Handling Missing Data

Methods for dealing with missing data

Here are some standard methods that can be used to handle missing data in analysis:

• Mean imputation: This method replaces the missing values with the mean of the observed values. We should use this method when the data is missing at random (MAR), and the analysis goals do not require a sophisticated approach.

• Median imputation: This method replaces the missing values with the median of the observed values. We should use this method when the missing data are not normally distributed or when extreme values heavily influence the mean.

• Multiple imputation: This method involves statistical analysis that involves generating multiple sets of imputed values for the missing data using a statistical model, performing the analysis on each of the imputed datasets separately, and combining the results by taking into account the uncertainty introduced by the missing data. We should use this method when the missing data are not missing at random (MNAR), and the analysis requires more accurate estimates of the missing values.

• Maximum likelihood estimation (MLE): This method uses a statistical model to estimate the missing values by maximizing the likelihood function. We should use this method when the missing data are not missing at random (MAR), and the analysis goals require the most accurate estimates of the missing values.

• Exclusion: This method involves removing observations with missing values from the analysis. It should be used when the missing data is entirely random (MCAR) and when the analysis goals justify the loss of information from the excluded observations.