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Using the Standardized Letters of Recommendation in Selection Results From a Multidimensional Rasch Model

机译：在多维Rasch模型的选择结果中使用推荐的标准字母

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摘要

In an effort to standardize academic application procedures, the authors developed the Standardized Letters of Recommendation (SLR) to capture important cognitive and noncognitive qualities of graduate school candidates. The SLR, which consists of seven scales, is applied to an intern-selection scenario. Both professor ratings (n = 414) during the application process and mentor ratings of the selected students (n = 51) are collected using the SLR. A multidimensional Rasch investigation suggests that the SLR displays satisfactory internal consistency, model fit, and item fit. The two cognitive scales, knowledge and analytical skills, are found to be the best predictors for intern selection. The professor ratings are systematically higher than the mentor ratings. Possible reasons for the rating discrepancies are discussed. Also, implications for how the SLR can be used and improved in other selection situations are suggested.
机译：为了使学术申请程序标准化，作者开发了推荐信标准化（SLR），以捕获研究生应聘者的重要认知和非认知素质。 SLR由七个等级组成，适用于实习生选择方案。使用SLR收集申请过程中的教授评分（n = 414）和所选学生的导师评分（n = 51）。 Rasch的多维研究表明，SLR具有令人满意的内部一致性，模型拟合和项目拟合。发现两个认知量表，知识和分析能力是实习生选择的最佳预测指标。教授的评分系统地高于导师的评分。讨论了等级差异的可能原因。此外，建议了在其他选择情况下如何使用和改进SLR的含义。

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