What to consider when using an ordered logit/probit regression?

I'm studing the effect of working hours on self-reported health where health is scaled 0-10 and working hours is the difference between actual hours and preferred hours. Based on this I created two dummies for working more than prefered and one for working less than preferred. these two dummies is what I want to estimate on self-reported health. I will offcourse include age, gender, education etc as independent variables also. I will make groups of variables, age education etc.

To do this regression I will use polr in the MASS package.
But before or after running the regression what are those steps (if any) I sould code or consider according to econometric theories? For instance there is so much to do/think about when using OLS but I can't find any other about polr/ordered regression.
In other communities/forums it was suggested to use brant or anova test for ordered regression, so I will do that. But is this enough? I don't feel it's enough.
What about heteroskedasticity, endogeneity, robustness, causality? I'm very much in doubt about what to do.
Can you suggest any kind of paper, book etc. I can read according to the above?

Because these questions are not specific for the R environment, you might have more success by looking at the Cross Validated website.
But maybe that was one of the 'communities/forums' you already consulted.

Basically, the issues that you identify are the same for probit/logit as they are for OLS. The important one is likely endogeneity. And that's the one that's hard to solve, of course.

@HanOostdijk Thanks I will do that from now.. One of the reason that I post it here was that I don't now how R can be used for my purpose.. so I thought if I post here then maybe someone could give me answer with some r codes or packages.

@startz oh okay.. I was just very much in doubt because I could really not find any examples with it.. seems like no one considered one of these things when I search.

7 Proportional Odds Logistic Regression for Ordered Category Outcomes | Handbook of Regression Modeling in People Analytics: With Examples in R, Python and Julia (peopleanalytics-regression-book.org)


Do anyone know if the polr function in the MASS package is better than oglmx package? In the oglmx package there is heteroskedastic ordered probit regression which sounds better but I want to hear oppion and why to use which?

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