Interpretaion of Logistic Coefficients

Question

All,

I ran a logistic Regression on a set of variables both categorical and continuous with a binary event as dependent variable.

Now post modelling, I observe a set of categorical variables showing negative sign which I presume is to understand that if that categorical variable occurs high number of times then the probability of the dependent variable occurring is low.

But when I see the % of occurrence of that independent variable I see the reverse trend happening. hence the result seems to be counter intuitive. Any reason why this could happen. I have tried explaining below with a pseudo example.

Dependent Variable - E Predictors: 1. Categorical Var - Cat1 with 2 levels (0,1) 2. Continuous Var - Con1 3. Categorical Var - Cat2 with 2 levels (0,1) Post Modelling: Say all are significant and the coefficients are like below, Cat1 - (-0.6) Con1- (0.3) Cat2 - (-0.4)

But when I calculate the % of occurrence of Event E on Cat 1, I observe that the % of occurence is high when Cat1 is 1, which I think is counter intuitive.

Pls help in understanding this.

Answer 1

There are at least two possibilities:

1. The parameterization of the categorical variable may not be what you think it is. SAS PROC LOGISTIC, in particular, has defaults that are (to me and many other) counter intuitive in many cases. R can also do some counterintuitive things with factors.

2. If that isn't it, then bear in mind that your logistic regression has other variables included. So, it is controlling for those variables. This has been discussed here multiple times.