Methods for selecting the best variable into the regression model I have constructed a continuous variable by using two different methods. Now I want to know the variable created under which way is the best and should be included in the model. Some preliminary results show that the variables constructed using both methods can significantly predict the outcome (p=0.03 vs. p=0.04). The incremental R-squared added by the variable constructed by both ways is the same. The AIC results for the model with the variable created using method one is 8000.2 and 8000.9 for the model with the variable constructed using method two.
Is there any other method I can use to compare which variable is relatively better?

Since both methods perform similarly well via p-value and AIC, another aspect you can consider is their abilities to generalize to new data. Cross validation will give you a good measure of this, since iteratively training on a susbet of data while testing on the remainder emulates this process and also gives a measure of how sensitive the metrics are to outliers.
