Interpreting coefficients in logistic regression after standardizing independent variables I am working with a dataset where the continuous independent variables are on different scales and I plan on standardizing them before running a binary logistic regression. 
However, I am not sure on how to explain the co-efficients from the model after standardizing the variables.
For example, without standardizing my variables I get Exp(B) value of 1.111 for Average Rate of Return (independent variable) and I would explain it as -> For every one percent increase in average rate of return, the odds of being Retirement Ready (dependent variable) is higher by 11%. How do i interpret the model output in a similar format after using standardized variables?
 A: Let's say Y = Being Retirement Ready, X1 = Average Rate of Return and X2 = (X1 - m1)/s1 is the standardized version of X1, where m1 is the sample mean of X1 and s1 is the sample standard deviation of X1. 
Assume we fit the binary logistic regression model:
log(odds of being retirement ready) = beta0 + beta1*X1,

and obtain an estimate b1 of beta1.  If exp(b1) = 1.11, say, then we can state the following:
For every 1-unit additive increase in X1 (Average Return Rate), we 
estimate that the odds of being retirement ready increase by a 
multiplicative factor of 1.11 (i.e., an 11% increase).

Next, assume we fit the binary logistic regression model:
log(odds of being retirement ready) = gamma0 + gamma1*X2, 

and obtain an estimate g1 of gamma1.  If exp(g1) = 1.12, say, then we can state either of the following:
For every 1-unit additive increase in X2 (Standardized Average Return 
Rate), we estimate that the odds of being retirement ready increase by 
a multiplicative factor of 1.12 (i.e., a 12% increase).

For every s1-units additive increase in X1 (Average Return Rate), we    
estimate that the odds of being retirement ready increase by a 
multiplicative factor of 1.12 (i.e., a 12% increase).

If Average Return Rate is expressed in percentage points, then a 1-unit additive increase means a 1 percent additive increase.  You need to calculate the value of s1 to figure out how many units additive increase in X1 you are dealing with (e.g., if s1 = 20%, then you are dealing with a 20 percent increase on the additive scale).  You could also say, more generically, something like this:
For every standard deviation additive increase in the value of X1 
(Average Return Rate), we estimate that the odds of being retirement 
ready increase by a multiplicative factor of 1.12 (i.e., a 12% 
increase).

