I have 60,000 data and around 45% of them is missing and the missing values are random. Can I simply use listwise or pairwise deletion or do I have to use imputation? If imputation is recommended which imputation is the best one?
It depends on
- Amount of missing data (what percentage of data is missing)
- Type of missing data (MAR, MCAR, NMAR)
According to this nice article (Tsikriktsis: A review of techniques for treating missing data in OM survey research, 2005), if more than 10% data is missing, the best solution is
- Maximum likelihood imputation if data are NMAR (non-missing at random)
- Maximum likelihood and hot-deck if data are MAR (missing at random)
- Pairwise deletion, hot-deck or regression if data are MCAR (missing completely at random)
In short: If your data is missing completely at random (MCAR), i.e., a true value of a missing value has the same distribution as an observed variable and missingness cannot be predicted from any other variables, your results will be unbiased but inefficient using listwise or pairwise deletion.
Multiple imputation by chained equations is regarded the best imputation method by many researchers.