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"If I back transform this I get 1.68. This does not seem
right, as a $1 increase in income would raise the odds of giving birth
in a given year by 68%. This would mean $1,000 raise would increase
the odds by 0.68*1000 or a 680% increase in the odds of giving birth."
Quite apart from the log transformation prob, careful with those marginal
effects. "Marginal" means for a small change so you cannot extrapolate that
to a huge increase like your 1000 bucks... Also, -mfx- stresses the fact
that the impact will be different depending on the value the covariates take
on. As As default, the mean is assumed but you can ask for other evaluation
points...
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Jason Davis
Gesendet: Freitag, 6. Februar 2009 16:19
An: [email protected]
Betreff: st: Interpretation of log transformed variables in logistic
regression?
I can use some help with this one. I have run a multivariate
logistical regression with log transformed continuous variables,
non-transformed continous variables, and some categorical variables.
The DV is birth outcome in a given year (yes/no) and the IV of
interest is income (log transformed). The results are in odds ratios.
My confusion is how do I interpret the odds ratio of the log
transformed continous variable. Specifically, the odds ratio of log
income is 5.4. If I back transform this I get 1.68. This does not seem
right, as a $1 increase in income would raise the odds of giving birth
in a given year by 68%. This would mean $1,000 raise would increase
the odds by 0.68*1000 or a 680% increase in the odds of giving birth.
Any suggestions would be greatly appreciated.
Jason
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