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Detecting and Measuring Rater Effects Using Many-Facet Rasch Measurement: Part Ⅱ

机译：使用多面相Rasch测量检测和测量评估者效果：第二部分

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The purpose of this two-part paper is to introduce researchers to the many-facet Rasch measurement (MFRM) approach for detecting and measuring rater effects. In Part Ⅱ of the paper, researchers will learn how to use the Facets (Linacre, 2001) computer program to study five effects: leniency/severity, central tendency, randomness, halo, and differential leniency/severity. As we introduce each effect, we operationally define it within the context of a MFRM approach, specify the particular measurement model(s) needed to detect it, identify group- and individual-level statistical indicators of the effect, and show output from a Facets analysis, pinpointing the various indicators and explaining how to interpret each one. At the close of the paper, we describe other statistical procedures that have been used to detect and measure rater effects to help researchers become aware of important and influential literature on the topic and to gain an appreciation for the diversity of psychometric perspectives that researchers bring to bear on their work. Finally, we consider future directions for research in the detection and measurement of rater effects.
机译：本文分为两部分，旨在向研究人员介绍用于检测和测量评估者效果的多面Rasch测量（MFRM）方法。在本文的第二部分中，研究人员将学习如何使用Facets（Linacre，2001年）计算机程序来研究五种影响：宽大/严重度，中枢倾向，随机性，晕轮和差异性宽大/严重度。在介绍每种效果时，我们在MFRM方法的上下文中在操作上对其进行定义，指定检测该效果所需的特定度量模型，识别该效果的组和个人级别统计指标，并显示Facets的输出分析，查明各种指标并解释如何解释每个指标。在本文结尾处，我们描述了用于检测和衡量评估者效果的其他统计程序，以帮助研究人员了解有关该主题的重要和有影响力的文献，并对研究人员带来的心理计量学观点的多样性获得赞赏。承担他们的工作。最后，我们考虑了评估和评估评估者效果的未来研究方向。

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