We extend the discrete data latent class literature by explicitly defining a latent variable for class membership as a function of both observables and unobservables, thereby allowing the equations defining the class membership and observed outcomes to be correlated. The procedure is then applied to modelling observed obesity outcomes, based upon an underlying ordered probit equation.