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人员与教育选拔中的机械决策教程

A Tutorial on Mechanical Decision-Making for Personnel and Educational Selection.

作者信息

Meijer Rob R, Neumann Marvin, Hemker Bas T, Niessen A Susan M

机构信息

Department of Psychometrics and Statistics, Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, Netherlands.

Department of Psychometrics and Research in Educational Measurement, Cito, Arnhem, Netherlands.

出版信息

Front Psychol. 2020 Jan 23;10:3002. doi: 10.3389/fpsyg.2019.03002. eCollection 2019.

Abstract

In decision-making, it is important not only to use the correct information but also to combine information in an optimal way. There are robust research findings that a mechanical combination of information for personnel and educational selection matches or outperforms a holistic combination of information. However, practitioners and policy makers seldom use mechanical combination for decision-making. One of the important conditions for scientific results to be used in practice and to be part of policy-making is that results are easily accessible. To increase the accessibility of mechanical judgment prediction procedures, we (1) explain in detail how mechanical combination procedures work, (2) provide examples to illustrate these procedures, and (3) discuss some limitations of mechanical decision-making.

摘要

在决策过程中,不仅要使用正确的信息,还要以最佳方式整合信息,这一点很重要。有可靠的研究结果表明,在人员选拔和教育选拔中,对信息进行机械整合的效果与整体整合信息相当,甚至更优。然而,从业者和政策制定者很少在决策中使用机械整合方法。科学成果能够应用于实践并成为政策制定一部分的一个重要条件是这些成果易于获取。为了提高机械判断预测程序的可及性,我们(1)详细解释机械整合程序的工作原理,(2)提供示例来说明这些程序,以及(3)讨论机械决策的一些局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a826/6990119/d9a37e2336f5/fpsyg-10-03002-g001.jpg

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