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- Can you run a regression with missing data?
- How do you deal with missing data in regression?
- How much missing data is acceptable for regression?
Can you run a regression with missing data?
With regression analysis, the default in all programs is to eliminate any cases with missing data on any of the variables (i.e., listwise deletion). As the amount of data that are missing increases, there can be a substantial reduction of sample size and a resulting loss of power.
How do you deal with missing data in regression?
When dealing with missing data, data scientists can use two primary methods to solve the error: imputation or the removal of data. The imputation method develops reasonable guesses for missing data. It's most useful when the percentage of missing data is low.
How much missing data is acceptable for regression?
Statistical guidance articles have stated that bias is likely in analyses with more than 10% missingness and that if more than 40% data are missing in important variables then results should only be considered as hypothesis generating [18], [19].
