评估缺少数据处理方法对规模链接准确度的影响
Tong Wu1,2, Stella Y Kim1, Carl Westine1
1University of North Carolina at Charlotte, USA.
在大规模评估中处理缺失的数据对于准确的规模链接至关重要. 响应函数赋值,多重赋值和全信息概率估计方法表现最好,最大限度地减少对象响应理论尺度链接中的错误.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 统计 统计 统计 统计
背景情况:
- 大规模评估经常遇到缺失的数据,影响结果的可靠性.
- 项目响应理论 (IRT) 被广泛使用,但其在规模链接中缺少数据的应用仍未得到充分探索.
- 规模链接确保了测试形式之间的可比性,这对于纵向研究和程序评估至关重要.
研究的目的:
- 评估六种不同的缺失数据处理方法对IRT规模链接准确性的影响.
- 在各种模拟条件和缺失数据机制下比较这些方法的性能.
- 在规模链接过程中确定最有效的策略,以解决在常见项目中缺失的响应.
主要方法:
- 模拟数据是在共同项目无等价组设计 (CINEG) 下生成的.
- 测试了六种方法:按列表删除 (LWD),将缺失视为不正确 (IN),更正的项目平均归算 (CM),响应函数归算 (RF),多重归算 (MI) 和全信息最大概率 (FIML).
- 根据估计的链接系数中的错误来评估链接的准确性.
主要成果:
- 响应函数赋值 (RF),多重赋值 (MI) 和全信息最大概率 (FIML) 展示了卓越的性能,产生了最低的链接错误.
- 按列表删除 (LWD) 在所有测试条件中产生了最高的链接错误.
- 缺少数据处理方法的选择显著影响了规模链接的准确性.
结论:
- 建议使用RF,MI和FIML来处理IRT尺度链接中缺少的数据,以确保准确可靠的结果.
- 应避免按列表删除,因为它会对尺度链接的准确性产生不利影响.
- 有效的缺失数据策略对于保持大规模评估的有效性和可比性至关重要.
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