Unforeseen complications during the administration of large-scale testing programs are inevitable and can prevent examinees from accessing all test material. For classification tests in which the primary purpose is to yield a decision, such as a pass/fail result, the current study investigated a model-based standard error approach, Bayesian Inference, Binomial Distribution, and Lord–Wingersky Recursion methods to estimate the consistency of making these classification decisions on an incomplete test. Using operational data from a high-stakes licensure examination, where items are presented in random order, results indicated that all methods were successful in eliminating misclassification when at least half the test was completed. Results from both Binomial and Recursion methods were nearly indistinguishable, yet differences emerged when item sequence was manipulated into difficulty order. Bayesian Inference was the most flexible, relatively unaffected by whether or not the items were randomly presented; however, representative prior data were required, which limits its practical utility. Implications for use in practice, relevant policy decisions, and feasibility for operational implementation are discussed.

中文翻译：

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估计不完整测试的分类决策

大规模测试项目管理过程中不可预见的并发症是不可避免的，并且会阻止考生访问所有测试材料。对于主要目的是产生决定（例如通过/失败结果）的分类测试，当前的研究调查了基于模型的标准误差方法、贝叶斯推理、二项分布和 Lord–Wingersky 递归方法来估计在不完整的测试中做出这些分类决策的一致性。使用来自高风险执照考试的操作数据，其中项目以随机顺序呈现，结果表明，当至少完成一半的测试时，所有方法都成功地消除了错误分类。二项式和递归方法的结果几乎无法区分，然而，当项目顺序被操纵成难度顺序时，差异就出现了。贝叶斯推理是最灵活的，相对不受项目是否随机呈现的影响；然而，需要有代表性的先验数据，这限制了它的实际效用。讨论了在实践中使用的影响、相关政策决定和操作实施的可行性。