I am doing some research using logistic regression. 10 variables influence the dependent variable. One of the aforementioned is categorical (e.g., express delivery, standard delivery, etc.). Now I want to rank those categories based on the "strength" of their effect on the dependent variable.
They are all significant (small p-value), but I think I can't just use the value of the odds for ranking purposes. I somehow need to figure out, if each category is also significantly different from the other categories. Is this correct?
I read about the possibility of centering the variable. Is this really an option? I do not want the rest of my model to be affected.
Stata output in order to support my comment to @subra's post:
Average marginal effects Number of obs = 124773
Model VCE : OIM
Expression : Pr(return), predict()
dy/dx w.r.t. : ExpDel
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ExpDel | .1054605 .0147972 7.36 0.000 .0798584 .1378626
------------------------------------------------------------------------------
Best Answer
Since you are interested in ranking the categories, you may want to re-code the categorical variables into a number of separate binary variables.
Example: Create a binary variable for express delivery- which would take the value 1 for express delivery cases and 0 otherwise. Similarly, a binary variable for standard delivery.
For each of these recoded binary variables you can calculate the marginal effects as indicated below:
Let me explain a bit on the above equation: lets say d is the re-coded binary variable for express delivery
Once you calculate the marginal effects for all the categories (re-coded binary variables) you can rank them.