Methods for Dealing with Missing Data
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Missing Completely At Random methods
Using mean values
This method replaces the missing data with the average for that variable.
Listwise deletion
Pairwise deletion
At an intuitive level, this method seems superior to listwise deletion: as each correlation is estimated using all the available data, it seems obvious that a larger effective sample size is used in the analysis. However, analytic and simulation studies of linear regression reveal that this is only true when the correlations between the variables are low. Further, the statistical tests used in many commercial programs are wrong. [1]. For this reason, pairwise deletion should not be consider a better method than pairwise deletion, and is perhaps best only used in situations where the missing data makes listwise deletion impractical.
Imputation
Stochastic imputation
Multiple imputation
Maximum likelihood estimation
Nonignorable methods
Dummy variable adjustment
Extra category
- ↑ Allison, Paul D. (2001). Missing Data (Quantitative Applications in the Social Sciences) (Page 8). SAGE Publications. Kindle Edition.