The current study aims to evaluate the performance of three non‐IRT procedures (i.e., normal approximation, Livingston‐Lewis, and compound multinomial) for estimating classification indices when the observed score distribution shows atypical patterns: (a) bimodality, (b) structural (i.e., systematic) bumpiness, or (c) structural zeros (i.e., no frequencies). Under a bimodal distribution, the normal approximation procedure produced substantially large bias. For a distribution with structural bumpiness, the compound multinomial procedure tended to introduce larger bias. Under a distribution with structural zeroes, the relative performance of selected estimation procedures depended on cut score location and the sample‐size conditions. In general, the differences in estimation errors among the three procedures were not substantially large.

中文翻译：

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具有非典型分数分布的分类一致性和准确性

当前的研究旨在评估三种非IRT程序（即正态近似，Livingston-Lewis和复合多项式）的性能，以便在观察到的分数分布显示非典型模式时估计分类指数：（a）双峰性，（b）结构（即系统性）颠簸，或（c）结构性零点（即无频率）。在双峰分布下，正态近似过程产生了很大的偏差。对于具有结构凸起的分布，复合多项式过程倾向于引入更大的偏差。在结构为零的分布下，所选估计程序的相对性能取决于切分位置和样本大小条件。通常，这三个过程之间的估计误差差异并不很大。