What are best practices for dealing with missing data?
Dealing with missing data is a critical aspect of statistical analysis, and it requires careful consideration to ensure the validity and reliability of study results. Here are some best practices for handling missing data: Understand the Mechanism of Missingness: Determine whether the missing data is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). Understanding the mechanism can guide the choice of appropriate imputation methods. Explore Patterns of Missing Data: Examine patterns of missingness…