Laureate Institute for Brain Research, Tulsa, OK, USA.
Department of Educational, School, and Counseling Psychology, University of Missouri, Columbia, MO, USA.
Behav Res Methods. 2024 Aug;56(5):4385-4402. doi: 10.3758/s13428-023-02189-z. Epub 2023 Aug 3.
In item response theory (IRT) modeling, the magnitude of the lower and upper asymptote parameters determines the degree to which the inflection point shifts above or below P = 0.50. The current study examines the one-parameter negative log-log model (NLLM), which is characterized by a downward shift in the inflection point, among other distinctive psychometric properties. After detailing the statistical foundations of the NLLM, we present a series of simulation studies to establish item and person parameter estimation accuracy and to demonstrate that this parsimonious model addresses the "slipping" effect (i.e., unexpectedly incorrect answers) via an inflection point < 0.50 rather than through computationally difficult estimation of the upper asymptote. We then provide further support for these simulation results through empirical data analysis. Finally, we discuss how the NLLM contributes to recent methodological literature on the utility of asymmetric IRT models.
在项目反应理论 (IRT) 建模中，下渐近和上渐近参数的大小决定了拐点向上或向下移动超过 P = 0.50 的程度。本研究检验了单参数负对数-对数模型 (NLLM)，该模型的特点是拐点向下移动，以及其他独特的心理测量特性。在详细介绍 NLLM 的统计基础后，我们进行了一系列模拟研究，以确定项目和个体参数估计的准确性，并证明这个简约模型通过拐点 < 0.50 而不是通过计算上渐近的困难估计来解决“滑动”效应（即，意外错误的答案）。然后，我们通过实证数据分析进一步支持这些模拟结果。最后，我们讨论了 NLLM 如何为最近关于非对称 IRT 模型效用的方法学文献做出贡献。
