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Using Multilevel Random Coefficient Modeling To Investigate Rater Effects in Performance Ratings

机译:使用多级随机系数建模来研究绩效评级中的评级者影响

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摘要

There has been recent interest in how rater attributes lead to systematic variance in ratings of job performance. Although numerous rater characteristics have been proposed to affect performance ratings, there has been little empirical research studying them. We suggest this has been because of methodological problems with levels of analysis and propose multilevel random coefficient (MRC) modeling as a solution. We present a multilevel model of rater effects in which ratees are nested within raters. We also present two examples of applying MRC modeling to criterion-related validity data to study how rater-level variables influence performance ratings and the relationships selection assessments have with those ratings.
机译:最近,人们对评估者属性如何导致工作绩效的评估系统地产生了兴趣。尽管已经提出了许多评定者特征来影响绩效评定,但是很少有实证研究对其进行研究。我们认为这是由于分析级别的方法学问题,并提出了多级随机系数(MRC)建模作为解决方案。我们提出了一个评估者效应的多层次模型,其中评估者嵌套在评估者中。我们还提供了两个将MRC建模应用于与标准相关的有效性数据的示例,以研究评估者级别的变量如何影响绩效等级以及与这些等级之间的关系选择评估。

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