在小样本量的背景下,通过链接随机森林的NEAT等式:一种机器学习方法。

IF 2.1 3区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Educational and Psychological Measurement Pub Date : 2023-10-01 Epub Date: 2022-09-04 DOI:10.1177/00131644221120899
Zhehan Jiang, Yuting Han, Lingling Xu, Dexin Shi, Ren Liu, Jinying Ouyang, Fen Cai
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引用次数: 0

摘要

锚定测试(NEAT)设计的非等效组中不存在的部分响应可以管理到计划缺失的场景。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,称为链式随机森林(CRF),以在NEAT设计中执行等式任务。具体而言,基于不同的数据扩充方法,提出了七种基于CRF的插补等值方法。通过仿真研究检验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)每个测试形式的样本量(50对100),(c)常见/锚定项目的比率(0.2对0.3),以及(d)采用两种形式的等效组与非等效组(无平均差异与0.5的平均差异),和(e)三种不同类型的锚定(随机、简单和坚硬),导致96种条件。此外,还有五种传统的等值方法,(1)塔克法;(2) Levine观察评分法;(3) 等百分比等值法;(4) 圆弧法;和(5)基于Rasch模型的并行校准,加上本研究中总共12种方法的7种基于CRF的插补等值方法。研究结果表明,得益于ML技术的优势,基于CRF的方法结合了Tucker方法的等式结果,如IMP_total_Tucker、IMP_pair_Tucker和IMP_Tucker_cirlce方法,可以对等式任务中的“缺失”产生更稳健和可信的估计,因此在小样本的短长度测试中,与其他同行相比,可以获得更准确的等式分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The NEAT Equating Via Chaining Random Forests in the Context of Small Sample Sizes: A Machine-Learning Method.

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.

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来源期刊
Educational and Psychological Measurement
Educational and Psychological Measurement 医学-数学跨学科应用
CiteScore
5.50
自引率
7.40%
发文量
49
审稿时长
6-12 weeks
期刊介绍: Educational and Psychological Measurement (EPM) publishes referred scholarly work from all academic disciplines interested in the study of measurement theory, problems, and issues. Theoretical articles address new developments and techniques, and applied articles deal with innovation applications.
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