I had a couple of questions about interpreting odds ratios for continuous variables in logistic regression. I've done some significant digging online and in textbooks and I'm left thinking that my questions are so basic that they answers aren't explicitly stated anywhere. Although I'm slightly ashamed that I don't know the answers, I'm gonna swallow my pride and ask them so I know them in the future!

Here's my situation...I'm looking at a sample of adjudicated youth who, as part of their probation, were enrolled in a job/life skills training program. I wanted to see the extent to which the age at which they were released from the program predicted employment six months post-release from the program.

(Also, keep in mind that there are other predictors in the model, but I've excluded them because they are not statistically significant and I want to keep this as clear as possible.)

Predictor:

Age of release from training program (Mean age = 17.4, SD=1.2, Range 14.3-20.5)

Outcome:

Employed or not (Employed=1, Not Employed=0)

Result:

Odds ratio 3.01 (p<.005)

(I've excluded goodness of fit stats, etc. because I'm seeking answers about the interpretation of the odds ratio only; I feel comfortable w/ the evaluation of model fit, CI's, etc.)

Putting it into words:

As age increases by one year, the odds of being employed six months post-discharge increase by three units.

Questions:

1) When I say, "As age increases by one year..." what is the starting point for age?

Does age start at zero? For example, "As age increases from 0 [i.e., the lowest age if you were to place this model on a graph]...”

Does age start at the the lowest age among the range of ages in the sample? For example, “As age increases from 14.3...”

OR

Does age start at the mean age of the sample? For example, “As age increases from 17.4...”,

2) Would centering help me interpret this result OR is that only effective in interpreting the y-int?

If it would help, I was thinking of doing either mean centering or subtracting the lowest age in the range from all the other ages in the sample.

Any suggestions?

3) Finally, is it appropriate to say that compared to a 14-year-old youth, a 17-year-old youth is nine times more likely to be employed?

I ask because I know that logistic regression assumes a sigmoidal relationship, and I’m curious as to whether this 3 unit increase in odds remains consistent at any point along the regression line.

Thanks so much!

Aaron