Sun Jianan, Ye Ziwen
Department of Mathematics, College of Science, Beijing Forestry University, Beijing, China.
Front Psychol. 2019 Aug 30;10:1944. doi: 10.3389/fpsyg.2019.01944. eCollection 2019.
Multidimensional computerized adaptive testing (MCAT) is one of the widely discussed topics in psychometrics. Within the context of item replenishment in MCAT, it is important to identify the item-trait pattern for each replenished item, which indicates the set of the latent traits that are measured by each replenished item in the item pool. We propose a pattern recognition method based on the least absolute shrinkage and selection operator (LASSO) to detect the optimal item-trait patterns of the replenished items via an MCAT test. Simulation studies are conducted to investigate the performance of the proposed method in pattern recognition accuracy under different conditions across various latent trait correlation, item discrimination, test lengths, and item selection criteria in the test. Results show that the proposed method can accurately and efficiently identify the item-trait patterns of the replenished items in both the two-dimensional and three-dimensional item pools.
多维计算机自适应测试（MCAT）是心理测量学中广泛讨论的主题之一。在MCAT的项目补充背景下，识别每个补充项目的项目-特质模式很重要，该模式表明了项目库中每个补充项目所测量的潜在特质集。我们提出一种基于最小绝对收缩和选择算子（LASSO）的模式识别方法，通过MCAT测试来检测补充项目的最优项目-特质模式。进行了模拟研究，以考察所提方法在不同条件下，跨越各种潜在特质相关性、项目区分度、测试长度和测试中的项目选择标准时，在模式识别准确性方面的表现。结果表明，所提方法能够准确、高效地识别二维和三维项目库中补充项目的项目-特质模式。
