Quote:
|
By "low risk" do you mean, say, for a variable which is Drink frequency with values of Never through to Everyday, the baseline category would be "Never"?
|
Yes
Quote:
|
Do you mind ellaborating on this? I would be most grateful. I have a strong pure maths background, but haven't done any statistics for 4 years (I left off my statistics career having completed the basics of normal distribution!). I'm not asking you to go into the smallest of detail with everything, but at the same time to put everything in laymans terms would be amazing!
|
Just try a run with variable selection using the options "Forward" and "Wald"
The rest is:
Hierarchical linear models commonly used by psychologists or psychometricians add groups of variables into a model simultaneously. Just scroogle "hierarchical logistic regression." The test to determine if entry of a group of variables is significant is called a likelihood ratio test (LRT) -- that is, you will get a p-value for the LRT. The test statistic happens to be a chi-square test, and chi-square tests are based on degrees of freedom, which happens to be equal to the number of variables in the group (added to the model) minus one.
I would start scroogling on terms you don't know about. And if serious about logisitic, then look at Hosmer & Lemeshow's Applied Logistic Regression textbook.
Univariate models only have one variable in them. If the beta p<0.25, for example, then you can add the variable to a larger model with many variables, and then run the larger model.