在具有锚定测试(NEAT)设计的非等效组中不存在的响应部分可以管理到计划中的缺失场景。在小样本量的背景下,我们提出了一种基于机器学习(ML)的插补技术,称为链接随机森林(CRF),以在NEAT设计中执行等同任务。具体来说,基于不同的数据增强方法,提出了7种基于CRF的插补方法。通过仿真研究检查了所提出方法的等效性能。考虑了五个因素:(a)测试长度(20,30,40,50),(b)每个测试表格的样本大小(50对100),(C)共同/固定项目的比率(0.2对0.3),(d)采用两种形式的等效组和非等效组(无平均差与平均差0.5),和(E)三种不同类型的锚(随机,easy,andhard),导致96个条件。此外,五种传统的等同方法,(1)塔克法;(2)莱文观察分数法;(3)等百分位数方法;(4)圆弧法;(5)基于Rasch模型的并行校准,还考虑了,加上7种基于CRF的归位方法,本研究共12种方法。研究结果表明,受益于ML技术的优势,基于CRF的方法,结合了Tucker方法的等同结果,例如IMP_total_Tucker,IMP_pair_Tucker,和IMP_Tucker_cirlce方法,可以为相等任务中的“错误”产生更可靠和可信的估计,因此比其他小样本短长度测试中的分数更准确。
The part of responses that is absent in the nonequivalent groups with anchor test (NEAT) design can be managed to a planned missing scenario. In the context of small sample sizes, we present a machine learning (ML)-based imputation technique called chaining random forests (CRF) to perform
equating tasks within the NEAT design. Specifically, seven CRF-based imputation
equating methods are proposed based on different data augmentation methods. The
equating performance of the proposed methods is examined through a simulation study. Five factors are considered: (a) test length (20, 30, 40, 50), (b) sample size per test form (50 versus 100), (c) ratio of common/anchor items (0.2 versus 0.3), and (d) equivalent versus nonequivalent groups taking the two forms (no mean difference versus a mean difference of 0.5), and (e) three different types of anchors (random, easy, and hard), resulting in 96 conditions. In addition, five traditional
equating methods, (1) Tucker method; (2) Levine observed score method; (3) equipercentile
equating method; (4) circle-arc method; and (5) concurrent calibration based on Rasch model, were also considered, plus seven CRF-based imputation equating methods for a total of 12 methods in this study. The findings suggest that benefiting from the advantages of ML techniques, CRF-based methods that incorporate the equating result of the Tucker method, such as IMP_total_Tucker, IMP_pair_Tucker, and IMP_Tucker_cirlce methods, can yield more robust and trustable estimates for the \"missingness\" in an equating task and therefore result in more accurate equated scores than other counterparts in short-length tests with small samples.