Methods of categorizing a continuous variable in regression analysis Is there any recent research papers or state of the art methods on how to categorize/dichotomize an explanatory continuous variable in regression analysis?
 A: In general, there is just one universally accepted, advisable way to categorize continuous data. And that is... Floating point numbers! Computers are incapable of representing the vast majority of real numbers, let alone all of the rational numbers. Even if we didn't rely on computers, we can't measure anything with infinite precision, so we still have to round at some point!
But, floating point aside, there is no general reason to categorize a continuous variable, except perhaps under specialized circumstances. You end up just ignoring real variability in your data and further increase the wrongness of your already wrong, but perhaps useful, model.
Some references explaining why one (unfortunately) commonplace technique for dichotomizing continuous predictors (median splits) is particularly problematic:


*

*Negative Consequences of Dichotomizing Continuous Predictor Variables 

*On the Practice of Dichotomization of Quantitative Variables

*Median Splits, Type II Errors, and False Positive Consumer Psychology: Don’t Fight the Power
One hypothetical scenario where dichotomization might be okay:
Imagine you have a measurement $W$ that does a decent job of distinguishing between $X=0$ and $X>0$ but has extreme imprecision given $X>0$. Perhaps you know that, in a given year, the number of days an individual spent incarcerated, on parole, on probation, or in court. But you don't know those days are split between incarceration, parole, probation, etc... It doesn't make sense to treat days in court the same way that you'd treat days in prison, so something like a zero-inflated count model or a hurdle model might be inappropriate or exceedingly difficult to estimate. However, you might instead create a dichotomous "legal contact" variable 
$$W = 
\begin{cases}
0 & \text{if } X= 0\\
1 & \text{if } X> 0\\
\end{cases}$$
and employ a logistic model. This doesn't solve your real problem, which is that your measurement is imprecise, but it might allow you to get some information from your data.
Again, if you were to instead have, for example, a measure of days in prison specifically, than something like a hurdle model would likely be more useful and more powerful.
