# Regression With Binned Variables

Today our professor was discussing Sutradhar, Gu and Paszat (2016). In this paper the authors decided to study the relationships between patient characteristics and following the "advice" of their doctors.

While reading this paper, I noticed (e.g., Page 4) that variables such as "Age" and "Neighborhood Income" are binned into quintiles. I have often read that arbitrary binning (e.g., why quintiles, why not deciles?) of a continuous variable should be avoided as it can both lead to biases as well as phenomena such as "p-hacking". As an example, is it possible that the authors tried different combinations of binning criteria until one of these criteria produced meaningful results?

My question: Ideally speaking, should this "binning" process not have been done and these variables (i.e., age, income) treated as continuous? Could it be said that this "binning" process might potentially take away from the results in this paper and add biases when compared to treating these variables as continuous?

• Yes, this could be the case, although it is also probable that the authors got this data in this format already, and in that case there was probably no hacking involved. Some surveys still ask the participants to put their age in one of the pre-defined age groups. Commented Jan 12, 2023 at 7:15
• Harrell’s Regression Modeling Strategies even discusses some reasons why surveys might do that. I recall an example about how people might be reluctant to give their exact salary but might be more willing to identify the range to which their salary belongs, so then it becomes a tradeoff between lower accuracy from having bins vs lower accuracy due to possible missing data.
– Dave
Commented Jan 13, 2023 at 16:55
• You can also use either interval arithmetic or Bayesian priors to represent the uncertainty in the unknown values within a bin. Commented Jan 13, 2023 at 17:03
• Thank you so much for your replies everyone! Commented Jan 16, 2023 at 16:27