Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai, China.
Beijing Insight Online Management Consulting Co.,Ltd, Beijing, China.
Behav Res Methods. 2024 Sep;56(6):6363-6388. doi: 10.3758/s13428-023-02315-x. Epub 2024 Feb 26.
High-stakes non-cognitive tests frequently employ forced-choice (FC) scales to deter faking. To mitigate the issue of score ipsativity derived, many scoring models have been devised. Among them, the multi-unidimensional pairwise preference (MUPP) framework is a highly flexible and commonly used framework. However, the original MUPP model was developed for unfolding response process and can only handle paired comparisons. The present study proposes the 2PLM-RANK as a generalization of the MUPP model to accommodate dominance RANK format response. In addition, an improved stochastic EM (iStEM) algorithm is devised for more stable and efficient parameter estimation. Simulation results generally supported the efficiency and utility of the new algorithm in estimating the 2PLM-RANK when applied to both triplets and tetrads across various conditions. An empirical illustration with responses to a 24-dimensional personality test further supported the practicality of the proposed model. To further aid in the application of the new model, a user-friendly R package is also provided.
高风险非认知测试常采用迫选（FC）量表来防止作弊。为了减轻由此产生的分数正向性问题，已经设计了许多评分模型。其中，多维成对偏好（MUPP）框架是一种高度灵活且常用的框架。然而，原始的 MUPP 模型是为展开反应过程而开发的，只能处理成对比较。本研究提出了 2PLM-RANK，作为 MUPP 模型的推广，以适应优势等级格式的反应。此外，还设计了改进的随机 EM（iStEM）算法，以实现更稳定和高效的参数估计。模拟结果普遍支持在应用于各种条件下的三元组和四元组时，新算法在估计 2PLM-RANK 方面的效率和实用性。对 24 维人格测试的反应的实证说明进一步支持了所提出模型的实用性。为了进一步帮助应用新模型，还提供了一个用户友好的 R 包。
