Which test to use?

What I want to have is this:
The effect of 1 continuous variable ("fixed factor"?) on a categorical DV controlling for 1 categorical variable (covariate) and 4 continuous variables (covariate).
I can also work with the continuous variables transformed into categorical variables.

Isn't this Binary Logistic Regression? How do I go about "controlling" in SPSS? As in, for the BLR test, there's no way to tell SPSS that some variables are covariates.. I guess SPSS does what I want to do it naturally anyway. That is, in the output table with all the variables and their Exp(B), Sig, df, Wald values etc., the test is designed to keep all but the single particular covariate constant. I have 6 covariates. For each one, the rest are controlled for. Is that true; do I get it right?
Also, on the results of the Logistic REgression, some variables have weird results on the AOR. For example, no upper limit; or huge Exp(B) or CI results such as 1,109E+15 or 59.535.


Fortran must die
A categorical DV might be binary logistic regression, ordinal logistic regression, or multinomial depending on how its measured. How many levels and what scale of data do you use for your DV.

Binary = Binary logistic regression
ordinal with more than two levels = ordinal logistic regression
nominal with more than two levels = multinomial logistic regression

In regression you don't go about controlling with the code. Your theory will tell you if the predictor is a control or not - not the software.

I am not sure what the BLR test is. Huge Exp are like a violation of one of the assumptions of the method. Did you run tests of the assumptions of the method (test for Multicolinearity, heteroscedastcity, etc)? Huge odds ratios are a common sign of a violation although I forget which one.
Well I checked it with a stats prof. and she said the reason why the numbers are off the charts is b/c the two groups are way off balance. The frequency is like %95 - %5 so there's no significance whatsoever..

BLR is just a fancy name we use as master statisticians to differ ourselves from the regular statisticians for the Binary Logistic Regression.