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Using cluster analysis to validate the angoff standard setting method in mixed-format assessments

机译:使用聚类分析来验证混合格式评估中的angoff标准设置方法

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Test validity is a property of the interpretation assigned to test scores. To provide an objective validating evidence for a standard-referenced assessment is especially important. In this study we utilize a statistical technique, cluster analysis, to explore the validity of one of the expert judgement technique-Yes/No Angoff standard setting method. We first segregated each examinee ability cluster using the hierarchical clustering (HC). Assume that each ability cluster is a Gaussian distribution and that the distribution of each test subject data can be modeled by mixture of Gaussians (MoG), where the mean, variance and the proportion of each cluster were initialized by the HC results. Finally, the ability clustering was implemented by the expectation maximization (EM) method. The results from the traditional standard-setting procedure and cluster analysis were compared. The study concludes that cluster analysis appears useful for helping to set standards on educational tests. In addition, it suggested that cluster analysis could be applied as a support tool to provide validating information in the process of standard setting for high-stakes achievement tests.
机译:测试有效性是分配给测试分数的解释的属性。为标准参考评估提供客观的验证证据尤为重要。在这项研究中,我们利用统计技术,聚类分析来探讨一种专家判断技术-是/否Angoff标准设置方法的有效性。我们首先使用层次聚类(HC)隔离每个考生能力聚类。假设每个能力簇都是高斯分布,并且可以通过混合高斯(MoG)来建模每个测试对象数据的分布,其中每个簇的均值,方差和比例都由HC结果初始化。最后,通过期望最大化(EM)方法实现了能力聚类。比较了传统标准制定程序和聚类分析的结果。该研究得出的结论是,聚类分析对于帮助制定教育测试标准很有用。此外,它建议将聚类分析用作支持工具,以在高风险成就测试的标准制定过程中提供验证信息。

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