Quote:
Originally Posted by lep11  Logistic is fine, but in SPSS make sure you treat any independent variables that have more than 2 categories as "Categorical", and then for each specify the "cornerpoint" or low risk category as the baseline. The rest of the continuous variables age and binary models can be added to the model. |
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"?
Quote:
|
Originally Posted by lep11 The easiest approach is to run forward stepwise logistic using e.g. the Wald of Likelihood ratio method of forward stepping. This will only build a model with significant predictors. Stepwise is somewhat biased, however, since you are only selecting the "fish from the of the bucket." Arguable, backwards stepping is a method to preserve subset correlation, resulting in a model that preserves correlation. |
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!
Quote:
|
Originally Posted by lep11 Most psychologists or psychometricians would probably use the hierarchical approach with SPSS, where all the family variables are added as a group, all the school environment variables added as a group, peer group, etc, added as a group. |
Not really sure what you mean by this, could you explain?
Quote:
|
This will give a chi-square test statistic (degrees of freedom = number of group variable minus one), that you can use to determine if each group of of variables is a siginificant predictor. Using this appraoch will also reflect that, by design, you have a theoretical idea about constructs and domains that predict risk. In fact, in psychometrics, I probably would never simply throw all variables into a logistic regression equation and see what happens.
|
I feel here that you're really answering my question, but I'm a bit lost...
Quote:
|
The last is to evaluate univariate models, and then add single variables whose beta coefficients are significant (e.g., p<0.25) into a full model. This full model can contain risk factors, adjustments (age, family income, grade point average), and nusaince factors(variable you don't want to study but are significantly different across drinkers and non-drinkers.
|
Whats a univariate model?
Quote:
|
Finally, I would recommend identifying variables that are significantly different across drinkers/non-drinkers, which are not really of interest to you, but nevertheless are different across the groups. In disease research, these are commonly the comorbidities that patients have, since patients have multitudes of problems at older ages (depression, electrolyte imbalnce, hypertension, etc.). Once you identify these variables, run a logistic regression (same dependent variable on only these variables). Before run-time, specify in SPSS you want the "logit". The logit is called the "propensity score." Do the run, and at the far rightmost column of the data set you will see "logit_1". Next, in your risk prediction models, use your primary risk and adjustment factors, and the logit to represent all the junk variables (which were different across drinker.non-drinker but not really of primary interest to you -- these are also called confounders). This latter model with the propesnity score representing nuisance factors may be better than models including all the nuisance variables.
|
So this is tackling the so called "nuisance variables", which is great. Before I try get my head around that i want to understand the stages you explained up to this point.
All in all I can't thank you enough for you reply, and hope to hear from you (or anyone else who can add to this post) soon.