# Large odds ratio in binary logistic regression - huge scale difference of continous variables

I'd like to ask for some help with a binary logistic regression. In SPSS I am building a binary logistic regression with 4 independent continuous variables (Sample size - 85).

However, with one of the variables (Bicaudatus_index) I get a huge odds ratio:

Maybe the scale of this variable is very different than other variables:

As this variable is a ratio of two measurements I try to multiply the variable 100 times and get a new variable. The odds ratio of the new variable in the same regression seems to be within normal range. However I don't know if it is appropriate to do that. If I multiply the variable not by 100, but e.g. 150 times, I get odds ratios that are different from the ones that I get with 100.

• What values for the coefficient do you get for the additional analyses which you mention? Oct 1 '16 at 16:40

Gelman, A. (2008). Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine, 27, 2865–2873. doi:10.1002/sim.3107