Improving IS Practical Significance through Effect Size Measures

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.