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使用易出错标准的诊断测试分数验证

Diagnostic Test Score Validation With a Fallible Criterion.

作者信息

Jewsbury Paul A

机构信息

Educational Testing Service, Princeton, NJ, USA.

出版信息

Appl Psychol Meas. 2019 Nov;43(8):579-596. doi: 10.1177/0146621618817785. Epub 2018 Dec 13.

Abstract

Criterion-related validation of diagnostic test scores for a construct of interest is complicated by the unavailability of the construct directly. The standard method, Known Group Validation, assumes an infallible reference test in place of the construct, but infallible reference tests are rare. In contrast, Mixed Group Validation allows for a fallible reference test, but has been found to make strong assumptions not appropriate for the majority of diagnostic test validation studies. The Neighborhood model is adapted for the purpose of diagnostic test validation, which makes alternate, but also strong, assumptions. The statistical properties of the Neighborhood model are evaluated and the assumptions are reviewed in the context of diagnostic test validation. Alternatively, strong assumptions may be avoided by estimating only intervals for the validity estimates, instead of point estimates. The Method of Bounds is also adapted for the purpose of diagnostic test validation, and an extension, Method of Bounds-Test Validation, is introduced here for the first time. All three point-estimate methods were found to make strong assumptions concerning the conditional relationships between the tests and the construct of interest, and all three lack robustness to assumption violation. The Method of Bounds-Test Validation was found to perform well across a range of plausible simulated datasets where the point-estimate methods failed. The point-estimate methods are recommended in special cases where the assumptions can be justified, while the interval methods are appropriate more generally.

摘要

由于无法直接获得感兴趣的构想,因此针对该构想的诊断测试分数的标准关联效度验证变得复杂。标准方法“已知组验证”假定使用无误的参考测试来替代该构想,但无误的参考测试很少见。相比之下,“混合组验证”允许使用有误差的参考测试,但已发现它做出的强假设不适用于大多数诊断测试验证研究。“邻域模型”是为诊断测试验证目的而改编的,它也做出了替代的但同样很强的假设。评估了邻域模型的统计特性,并在诊断测试验证的背景下审查了这些假设。或者,可以通过仅估计效度估计值的区间而不是点估计值来避免强假设。“边界法”也适用于诊断测试验证目的,这里首次引入了其扩展方法“边界-测试验证法”。发现所有三种点估计方法都对测试与感兴趣的构想之间的条件关系做出了强假设,并且这三种方法都缺乏对假设违背的稳健性。在一系列合理的模拟数据集中,当点估计方法失败时,发现边界-测试验证法表现良好。在假设可以得到证明的特殊情况下推荐使用点估计方法,而区间方法更普遍适用。

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