Abstract

Assuming a “synthetic population” and imposing strong assumption to estimate score distributions has been the traditional practice when performing equating under the nonequivalent groups with anchor tests design (NEAT). In this paper, we use the concept of partial identification of probability distributions to offer an alternative to this traditional practice in NEAT equating. Under this approach, the score probability distributions used to obtain the equating transformation are bounded on a region where they are identified by the data. The advantages of this approach are twofold: first, there is no need to define a synthetic population and, second, no particular assumptions are needed to obtain bounds for the score probability distributions that are used to build the equating transformation. The results show that the uncertainty about the score probability distributions, reflected on the width of the bounds, can be very large, and can thus have a big impact on equating