Item parameter estimates of a common item on a new test form may change abnormally due to reasons such as item overexposure or change of curriculum. A common item, whose change does not fit the pattern implied by the normally behaved common items, is defined as an outlier. Although improving equating accuracy, detecting and eliminating of outliers may cause a content imbalance among common items. Robust scale transformation methods have recently been proposed to solve this problem when only one outlier is present in the data, although it is not uncommon to see multiple outliers in practice. In this simulation study, the authors examined the robust scale transformation methods under conditions where there were multiple outlying common items. Results indicated that the robust scale transformation methods could reduce the influences of multiple outliers on scale transformation and equating. The robust methods performed similarly to a traditional outlier detection and elimination method in terms of reducing the influence of outliers while keeping adequate content balance.

中文翻译：

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在IRT真实分数相等下评估具有多个外围公共项目的稳健规模转换方法。

由于项目过度暴露或课程变更等原因，新测试表格上常见项目的项目参数估计可能会异常更改。将其更改不符合正常行为的普通项目所暗示的模式的普通项目定义为异常值。尽管提高了等值精度，但检测和消除异常值可能会导致常见项目之间的内容不平衡。当数据中仅存在一个异常值时，最近提出了鲁棒的尺度变换方法来解决该问题，尽管在实践中经常会看到多个异常值。在此模拟研究中，作者检查了在存在多个外围常见项目的情况下的鲁棒规模转换方法。结果表明，鲁棒的尺度变换方法可以减少多个离群值对尺度变换和等式的影响。健壮的方法在减少异常值的影响的同时保持足够的内容平衡，其性能与传统的异常值检测和消除方法相似。