在渐进矩阵中建模顺序依赖:自动回归物件响应理论 (AR-IRT) 方法
Nils Myszkowski1, Martin Storme2
1Department of Psychology, Pace University, New York, NY 10004, USA.
Journal of Intelligence
|January 22, 2024
概括
这项研究引入了对物体反应理论的自回归模型,考虑认知测试中的顺序依赖. 这些模型改善了适合性,并为渐进矩阵提供了更可靠的估计.
科学领域:
- 心理测量 心理测量 心理测量
- 认知心理学 认知心理学
- 教育测量教育的测量
背景情况:
- 传统的测量模型假定项目响应独立,往往忽视了顺序依赖.
- 自动回归模型在物体响应理论中未得到充分研究,特别是在认知能力测试中.
研究的目的:
- 扩展二进制项目响应模型,特别是2参数后勤 (2PL) 模型,以纳入自动回归序列依赖.
- 调查顺序效应对认知能力测试的影响,使用渐进矩阵作为案例研究.
主要方法:
- 开发一个自动回归的lag-1 2PL模型.
- 使用公开可用的渐进矩阵数据集,将拟议模型与传统的2PL模型进行应用和比较.
主要成果:
- 与传统的2PL模型相比,自动回归的lag-1 2PL模型显示出更好的模型适应性.
- 拟议的模型产生了更保守的歧视参数和标准错误,表明精度提高.
结论:
- 顺序依赖可能是认知能力测试中的重要,被忽视的因素,特别是在渐进式矩阵中.
- 自动回归模型在连续测试场景中提供了更准确的响应过程表示.
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