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.