There may be times when you would like to run a logistic regression with no
predictor variables; in other words, just the constant (a.k.a. the
intercept).  For example, one may do this when calculating deviance scores
between various models.  If you try to run the logistic regression command
in SPSS without a method subcommand or a method = enter
subcommand with no variables after it, SPSS will give you an error message and
not run the logistic regression.  There is a way to “trick” SPSS
into running this type of logistic regression model.  First, you will need
to create a new variable that is a constant in the dataset.  Next, when you
run the logistic regression, use this new (constant) variable as the independent
variable with the noconst subcommand.  In effect, you are simply
substituting the constant that you create for the one that would normally be
included in the model.  (Please note that this trick does not work with the
regression command.  According to SPSS technical support, the
regression command cannot be run without predictors; in other words, you
cannot get an intercept only model.  If you want an intercept only model,
you will need to use the glm command.)

For example, let’s use the /spss/faq/hsb2.sav
dataset.  First, we will create the constant
variable.  Next, we will run the logistic regression using female as the dependent
variable (we understand that this is an unusual choice for a dependent
variable, but we just needed a dichotomous variable for the example).

compute constant = 1.
execute.

logistic regression  var = female
 /method = enter constant
 /noconst.

Case Processing Summary

Unweighted Cases(a)		N	Percent
Selected Cases	Included in Analysis	200	100.0
	Missing Cases	0	.0
	Total	200	100.0
Unselected Cases		0	.0
Total		200	100.0
a If weight is in effect, see classification table for the total number of cases.

Dependent Variable Encoding

Original Value	Internal Value
male	0
female	1

Classification Table(a,b,c)

			Predicted
			FEMALE		Percentage Correct
	Observed		male	female
Step 0	FEMALE	male	0	91	.0
		female	0	109	100.0
	Overall Percentage				54.5
a No terms in the model.
b Initial Log-likelihood Function: -2 Log Likelihood = 277.259
c The cut value is .500

Variables not in the Equation

			Score	df	Sig.
Step 0	Variables	CONSTANT	1.620	1	.203
	Overall Statistics		1.620	1	.203

Omnibus Tests of Model Coefficients

		Chi-square	df	Sig.
Step 1	Step	1.622	1	.203
	Block	1.622	1	.203
	Model	1.622	1	.203

Model Summary

Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square
1	275.637	.008	.011

Classification Table(a)

			Predicted
			FEMALE		Percentage Correct
	Observed		male	female
Step 1	FEMALE	male	0	91	.0
		female	0	109	100.0
	Overall Percentage				54.5
a The cut value is .500

Variables in the Equation

		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1(a)	CONSTANT	.180	.142	1.616	1	.204	1.198
a Variable(s) entered on step 1: CONSTANT.