通过基于回归的持续规范化来改善标准得分质量
Wolfgang Lenhard1, Alexandra Lenhard2
1University of Wuerzburg, Wuerzburg, Germany.
Educational and psychological measurement
|November 6, 2023
概括
与传统规范化相比,半参数连续规范化 (SPCN) 提供了一种更准确,更有效的方法来解释心理测试结果. SPCN 需要更小的样本大小来实现最佳结果,减少规范表中的错误和缺失数据.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 心理评估 心理评估
背景情况:
- 心理测试的解释依赖于标准分数.
- 传统的规范化方法包括排序程序和编制规范表.
- 传统规范的准确性可能会受到样本大小和数据特征的影响.
研究的目的:
- 将半参数连续规范 (SPCN) 与传统规范方法进行比较.
- 用对象响应理论模拟来评估不同规范方法的性能.
- 识别传统规范程序中的潜在错误和局限性.
主要方法:
- 模拟心理测试数据使用物品反应理论,具有不同的物品属性.
- 模拟的规范得分使用随机样本与常规排名和SPCN.
- 与大型代表性样本 (N=840,000) 进行交叉验证的规范和计算的规范错误.
- 重复了9万次的模拟和验证过程.
主要成果:
- 随着更大的样本大小,SPCN和传统规范都得到了改善.
- 与传统规范化相比,SPCN的样本显著较小,可以获得最佳的结果.
- 传统的规范表现出较差的数据匹配,更多的与年龄相关的错误,以及在规范表中缺失的值的数量较多.
- 与传统规范相适应的数据因年龄段的细粒度而异,质疑样本大小建议.
结论:
- SPCN是心理测试规范的更有效,更准确的方法.
- 传统的规范化方法在数据合适性,错误率和完整性方面存在局限性.
- 测试规范应该来自原始分数分布的统计模型,而不是传统的排名程序.
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