# Ordinal regression, multinomial regression or linear regression with dummy variables?

I am conducting a study looking at the quality of clinical trials. I am trying to find out what factors are predictors of the methodological quality of the trials. My dependent variable is a score grading their methodology (score range is from 0 to 10). I have a number of independent variables. Some are multi-category nominal like country, trial type (2-arm, multi-arm, etc) or discipline (cardiology, oncology, radiology, etc.). Others are continuous and binary.

I was initially under the impression that for the multi-category ones, creating dummy variables and then proceeding with linear regression was the correct way. One question I have is that after coding for the dummy variables, depending on which I use as the reference, the significance of the others will change. I understand the math behind it, but I am confused as to which category I should then use as a reference. How does one decide on which category should be used as the reference? The categories are all of equal importance. For example, one of the multi-category variables is funding source - government, non-profit or private. Depending on which I use as the reference, the significance of the other two varies.

Second question: after reading some texts, some are suggesting ordinal or multinomial (depending on the distribution of data within each independent variable). Is this a better method for what I need rather than doing linear regression with dummy variables?

• It doesn't matter which category is the reference; this changes the parameterization, not the model. If you use R, you can use emmeans to look at the contrasts (comparisons between categories) that are of interest. And yes you should consider ordinal regression because the outcome variable is a ranking of quality on a scale 0-10. Commented Feb 24 at 6:46