MISSINGSUMMARIES Subcommand (MULTIPLE IMPUTATION command)

The MISSINGSUMMARIES subcommand controls the display of missing values of analysis variables.

OVERALL Keyword

The OVERALL keyword displays a graph that summarizes missingness for cases, variables, and individual data (cell) values:

  • Number and percent of analysis variables that have one or more missing values.
  • Number and percent of cases that have one or more missing values on analysis variables.
  • Number and percent of individual data values that are missing among all analysis variables and cases.

Overall summaries are displayed by default.

VARIABLES Keyword

The VARIABLES keyword displays a table of analysis variables sorted by percent of missing values. The table includes descriptive statistics (mean and standard deviation) for scale variables. The table is not shown by default.

You can optionally control the maximum number of variables to display (default=25) and minimum percentage missing (default=10) for a variable to be included in the display. Specify one or both in parentheses. For example, VARIABLES(MAXVARS=50 MINPCTMISSING=25) requests that the table display up to 50 variables that have at least 25% missing values. The set of variables that meet both criteria are displayed. For example, by default, if there are 25 analysis variables but only 5 have 10% or more missing values, the output includes only 5 variables. If there are more analysis variables than MAXVARS and all meet the MINPCTMISSING criteria, then variables with the most missing values are displayed.

  • MAXVARS must be a positive integer.
  • MINPCTMISSING must be a nonnegative number that is less than 100.

PATTERNS Keyword

The PATTERNS keyword displays tabulated patterns of missing values. Each pattern corresponds to a group of cases with the same pattern of incomplete and complete data on analysis variables. You can use PATTERNS output to determine whether monotone imputation method can be used for your data, or if not, how closely your data approximate a monotone pattern.

To display patterns, specify the PATTERNS keyword. The chart orders analysis variables and patterns to reveal monotonicity where it exists. Specifically, variables are ordered from left to right in increasing order of missingness. Rows (patterns) are the sorted first by the last variable (nonmissing values first, then missing values), then by the second to last variable, and so on, working from right to left.

You can use PATTERNS output to determine whether monotone imputation method can be used for your data, or, if not, how closely your data approximate a monotone pattern. If no nonmonotone pattern exists after reordering you can conclude that the data have a monotonic pattern when analysis variables are ordered as such. If data are not monotone, the chart shows how to achieve reach monotonicity.

NONE Keyword

NONE suppresses all MISSINGSUMMARIES output.

NONE generates an error when used with any other keyword.