What is mean value imputation?
What is mean value imputation?
Mean imputation (MI) is one such method in which the mean of the observed values for each variable is computed and the missing values for that variable are imputed by this mean. This method can lead into severely biased estimates even if data are MCAR (see, e.g., Jamshidian and Bentler, 1999).
Is mean imputation good?
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. That’s a good thing. Plus, by imputing the mean, you are able to keep your sample size up to the full sample size.
What are the types of imputation?
- Complete Case Analysis(CCA):- This is a quite straightforward method of handling the Missing Data, which directly removes the rows that have missing data i.e we consider only those rows where we have complete data i.e data is not missing.
- Arbitrary Value Imputation.
- Frequent Category Imputation.
How does EM imputation work?
It uses the E-M Algorithm, which stands for Expectation-Maximization. It is an iterative procedure in which it uses other variables to impute a value (Expectation), then checks whether that is the value most likely (Maximization). If not, it re-imputes a more likely value.
What is the problem of imputation?
Mean imputation reduces the variance of the imputed variables. Mean imputation shrinks standard errors, which invalidates most hypothesis tests and the calculation of confidence interval. Mean imputation does not preserve relationships between variables such as correlations.
What does imputation of missing data mean?
In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as “unit imputation”; when substituting for a component of a data point, it is known as “item imputation”.
What does imputation mean in research?
Imputation, also called ascription, is a statistical process that statisticians, survey researchers, and other scientists use to replace data that are missing from a data set due to item nonresponse. Researchers do imputation to improve the accuracy of their data sets.
Why is imputation used?
Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data.
Does mean imputation reduce variance?
Mean imputation reduces the variance of the imputed variables. Mean imputation shrinks standard errors, which invalidates most hypothesis tests and the calculation of confidence interval.
How many imputations are really needed?
An old answer is that 2 to 10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error (SE) estimates that would not change (much) if you imputed the data again.