Logo image
Improving IS practical significance through effect size measures
Journal article   Peer reviewed

Improving IS practical significance through effect size measures

N. Thompson, X. Wang and R. Baskerville
Journal of Computer Information Systems
2021
url
Link to Published Version *Subscription may be requiredView

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.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: InCites

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
1 Clinical & Life Sciences
1.155 Medical Ethics
1.155.611 Evidence Based Medicine
Web Of Science research areas
Computer Science, Information Systems
ESI research areas
Computer Science
Logo image