A Critical View on the NEAT Equating Design: Statistical Modeling and Identifiability Problems

作者：Ernesto San Martı́n, Jorge González · 发表于：Journal of Educational and Behavioral Statistics · 年份：2022 · DOI：10.3102/10769986221090609 · 被引用次数：3 · 研究领域：Psychometric Methodologies and Testing、Advanced Statistical Modeling Techniques、Statistical Methods in Clinical Trials

The nonequivalent groups with anchor test (NEAT) design is widely used in test equating. Under this design, two groups of examinees are administered different test forms with each test form containing a subset of common items. Because test takers from different groups are assigned only one test form, missing score data emerge by design rendering some of the score distributions unavailable. The partially observed score data formally lead to an identifiability problem, which has not been recognized as such in the equating literature and has been considered from different perspectives, all of them making different assumptions in order to estimate the unidentified score distributions. In this article, we formally specify the statistical model underlying the NEAT design and unveil the lack of identifiability of the parameters of interest that compose the equating transformation. We use the theory of partial identification to show alternatives to traditional practices that have been proposed to identify the score distributions when conducting equating under the NEAT design.