I am running the a logistic regression model to test the effects of task variables on choice (left/right). I set up a logistic regression model per subject and test the regression coefficients against zero across subjects later on. One predictor is continuous and I normalize it to account for different possible values across subjects. One regressor is binary and I don't normalize it. One regressor can take on four different values (10,20,30,40) whereas their order and distances are meaningful. However it is still a discrete parameter. Would you normalize the regressor in this case? The results are different whether I do or don't and I wanted to hear your opinion.
I use matlab
to regress the design matrixx
with the following optionsbetas = glmfit (x,y,'binomial','link','logit')
. When normalize all variables, the respective regression weights for one example subject are (-7.14 4.283 -0.47 -0.49; intercept included). When I only normalize the continuous variablex1` the respective weights are (-5.51 4.283 -0.088 -1.01).
The t values against zero across all participants are [41.52 -3.985 and -0.032] if I normalize all values. If I only normalize the continuous variable they are [20.14 -3.89 -0.48].