Different ranking approaches defining association and agreement measures of paired ordinal data
Different ranking approaches defining association and agreement measures of paired ordinal data
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DOI：
10.1002/sim.5382
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发表时间：
2012-11-20
影响因子：
2
通讯作者：
Svensson, Elisabeth
中科院分区：
文献类型：
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作者：
Svensson, Elisabeth
评价表通常用于定性变量的自我评估，也用于残疾严重程度、结果等的专家评分。评价表评估和其他有序分类产生的有序数据只具有等级不变属性。因此，统计方法通常是基于排名的。其目的是集中于配对顺序数据中关联性和不一致性度量之间的排序方法的差异。Spearman相关系数是在将每个数据集转换为排名时，两个变量之间关联性的衡量标准。评估不一致的扩大排名办法考虑到数据对提供的信息，并在存在系统性不一致的情况下，与评估中额外的个人可变性的测量分开提供识别和测量。这两种方法被应用于关于感知疼痛与身体健康之间的关系以及患者进行疼痛评估的可靠性的经验数据。还评估了患者在治疗后感觉到的结果水平与医生基于标准的评分之间的不一致艺术。对观察到的分歧进行的系统性和个体性分歧的综合评价，提供了关于其来源的有价值的可解释信息。系统性分歧的存在可以调整和/或理解。个体差异很大可能是量表质量不佳或评分者之间存在异质性的迹象。会议还表明，不得将关联度作为一致性的衡量标准，即使这种滥用相关系数的情况很常见。版权所有(C)2012 John Wiley&Sons，Ltd.
Rating scales are common for self-assessments of qualitative variables and also for expert-rating of the severity of disability, outcomes, etc. Scale assessments and other ordered classifications generate ordinal data having rank-invariant properties only. Hence, statistical methods are often based on ranks. The aim is to focus at the differences in ranking approaches between measures of association and of disagreement in paired ordinal data. The Spearman correlation coefficient is a measure of association between two variables, when each data set is transformed to ranks. The augmented ranking approach to evaluate disagreement takes account of the information given by the pairs of data, and provides identification and measures of systematic disagreement, when present, separately from measures of additional individual variability in assessments. The two approaches were applied to empirical data regarding relationship between perceived pain and physical health and reliability in pain assessments made by patients. The art of disagreement between the patients' perceived levels of outcome after treatment and the doctor's criterion-based scoring was also evaluated. The comprehensive evaluation of observed disagreement in terms of systematic and individual disagreement provides valuable interpretable information of their sources. The presence of systematic disagreement can be adjusted for and/or understood. Large individual variability could be a sign of poor quality of a scale or heterogeneity among raters. It was also demonstrated that a measure of association must not be used as a measure of agreement, even though such misuse of correlation coefficients is common. Copyright (c) 2012 John Wiley & Sons, Ltd.