15.7.1.1 Categorical Variables
An axis expression that names a categorical variable divides the data
into cells according to the values of that variable. When all the
variables named on TABLE
are categorical, by default each cell
displays the number of cases that it contains, so specifying a single
variable yields a frequency table, much like the output of the
FREQUENCIES
command (see FREQUENCIES):
Custom Tables
|
|
Count |
Age group |
15 or younger |
0 |
16 to 25 |
1099 |
26 to 35 |
967 |
36 to 45 |
1037 |
46 to 55 |
1175 |
56 to 65 |
1247 |
66 or older |
1474 |
|
Specifying a row and a column categorical variable yields a
crosstabulation, much like the output of the CROSSTABS
command
(see CROSSTABS):
CTABLES /TABLE=ageGroup BY gender.
Custom Tables
|
|
S3a. GENDER: |
|
|
Male |
Female |
|
|
Count |
Count |
Age group |
15 or younger |
0 |
0 |
16 to 25 |
594 |
505 |
26 to 35 |
476 |
491 |
36 to 45 |
489 |
548 |
46 to 55 |
526 |
649 |
56 to 65 |
516 |
731 |
66 or older |
531 |
943 |
|
The ‘>’ “nesting” operator nests multiple variables on a single
axis, e.g.:
CTABLES /TABLE likelihoodOfBeingStoppedByPolice BY ageGroup > gender.
Custom Tables
|
|
|
|
86. In the past year, have you hosted a social event or party where alcohol was served to adults? |
|
|
|
|
Yes |
No |
|
|
|
|
Count |
Count |
Age group |
15 or younger |
S3a. GENDER: |
Male |
0 |
0 |
Female |
0 |
0 |
16 to 25 |
S3a. GENDER: |
Male |
208 |
386 |
Female |
202 |
303 |
26 to 35 |
S3a. GENDER: |
Male |
225 |
251 |
Female |
242 |
249 |
36 to 45 |
S3a. GENDER: |
Male |
223 |
266 |
Female |
240 |
307 |
46 to 55 |
S3a. GENDER: |
Male |
201 |
325 |
Female |
282 |
366 |
56 to 65 |
S3a. GENDER: |
Male |
196 |
320 |
Female |
279 |
452 |
66 or older |
S3a. GENDER: |
Male |
162 |
367 |
Female |
243 |
700 |
|
The ‘+’ “stacking” operator allows a single output table to
include multiple data analyses. With ‘+’, CTABLES
divides
the output table into multiple sections, each of which includes
an analysis of the full data set. For example, the following command
separately tabulates age group and driving frequency by gender:
CTABLES /TABLE ageGroup + freqOfDriving BY gender.
Custom Tables
|
|
S3a. GENDER: |
|
|
Male |
Female |
|
|
Count |
Count |
Age group |
15 or younger |
0 |
0 |
16 to 25 |
594 |
505 |
26 to 35 |
476 |
491 |
36 to 45 |
489 |
548 |
46 to 55 |
526 |
649 |
56 to 65 |
516 |
731 |
66 or older |
531 |
943 |
1. How often do you usually drive a car or other motor vehicle? |
Every day |
2305 |
2362 |
Several days a week |
440 |
834 |
Once a week or less |
125 |
236 |
Only certain times a year |
58 |
72 |
Never |
192 |
348 |
|
When ‘+’ and ‘>’ are used together, ‘>’ binds more
tightly. Use parentheses to override operator precedence. Thus:
CTABLES /TABLE hasConsideredReduction + hasBeenCriticized > gender.
CTABLES /TABLE (hasConsideredReduction + hasBeenCriticized) > gender.
Custom Tables
|
|
|
|
Count |
26. During the last 12 months, has there been a time when you felt you should cut down on your drinking? |
Yes |
513 |
No |
3710 |
27. During the last 12 months, has there been a time when people criticized your drinking? |
Yes |
S3a. GENDER: |
Male |
135 |
Female |
49 |
No |
S3a. GENDER: |
Male |
1916 |
Female |
2126 |
Custom Tables
|
|
|
|
Count |
26. During the last 12 months, has there been a time when you felt you should cut down on your drinking? |
Yes |
S3a. GENDER: |
Male |
333 |
Female |
180 |
No |
S3a. GENDER: |
Male |
1719 |
Female |
1991 |
27. During the last 12 months, has there been a time when people criticized your drinking? |
Yes |
S3a. GENDER: |
Male |
135 |
Female |
49 |
No |
S3a. GENDER: |
Male |
1916 |
Female |
2126 |
|