# Which regression is useable for an ordinal dependent and multiple discrete/ordinal/binary independent variables?

For a paper of mine I am trying to figure out if there is a correlation between success as a musician and multiple other factors. These are my variables:

1. Dependent variable: Success from 2016 based on RIAA certifications. (ordinal, key: 0 = No cert;
1 = Gold; 2 = Platinum; 3 = Multiplatinum)
2. Independent variables:
• network degree (discrete, non-gaussian)
• male (binary)
• Success from 2013-2015 (ordinal, key: same as above)
• 5 dummies for different regions (binary, made using One Hot Encoding)

So my datatypes for the regression look like this: $$ordinal = discrete + binary + ordinal$$ .

I am unsure which type of regression to use. An ordered probit looks like the best option, although I am unsure if it is the best possible choice (my statistics are rusty). Are the any limitations that I need to be aware of in regards to the different data types and what are other useable options that could help me?