通过混合物模型扩展来减少人体适应性评估的校准偏差
Johan Braeken1, Saskia van Laar2
1University of Oslo, Oslo, Norway.
Educational and psychological measurement
|September 9, 2025
概括
这项研究引入了一种新的单阶段方法,用于评估个人是否适合参加调查,从而提高了测量的适当性. 新方法克服了传统的两阶段方法的局限性,提供了更准确的个人反应模式的评估.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 统计建模 统计建模
背景情况:
- 测量的适当性对于有效的个人评估至关重要.
- 异常反应模式表明一个人与测试/调查之间的不合适.
- 传统的人体适应统计 (例如,Lz) 由于两阶段估计而遭受校准偏差.
研究的目的:
- 提出和评估一个一阶段的人适合评估方法.
- 解决现有的两阶段估计程序的局限性.
- 评估人适合在具有挑战性的条件下,如短的多种尺度.
主要方法:
- 通过对异常响应模式的混合组件进行模型扩展校准,开发了一个单阶段解决方案.
- 进行模拟研究以评估拟议的方法.
- 专注于短的多种调查尺度,在大规模评估中很常见.
主要成果:
- 一个阶段的方法为传统方法提供了可行的替代方案.
- 模拟研究表明,在不利的条件下,该方法的有效性.
- 这种方法有可能减少与双阶段程序固有的校准偏差.
结论:
- 拟议的单阶段人适合方法提供了对测量适当性的更可靠的评估.
- 这种方法在大型教育评估中对短的多种尺度特别有价值.
- 未来的研究应该探索其在各种评估环境中的应用.
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