CTABLES Categorical Variable Basics (PSPP)

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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):

CTABLES /TABLE=ageGroup.

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
	15 or younger	S3a. GENDER:	Female	0	0
	16 to 25	S3a. GENDER:	Male	208	386
	16 to 25	S3a. GENDER:	Female	202	303
	26 to 35	S3a. GENDER:	Male	225	251
	26 to 35	S3a. GENDER:	Female	242	249
	36 to 45	S3a. GENDER:	Male	223	266
	36 to 45	S3a. GENDER:	Female	240	307
	46 to 55	S3a. GENDER:	Male	201	325
	46 to 55	S3a. GENDER:	Female	282	366
	56 to 65	S3a. GENDER:	Male	196	320
	56 to 65	S3a. GENDER:	Female	279	452
	66 or older	S3a. GENDER:	Male	162	367
	66 or older	S3a. GENDER:	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
	Yes	S3a. GENDER:	Female	49
	No	S3a. GENDER:	Male	1916
	No	S3a. GENDER:	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
	Yes	S3a. GENDER:	Female	180
	No	S3a. GENDER:	Male	1719
	No	S3a. GENDER:	Female	1991
27. During the last 12 months, has there been a time when people criticized your drinking?	Yes	S3a. GENDER:	Male	135
	Yes	S3a. GENDER:	Female	49
	No	S3a. GENDER:	Male	1916
	No	S3a. GENDER:	Female	2126