Abstract

This article describes a marginal maximum likelihood (MML) estimation algorithm for Wilson's (1990) ordered partition model (OPM), a measurement model that does not require the set of available responses to assessment tasks to be fully ordered. The model and its estimation algorithm are illustrated through the analysis of an example data set. In the example, we use the ordered partition model to compare a set of alternative scoring schemes for open-ended science items.