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对混合格式项目测试应用的顺序贝叶斯能力估计
Jiawei Xiong1, Allan S Cohen2, Xinhui Maggie Xiong3
1Pearson, Athens, GA, USA.
Applied psychological measurement
|October 9, 2023
概括
与传统的并发贝叶斯式 (CB) 方法相比,新的顺序贝叶斯式 (SB) 方法为混合格式测试提供了更准确的能力估计,特别是在较小的样本大小的情况下. 这种方法提高了整体评估可靠性.
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
- 统计建模 统计建模
背景情况:
- 大规模评估经常使用混合格式的项目,结合多选项 (MC) 和构建响应 (CR) 格式.
- 同时分析混合格式的项目可能不会产生最佳能力估计.
- 现有的方法,如并发贝叶斯校准 (CB),在准确估计考生能力方面可能存在局限性.
研究的目的:
- 探索混合项目响应模型的两步顺序贝叶斯式 (SB) 分析方法.
- 将SB方法的准确性和可靠性与传统的并发贝叶斯式 (CB) 方法 (EAPsum) 进行比较.
- 评估SB方法在各种因素的性能,包括样本大小和测试长度.
主要方法:
- 开发并应用了一种两步顺序的贝叶斯式 (SB) 方法,将MC和CR项目中的能力估计整合起来.
- 利用从MC项目估计的个体水平样本依赖的先前分布来估计后来的能力.
- 进行模拟研究以评估参数恢复,并在不同的条件下将SB与CB (EAPsum) 进行比较.
主要成果:
- SB方法比CB方法更准确,更可靠地估计了能力,特别是在小样本大小 (N=150,500) 的情况下.
- 这两种方法都显示了多选项参数的可比回收.
- 在恢复构造响应项目参数方面,CB方法略高于SB方法,尽管SB方法的后置能力估计在实证示例中显示出更高的可靠性.
结论:
- 序列贝叶斯式 (SB) 方法提供了一个更准确和可靠的方法,用于能力估计在混合格式的评估与并发贝叶斯式 (CB) 方法相比.
- 在有限的样本规模的情况下,SB方法的优势尤其明显.
- 拟议的SB方法为混合项目评估提供了更好的后置能力估计可靠性.
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