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Erschienen in:
2023 | OriginalPaper | Buchkapitel
5. General Considerations on Univariate Methods: Single and Multiple Imputation
verfasst von : Matthias Templ
Erschienen in: Visualization and Imputation of Missing Values
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Abstract
Missing or invalid values clearly affect the quality of data analysis, model results, and classification performance. However, the methods and principles for imputing data vary widely.
This chapter focuses on general considerations for imputing missing values. At the outset, full case analysis, where observations with missing values are deleted before analysis, and univariate imputation methods are criticized. They are discussed once here and then only used as a (worst-case) benchmark. Important in this chapter is the introduction to multiple imputation, a principle for properly estimating the variance of estimators, and the types of randomness introduced for imputation. The concept of multiple imputation is often used in simple production environments, but rarely when the production of statistics is complex, for example, in official statistics. Finally, the difference between joint modeling approaches and fully conditional modeling is explained.