Missing Data Patterns

The MI Procedure

Missing Data Patterns

The MI procedure sorts the data into groups based on whether an individual's value is observed or missing for each variable to be analyzed. The input data set does not need to be sorted in any order.

For example, with variables Y₁, Y₂, and Y₃ (in that order) in a data set, up to eight groups of observations can be formed from the data set. The following figure displays the eight groups of observations and an unique missing pattern for each group:

Missing Data Patterns

Group	Y1	Y2	Y3
1	X	X	X
2	X	X	.
3	X	.	X
4	X	.	.
5	.	X	X
6	.	X	.
7	.	.	X
8	.	.	.

Figure 9.6: Missing Data Patterns

Here, an "X" means that the variable is observed in the corresponding group and a "." means that the variable is missing.

The variable order is used to derive the order of the groups from the data set, and thus determines the order of missing values in the data to be imputed. If you specify a different order of variables in the VAR statement, then the results are different even if the other specifications remain the same.

A data set with variables Y₁, Y₂, ..., Y_p (in that order) is said to have a monotone missing pattern when the event that a variable Y_j is missing for a particular individual implies that all subsequent variables Y_k, k > j, are missing for that individual. Alternatively, when a variable Y_j is observed for a particular individual, it is assumed that all previous variables Y_k, k < j, are also observed for that individual.

For example, the following figure displays a data set of three variables with a monotone missing pattern. Note that this data set does not have any observations with missing patterns such as in Groups 3, 5, 6, 7, or 8 in the previous example.

Monotone Missing Data Patterns

Group	Y1	Y2	Y3
1	X	X	X
2	X	X	.
3	X	.	.

Figure 9.7: Monotone Missing Patterns

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