The Measurement and Communication of Effect Sizes in Management Research

Carl F. Fey*, Tianyou Hu, Andrew Delios

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)

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.

Original languageEnglish
Article number174087762200002
Pages (from-to)176-197
Number of pages22
JournalManagement and Organization Review
Volume19
Issue number1
Early online date21 Apr 2022
DOIs
Publication statusPublished - 1 Feb 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • confidence interval
  • data visualization
  • effect size
  • statistical analysis
  • statistical reporting

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