This paper discusses two forms of separability of item and person parameters in the context of response time (RT) models. The first is “separate sufficiency”: the existence of sufficient statistics for the item (person) parameters that do not depend on the person (item) parameters. The second is “ranking independence”: the likelihood of the item (person) ranking with respect to RTs does not depend on the person (item) parameters. For each form a theorem stating sufficient conditions, is proved. The two forms of separability are shown to include several (special cases of) models from psychometric and biometric literature. Ranking independence imposes no restrictions on the general distribution form, but on its parametrization. An estimation procedure based upon ranks and pseudolikelihood theory is discussed, as well as the relation of ranking independence to the concept of double monotonicity.
