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
According to this nice article, if more than 10% data is missing, the best solution is
Here is link to that article, if it cannot be downloaded from sciencedirect.
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.