiple imputation?
Because multiple imputation only uses observed information from other variables to predict the missing data on a specific variable, it will only give unbiased results as long as the missingness can be explained completely from the observed variables.
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