Realizing the full potential of psychometric meta-analysis for a cumulative science and practice of human resource management

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• 作者：Deniz S. Ones^a ; ^{deniz.s.ones-1@tc.umn.edu} ; Chockalingam Viswesvaran^b ; ^vish@fiu.edu ; Frank L. Schmidt^c
• 关键词：Meta-analysis ; Replication ; Literature search ; Open data ; Practitioner research ; Publication bias ; Interrater reliability
• 刊名：Human Resource Management Review
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：27
• 期：1
• 页码：201-215
• 全文大小：378 K
• 卷排序：27

文摘

This might be the most opportune time for Human Resource Management (HRM) to benefit from psychometric meta-analysis. Explosion of empirical research, often with conflicting results, hide important takeaways that can guide evidence-based applications of HRM. The science of HRM can turn to meta-analyses and meta-analytic thinking as the antidote to the so-called replication crisis afflicting social sciences in general. In this paper, we focus on issues and potential problems that may threaten the veracity and usefulness of contemporary meta-analyses in HRM. We contend that these problems must be correctly tackled for meta-analyses to realize their full potential in advancing HRM science and practice. We address the problems of identification and inclusion of all relevant effect sizes, as well as appropriate corrections for unreliability and range restriction. We offer concrete proposals to enable inclusion of unpublished, practitioner research and data in HRM meta-analyses.