在真实得分关系中使用组差异来评估测量偏差.
1Retired from Educational Testing Service, Princeton, NJ, USA.
Applied psychological measurement
|July 10, 2025
概括
本研究引入了一种新的测量偏差线性模型,与预测偏差模型不同. 它强调了忽视差异如何导致关于测试偏见的错误结论,特别是在大学招生和就业测试中.
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:
- 测量偏差和预测偏差在标准化测试中至关重要.
- 像Cleary模型这样的现有模型主要解决预测偏差.
- 了解这些偏见对于在招生和就业方面进行公平评估至关重要.
研究的目的:
- 开发一种新的线性模型,用于使用变量中的错误 (EIV) 回归来评估测量偏差.
- 为了区分测量偏差和预测偏差,特别是当真得分的平均值在组之间不同时.
- 重新审视大学招生和就业背景下关于测试偏差的经验研究结果.
主要方法:
- 为测量偏差开发一个线性变量误差 (EIV) 回归模型.
- 在不同的真分数平均值下对测量偏差和预测偏差进行比较分析.
- 对少数群体测试绩效预测现有的经验数据进行重新评估.
主要成果:
- 拟议的EIV模型为测量偏差评估提供了一种新的方法.
- 忽视测量和预测偏差之间的区别可能会导致误解,原因是向平均值回归.
- 对少数群体过度预测的实证发现与对他们的重大测量偏见一致.
结论:
- 该研究为理解和量化测量偏差提供了一个精细的框架.
- 它强调了区分测量和预测偏差对于准确的测试评估的重要性.
- 结果表明,在少数群体中观察到的过度预测可能源于固有的测量偏差,而不是仅仅是预测问题.
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