The Measurement and Communication of Effect Sizes in Management Research

Abstract

The measurement and communication of the effect size of an independent variable on a dependent variable is critical to effective statistical analysis in the Social Sciences. We develop ideas about how to extend traditional methods of evaluating relationships in multivariate models to explain and illustrate the statistical power of a focal independent variable. Even with a growing acceptance of the need to report effect sizes, scholars in the management community have few well-established protocols or guidelines for reporting effect sizes. In this editorial essay, we: (1) review the necessity of reporting effect sizes; (2) discuss commonly used measures of effect size and accepted cut-offs for large, medium, and small effect sizes; (3) recommend standards for reporting effect sizes via verbal descriptions and graphical presentations; and (4) present best practice examples of reporting and discussing effect size. In summary, we provide guidance for authors on how to report and interpret effect sizes, advocating for rigor and completeness in statistical analysis.

Reaching an Optimally Distinctive CSR Strategy: Examining the Antecedents of CSR Scope Conformity and Emphasis Differentiation Among Chinese Publicly Listed Companies

“…= 0.060, p = 0.046), which is consistent with our prediction that private ownership is positively associated with high-level CSR scope conformity (Hypothesis 1a). Following the suggestions of prior studies on assessing a variable's effect size (Fey, Hu, & Delios, 2022; Lewin et al, 2016), we calculated the effect size of private ownership. The result shows that private ownership leads to an increase of 0.13 SD in scope conformity.…”

Interspecific variation in leaf traits, photosynthetic light response, and whole-plant productivity in amaranths (Amaranthus spp. L.)

“…It is also important to report some effect size measures that indicate whether the observed statistical differences among groups are of practical significance. For a two-way ANOVA and small sample size, the effect size measure omega squared (ω 2 ) is recommended [ 77 – 82 ]. ω 2 also determines the percentage of the variation in the dependent variable attributable to the individual independent factors (i.e., species and measurement dates) [ 78 ].…”

“...and the crowd goes wild!” – The impact of crowds and simulated crowd noise on performance in professional darts players during the COVID-19 pandemic

“…A real audience resulted in larger performance decrements, breal=-3.62, t( 22,835)=-11.14, p<.001, bstand_real=-.20. The partially standardized beta coefficients can be considered small 60 The control variables "round of the tournament", bround=-0.013, t (22,325)=-4.53, p<.001, and the level-two predictor "Pro Tour order of merit", brank=-0.024, t(390)=-6.38, p<.001, significantly impacted CP. Both ranking systems correlated with r=.90, p<.001.…”

“…Playing with a simulated audience resulted in an increase in three-dart average, bsim=0. 54 60 . Pro Tour order of merit, CP, and the round of the tournament were significant predictors of 3DA (all ps<.001, for effect size, see Table 2) and were added to the final model.…”