A Nonparametric Framework for Comparing Trends and Gaps Across Tests
A Nonparametric Framework for Comparing Trends and Gaps Across Tests
复制标题
用于比较测试中的趋势和差距的非参数框架
DOI：
10.3102/1076998609332755
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发表时间：
2009
影响因子：
2.4
通讯作者：
Andrew D. Ho
中科院分区：
文献类型：
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作者：
Andrew D. Ho
当比较不同测试的测试分数趋势、差距和差距趋势时，通常会出现规模问题。为了克服这些困难，可以用在单调尺度变换下不变的非参数图或统计量来表示相同分数尺度上的测试分数分布。本文激发并开发了一个框架，用于跨测试比较这些非参数趋势、差距和差距趋势表示。强调了该框架与其他非参数工具之间的联系，包括概率-概率（PP）图、Mann-Whitney U检验和被称为P（Y > X）的统计量。作者描述了该框架相对于依赖规模的趋势和差距统计的优势，并演示了这些非参数方法在经常被问到的政策问题上的应用。
Problems of scale typically arise when comparing test score trends, gaps, and gap trends across different tests. To overcome some of these difficulties, test score distributions on the same score scale can be represented by nonparametric graphs or statistics that are invariant under monotone scale transformations. This article motivates and then develops a framework for the comparison of these nonparametric trend, gap, and gap trend representations across tests. The connections between this framework and other nonparametric tools, including probability–probability (PP) plots, the Mann-Whitney U test, and the statistic known as P(Y > X), are highlighted. The author describes the advantages of this framework over scale-dependent trend and gap statistics and demonstrates applications of these nonparametric methods to frequently asked policy questions.