Missing Data, Imputation of

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

Categorizing Missingness

Missingness can be categorized in two ways: the physical structure of the missings and the underlying nature of the missingness. First, the structure of the missings can be due to item or unit missingness, the merging of structurally different data sets, or barriers attributable to the data collection tools. Item missingness refers to the situation in which a single value is missing for a particular observation, and unit missingness refers to the situation in which all the values for an observation are missing. Figure 1 provides an illustration of missingness.

Table 1 Underlying Nature of Missingness

Second, missings can be categorized by the underlying nature of the missingness. These three categories are (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR), summarized in Table 1 and discussed below.

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