How to Handle Missing Data in SAS

Handling missing data is a crucial step in data analysis using SAS. Here are some common techniques:

• Use PROC MI: PROC MI is a SAS procedure for multiple imputation, which allows you to create multiple datasets with imputed values for missing data.
• Use PROC MEANS and PROC FREQ: These procedures can provide descriptive statistics for variables with missing data, helping you understand the extent of missingness in your dataset.
• Use DATA Step with IF-THEN-ELSE: You can use the DATA step in SAS along with conditional logic (IF-THEN-ELSE) to handle missing data by assigning specific values or imputed values.
• Use PROC SQL: SQL can be used to create new datasets with missing data handled through aggregation functions or subqueries.

By implementing these techniques, you can effectively handle missing data in SAS and ensure a more accurate analysis of your dataset.