The first category of dependent variables presented in Table 1 is discrete outcomes. These variables are characterized by the fact that they only assume a finite number of integer values. There are four types of discrete outcomes. Binary, or dichotomous, outcomes take on only two values. Binary outcomes can be analyzed via probit regression or logistic regression.
Ordinal outcomes take on three or more values that can be rank ordered.
In addition, an assumption of equal-sized intervals between the response options of ordinal outcomes is usually not warranted. Ordinal outcomes can be analyzed via ordered probit regression or ordered logistic regression. Nominal outcomes assume three or more values that cannot be rank ordered. A nominal outcome can be analyzed via multinomial logistic regression. In a multinomial logistic regression, K-1 dichotomous outcomes are analyzed simultaneously, where K is equal to the number of values in the nominal outcome variable.