A common language effect size statistic.

By K. McGraw, S. Wong. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Comparing Code Explanations Created by Students and Large Language Models (2023, methodology): “Following the guidelines of [46] and the broader discussion in [37], we use 𝑝 values as only one source of evidence and outline supporting statistics including two effect sizes – Rank-Biserial (RBC) Correlation [19] and Common-Language Effect Size (CLES) [30] – when presenting the results of the st”
Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs (2013, background): “For correlated samples, Z = Mdiff/Sdiff (McGraw and Wong, 1992), and the percentage associated with the upper tail probability of this value is 0.”
What Five Decades of Research Tells Us About the Effects of Youth Psychological Therapy: A Multilevel Meta-Analysis and Implications for Science and Practice (2017, background): “Perhaps the broadest implication derives from translating our overall mean ES of 0.46 at posttreatment into a “common language effect size” (McGraw & Wong, 1992): The probability that a randomly selected youth in the treatment condition would be better off after treatment than a randomly selected…”
A Comparison of Predictors of General and Violent Recidivism Among High-Risk Federal Offenders (2002, methodology): “The CLES represents 242 CRIMINAL JUSTICE AND BEHAVIOR 243 244 the probability that an individual sampled randomly from one group will have a higher score than a person sampled randomly from a second group.”
Comparing Effect Sizes in Follow-Up Studies: ROC Area, Cohen's d, and r (2005, unknown): “AUC also has a plain language interpretation because it is conceptually and mathematically similar to the common language effect size or CL ( McGraw & Wong, 1992; see also Delaney & Vargha, 2002).”
An improved effect size for single-case research: nonoverlap of all pairs. (2009, methodology): “Applied to continuous data it is the Common Language Effect Size (CL) (McGraw & Wong, 1992).”
Measures of Effect Size for Comparative Studies: Applications, Interpretations, and Limitations. (2000, background): “…size measures (Dwyer, 1974; Glass & Hakstian, 1969; Maxwell, Camp, & Arvey, 1981; Richardson, 1996), suggesting alternative indices (Friedman, 1968; McGraw & Wong, 1992; Rosenthal & Rubin , 1982), and offering cautionary notes on the interpretation of these indices (Mitchell & Hartmann, 1981;…”
The value of prior knowledge in machine learning of complex network systems (2016, unknown): “CLES for network sizes of 90 nodes vs 180 nodes is 64%.”
Psychological injury in victims of child sexual abuse: A meta-analytic review (2015, methodology): “In terms of practical utility, victims of CSA/ASA had a 70% higher probability (CLES) of internalizing injury (general sequelae) than non-victims, with 66% for depression and 68% for anxiety.”
The effects of gender on group work process and achievement: an analysis through self‐ and peer‐assessment (2014, methodology): “In order to ease interpretation of the resulting values, we convert Hedges’ g to the probability of superiority (Grissom, 1994; see also McGraw & Wong, 1992 who refer to it as the ‘common language effect size’).”
The Other Half of the Story: Effect Size Analysis in Quantitative Research (2013, background): “Additionally, McGraw and Wong (1992) developed a measure to report what they call “the common language effect size indicator,” which describes the probability that a random value sampled from one group will be greater than a random value sampled from a comparison group (McGraw and Wong, 1992).”