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).