Using ordinary least squares (OLS) does not solve the problem you are facing. It only assumes it away. If you are using OLS you are implicitly assuming that the different points on your scale are equally spaced. If you are comfortable with this assumption, push the OLS button and try to convince your audience.
I would tackle the problem differently. You have already mentioned of the solutions. Indeed, it could make sense to recode the control variables and to reduce the number of categories. Sparsely populated categories could be merged to other categories. Use your topical knowledge to merge and redefine categories.
You can also try to recode the dependent variables. Even on a 10 point scale, responses are usually clustered around some modalities. Again, guided by topical knowledge, you could redefine the dependent variable.
This topic is not new on CrossValidated. Under the Likert tag you will find plenty of discussions that may be of interest to you.
Results from an ordered logit/probit regression are always unintuitive, but categorical explanatory variables are as meaningful as continuous ones. I'd even say that they are easier to interpret.
For a concrete example, you could look at Dobson, An Introduction to Generalizer Linear Models, 2002, 2nd ed., Chapter 8. In her "car preferences" example, the dependent variable is the importance of air conditioning and power steering (three levels: "no or little importance", "important", "very important") and the two explanatory variables are gender (male or female, coded as 1 and 0) and age (18-23, 24-40, >40, coded as age2440 = 1 or 0, and agegt40 = 1 or 0).
Fitting an ordered probit model you get (I've used R, MASS library, polr() function):
Coefficients:
male age2440 agegt40
-0.3467 0.6817 1.3288
Intercepts:
NoImp|Imp Imp|VeryImp
0.01844 0.97594
Then you can compute the probabilities for women (male = 0) over 40 (age2440 = 0, agegt40 = 1):
NoImp Imp VeryImp
0.095 0.267 0.638
and for men over 40 (male = 1):
NoImp Imp VeryImp
0.168 0.330 0.502
Their difference is the gender partial effect:
NoImp Imp VeryImp
-0.073 -0.063 0.136
I think that it's meaningful ;-)
Best Answer
That is not a robustness check because the ordinary linear model is guaranteed not to fit. It will yield probabilities estimates outside $[0,1]$. A better approach to checking the assumptions of an ordinal regression model are:
For the logistic ordinal model (proportional odds model) the equal slopes (proportional odds) assumption can be checked in several ways, including: