2012/01/01 by Ken Kelley, Kristopher J. Preacher · 1 citation
Decision Sciences · Computer Science · Psychology · Mathematics · #Meta-analysis and systematic reviews #Advanced Statistical Modeling Techniques #Psychometric Methodologies and Testing #Sample size determination #Population size #Dimension (graph theory) #Statistics #Population #Confusion #Econometrics #Contrast (vision) #Value (mathematics) #Term (time) #Psychology #Computer science #Mathematics #Artificial intelligence #Physics #Sociology
paper · doi:10.1037/a0028086
openalex publication_date 2012/01/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.