
The nonequivalent groups with anchor test design (NEAT) is widely used in test equating. In this design, two groups of examinees are administered separate test forms with each test form containing a common subset of items. Because test takers from different populations are assigned only one test form, missing score data emerge by design rendering some of the score distributions unavailable. The equating literature has treated this problem from different perspectives, all of them making different assumptions in order to estimate the missing score distributions. In this talk I’ll use the concept of partial identification of probability istributions to offer an alternative to this traditional practice in NEAT equating. Comparisons with current methods of equating under the NEAT design are made and illustrated.