MISSING Subcommand (REGRESSION command)

MISSING controls the treatment of cases with missing values. By default, a case that has a user-missing or system-missing value for any variable named or implied on VARIABLES is omitted from the computation of the correlation matrix on which all analyses are based.

• The minimum specification is a keyword specifying a missing-value treatment.

LISTWISE. Delete cases with missing values listwise. Only cases with valid values for all variables named on the current VARIABLES subcommand are used. If INCLUDE is also specified, only cases with system-missing values are deleted listwise. LISTWISE is the default if the MISSING subcommand is omitted.

PAIRWISE. Delete cases with missing values pairwise. Each correlation coefficient is computed using cases with complete data for the pair of variables correlated. If INCLUDE is also specified, only cases with system-missing values are deleted pairwise.

MEANSUBSTITUTION. Replace missing values with the variable mean. All cases are included and the substitutions are treated as valid observations. If INCLUDE is also specified, user-missing values are treated as valid and are included in the computation of the means.

INCLUDE. Includes cases with user-missing values. All user-missing values are treated as valid values. This keyword can be specified along with the methods LISTWISE, PAIRWISE, or MEANSUBSTITUTION.

Example

REGRESSION  VARIABLES=POP15,POP75,INCOME,GROWTH,SAVINGS
 /DEPENDENT=SAVINGS
 /METHOD=STEP
 /MISSING=MEANSUBSTITUTION.

• System-missing and user-missing values are replaced with the means of the variables when the correlation matrix is calculated.