Question

What are three ways of dealing with missing data that don't require imputation?

1) Exclude the missing data from the analysis
2) Use mean substitution to replace missing values
3) Use regression imputation to estimate missing values
4) Use multiple imputation to generate plausible values for missing data

Answer 1

Three ways of dealing with missing data that don't require imputation are excluding the missing data from analysis, using mean substitution, and regression imputation.

Step-by-step explanation:

When dealing with missing data, there are three ways that don't require imputation:

1. Exclude the missing data from the analysis: If the missing data is negligible or doesn't affect the overall analysis, you can choose to exclude it.
2. Use mean substitution to replace missing values: Another option is to replace missing values with the mean of the available data. This can help maintain the overall distribution of the variable.
3. Use regression imputation to estimate missing values: Regression imputation involves using regression models to predict missing values based on other available variables.