How do you handle missing values in SAS?

Using options like NMISS or the COALESCE function is an effective way to handle missing values in SAS. The NMISS function counts the number of missing values in a list of variables, which helps assess the extent of missing data. This information is crucial for making informed decisions about how to handle these missing values in subsequent analysis.

The COALESCE function is particularly useful for replacing missing values with the first non-missing value in a list. This allows for a more sophisticated approach, ensuring that the data remains as representative as possible. Such methods enable analysts to retain valuable information that might otherwise be lost through methods like removal.

In contrast, the other options may not be optimal for every situation. For example, merely removing missing values from the dataset can lead to loss of potentially useful data and reduce the size of your analysis, which may not always be desirable. Replacing missing values with zero might introduce bias, as it assumes that zero is a meaningful and accurate value for the variable, which is not always the case. Similarly, calculating averages for imputation can skew results since it may not accurately reflect the underlying data distribution, particularly if the missing data is not random. Thus, choosing functions like NMISS and COALESCE provides a versatile framework for