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Improving IS Practical Significance through Effect Size Measures
Journal of Computer Information Systems ( IF 2.8 ) Pub Date : 2021-02-05 , DOI: 10.1080/08874417.2020.1837036
Nik Thompson 1 , Xuequn Wang 2 , Richard Baskerville 3
Affiliation  

ABSTRACT

Evidence-based practice in management assigns a high value to research results as a guide to practices that have been rigorously shown to be effective. To emphasize the practical relevance and outcomes for information systems research, statistical research should generally report its effect sizes. Specifying effect sizes not only reveals the utility of our results, but it also enables evidence-based practitioners to easily compare the known effects of different interventions applied in different studies. Effect size reporting has become a standard practice in many fields, however, though information systems researchers have adopted many other elements of statistical rigor, effect sizes are often overlooked. This paper surveys the current use of effect size calculations in information systems research, explains how such effects sizes are calculated, offers recommendations on when each of the different formulae is appropriate, and provides foundational work toward an index of expected effect sizes in information systems research.



中文翻译:

通过影响大小措施提高 IS 的实际意义

摘要

管理中的循证实践高度重视研究成果,作为已被严格证明有效的实践的指南。为了强调信息系统研究的实际相关性和结果,统计研究通常应报告其影响大小。指定效应量不仅揭示了我们结果的实用性,而且还使循证从业者能够轻松地比较不同研究中应用的不同干预措施的已知效果。效应量报告已成为许多领域的标准做法,然而,尽管信息系统研究人员采用了许多其他统计严格要素,但效应量常常被忽视。本文调查了信息系统研究中影响大小计算的当前使用,解释了如何计算这些影响大小,

更新日期:2021-02-05
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