David Eccles School of Business, University of Utah.
Freeman School of Business, Tulane University.
J Appl Psychol. 2014 Mar;99(2):239-61. doi: 10.1037/a0034556. Epub 2013 Oct 7.
Despite the widespread use of interrater agreement statistics for multilevel modeling and other types of research, the existing guidelines for inferring the statistical significance of interrater agreement are quite limited. They are largely relevant only under conditions that numerous researchers have argued rarely exist. Here we address this problem by generating guidelines for inferring statistical significance under a number of conditions via a computer simulation. As a set, these guidelines cover many of the conditions researchers commonly face. We discuss how researchers can use the guidelines presented to more reasonably infer the statistical significance of interrater agreement relative to using the limited guidelines available in the extant literature.
尽管多水平模型和其他类型的研究广泛使用了评分者间一致性统计,但现有的评分者间一致性统计显著性推断指南非常有限。它们主要只适用于许多研究人员认为很少存在的条件。在这里,我们通过计算机模拟生成了在多种条件下推断统计显著性的指南来解决这个问题。作为一个集合,这些指南涵盖了研究人员通常面临的许多情况。我们讨论了研究人员如何使用这些指南更合理地推断评分者间一致性的统计显著性,而不是使用现有文献中有限的指南。