*** NOTE TO ANYONE INTERESTED IN THE CONTENTSOF, OR RESPONDING TO, THIS THREAD: a new thread has been created on the subject, titled "Logistic Regression 2". ***

I have a number of variables regarding teenage drinking and attitudes towards school. These include a mixture of binary/dichotomous variables and variables of the form "You teachers treat you fairly" (Strongly Agree, Agree, Disagree, Strongly Disagree). I want to analyse what the causal variables are on responses to "Have you ever drunk alcohol?", and take it that the way forward is logistic regression. I am using SPSS.

Here is what I plan to do, if anyone has any advice please respond!

Run a logistic regression for my dependent variable being "Have you ever drunk alcohol?", and with the independent variables as all of those variables I consider to have an affect on this dependent variable

Question 1: is there a concrete method for testing which variables have a "significant" affect on this dependent variable, and as a result I can justify using this as an independent variable in my logistic regression model?.

My uncle told me that the best thing to do is to look at a matrix of collinearity for all the independent variables I plan to chuck in, and if two variables are stongly correlated then you should leave one of them out (which one?) because this would lead to instability in the model. Can someone clarify this and is there a more concrete method of this process.

Question 2: So when I've got all my independent variables right I'll have my model, and all my beta_i's etc... but what kind of conclusion can I draw if, say, my final model has beta_1 = 0.8 (this being the parameter corresponding to age)?

Basically, I have a lot to learn.

Thanks.

L-dawg