When Is Imputation Appropriate?
When It Is Plausible That Data Are Missing at Random, but Not Completely at Random, Analyses Based on Complete Cases May Be Biased. Such Biases Can Be Overcome...
When it is plausible that data are missing at random, but not completely at random, analyses based on complete cases may be biased. Such biases can be overcome using methods such as multiple imputation that allow individuals with incomplete data to be included in analyses.
Under what circumstances would you use imputation technique?
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Multiple imputation
- Imputation – Similar to single imputation, missing values are imputed. ...
- Analysis – Each of the m datasets is analyzed.
When should I impute?
Contrary to mean im- putation, regression imputation can also be used when more than 10% of the data is missing and when the data contains highly correlated variables (Little & Ru- bin, 1989).
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