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Imputation involves replacing missing values, or missings, with an estimated value. In a sense, imputation is a prediction solution. It is one of three options for handling missing data. The general principle is to delete when the data are expendable, impute when the data are precious, and segment for the less common situation in which a large data set has a large fissure. Imputation is measured against deletion; it is advantageous when it affords the more accurate data analysis of the two. This entry discusses the differences between imputing and deleting, the types of missings, the criteria for preferring imputation, and various imputation techniques. It closes with application suggestions.

Figure 1 Missing Data Structure

Impute or Delete

The trade-off is between inconvenience and bias. There are two choices ...

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