15.7.9.3 Scale Missing Values
/SMISSING
{VARIABLE
| LISTWISE
}
The SMISSING
subcommand, which must precede the first
TABLE
subcommand, controls treatment of missing values for
scalar variables in producing all the output tables. SMISSING
is optional.
With SMISSING=VARIABLE
, which is the default, missing values
are excluded on a variable-by-variable basis. With
SMISSING=LISTWISE
, when stacked scalar variables are nested
together with a categorical variable, a missing value for any of the
scalar variables causes the case to be excluded for all of them.
As an example, consider the following dataset, in which ‘x’ is a
categorical variable and ‘y’ and ‘z’ are scale:
Data List
x |
y |
z |
1 |
. |
40.00 |
1 |
10.00 |
50.00 |
1 |
20.00 |
60.00 |
1 |
30.00 |
. |
|
With the default missing-value treatment, ‘x’’s mean is 20, based
on the values 10, 20, and 30, and ‘y’’s mean is 50, based on 40,
50, and 60:
CTABLES /TABLE (y + z) > x.
Custom Tables
|
|
|
Mean |
y |
x |
1 |
20.00 |
z |
x |
1 |
50.00 |
|
By adding SMISSING=LISTWISE
, only cases where ‘y’ and
‘z’ are both non-missing are considered, so ‘x’’s mean
becomes 15, as the average of 10 and 20, and ‘y’’s mean becomes
55, the average of 50 and 60:
CTABLES /SMISSING LISTWISE /TABLE (y + z) > x.
Custom Tables
|
|
|
Mean |
y |
x |
1 |
15.00 |
z |
x |
1 |
55.00 |
|
Even with SMISSING=LISTWISE
, if ‘y’ and ‘z’ are
separately nested with ‘x’, instead of using a single ‘>’
operator, missing values revert to being considered on a
variable-by-variable basis:
CTABLES /SMISSING LISTWISE /TABLE (y > x) + (z > x).
Custom Tables
|
|
|
Mean |
y |
x |
1 |
20.00 |
z |
x |
1 |
50.00 |
|