Why is it the case that when I run logistic regression with one categorical predictor, my regression is not significant whereas if I run the logistic regression with the same variable except it is continuous, the logistic regression automatically becomes significant?
Solved – Logistic regression: categorical predictor vs. quantitative predictor
binningcategorical datacontinuous dataregression
Best Answer
That is not a necessary result, but it is certainly plausible. If you turn a quantitive predictor into a single categorical predictor you lose a lot of information; with the categorical predictor you only know whether an observation is below or above a certain threshold (e.g. the mean or median), while with a quantitative predictor you also know how much below or above the threshold that observation is. It is not unreasonable to suspect that if you feed your model more information (i.e. add your variable as a quantitative predictor), you will get more precise results.
One of the reasons why this is not necessarily true is that if you add a variable to a regression model as a quantitative variable you assume the effect of that variable to be linear. If the effect is strongly non-linear, then that may undo the advantage of adding quantitative variables. There are however easy ways to check whether that is the case (plots of residuals against predictors), and easy ways to solve it (adding your variables as splines or polynomials are probably the easiest solutions).